| LACO
| LACO

The evolution of ESG Reporting: a new era of transparency

In today’s increasingly sustainability-focused world, businesses are more than mere profit-making entities. They play a vital role in shaping our planet’s future. As such, Corporate Sustainability Reporting – the act of publicly sharing a company’s environmental, social, and governance (ESG) goals and their progress towards them – has become an essential aspect of modern business practice. This level of transparency not only showcases a company’s commitment to sustainable practices but also builds trust with stakeholders – such as investors, customers, employees, and the wider community.

In Europe, this move towards transparency has been significantly propelled by the Non-Financial Reporting Directive (NFRD). This key piece of legislation mandates large companies to disclose certain non-financial data related to sustainability, creating a culture of accountability.

European Corporate Sustainability Reporting has evolved from the implementation of NFRD, through the rise of the European Green Deal, to the birth of the Corporate Sustainability Reporting Directives (CSRD) and the European Sustainability Reporting Standards (ESRS). What do these distinct directives and standards signify? How have they developed? And what is their impact on business?

Introducing the Non-Financial Reporting Directive (NFRD)

The Non-Financial Reporting Directive (NFRD) is a significant piece of legislation adopted by the European Union in 2014 (see figure 1). It requires certain companies to provide non-financial information, often in the form of ‘sustainability reports’, along with their annual reports. This directive applies to large public-interest entities, such as listed companies, banks, and insurance companies, with more than 500 employees, constituting approximately 11,6000 companies and groups within the EU.

The NFRD aims to evaluate the non-financial performance of these companies and encourages them to develop a responsible approach to business. The directive requires public disclosure documents to include topics such as

  • environmental protection,
  • social responsibility,
  • treatment of employees,
  • respect for human rights,
  • anti-corruption, and
  • bribery issues.

The main purposes of the NFRD are to make non-financial information available to stakeholders and investors, and to increase business transparency and accountability. While these EU guidelines are not mandatory, they have set a clear course towards greater corporate sustainability reporting.

The interplay between NFRD and ESG

Environmental, Social, and Governance (ESG) refers to three central factors used in measuring the sustainability and societal impact of a company or business.

  • The Environmental aspect focuses on how a company’s operations affect the natural environment, considering elements such as waste management, energy efficiency, and carbon footprint.
  • The Social component examines how a company manages relationships with its employees, suppliers, customers, and communities where it operates. This includes aspects like labor practices, diversity and inclusion, and human rights.
  • Governance pertains to a company’s leadership, executive pay, audits, internal controls, and shareholder rights. It reflects how a company is governed and the standards it upholds in its business practices.

The European Union’s Non-Financial Reporting Directive (NFRD) is important for ESG reporting. It encourages businesses to take ESG factors into account and report on them, which promotes sustainable business practices throughout Europe.

The emergence of the European Green Deal

The European Green Deal is a set of policy initiatives by the European Commission aimed at making Europe climate neutral by 2050. Unveiled in December 2019 (see figure 1), it represents a roadmap towards a sustainable economy and involves significant investment in green technologies, sustainable solutions, and innovative businesses. It outlines specific policy initiatives across various sectors, from significantly cutting greenhouse gases, investing in innovative research and innovation, to rolling out cleaner, cheaper, and healthier forms of private and public transport.

The NFRD, on the other hand, is a key tool that aligns with and supports these goals. By requiring large companies to report on their environmental and social impacts, and governance practices, the NFRD helps ensure that the corporate sector is contributing to – rather than hindering – the objectives of the European Green Deal. For instance, a company reporting under NFRD would need to disclose its carbon emissions and plans for reduction, which directly ties into the Green Deal’s goal of reducing greenhouse gas emissions.

In essence, while the European Green Deal sets the broader vision and targets for a sustainable European economy, the NFRD provides a framework for businesses to contribute to this vision through increased transparency and accountability. This reporting not only increases transparency but also encourages companies to develop more sustainable business models and practices.

| LACO

Figure 1: Timeline of the evolution of the European Corporate Sustainability Reporting Directive

The evolution from NFRD to the Corporate Sustainability Reporting Directives (CSRD)

The NFRD has been a significant step towards enhancing corporate transparency in Europe. However, recognising the need for more comprehensive and standardised reporting, the European Union has recently introduced a new directive, the Corporate Sustainability Reporting Directive (CSRD), which builds upon and expands the scope of the NFRD.

1. Extending the company scope

One of the key changes brought about by the CSRD is the increase in the number of companies required to provide non-financial information (see Figure 2). While the NFRD applied to large public-interest entities with over 500 employees, the CSRD extends this requirement to all large companies and all companies listed on regulated markets (except for micro-enterprises). This expansion of regulations encompasses a total of 49,000 entities, a significant increase from the previous 11,600.

2. Introduction of mandatory EU reporting standards

The CSRD also provides more detailed reporting requirements. It introduces mandatory EU sustainability reporting standards, aiming to ensure that reports across different companies and sectors are comparable. This is a significant shift from the NFRD, which provided only general guidelines for reporting.

3. Obligatory audit

Furthermore, the CSRD requires an audit (assurance) of reported information, similar to the audits required for financial information. This marks a major step towards ensuring the reliability and accuracy of non-financial reports.

The introduction of the CSRD represents a substantial advancement in the EU’s commitment to sustainable finance and corporate transparency. As companies begin to adapt to these new regulations, they will play a critical role in Europe’s broader ambition to achieve a sustainable, net-zero economy.

| LACO

Figure 2: Extended coverage from NFRD to CSRD

The implications of the European Sustainability Reporting Standards (ESRS)

“The CSRD determines which companies must report, on what topics, where and when. The ESRS provides the ‘how’.”

While the CSRD is an overarching directive that mandates companies to disclose specific non-financial information, including their impacts on the environment and society. The European Sustainability Reporting Standards (ESRS), on the other hand, are the detailed standards that companies subject to the CSRD will have to use when preparing their sustainability reports. These standards, drafted by the European Financial Reporting Advisory Group (EFRAG), specify what companies must report on, providing detailed guidelines on various topics such as climate change, water and biodiversity, and employee-related matters.

In essence, while the CSRD determines which companies must report, on what topics, where and when, the ESRS provides the ‘how’ – the specific principles and requirements that companies must follow when reporting on these topics.

Example of difference between CSRD and ESRS

An example to illustrate this relationship could be seen in the area of climate change reporting. The CSRD might require a company to report on its climate impacts, risks, and opportunities, whereas the ESRS would provide detailed instructions on how the company should measure and report its greenhouse gas emissions, how it should assess and explain its climate-related risks, and how it should disclose its strategies and targets for climate mitigation and adaptation.

Therefore, the ESRS and the CSRD work hand-in-hand to ensure that companies across the EU provide comprehensive, consistent, and comparable sustainability disclosures, supporting the EU’s broader goals of sustainability and transparency in the corporate sector.

Conclusion: the role of data in Corporate Sustainability Reporting

Data plays an integral role in the context of CSRD and ESRS. In the context of corporate sustainability, data can be thought of as concrete evidence that demonstrates a company’s environmental, social, and governance performance. For example, a company might record data on its carbon emissions, its waste management procedures, or its diversity policies.

From CSRD’s perspective, accurate and reliable data is criticalas it forms the basis for these disclosures and ensures that sustainability reports across different companies and sectors are comparable. Therefore, maintaining high-quality data is not just a compliance requirement, but a vital element in driving forward the sustainability agenda. Data Governance plays a critical role here , acting as the backbone that ensures the integrity, accuracy, and reliability of reported information. In essence, data governance provides the framework to ensure that companies are able to provide accurate and trustworthy sustainability information, thereby leveraging their data as a strategic asset.

For business executives, the European Green Deal presents both challenges and opportunities, as companies will need to adapt to new regulations and standards, but also stand to benefit from increased investment in the green economy. And in the world of CSRD, data is not just numbers, but a powerful tool for driving positive change. With data governance at its core, companies can ensure that their sustainability disclosures are reliable and trustworthy, positioning them to seize the opportunities presented by this new era of responsible business.

The evolution of ESG Reporting: a new era of transparency2026-02-16T08:37:21+00:00

Which AI model fits your business need?

Artificial intelligence (AI) is everywhere. AI algorithms power fraud detection systems, automates customer conversations, analyses medical scans, generates content, and supports decisions at every level of an organisation. Despite this growing presence, the term “AI” is often used without much precision. It serves as a label for a wide range of technologies that operate in fundamentally different ways.

The starting point is not the technology itself. It is the business challenge. Once the problem is clearly defined, along with its context and constraints, it becomes possible to assess which type of AI offers the best solution. A well defined business challenge can help choose the correct AI toolings.

This article provides a structured overview of today’s AI landscape. It explains the major types of AI solutions in practical terms, showing what they are, how they work, and where they fit. The goal is not to hype any particular method, but to help organisations make informed choices. Finally, you will find a practical framework for selecting the right approach based on your business’s specific needs.

Different AI models explained

1. Rule-based systems: when logic is enough

Some problems do not require learning or prediction. They simply require a logical structure. Rule-based systems are the most straightforward form of artificial intelligence. They operate through a predefined set of logical instructions. If the condition(s) is met, one or more specific action follows. The system is not required to adapt itself or improve over time. It behaves consistently, based entirely on the rules written by humans.

These systems are ideal when business processes are stable and clearly defined. They are often used in eligibility checks, validation tools, compliance workflows or basic automation. Since there is no training involved, they do not require data science expertise or historical trends.

