| LACO
| LACO

How analytics engineering enables your business

Data and AI initiatives are scaling fast but many organisations still find themselves stuck in the same place: waiting on the central data team. Every new dashboard, insight or metric request ends up in the same queue, handled by the same overstretched experts. Self-service BI and decentralised models sound great in theory, but in practice they often raise a new question: how do you empower more people to work with data without losing control over quality, security and consistency?

Analytics engineering is the missing piece. It bridges the gap between raw data and reliable insights, turning a well-intentioned mess into a governed, scalable and business-ready foundation.

The challenge

As organisations scale their data environments, traditional centralised models start to crack. The data team becomes a bottleneck, handling every extract, dashboard update and metric debate. In the rush to deliver, quick fixes pile up leading to inconsistent logic, duplicated effort and KPIs that don’t quite match across teams.

Meanwhile, the pressure to enable self-service keeps growing. Business units want to move faster, but opening up access to raw or poorly modelled data only creates new risks: errors, misinterpretation, and dashboards that tell five versions of the truth. Add to that the growing complexity of data platforms — from warehouses to lakes to lakehouses on Microsoft Azure and Microsoft Fabric and suddenly the data landscape feels more like a maze than a launchpad.

The real issue isn’t technology. It’s the lack of a scalable operating model that balances flexibility with control.

The solution

Analytics engineering provides that model. It’s the discipline that sits between data engineering and business analytics, focused on designing clean, reusable and trusted data products. Instead of building dashboards or pipelines in isolation, analytics engineers take ownership of the semantic layer — the structured, business-aligned view of the data that everyone can build on.

At LACO, analytics engineers create this layer with governance and quality by design. They embed validation rules, document logic, and make sure key definitions are consistent across domains. Rather than duplicating effort in every report, teams work with shared, curated datasets which speeds up delivery and reduces rework.

This approach is deeply integrated with Microsoft Azure and Microsoft Fabric. LACO combines warehousing and lakehouse expertise to design scalable, high-performance architectures that business users can actually understand. Starting from business problems, analytics engineers work backwards to define what data is needed, how it should be modelled, and how it can be safely exposed.

The central team shifts from reacting to every ticket to enabling self-service through strong foundations and clear guardrails.

The results

By introducing analytics engineering as a core capability, organisations remove the bottleneck without losing control. Business teams gain faster access to insights, working directly with trusted data products instead of relying on ad-hoc extracts and one-off reports.

The central data team gets breathing room to focus on long-term value instead of short-term fixes. Governance improves, definitions align, and KPI discussions move from “which number is right?” to “what should we do next?”.

Self-service becomes a scalable capability and not a source of chaos. Domain teams explore and innovate within a clear, governed framework. And with a strong Microsoft Azure and Microsoft Fabric foundation, the organisation is better prepared for future analytics and AI growth without having to rebuild the basics each time.

In short: analytics engineering turns your data team from a bottleneck into a strategic enabler.

Ready to remove your analytics bottleneck?

Want to see how analytics engineering can unlock governed self‑service in your organisation? LACO’s analytics engineers help you design and build robust data products on Azure and Microsoft Fabric, so your teams can move faster without losing control.

How analytics engineering enables your business2026-01-08T11:45:55+00:00

Conversational BI: the AI‑powered future of data intelligence

Dashboards used to be the answer,until business users started asking better questions. In fast-paced environments where decisions can’t wait for the next reporting cycle, static views just don’t cut it anymore. Users expect to engage with data as naturally as they would with a colleague: by asking a question and getting a clear, useful answer.

Conversational BI makes that possible. By combining large language models (LLMs) with governed data platforms like Microsoft Fabric and Microsoft Azure, it turns raw data into on-demand intelligence. Less digging, more deciding.

The challenge

Traditional data intelligence environments were built to answer known questions through pre-defined dashboards and reports. That worked until business needs changed faster than reports could be updated. As data volumes grow and decision cycles shorten, teams no longer want to wait days for a new dashboard. They want to ask ad-hoc questions and get answers, instantly.