In contrast to machine learning or Generative AI, rule-based systems do not rely on pattern recognition or large datasets. They are easy to audit and explain, which makes them useful in sectors where traceability and consistency are paramount. However, their biggest strength is also their biggest limitation. They cannot handle ambiguity, complexity or variation well. When things change or when data becomes unpredictable, rule-based systems break down quickly.

2. Traditional machine learning: recognising patterns in data

When the decision logic is complex to write manually but patterns exist in historical data, machine learning becomes a more suitable option. In a typical machine learning workflow, a business appropriate model is trained using a dataset that includes both input variables and known outcomes. The system then learns to associate certain input combinations with specific results and then applies that understanding to make predictions on new data that it has not seen before.

This approach is widely used in applications such as churn prediction, demand forecasting, customer segmentation or fraud detection. The models work best when the data is structured and clean, meaning it is presented in well-organised tables with consistent fields. The algorithms rely on mathematical relationships and statistical reasoning rather than human-defined rules.

Traditional machine learning, sometimes referred to as classical AI, differs fundamentally from newer forms like Generative AI. It does not create content or simulate conversation but instead focuses on extracting predictive value from structured input data. Unlike deep learning (see next section), traditional machine learning typically produces models that are more interpretable. Decision trees, linear regressions and support vector machines, for instance, offer insight into how the model reaches its conclusions. One can follow the path of decisions to see how each input affects the output. This balance between accuracy and explainability makes traditional machine learning a reliable option for many business cases, especially where regulation or stakeholder trust requires transparency.

3. Deep learning: mastering complexity

Some tasks are simply too complex for traditional models. When dealing with unstructured data such as images, video, sound or natural language, traditional models hit their limitations. In such scenarios deep learning becomes essential. Deep learning relies on neural networks with many interconnected layers. Each layer extracts increasingly abstract features from the data. These deep neutral networks are capable of recognizing nonlinear patterns that humans would struggle to describe explicitly.

Deep learning has enabled major advances in areas such as speech recognition, medical image analysis, real-time translation and autonomous vehicles. It is also the foundation for many Generative AI systems, including large language models and image synthesis tools. Unlike traditional machine learning, deep learning does not require manual feature selection, as the model discovers the relevant patterns on its own during training. However, this also makes deep learning models harder to interpret. They behave like black boxes, producing accurate results without offering much insight into the chain of reasoning behind them.

Training these models demands large volumes of labelled data and significant computational power. The development process is more complex and more resource-intensive than traditional methods. Yet the performance gains can be substantial when the right data and infrastructure are available.

4. Foundation models: general intelligence at scale

Foundation models are large-scale deep learning systems trained on vast and diverse datasets, both structured and unstructured, across many domains. Unlike task-specific models, foundation models are designed to perform a broad range of functions. They are not built for a single goal such as predicting sales or segmenting customers. Instead, they are built to understand language, generate content, answer questions and complete tasks in a variety of contexts and processes.

These models include well-known large language models (LLMs) like GPT or PaLM, and are often grouped under the label of Generative AI. They are trained once and then reused across many use cases. This makes them extremely efficient for organizations that want to explore different types of AI capabilities without building custom models from scratch. The general knowledge encoded in a foundation model allows it to understand prompts, summarize documents, generate responses and even reason over open-ended questions.

However, foundation models are not perfect. As they are trained on publicly available data, they can reflect biases when answering on certain contexts, produce inaccurate statements or fail to grasp the nuances of specialized domains. They are broad in scope but shallow in domain expertise. Their outputs are based on statistical likelihood of words following the previous words and not on verified facts. For critical business tasks, that distinction is crucial.

5. Fine-tuning foundation models: adapting general AI to specific needs

To make foundation models more useful in specific environments, organizations can fine-tune them. Fine-tuning involves retraining a general model on smaller, more targeted datasets that reflect a particular industry, tone or process. This enables the model to use its broad general knowledge more effectively within a defined business context. In machine learning terms, this process is known as domain adaptation.

Foundation models serve as base models, which can then be adapted to support specialized tasks without having to rebuild everything from scratch. For example, a healthcare provider might fine-tune a model using anonymised patient records to improve its ability to summarize medical reports. A legal team might refine legal terminologies to reduce ambiguity during a contract review. A retailer could adjust tone and vocabulary to match brand guidelines in their automated communications.

Fine-tuning offers a practical middle ground. It avoids the cost and complexity of building a model from zero, while still offering more precision and relevance than an off-the-shelf tool. However, the process requires expertise in prompt design, data preparation and human evaluation. In some cases, the customised version might make more mistakes or lose the broad knowledge that made the original model useful. So even though fine-tuning is meant to improve performance, doing it poorly can lead to worse outcome, and the general version of the AI might actually work better.

6. Custom models: building from the ground up

Some organizations have access to high-quality proprietary data that gives them a competitive advantage. In those cases, it may be more useful to build a custom model from scratch — a route often referred to as developing a proprietary AI model. This involves collecting relevant data, defining a clear objective, selecting appropriate algorithms, training the model and integrating it into production systems.

Custom models provide full control over inputs, outputs and behaviour. They are especially useful in cases where off-the-shelf tools or general-purpose models fail to capture business-specific nuances. They also offer greater flexibility in adapting to changing requirements or integrating with existing systems and workflows of the organization.

However, custom development is not a light decision. It demands significant time, talent and financial investment. Success depends on both data maturity and operational readiness. While building proprietary AI can offer long-term differentiation, it only makes sense when the business case clearly outweighs the cost of adapting an existing solution. When done well, it results in models that are deeply aligned with organizational goals and difficult for competitors to replicate in short term.

Explainable AI: clarity as a design principle

In some domains, being right is not enough. Organizations must also be able to explain how and why an AI system reached its conclusion. This is where explainable AI, often referred to as XAI, comes in. It refers to the use of models and techniques that prioritise interpretability alongside performance.

This can be achieved by choosing inherently simple models such as decision trees, or by applying post hoc explanation tools that provide insights into more complex models. In both cases, the goal is to provide decision-makers with understandable justifications that go beyond model outputs.

Explainability is essential in various sectors, particularly those that are regulated or high stakes such as healthcare, finance, insurance and government. It also plays a key role in building trust among users and stakeholders. In many situations, an understandable model that performs slightly less well is preferred over a high-performing black box.

AI act: risk based assessments

On top of all these, we need to be compliant of the AI act that is being enforced in phases starting August 2024. The AI Act, introduced by the European Union, is the first major legal framework designed to govern the use of artificial intelligence.

It takes a risk-based approach, meaning that the level of oversight depends on how an AI system is used and the potential impact it may have. Companies will face strict requirements related to transparency, accuracy, and human oversight. Systems deemed unacceptable, like those that manipulate behaviour or use social scoring, will be banned entirely. For companies developing or deploying AI, the Act brings new responsibilities, including documentation, testing, and ongoing monitoring. For users and the public, it offers greater protection, aiming to ensure that AI serves people in a fair and trustworthy way.

Which AI model is the right one for my use case?

By now, the key takeaway should be clear. Artificial intelligence is not a single system, and choosing the right approach depends entirely on what you are trying to achieve. Every AI model comes with trade-offs. Simpler AI models such as rule-based systems or traditional AI techniques offer clarity and control. More complex solutions like deep learning or Generative AI provide power and flexibility, but often at the cost of transparency. General-purpose foundation models are quick to deploy and highly versatile, while custom models offer a near perfect fit with business-specific requirements but require significant investment for development.

To help navigate these choices, LACO developed a practical decision tree that maps the reasoning behind each AI option. It connects your use case characteristics with the most suitable technology, whether that is a fine-tuned large language model, a classic supervised learning algorithm, or a rule-based automation. The tool is designed to support teams in making grounded, informed decisions based on data availability, business goals and regulatory expectations.

You can download the full decision tree here and use it as part of your internal planning or evaluation process.

Pros
  • Relatively flat organization.
  • Informal data governance bodies.
  • Relatively quick to establish and implement.
Cons

  • Consensus discussions tend to take longer than with a centralised model.

  • Many participants, which can compromise governance bodies.
  • May be difficult to sustain over time.
  • Provides the least value.
  • Difficult to coordinate.
  • Business as usual can interrupt data governance.
  • Issues around data co-ownership and accountability.

The fight for a better planet

Next to people, LACO is also focused on the fight for a better planet. Using cutting-edge data science technologies, we worked out a sustainable business approach for the aviation industry to combat food waste and the wasted fuel to transport 6.1 million tonnes of unnecessary food. Check it out!

Which AI model fits your business need?2026-02-16T08:37:33+00:00

Data governance: 7 best practices for success

Are you struggling to get your data governance initiative off the ground? Or perhaps finding it difficult to maintain momentum in your existing programme? You’re not alone. Launching a data governance programme can feel overwhelming. With so many elements to consider—from roles and responsibilities to policies and processes—it’s easy to lose sight of the fundamentals.

The seven essential best practices below will help you build a strong foundation for your data governance programme and set you up for success. Ultimately, the goal is to make data governance a natural part of how your organisation operates, leading to the consistent and effective management of data as a valuable asset. Whether you’re just getting started or looking to refine your existing approach, these insights will help you build a sustainable, effective programme that will stand the test of time.

Best practice #1 – Rally support at every level

For your data governance programme to thrive, it needs to be supported from the top of the company down and from the bottom up. This means you need committed leaders and actively involved employees. When management and staff are both engaged, the link between strategy and execution is strong, and your programme is more likely to be successful and sustainable.

Bottom-up support

To generate grassroots support, you need to build enthusiasm and understanding among employees. The key here is communication and education—employees need to see how data governance practices will positively impact their daily tasks, making their work more efficient and less prone to errors. Once they understand that these practices will likely lead to more accurate data for decision-making, fewer mistakes, and time saved from not having to correct data, they are more likely to embrace and advocate for the programme.

Involving employees in the early stages, such as during the policy development process, can also foster a sense of ownership and accountability. This participatory approach turns employees into advocates who expand company awareness about the importance of data governance.

Top-down support

Best practice #2 – Assess your current situation

To get your organisation’s data governance to where it needs to be, you need to know exactly where it is right now. This means conducting a thorough assessment of how data is currently managed, stored, accessed and utilised at your organisation. You will identify gaps, recognize areas for improvement and determine the capacity for change. This assessment will enable you to align your efforts with your organisation’s specific needs and help you to address the most pressing issues first.

Start by conducting interviews and surveys with key stakeholders across all departments. These conversations can reveal inconsistencies in data handling, data-quality pain points, lack of ownership, and other underlying issues that hinder effective data governance. A data maturity assessmentcan also be beneficial because it provides a framework for understanding how advanced or rudimentary your current practices are. You can utilise industry frameworks like DCAM or CMMI to do this—these offer structured approaches for identifying strengths and areas for improvement.

Best practice #3 – Zero in on the data

It’s crucial to get to know the actual data you are governing and why it’s useful. This lets you focus your resources where they will have the most impact. By concentrating on high-value data, you can demonstrate the tangible benefits of data governance early on, such as improved data quality, comprehensive data documentation in a business glossary, and compliance with critical regulations like GDPR or CSRD.

The best place to start is by identifying your critical data elements (CDEs). These are the data assets that are most vital to your organisation’s operations, decision-making and strategic goals. CDEs might include customer information, financial records, or operational metrics—any data that, if compromised or mismanaged, could significantly impact your business.

Identifying your CDEs means consulting with your organisation’s different departments and stakeholders to understand which data points are essential and why. This not only highlights the data that needs the most robust governance but also fosters a sense of ownership among stakeholders, as they see their priorities reflected in the governance strategy. It’s also a good idea to engage with business leaders, data stewards and analysts to gather insights into how different data sets drive key business processes.

Document and categorize your CDEs according to their sensitivity and criticality as you go. Doing this will guide the implementation of specific policies and controls tailored to each type of data, ensuring that the most sensitive data receives the highest level of protection.

Best practice #4 – Demonstrate value early

Quick wins are a wonderful way to encourage everyone at your company to value and adhere to your data governance programme. A quick win is a small, achievable goal that produces immediate, tangible benefits. For example, you might focus on improving the accuracy of critical reports, streamlining access to frequently used data sets, or resolving a persistent data-quality issue that has been causing operational inefficiencies.

By setting and achieving small goals, you create opportunities to showcase the benefits of data governance from the get-go. Quick wins generate enthusiasm and buy-in across all levels of the organisation. When employees see how data governance can make their jobs easier—whether by reducing the time spent on data retrieval or improving the reliability of the data they work with—they become advocates for the programme. This grassroots support is invaluable for driving change and embedding data governance practices in your company culture.

Quick wins also establish credibility for the data governance programme by proving that it delivers real value. Stakeholders who see immediate improvements are more likely to support and engage with the programme, which is crucial for its long-term success. Early success helps you secure the resources and support needed for more extensive governance initiatives down the line. You create a positive feedback loop in which initial successes lead to greater investment in data governance, enabling even more significant improvements in the future.

Best practice #5 – Focus on process, not tools

A common pitfall in launching a data governance programme is the temptation to purchase tools before establishing robust processes. While tools can be incredibly valuable, they are not a substitute for a solid foundation of governance practices. Tools should serve to enhance and streamline processes that are already well-defined, not act as a shortcut or replacement for proper governance structures.

Focusing on the process first means embedding governance practices into your organisation’s daily operations. You need to clearly define roles, responsibilities and workflows related to data management. Establishing these processes helps to ensure that your data governance efforts are aligned with your organisation’s strategic goals and operational needs. It also creates a strong framework that can be scaled and adapted as your data governance programme evolves.

Once your foundational processes are in place, you can start evaluating tools that will complement and enhance your efforts. Selecting tools without having first established processes can lead to mismatches between your needs and the tool’s capabilities, resulting in wasted resources and potential setbacks. By prioritising processes over tools, you ensure that any technology you implement is a good fit for your organisation’s specific requirements, ultimately contributing to the success of your data governance programme.

Best practice #6 – Create cultural change

For data governance to succeed, it must go beyond just policies and procedures—it requires a cultural shift within the organisation. You need to embed the importance of data governance into the very fabric of your organisation’s daily operations. When employees see how data governance directly benefits their work, they are more likely to embrace and advocate for it, leading to a stronger, company-wide commitment to managing data as an asset.

Start by communicating the value of data governance in a way that resonates. In other words? Let employees know what’s in it for them. Data governance isn’t just about compliance or better decision-making; it’s about empowering every employee to work more efficiently, reducing the time they spend on data-related issues, and ensuring they have access to accurate, reliable information that supports their daily tasks.

Cultural change requires ongoing engagement, not just a one-off training session. Regular workshops, updates and discussions about the benefits and progress of data governance can keep the momentum going. Leadership plays a crucial role here. When executives not only endorse but actively participate in data governance efforts, it signals to the rest of the organisation that this is a priority, not a passing initiative.

Creating a culture that values data governance also means recognizing and rewarding adherence to these practices. Whether it’s through formal recognition programmes or simply acknowledging efforts in meetings, reinforcing positive behavior helps to cement the importance of data governance in your organisational culture.

Best practice #7 – Define control measurements

Defining control measurements is vital for ensuring the success of your data governance programme. These metrics provide a framework for evaluating the effectiveness of your data governance efforts and identifying areas that require improvement.

Start by determining the key metrics that align with your organisation’s data governance goals. Common metrics include data-quality indicators, data-steward onboarding rates, compliance with data-handling procedures, and the number of documented data terms. For instance, tracking the accuracy and completeness of critical data elements (CDEs) can provide insight into how well your governance practices are being implemented. Similarly, measuring compliance rates with data policies can highlight areas where additional training or communication may be needed.

Once these metrics are established, develop a regular monitoring process. This might involve scheduled reviews where governance teams assess progress against predefined benchmarks or automated dashboards that provide real-time insights. The goal is to create a feedback loop where data governance practices are continuously refined based on measurable outcomes.

Regular reporting is also crucial. Sharing these metrics with stakeholders across the organisation helps maintain transparency and accountability. It allows leaders to see the tangible benefits of data governance and supports ongoing investment in governance initiatives.

| LACO
Data governance: 7 best practices for success2026-02-16T08:37:44+00:00

5 key takeaways from SAS Innovate that could reshape your AI strategy

SAS Innovate is a showcase of how SAS tools can be used to build AI-driven applications that reshape how businesses harness data and AI.

It’s also a chance to gain insights and knowledge that simplify and enhance data and AI ecosystems, save businesses time and make it easier to make the right decisions.
From synthetic data to hands-on experimentation, here’s what stood out, and what it means for your organisation.

Takeaway 1:
Synthetic data in AI has potential, but its value remains to be proved

Synthetic, AI-generated data allows for analysis without using real information, keeping data blinded and secure. It’s used for a wide range of purposes, like:

  • fraud detection
  • cybersecurity monitoring
  • policy development
  • law enforcement
  • economic analysis
  • clinical trials
  • worker safety
  • quality control

It preserves the statistical properties and patterns of the original data and can increase the volume of minority data, enabling models to be better trained to recognise this group.

Augment & generate data

This is what the SAS Data Maker does. It unlocks the potential of existing data using a low-code/no-code interface to quickly augment or generate data.

While it can be very useful for specific sectors with strict data privacy requirements, its practical value will really depend on pricing and packaging, especially when compared to more DIY solutions using custom scripts and blinded datasets. Our verdict: watch this space.

Read more: How to create an impactful data governance policy

Takeaway 2:
We’re quickly moving towards smarter automation, safer workplaces & sensitive chatbots

Several practical AI innovations are happening within SAS. A highlight from the showcase was the introduction of an SAS custom step in Viya — an easy-to-use, modular feature accessible to both SAS and non-SAS users, with use cases like auto-documenting code and supported by a public GitHub repository.

Agentic AI addresses hallucinations in AI-generated responses by using tools like SAS Model Manager to track prompts and available language models.

Guardrails for chatbot interactions use sentiment analysis to handle sensitive scenarios like missed payments or emergencies, and can automatically derive actionable steps from conversations.

Lastly, event stream processing is used to detect and prevent workplace incidents by monitoring CCTV footage for safety compliance (e.g. missing PPE) and alerting relevant teams in real-time.

Takeaway 3:
SAS Viya Workbench is a big step forward

The SAS Viya Workbench is a coding interface for developers that appears to be a solid move by SAS with lots of potential. What are the benefits?