Meanwhile, data teams are buried in repeat requests: small tweaks, new views, slightly different filters. Time that could be used for value-added analytics is lost to backlog and maintenance. Despite all the tech in place, the experience often feels rigid and slow.

The solution

Conversational BI redefines how people interact with data, using AI to deliver fast, contextual answers in natural language. Instead of clicking through a forest of dashboards, users can simply ask, “How did revenue evolve last quarter by region?”, “Which products are driving margin decline?” or “What changed in churn after the price update?”.

Here’s how it works in a modern Microsoft Azure and Microsoft Fabric environment:

  • Governed data as the foundation: It all starts with a strong, governed data model. Microsoft Fabric, Azure and lakehouse structures expose clean, curated datasets with business-friendly entities like customers, products or orders. Centralised rules around quality, lineage and security ensure that answers are trustworthy.
  • LLM-powered intelligence: Large language models interpret the user’s question, map it to the correct metrics and dimensions, and generate the necessary queries. They summarise insights, highlight trends, and suggest follow-up questions — even visualising results in tools like Power BI. The outcome is not just data, but narrative: a story users can act on.
  • Built-in governance and control: Conversational BI doesn’t bypass governance, it builds on it. Role-based access, row-level security and built-in guardrails ensure users only see what they’re allowed to see. AI responses are explainable and traceable, with previews and validation tools that help data teams monitor, review and improve the system over time.

Together, these elements allow organisations to embed AI-driven experiences directly into the tools their people already use. From embedded chat to smart search, Conversational BI makes data as accessible as a conversation while keeping full control behind the scenes.

The results

For business users, conversational BI feels like having an analyst on standby. Questions that used to take days now take minutes. The data becomes more accessible, decisions become faster, and insights are easier to trust because they’re delivered in plain language.

For data teams, the shift is equally powerful. Instead of acting as dashboard factories, they focus on governance, modelling and quality, the building blocks of a trusted data environment. The result: fewer ad-hoc requests, less firefighting, and a more scalable approach to analytics.

At organisation level, decision-making becomes more democratic and consistent. When more people can safely ask better questions and actually understand the answers. Data intelligence evolves from a reporting system into a strategic conversation partner.

The future of BI isn’t just visual. It’s conversational.

Ready to explore Conversational BI?

LACO helps you design and implement AI-powered conversational BI, combining LLM capabilities with governed data so your teams can ask questions in plain language and get trustworthy answers when they need them.

Conversational BI: the AI‑powered future of data intelligence2026-01-15T10:17:12+00:00

Marketing mix modelling

Marketing leaders today face a growing challenge: deliver measurable results in a landscape that’s more complex, fragmented and regulated than ever. Budgets are under pressure, while customer journeys span more channels and more blind spots than before.

With traditional tracking becoming less reliable, it’s harder to understand what’s truly driving performance. That’s where marketing mix modelling comes in. By connecting the dots between campaigns, spend and business outcomes, it brings clarity back to decision-making and replaces assumptions with insight.

The challenge

Today’s marketing landscape is a paradox: more channels, more data, yet less visibility. Tracking customer behaviour has become harder thanks to GDPR, strict consent rules and the looming end of cookies. At the same time, marketing spend is spread across a growing mix of online and offline touchpoints, making it difficult to see what’s really working.

Traditional tracking methods fall short. ROI and performance are often judged by what’s easiest to measure — last-click metrics, web analytics or internal assumptions — rather than by a complete, objective view. The result: fragmented insight, unclear attribution, and growing pressure on marketing leaders to justify budgets without solid evidence.

The solution

LACO helps organisations cut through this complexity with an AI-powered marketing mix modelling (MMM) approach, built on a robust Microsoft Azure and Microsoft Fabric foundation. Rather than relying on user-level tracking, MMM uses advanced statistical and machine learning techniques to connect consolidated marketing inputs with business outcomes like sales, leads or conversions.