It allows users to work with SAS, Python or R in a notebook way of working.
It focuses on self-service, enabling developers to allocate the resources they need and choose their preferred IDE.
It’s a standalone tool that doesn’t require additional SAS Viya or SAS 9 licences.
Improvements coming in Q2 and Q3 include customisable environments and batch scheduling.

Stepping up

The Viya Workbench feels like a promising step forward. With Microsoft setting strong examples like Databricks, Fabric and Synapse, it’s encouraging to see SAS stepping up with similar products. If development continues, Viya Workbench has the potential to become a widely adopted multilanguage platform — something many enterprises will find appealing.

Want to know more? SAS has created a webinar with additional information here

Takeaway 4:
Clear communication remains essential

The evolution of the data and analytics ecosystem into an integrated cohesive (cloud) platform offers exciting prospects for organisations seeking to unlock the full potential of their data assets. These figures show that if organisations can learn to navigate the evolving landscape, they can harness the transformative power of integrated cloud data platforms.

  • By 2024, enterprises that primarily build applications leveraging a D&A ecosystem from a single cloud service provider will outperform competitors, despite vendor lock-in.
  • By 2024, 50% of new system deployments in the cloud will be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions.
  • Through 2025, powerhouse cloud ecosystems will consolidate the vendor landscape by 30% leaving customers with fewer choices and less control of their software destiny.

Takeaway 5:
SAS 9.4 M9 released, a major leap in security and integration

The latest maintenance release of SAS 9.4, M9, was launched on 17 June 2025. It delivers a robust security enhancements, Azure integration improvements and a clear roadmap for the future beyond M9.

  • Security first: M9 introduces automated TLS setup, drastically reducing manual configuration and errors. A centralised certificate management improves governance and consistency. The release also brings Multi-Factor Authentication (MFA) with Single Sign-On (SSO) integration, and commits to quarterly security updates
  • Azure & Viya integration: M9 strengthens interoperability with SAS Viya and Microsoft Azure, making hybrid deployments smoother and more scalable.
  • Extended support: SAS has confirmed standard support for 9.4 M9 through 2030, giving organisations long-term stability.
  • Future-proofing: Even more importantly, SAS has confirmed that another maintenance release (M10) is on the roadmap, reassuring us for continued investment in the 9.4 platform.

Why these insights matter

SAS Innovate made one thing clear: the data and AI landscape is evolving fast, and organisations need to keep pace. From synthetic data and agentic AI to hands-on tools like Viya Workbench, the innovations on display aren’t future concepts, they’re practical enablers that can drive smarter, safer, and more efficient decisions today.

For businesses aiming to future-proof their data strategy, SAS is positioning itself as a powerful ecosystem to build on. And for data & AI teams like ours at LACO, the event reaffirmed the importance of staying curious, experimenting with new tools, and continuously refining your approach in an ever-changing environment.

5 key takeaways from SAS Innovate that could reshape your AI strategy2026-02-16T08:37:54+00:00

How to create an impactful data governance policy

Want to harness the full potential of your organisation’s data assets? Then you’ll need a robust data governance policy. This is a comprehensive set of guidelines and rules that define the ways in which your organisation’s data is handled, stored, accessed and protected.

With an impactful data governance policy, you don’t just manage data effectively, maintain high data quality, and ensure you comply with regulatory requirements like GDPR, CSRD or the AI Act, but you also foster a company culture of data responsibility and turn your organisation’s data into a valuable asset that feeds your business objectives.

Setting up a data governance policy might seem complicated at first, but it all becomes clear once you understand the basics. Let us walk you through it …

What is a data governance policy?

Your first step in taking control of your organisation’s data is to develop a data governance framework. Once you have this, and you’ve defined the roles and responsibilities for your organisation, it’s time to lay out the guidelines and rules that will enable data governance to run smoothly—this is your data governance policy. It translates your data governance framework into actionable steps that enable your organisation to meet its objectives.

You may or may not already have a data governance charter that you can use to guide your policy. This is closely related to a data governance policy, but the two documents serve different purposes.

Data governance charter

The charter is a high-level document outlining the overall data governance framework, including the organisation’s vision, mission, and objectives for data governance. It sets the stage for developing specific policies and standards.

Data governance policy

The policy is more detailed and focuses on specific guidelines and rules for managing data. Derived from the principles outlined in the charter, the policy provides actionable steps for data governance, ensuring that the organisation’s data management practices are aligned with its strategic goals.

The four key components of a data governance policy

To build a successful data governance policy, you need to understand the components that form its foundation: principles, policies, standards and processes. These elements work together to ensure data is managed effectively and securely.

1. Principles – Your organisation’s views and values in relation to data

Principles are the fundamental beliefs that guide your organisation’s approach to data. They are like the North Star, providing direction and setting the stage for how data is treated within your company. For instance, a principle might be “Data is an asset,” which underscores the value placed on data within the organisation.

2. Policies – The rules and guidelines that align practices with principles

Policies are the rules of the game; the do’s and don’ts for managing data at your organisation. Your policies dictate how data is handled, ensuring company-wide consistency and compliance. To create an effective data governance policy, you also need individual data policies that maintain the quality, security and usability of your data.

Data governance policies vs individual data policies. What’s the difference?
  • A data governance policy outlines the overarching principles and framework for managing data across your organisation.
  • Individual data policies are guidelines and rules that address specific aspects of data management, such as quality, security, and access.

Must-have individual data policies

1. Data quality policy

A data quality policy ensures you have the accurate, complete and reliable data you need for making informed business decisions. Organisations should implement regular audits, validation processes and error correction mechanisms to maintain high data quality standards. Additionally, data quality metrics and dashboards can be employed to continuously monitor the status of data across the organisation. By doing this, organisations can quickly identify and address any issues that arise, ensuring that the data remains a valuable asset.

2. Data security policy

Data security is paramount in protecting sensitive information from unauthorized access and breaches. Implementing a data security policy that ensures robust encryption, access controls and security monitoring is essential for safeguarding data. Regularly updating security protocols to address new threats and vulnerabilities is also critical. Organisations must ensure that their data security measures comply with relevant regulations and standards to prevent data breaches and protect against potential legal ramifications.

3. Data access and usage policy

Defining clear guidelines for data access and usage is crucial for ensuring data is used ethically and in compliance with regulations. This policy should specify who can access data and under what conditions. Establishing guidelines for data sharing, both internally and externally, helps prevent misuse and ensures that data is used appropriately. By implementing strict access controls and monitoring data usage, organisations can maintain the integrity and confidentiality of their data.

4. Data retention policy

A data retention policy specifies how long data should be retained and the guidelines for its disposal. This policy is crucial for managing the data lifecycle effectively and ensuring compliance with legal and regulatory requirements. Secure disposal methods, such as data wiping or physical destruction of storage media, are outlined to ensure that obsolete data is irretrievably removed, protecting the organisation from potential data misuse.

3. Standards – Detailed instructions that ensure consistency and quality

Standards are the specific criteria or benchmarks that need to be met to comply with policies. Think of standards as the rulebook’s detailed instructions. If the policy is the rule that says you must score goals to win a game, standards explain how big the goalposts should be and what counts as a valid goal. They provide detailed, measurable requirements to ensure quality and security.

4. Processes – Step-by-step strategies for ensuring policy compliance

Processes are the step-by-step actions taken to meet standards and comply with policies. Consider processes as the playbook or strategy manual. They outline how to execute plays effectively to follow the game’s rules and achieve goals. In data governance, processes include data handling procedures, monitoring activities and compliance checks that ensure policies and standards are followed correctly.

Six steps for creating an effective data governance policy

Whether you’re establishing a new framework or updating an existing one, these guidelines will help you build a policy that supports your organisation’s goals.

Step 1 – Gather stakeholder feedback

First up, engage with your organisation’s key stakeholders to make sure you understand their needs and expectations. This might mean conducting interviews or workshops, or simply having informal discussions. Think of this as essential research; you need to know what is important to each stakeholder in order to create a policy that caters to everyone.

Step 2 – Establish data governance principles

Identify the core principles that will guide your data governance policy. These principles should reflect the organisation’s values and strategic vision regarding data. They are the values that shape your company’s culture and light the way forward. For example, a principle might be “Data quality and security are assured at all times.”

Step 3 – Align your data governance policy with your business objectives

Your data governance policy should support the strategic objectives of your organisation. This alignment ensures that all data management practices contribute directly to your business goals, such as enhancing customer satisfaction, improving decision-making and driving innovation.

Step 4 – Define the purpose and scope of your data governance policy

Clearly articulating your policy’s purpose and scope sets the foundation for your data governance practices. The purpose explains why the policy exists and how it supports the organisation’s mission and business objectives. The scope specifies who is affected by the policy and the types of data it covers.

Step 5 – Draft your data governance policy document

Now it’s time to create a blueprint for your data governance practices. To draft the policy document, you need to outline the essential components:

  • Purpose: Explain why the policy exists and how it supports your business objectives.
  • Scope: Define who is affected by the policy and what types of data it covers.
  • Roles and responsibilities: Clearly defining roles and responsibilities is essential for avoiding confusion and ensuring accountability. Each role, from data stewards to data custodians, should have well-defined tasks and expectations.
  • Policy rules and classifications: Draft the rules that will govern data handling within your organisation. These rules should cover data quality standards, security protocols, access controls, and usage guidelines. Classify data to ensure it is handled appropriately according to its sensitivity and importance.
  • Compliance and security measures: Detail the measures in place to ensure compliance with policy rules, regulations and data security.
  • Review process: Establish a process for regular reviews and updates. This ensures the policy remains relevant and effective in the face of evolving business needs and regulatory changes.

Step 6 – Review and validate your data governance policy

Once your draft is ready, share it with stakeholders for feedback and validation. This collaborative approach ensures your data governance policy addresses all concerns and aligns with your organisation’s goals. Just like having a team review a project plan before its execution, this review ensures everyone is on the same page and committed to the plan.

How to put your data governance policy into action

Implementing your data governance policy effectively requires careful planning and communication, as well as ongoing (change) management. Your goal is not just to make sure everyone knows about the policy, but to embed these priorities and practices into the company culture so they become second nature.

Use the following tips to help structure your implementation strategy and ensure you cover all the bases.

1. Secure executive sponsorship

Gaining support from your organisation’s top leaders is essential. Executive sponsors can provide the necessary resources, remove obstacles, and reinforce the importance of data governance across the organisation.

2. Establish a governance body

Form a data governance council or committee to oversee the policy’s implementation. This body should include representatives from key departments such as IT, legal, compliance and business units. The council’s responsibilities include setting priorities, resolving issues and ensuring alignment with organisational goals.

3. Educate and train employees

Educating employees about the data governance policy and its importance is critical. Training sessions should be tailored to different roles within the organisation, ensuring that everyone understands their responsibilities. Awareness programs can help foster a data-centric culture by emphasizing the benefits of data governance and the role each employee plays in maintaining data integrity and security.

4. Communicate effectively

Communication is the key to successful implementation. Develop a communication plan that includes regular updates, newsletters and workshops. Use various channels to reach all employees and ensure that your data governance policy’s objectives and procedures are clearly understood. Transparent communication helps in gaining buy-in and promoting a culture of accountability.

5. Monitor and measure

Define metrics to assess the policy’s effectiveness and ensure compliance. Regularly review these metrics to identify areas for improvement. Monitoring should include audits, surveys, and performance indicators to track adherence to the policy. Use this data to make informed adjustments and keep the policy relevant and effective.

Reap the benefits of successful data governance

Fully implemented, your data governance policy transforms from a static document into a dynamic framework that enables you to harness the power of your company’s data and use it to pave new paths to even more success. Need help getting started? Contact us—our experts will happily help you on your way.

How to create an impactful data governance policy2026-02-16T08:38:08+00:00

The essential role of data stewards

In the digital age, data flows through businesses as profusely as water flows down mountains. The people responsible for protecting and directing a company’s data are called data stewards. They are the guardians of data integrity, ensuring information is accurate, accessible, reliable and secure. Moreover, they are strategic enablers that bridge the gap between raw data and actionable insights, helping organisations harness the power of data to maintain a competitive edge in a rapidly changing world.

Along with protecting sensitive information by collecting, analysing and reporting data accurately and transparently, data stewards ensure compliance with stringent data protection laws like GDPR, and newly introduced sustainability reporting regulations, such as the European Corporate Sustainability Reporting Directive (CSRD).
Data stewards are crucial. But what is it they do, exactly? How do they protect and manage company data? And what do you need to consider when selecting the best people for the role? Read on to discover how to set up your company for successful data stewardship.

What do data stewards do?

Businesses generate vast amounts of data from various sources, and the complexity of managing this data—known as data governance—has increased dramatically in recent years. Data governance and data stewardship go hand in hand. While data governance encompasses the overall management of data within an organisation, data stewardship focuses on the day-to-day tasks of maintaining and safeguarding data. However, data stewards do more than just play an operational role, they also implement key strategies that keep a company’s data-focused initiatives running smoothly and effectively.

Imagine a bustling restaurant kitchen with different workstations: the pastry station, grill, appetizers, sauces, etc. Each station has a chef skilled in their specific culinary area, but there is also a head chef overseeing them all. The head chef is the resident expert. They ensure every dish is prepared to perfection. Similarly, a data steward is a subject matter expert within their data domain. They possess a deep understanding of specific data sets and how to work with them.

In a restaurant kitchen, each dish has a recipe and a list of ingredients—the input that makes up the meal. In data terms, this is metadata. Like chefs planning dishes, data stewards create and manage metadata for their data sets. Head chefs also perform tasks behind the scenes, such as ensuring the quality of ingredients and choosing the best cooking techniques or presentation methods. Data stewards similarly ensure data quality through activities like data profiling, cleansing and establishing governance standards.

Data governance mitigates risks

The data domains within an organisation are akin to the workstations in a kitchen. They’re logical groupings of types of data, such as customer, product and location data. A data steward oversees one or more data domains, taking on a range of primary responsibilities:

  • Ensuring data quality: This involves data profiling, cleansing, root cause analysis, and regular auditing.
  • Managing metadata: Maintaining business glossaries, reviewing term submissions, and ensuring clear, concise definitions.
  • Enforcing data governance: Implementing and monitoring data-related roles, policies, standards and processes.
  • Contributing to documentation: Helping to create and document data roles and standards based on professional expertise and stakeholder feedback.
  • Monitoring data usage: Identifying ways to leverage data to drive enterprise objectives.

As you can see, the role of a data steward encompasses a wide array of responsibilities tailored towards optimising the use of data while ensuring its integrity.

One of the primary roles of data stewards is data quality management, which includes identifying and correcting errors, ensuring consistency, and verifying that the data in use is up to date. This involves auditing, reporting and remediation activities that help maintain the data at the standard necessary for operational and analytical accuracy.

Data stewards are also deeply involved in metadata management. They create and maintain clear documentation that describes various data elements and their contexts, making data understandable and usable for technical and non-technical stakeholders alike. This includes defining data terms clearly, documenting data lineage, and ensuring that everyone in the organisation understands what data is available, what it can be used for, and what its limitations are.

Furthermore, data stewards enforce data governance policies. They work collaboratively across departments to implement data access controls, ensuring that sensitive information is only accessible to authorised personnel. This role is crucial in preventing unauthorised data access and leaks, thereby protecting the enterprise from potential data misuse or theft. Through these efforts, data stewards not only safeguard data but also build a culture of accountability and data literacy within the organisation.

How do you select a data steward?

Selecting a data steward is an important decision that requires careful consideration. While the specific requirements may vary depending on the organisation’s needs, there are a few key qualities to look for in a potential data steward:

  • Subject matter expertise: Data stewards should have a deep understanding of the data for which they are responsible. This includes knowledge of data sources, usage and potential risks associated with the data.
  • Data-driven mindset: Data stewards must have a deep understanding and appreciation of the value of data. They should approach their role with an analytical mindset and be comfortable working with large amounts of information.
  • Attention to detail: Data stewards must demonstrate excellent attention to detail and have a keen eye for spotting inconsistencies or errors in data. They should also be able to identify patterns and trends within data sets.
  • Communication skills: Data stewards need to effectively communicate complex technical concepts to non-technical stakeholders. They should also be able to explain data-related issues and solutions in a clear and concise manner.
  • Collaborative nature: Data stewards often work with various teams and departments within an organisation. Therefore, they should have strong collaborative skills and be able to build relationships with others.

The role of data steward requires a unique blend of skills, including technical knowledge, business insight and interpersonal abilities. These competencies enable data stewards to manage data effectively and advocate for its strategic use within their organisation. Their ability to act as a liaison between IT and business units is critical for translating data-related activities into business value.

Everybody plays a part in data stewardship

Data stewardship is not just limited to those specifically designated as data stewards. In today’s data-driven world, almost everything we do involves some form of data and everyone within an organisation has a role to play in ensuring the accuracy of data and its responsible use.

Data literacy is important for employees at all levels. To ensure they are using data correctly and ethically, all employees must have a clear understanding of the organisation’s data policies and procedures. They need to know how data is collected, stored and used within the organisation. Additionally, everyone within the organisation is responsible for the quality of the data they produce. This inclusive approach to data stewardship empowers every employee to take responsibility for the data they handle, thereby decentralising data governance and enhancing overall data quality.

How to implement data stewardship: Overcoming the 4 biggest challenges

Implementing effective data stewardship not only requires new processes, but also new attitudes and behaviours. It’s important to be prepared for the challenges that may arise during this process:

  • Shifting the company culture: It is integral to cultivate a company culture that views data as a vital strategic asset—everyone must learn to prioritise data accuracy and integrity. Data stewards often lead the charge in advocating for the prioritisation of data governance by demonstrating how it can directly benefit the different departments.
  • Getting everyone on board: Change management is a crucial aspect of successful data stewardship. Changes are often met with resistance from employees who are comfortable with the current processes and systems. This challenge can be overcome by involving stakeholders in the decision-making process and addressing their concerns.
  • Allocating resources: Data stewardship requires dedicated resources, in terms of both time and budget. Organisations may struggle to allocate these resources due to competing priorities. To overcome this, data stewards can demonstrate the ROI of effective data management by showcasing its impact on business outcomes.
  • Breaking down data silos: Data stewards need to work collaboratively across departments to ensure that data is not only shared but also consistently managed across the enterprise. This can necessitate the dismantling of data silos that have been entrenched in an organisation for years. Achieving this requires robust communication channels and often, the implementation of integrated data management systems that support data visibility and accessibility across the organisation.

By addressing these challenges head-on, data stewards can help to foster a culture that sees data as a critical asset, leading to sustained business growth and success.

The future is data-based

The role of data stewards in a modern business transcends traditional data management. They do much more than simply maintain data; they enhance data quality, enforce regulatory compliance, manage metadata, and ultimately, unlock the true potential of data-driven decision-making. They are the head chefs in the complex kitchen of data management, playing crucial roles in ensuring the precision and reliability of the data their companies depend on.

With the rapid growth of data and the swift advancement of technologies and regulations, the need for skilled data stewards is more pressing than ever. Modern businesses that recognise and empower their data stewards are better equipped to leverage their data for competitive advantage, regulatory compliance, and enhanced operational efficiency. In the digital age, data stewards are essential for business success in any data-driven enterprise.

The essential role of data stewards2026-02-16T08:38:17+00:00

Data governance roles and responsibilities

Data is everywhere in today’s business world. It’s an integral part of every workday, helping with everything from providing customer insights to enabling strategy analysis and driving process optimisation. The ever-growing volume and complexity of data in today’s business landscape means that if you want your business to thrive, you need to prioritise data governance.

Like a trusted compass, a well-designed data governance model guides your company’s data journey, ensuring accuracy, consistency and security of your data assets. However, this doesn’t happen automatically. To keep data governance running smoothly and make sure nothing falls through the cracks, you need to delegate clear roles and responsibilities for data management.

There are five essential roles to consider as you embark on this journey. You need a leader—your Chief Data Officer (CDO)—as well as data owners, data stewards, data custodians and data consumers. In a full-fledged data governance programme, there are other important roles to consider, such as data architects, data protection officers, data analysts, and more. But in the beginning, the most important thing is to define the five essential roles and assign them to the most suitable employees.

The 5 essential roles in data governance

An easy way to understand data governance is to think of your organisation as a bustling city. Data is the electricity that powers everything from streetlights to skyscrapers. And just like a city needs electricians, engineers and policymakers to manage its power supply effectively, an organisation needs people in specific roles to govern its data. These roles each require certain skills and come with unique responsibilities.

Whether you’re a small startup or a large corporation, understanding these roles is the first step towards designing your data governance operating model.

The Chief Data Officer

Your Chief Data Officer (CDO) is your data governance leader—the mayor of your data-powered city. They orchestrate the flow of electricity (data). Armed with expertise and experience, the CDO ensures the city is always powered up and functioning optimally. They are the visionary leader who charts the course for the city’s growth. They shape the policies and procedures that underpin how data is collected, stored, managed and utilised across the organisation.

The CDO engages with stakeholders across the company, educating them about the value of data governance, addressing their concerns, and securing their buy-in, much like a mayor engaging with citizens and local businesses. As the city’s chief regulator, the CDO ensures all data practices comply with relevant laws, regulations and industry standards.

The CDO weaves data governance into the very fabric of your organisation. Embracing the ever-changing nature of the business landscape, they ensure a nimble programme that evolves alongside your organisation, tackling new data governance challenges and seizing opportunities along the way.

Ideally, the CDO should be part of the C-suite, with a direct line of communication to the top-level leaders. This allows them to make strategic decisions that impact how data is managed and utilised, just like a mayor shaping city policies.

The foundational data governance roles

It’s important to build a strong foundation on which your data governance system can thrive. This means making sure you understand the different roles and responsibilities associated with data governance and choosing how you will define them for your company.

Data owners: steering data accountability

Think of data owners as the city’s policymakers. They are responsible for making strategic decisions about how the city’s electricity (your company’s data) should be used.

Definition

Data owners are high-level individuals within an organisation who have legal authority and control over a particular set of data.

Affiliation

Business

Responsibilities
  • Setting policies for data usage and security.
  • Making strategic decisions about data-related issues or investments.
  • Ensuring compliance with data regulations and standards.
Ideal fit

Senior executives or department heads who understand the strategic importance of data and can make decisions that align with the organisation’s overall goals.

Data stewards: ensuring data quality and usage

Data stewards are like city planners. They ensure the city’s power infrastructure (data flow) is well maintained and meets the needs of its inhabitants.

Definition

Data stewards are responsible for maintaining the quality and usage of data within an organisation.

Affiliation

Business

Responsibilities
  • Implementing the policies set by data owners.
  • Overseeing data quality and resolving any data-related issues.
  • Coordinating with other roles to ensure data is accessible, reliable, and secure.
Ideal fit

Individuals with strong project management skills who have a deep understanding of your organisation’s data landscape.

Data custodians: safeguarding the technical side of data

Data custodians are like the city’s electricians. They ensure the city’s power grid (data governance model) is functioning properly and safely.

Definition

Data custodians are responsible for the technical aspects of data management. They ensure that data is stored securely, backed up regularly, and accessible when needed.

Affiliation

Technical

Responsibilities
  • Managing data storage and backup procedures.
  • Implementing data security measures.
  • Ensuring data is accessible and retrievable for data consumers.
Ideal fit

IT professionals with expertise in data management and security.

Data consumers: using data for informed decision taking

Data consumers are the city’s inhabitants. They use the city’s electricity (your company’s data) for various purposes, from powering their homes to running businesses.

Definition

Data consumers are the users of data within the organisation. They rely on accurate and reliable data to perform their job functions.

Affiliation

Business

Responsibilities
  • Using data responsibly and in accordance with company policies.
  • Providing feedback on data quality and accessibility.
  • Participating in data-related training and development programmes.
Ideal fit

Any employee within the organisation who uses data to perform their duties, from sales and marketing to finance and operations.

Allocating roles and responsibilities

Once you’re clear on the roles and responsibilities required for smooth-flowing data governance, it’s time to take a close look at your team.

  • Identify key players: Identify the key players within your organisation—these are the people who will implement your data governance system.
  • Role definition: Decide who will perform the roles of data owners, data stewards, data custodians and data consumers. Then, define the responsibilities of each employee’s individual role accurately and with as much detail as possible—clarity is key! A data steward shouldn’t be expected to handle the technical aspects of data management and a data consumer shouldn’t be put in charge of ensuring data quality. Stick to the roles and responsibilities you have defined.
  • Match skills and expertise: It’s also important to assign roles based on an individual’s skills and experience. In the same way that city managers would select a trained electrician to manage the power grid, you should choose individuals with the appropriate expertise for each role in your data governance system.

Preparing for long-term success

With clear roles and responsibilities in place, your data governance is off to a flying start. But this isn’t a set-and-forget solution. Change is inevitable. You need to embrace change management to keep your data governance system agile and adaptable. Your system—and the team of people implementing it—must continuously evolve to succeed in the long term. This means you need to be open to feedback, ready to implement improvements, and prepared to manage the impact of any changes.

Important things to remember:

  • Communication is key. Clear communication ensures everyone understands their role and their individual responsibilities. They know what is expected of them, what they need to do, and the ways in which they contribute to the system’s overall goals.
  • Ongoing development is essential. Foster an environment of learning and improvement to ensure your team remains effective and adaptable.
  • And be sure to provide regular training to keep all participants up to date with the latest data governance practices and regulations.

A robust data governance model is essential for modern businesses. And with the right people in the right roles, your organisation is not only able to reap the benefits of well-managed data, but also able to ensure regulation compliance and better serve your customers.

Data governance roles and responsibilities2026-02-16T08:38:26+00:00

The seven key elements of a data governance framework

In our rapidly evolving digital age, with a rising number of data breaches and new rules like CSRD coming into play, being able to handle accurate, secure, and trusted data has become crucial for today’s businesses. This is why companies need to get really good at data governance – that’s how they can effectively manage data as a valuable resource. But as the volume of data grows exponentially, the way we manage and govern this data becomes increasingly critical. This is where understanding the distinction between data governance and data management comes into play.

So, you’ve determined that data governance is important for your business and you’re ready to integrate it. The question now is, where do you start? This is where the seven crucial elements come in handy. These elements should be part of your action plan to govern your data effectively.

In the following sections, we will demystify the differences between data governance and data management and deep dive into the essential components of a robust data governance framework. Whether you’re a C-level executive seeking to leverage data more effectively, or simply interested in enhancing your organisation’s data practices, this blog post will provide valuable insights to steer you in the right direction.

Data governance and data management: The symphony of data success

Before we begin discussing the elements to include in your data governance framework, it’s important to clarify the difference between data governance and data management. In the vast world of business data, two terms often stand out: data governance and data management. Both are crucial in the journey to effective data utilisation, but they are not interchangeable. For C-level executives navigating the complexities of modern data landscapes, understanding the separation of duties between these two concepts is vital.

| LACO

While they have distinct responsibilities and objectives, data governance and data management complement each other. Data governance sets the pace and direction, while data management plays the notes, executing on the ground. Both are essential for creating a harmonious symphony of effective and efficient data use.

Consider this: Without governance, management could become disjointed, lacking clear guidelines and oversight. Conversely, without management, governance strategies and policies would remain theoretical, never quite making it into practice.

In conclusion, data governance and data management are two sides of the same coin, each carrying out separate but complementary duties. Understanding their unique roles and how they work together is key to leveraging data effectively within your organisation. Remember, it’s not about choosing between data governance and data management. Instead, it’s about ensuring they work in harmony, guiding your organisation towards a more informed, data-driven future. By achieving this balance, you’ll be well on your way to conducting your own successful data symphony.

The emergence of the European Green Deal

“The seven key components of a data governance framework”

Navigating the world of data can be like maneuvering through a complex labyrinth. That’s where a data governance framework comes in – it’s your map and compass, guiding you towards effective data usage. Think of a data governance framework as a recipe for a successful meal. It’s a detailed list of all the ingredients (or components) you need to create an effective data governance program. Just as you would consider different elements like vegetables, proteins, and spices when cooking a meal, in data governance, you need to consider various aspects such as strategy, organisation, directives, and technology. This ‘recipe’ or framework ensures that you’re not missing any crucial steps or components when setting up data governance for your business. It’s essentially your guide to making sure you’re doing everything necessary to manage, protect, and optimise your data effectively.

At LACO, we have identified seven key components to build a solid data governance framework:

  • strategy,

  • organisation,

  • directives,
  • measurement,

  • organisational change management,

  • technology, and
  • data and enterprise architecture.

Together, they provide a comprehensive approach to managing, protecting, and optimising data, turning it from raw information into a strategic asset that drives informed decision-making, promotes compliance, reduces costs, and ultimately propels business success.

| LACO

The seven building blocks of a Data Governance Framework

1. Strategy

“A strategy is crucial to make everyone understand the importance of data governance and what it means for the organisation as well as for themselves.”

When we talk about ‘strategy’ in a data governance framework, we’re essentially talking about the game plan for managing your company’s data. It’s like a roadmap that outlines where you want to go with your data managementand how you plan to get there. This includes setting goals like improving data quality, ensuring data security, or using data to make better business decisions.

The strategy allows you to document your goals, reasons for pursuing them, and how they align with the objectives and strategies of your organisation. This is crucial to help everyone in your organisation understand the importance of governance and what it means for both the organisation and themselves as individuals. In essence, a data governance strategy is your master plan for making sure your data works for you, helping you achieve your business objectives.

2. Organisation

The ‘Organisation’ part focuses on the people who make data governance happen. It’s all about who does what – from the data stewards, who make sure the data is accurate and reliable, to the top-level executives, who support and promote data governance within the company. Let’s say in a hospital, there might be a person assigned as a data steward whose job is to look after patient data and make sure it’s correct and used properly.

This component also includes how data governance works in practice, also known as the operating model. This includes things like setting up data committees, determining how decisions are made, and how processes are carried out. Think of this as the groundwork for setting up a data governance organisation. It also deals with rules around who owns the data and who is responsible for it.

3. Directives

Directives include the policies, standards, and procedures that govern how data is handled. They’re the rules of the road, ensuring everyone knows what they should and shouldn’t do. For instance, a retail company might have a policy that customer data must be anonymised before being used in market research.

Sure, creating policies, processes, and standards is a big part of data governance. But these alone don’t guarantee success. They need to be connected to a strategy that’s driven by business needs. Also, there have to be people in charge who are responsible for carrying out, checking, and keeping an eye on these policies.

Additionally, there must be systems in place to measure and monitor how well the policies are working. And it’s important to think about how these policies and standards are communicated. Simply put, just having a policy doesn’t mean your data governance efforts are successful. It’s all about how these elements work together to support your business goals.

4. Measurement

Measuring data governance is like checking the health of your business. It’s about making sure your data practices are working well and helping your business grow. This involves keeping track of things like the quality of your data – is it accurate and reliable? Are you following all the rules and regulations related to your data? You might also look at whether your team is using data effectively to make decisions. By regularly checking these aspects, you can spot any issues early on and make necessary changes. In simple terms, measuring the effectiveness and efficiency of your data governance efforts helps you ensure your data is in good shape and your business is getting the most out of it.

5. Organisational change management

Data governance always involves changing how people work, which can be challenging. Organisational change management is all about preparing your team for changes related to how you handle data. It’s like coaching a sports team to adapt their game plan when rules or strategies change. This could involve training your staff on new data policies, helping them understand why these changes are important, and supporting them through the transition. The goal is to ensure everyone is on board and ready to work with the new data practices. This way, you can ensure your data governance plan is successful and your business continues to run smoothly, even as you make changes to how you manage data.

6. Technology

Think of technology in a data governance framework as the toolbox that helps you put your data governance plan into action. This might include tools for managing metadata, like a data catalog which is essentially a ‘library’ of your data. Or it could involve a data quality reporting tool, which is like a health check-up for your data.

But remember, technology is like your supporting actor. Its job is to back up the processes you establish. For example, if you have a process for regularly cleaning up your data, a data quality tool can help automate this task and make it more efficient.

A common mistake is to get the technology first and then figure out the processes. It’s like buying a fancy car before you know how to drive. Instead, you should first establish what you want to achieve with your data governance (i.e. your processes), and then find the technology that best supports these goals.

7. Data and enterprise architecture

This component is about how your data is structured and integrated with your broader business and IT environment. Think of it as the blueprint for a building – it outlines how all the different parts fit together. In terms of data governance, this architecture maps out:

  • how data flows through your organisation,
  • where it’s stored,
  • who has access to it, and
  • how it’s protected.

For instance, your sales team might collect customer data which then gets used by the marketing team for targeted campaigns. The architecture ensures this data transfer happens smoothly, accurately, and securely.

By having a clear data and enterprise architecture, you can ensure data is managed effectively across your business. It helps avoid confusion, prevents data mishaps, and makes sure everyone is using data in a way that benefits your company. It’s like having a well-planned city – everything runs more efficiently, and you’re better equipped to reach your goals.

Having a solid data governance framework is no longer a luxury but a necessity for every modern business. To manage data as an asset effectively, organisations must understand and implement the essential components of a successful data governance framework. It’s not enough just to have policies in place – you need the right people, processes, technology, and architecture to make sure your data governance efforts are successful. By understanding each of these components and how they work together, you can create an effective plan that helps you reach your goals.

The data governance framework: not a one-size-fits-all

In a nutshell, implementing a data governance framework in your organisation is not a cookie-cutter process – it’s more like creating a custom-tailored suit. There are many factors to consider, and the approach will be unique to your organisation, depending on your specific needs, goals, and the importance of data governance in your business strategy.

For instance, if your organisation is heavily driven by compliance, you might spend more energy on establishing policies, rules, and standards. On the other hand, if your goal is to extract greater value from your data, you may emphasize improving data usage, refining definitions, and metadata, and enhancing understanding of data.

Essentially, the implementation of this framework is a customised journey. It’s not a one-size-fits-all solution but rather a tailored approach that aligns with your organisation’s specific needs and goals. The direction and focus of this journey will ultimately be determined by you and your organisation.

The seven key elements of a data governance framework2026-02-16T08:38:36+00:00

The importance of Data Governance

As data becomes the lifeblood of virtually every organisation and its processes, data governance is becoming a critical factor for success. The value of data has been established for years and continues to grow. These advancements have made organisations realize that sound data management practices are essential to their success. This is why data governance has become increasingly important for streamlining business processes, ensuring compliance, and optimizing the use of data to drive value.

Without an effective data governance program, organisations may be at risk of wasting resources and missing out on the opportunities that come with having trusted and qualitative data. Therefore, it is more important than ever for Chief Data Officers (CDOs), regardless of their industry, to acknowledge that data governance enhances an organisation’s data reliability and comprehension through stewardship and a robust business glossary, among others. This, in turn, accelerates enterprise-wide digital transformation.

But is data governance really important anymore with AI disrupting businesses? And how relevant will it be in the future and beyond?

What is data governance?

“At LACO, we define data governance as the organising framework that establishes an organisation’s strategy, objectives, and policies regarding the management of its data assets.”

The term “data governance” can have multiple definitions that are only slightly different from each other. At LACO, we define data governance as the organising framework that establishes an organisation’s strategy, objectives, and policies regarding the management of its data assets.

We often use the analogy that data governance provides for data assets what Human Resources (HR) delivers for human assets. This comparison is relevant for several reasons. The main responsibility of HR is to plan, direct, and coordinate the management of their human assets. They are not responsible for the daily operations of the business. HR is responsible for sourcing new employees, preparing them for their roles, managing their employment, and eventually retiring or terminating them. They play a critical role in every stage of the employee journey.

While data governance professionals may not be as hands-on as their HR counterparts, they must still establish policies for acquiring, onboarding, utilising, and retiring data assets. Just like HR, data governance also needs to take a strategic view. It should provide for the present needs while also planning for the future.

So, data governance provides policy and guidance for defining data, establishing ownership, and managing the strategic data asset of the enterprise. It encompasses the people, processes, and technologies required to manage and protect data assets. This is all to guarantee that the data used in the organisation is available, usable, secure, consistent, auditable, and has integrity.

The importance of data governance

Data governance is highly important for businesses because of

  • the increased availability of data,
  • the rise of digital transformation initiatives,
  • and increased regulatory and compliance requirements.

Data is available everywhere and in many different forms. It is no longer just structured data stored in databases; it’s big data, unstructured data, streaming data, and more. This increases the complexity of understanding what a company has and how to use it effectively, which is why having a comprehensive plan for managing data is so important.

Data governance also plays a critical role in digital transformation initiatives. Companies need to understand the quality, origin, and regulatory requirements of their data before they can move it into cloud computing systems or leverage AI and machine learning algorithms. This is why having clear policies and standards for managing data is paramount.

Finally, regulatory and compliance requirements are also increasing. From the GDPR to the European AI Act, organisations need to ensure that they have the right policies in place to protect their customers’ data. Data governance is essential for helping companies comply with these regulations and meet customer expectations around data privacy and security.

Five signs that you need data governance

| LACO

If your company is facing any of the following challenges, then it’s a sign that you need data governance:

  • Lack of clarity about data usage: If you are unsure about where your company’s data resides, who owns it, how it’s being used, or what policies should be in place for handling it, then your company needs data governance.
  • Inconsistent data quality: If different departments or teams are using different definitions or standards for the same term or metric, then it’s an indication that your company needs data governance.
  • Increased risk of data breaches: The increasing amount of data collected by organisations means there’s an equally increased risk of data breaches.
  • Inability to leverage data for decision-making: If you’re struggling to get valuable insights from your data or you’re unable to answer key business questions, then you’re not fully leveraging the potential of your data.
  • Difficulty in complying with regulations: As more countries and regions introduce regulations around data usage and privacy, it’s crucial for organisations to comply with these regulations.

Without data governance capabilities embedded in your organisation, data assets can become siloed, misused, and neglected. The lack of consistent processes for managing data can result in increasing costs linked to

  • operational inefficiencies,
  • poor decision-making,
  • compliance risks,
  • and missed opportunities.

The significant benefits of data governance

Implementing data governance in your company can result in significant benefits that can impact your risk management, cost reduction, and revenue generation efforts.

Data governance mitigates risks

With data breaches becoming increasingly common and regulations around data usage and privacy becoming stricter, having robust data governance practices in place is critical. By having clear policies and procedures in place for managing data, your company can mitigate the risk of data breaches, data theft, and data misuse. It can help you manage access to sensitive data and monitor data usage. So, in terms of risk management, data governance ensures that data is secure, consistent, and auditable. Data governance also helps your company comply with regulations, such as the GDPR and CCPA, reducing the risk of regulatory fines and penalties or even reputational harm.

Data governance reduces costs

“Most of the time, data analysts dedicate around 80% of their time to searching for and preparing data for analysis. The remaining 20% is spent on actually performing analytics and gaining insights. Data governance can change that ratio.”

Data governance can reduce costs by improving operational efficiency. By streamlining data management processes, you can reduce duplication of effort, minimize errors, and improve productivity. Inconsistent data quality often leads to costly operational inefficiencies. Most of the time, data analysts dedicate around 80% of their time to searching for and preparing data for analysis. The remaining 20% is spent on actually performing analytics and gaining insights. Data governance can change that ratio. Having access to all relevant and trusted data allows analysts to see information in context and make informed business decisions.

By integrating data governance into your company’s processes, you can ensure that data is accurate, complete, and up to date, thereby reducing the risk of operational inefficiencies and decreasing the overall cost of your operations. Additionally, having a clear and shared understanding of the data you have and how it is being used can help you optimize your data storage and infrastructure, reducing costs associated with unnecessary data storage and management.

Data governance generates revenue

Finally, data governance can also generate revenue for your company by helping you leverage the potential of your data. With a clear and shared understanding of data, your company can make informed decisions around product development, customer segmentation, and marketing initiatives. By leveraging data governance to ensure data is comprehensive and timely, your company can gain insights into customer preferences and behaviours, allowing you to make better products, offer more personalized services, and create more effective marketing campaigns. This, in turn, allows you to identify new market opportunities and capitalize on them. With data governance in place, your company can make more informed decisions about product design, launch dates, pricing strategies, and promotional activities that are based on facts, not just assumptions.

The increasing importance of data governance in the future

“As the volume and complexity of data continue to grow, so do the risks associated with poor data management.”

It’s undeniable that data governance is here to stay, for the simple reason that making business decisions based on data isn’t going anywhere. An increasing number of businesses are turning to data analytics to optimize their performance, but to get the best analysis out of your data, you need to be working with well-organised, clean data sets that every member of your organisation can trust.

Moreover, as the volume and complexity of data continue to grow, so do the risks associated with poor data management. The rise of cyber threats, data breaches, and privacy concerns makes it even more critical to have clearly defined policies and procedures for managing data. With increasing regulations, like the GDPR, organisations need to ensure that they have proper procedures in place for their data handling.

Additionally, as more businesses move online and digital transformation continues, the amount of generated data is expected to increase exponentially over time. This means that organisations must develop effective ways of managing all this data, so it remains accessible but secure at the same time. As both technological advancements and new regulations keep emerging, organisations will require effective methods, such as data governance, to ensure they remain up to date on all aspects of handling their information assets safely and securely.

Conclusion

“Data governance is not just about rules; it’s about creating an environment where everyone takes ownership of their data assets to derive business value from them.”

The significance of data governance is going to rise in the future since organisations need to safeguard and handle their data assets while keeping up with the constantly evolving technology and strict privacy regulations. Any forward-thinking organisation that needs trustworthy data to make accurate, data-driven decisions must consider how data governance can help them reach their goals. Data governance is not just about rules; it’s about creating an environment where everyone takes ownership of their data assets to derive business value from them.

The importance of Data Governance2026-02-16T08:38:49+00:00

Moving your SAS platform to the cloud: business lessons learned

Once you’ve moved your data platform to the cloud, your work as an IT professional tends to get a lot easier. But to be honest, getting that platform there in the first place can be quite a daunting task. How to tackle that?

Now, when it comes to SAS migration in general, LACO has always been somewhat of a pioneer on the Belgian market. As a matter of fact, LACO was the very first SAS partner to successfully deliver a SAS cloudification project in Belgium. Here are three important lessons we’ve learned from our experience with SAS cloudification so far. You might use them to your advantage!

Lesson 1: Take on the legal and regulatory hurdles from the start

The days that IT professionals sincerely worried about cloud security are long past. Most of us came to trust the high level of security that is built into the major cloud platforms, protecting data by design and by default when used correctly. Unfortunately, however, that hasn’t stopped some of our business colleagues from still worrying about these issues. Their fears need to be acknowledged too, of course. But then it is up to us to address those concerns with clear and hard facts we have at our disposal today. Even more so, as those persistent fears could turn out to be a real showstopper. If your CFO is not comfortable with moving his data to the cloud, for instance, then your project risks never taking off in the first place.

And talking about showstoppers: in certain sectors, such as the insurance industry, there is a set of mandatory legal rules and government restrictions that you absolutely have to take into account, before you can even think of moving your data platform to the cloud. These compliance demands are not insurmountable, but they will require you to obtain several official approvals, sometimes even undergoing a risk assessment. And that usually takes time, as there are no shortcuts or detours for it. Which is why, in these specific sectors, we always start a cloudification project by tackling the legal and regulatory hurdles. If these cannot be overcome, the project simply cannot move ahead.

Lesson 2: Be clear on the business case

A popular misconception that we often come across, even though people should really know better by now, is the idea that running your IT infrastructure in the cloud is by definition cheaper than running it in your own data centre – or having a hosting partner run it for you. In our experience, however, simply migrating your servers to the cloud rarely brings any real value to your business, not even from a purely financial perspective. On the contrary: it often turns out to be more expensive.

No misunderstanding, however, this only applies if you use the cloud the way you would use your former on-premise data centre by leaving all your servers running 24/7 all year round. The great thing about the cloud is that it allows you to run only those servers you require, switching systems on and off at any given notice. It basically lets you add or remove hardware resources in function of your actual computing needs. So if, say you have a reporting environment that is only used intensively by your business colleagues during working hours, you can decrease the server capacity for that environment before and after those hours. Another typical example for cost savings is a testing environment. Instead of keeping it running all the time, even during weekends, you could limit yourself to using that part of your infrastructure only when you actually need to do some testing.

So by using that flexibility, which is typical for the cloud, you can effectively optimise and strengthen your financial business case. Nevertheless, if you ask us, the real key to cloud success is in going beyond the financials approaching SAS cloudification not as a migration project but as an optimisation project. Instead of regarding the cloud merely as an alternative for your own data centre or that of your hosting provider and in a way continuing what you’ve always been doing, you should treat it as a springboard to a new world with possibilities you could only dream of before.

If you look at the cloud for new capabilities and extra functionality, you might just discover that there are applications and functionality within your reach, such as advanced disaster recovery features, that you would never have been able to deploy with just your own data centre.

Lesson 3: Match your licensing models

Moving your SAS data platform to the cloud also requires matching the different licensing models. This is especially challenging when you’re dealing with an older licensing model for your data platforms, since these older models – and not just those used by SAS – are still very much bound to physical hardware such as CPU cores. That is not necessarily the case, of course, with virtualised and cloud environments, where usage- and client-based licensing models continue to grow in popularity.

Matching licensing models is somewhat less challenging, as you can probably imagine, for those customers who have already moved on to SAS’ latest data platform: SAS Viya. Running on a scalable, cloud-native architecture, SAS Viya is an open and cloud-ready platform. Consequently, SAS Viya customers can entirely benefit from an easier cloud migration concerning licence fees.

The exercise of matching your software vendor’s licensing agreement with your needs in terms of scalability and elasticity, has to be done right from the start, as it might be another showstopper. Therefore, we invariably advise our customers to reach out and establish a satisfying agreement with their vendor and in some cases even their cloud provider. Not only they usually have a number of licensing programmes to choose from, sometimes with discounts that customers can profit from. They can also help to establish a smooth transition period. After all, you don’t move to the cloud overnight, do you?

So far for the business lessons learned from our SAS cloudification projects. Feel like diving a little deeper into the actual technology? Head quickly to our blog post with technical lessons learned . But first: check out our SAS cloudification page!

Moving your SAS platform to the cloud: business lessons learned2026-02-16T08:39:24+00:00
| LACO

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