  • It starts with the data. We build a secure, scalable platform on Azure to integrate all relevant marketing inputs — media spend, CRM, web and app analytics, and external data like seasonality or macro-economic indicators. The result: a centralised, governed environment that’s consistent, traceable and ready for modelling.
  • From there, LACO defines a unified marketing data model that brings together online, offline and contextual factors. Search, social, display, TV, radio, events. All channels are harmonised into one view, enabling consistent comparisons and meaningful insights.
  • We then apply AI-powered MMM models that estimate how each driver — from media spend to timing to external influences — contributes to business results. These models are deployed directly into the Microsoft Azure or Microsoft Fabric environment and made accessible via tools like Power BI, so marketing and business teams can explore insights independently. Clear visuals and practical explanations show how spend, saturation and timing affect performance.

A key feature?
Scenario planning. Decision-makers can run what‑if analyses via a conversational interface that acts as an AI agent for the marketing organisation to test budget shifts, channel reallocations or campaign timing. What happens if we boost spend on social and cut TV? Launch a promo earlier? Shift regional targeting? These simulations support smarter, evidence-based decisions before money is spent.

Crucially, the solution is transparent and privacy-friendly. Instead of black-box algorithms built on personal data, LACO’s MMM approach relies on aggregated, governed inputs. Assumptions, sources and model logic are documented, building trust and ensuring compliance, even as regulations evolve.

Finally, MMM models feed into a predictive ROI engine that forecasts the impact of future marketing investments. Budget planning becomes proactive, grounded in data rather than gut feeling. Finance and marketing teams get forward-looking guidance on where to invest, at what level, and with what expected return.

The results

With AI-driven MMM on a strong Microsoft Azure and Microsoft Fabric data platform, organisations move from reactive reporting to confident, evidence-based marketing decisions. ROI attribution becomes transparent across all channels, enabling smarter budget allocation and better performance, often without increasing spend.

Forecasting improves, planning becomes more strategic, and marketing finally earns its place as a measurable growth driver. Because the entire approach is built on governed, aggregated data, organisations stay agile and compliant even as privacy rules continue to change. The end result? Clarity. Control. And a marketing function that moves to knowing.

Ready to bring clarity to your marketing mix?

LACO helps you build an AI-powered marketing mix modelling framework on Microsoft Azure and Microsoft Fabric, turning fragmented marketing data into transparent ROI insights and forward-looking scenarios for smarter budget decisions.

Marketing mix modelling2026-01-15T10:15:27+00:00

AI readiness: are you solving the right problems?

Everyone’s talking about AI. From the boardroom to the breakroom, it’s being pitched as the next big thing. But between the buzzwords and bold promises, one question often gets lost: are we solving real business problems, or just playing with shiny tools?

For many organisations, the excitement around AI has led to rushed pilots, unclear goals and underwhelming results. Without a strong link to decisions, data and adoption, even the most promising AI use cases fall flat. It’s time to get practical.

The challenge

AI has officially made it to the boardroom. It’s no longer just a pet project for innovation labs or data scientists with too much time on their hands. But as the hype grows, so does the risk of missing the point. Too many organisations still approach AI as a technology exercise, not a business change.

Ambitious projects take off — generative AI, copilots, predictive models — but without a clear link to decisions, processes or outcomes. Budgets get eaten, time disappears, and valuable expert capacity is spent on pilots that never leave the lab.

The most common pitfalls? Unclear business relevance, poor data quality, and solutions that simply don’t fit how people actually work. Somewhere along the way, teams discover that the required data doesn’t exist or isn’t reliable, that the Azure or Fabric setup can’t support what’s needed, or that users don’t trust — let alone understand — the AI output.

The result? A trail of disconnected pilots, sceptical stakeholders and the growing sense that “AI is expensive and doesn’t deliver”.

The solution

Before you build, pause. LACO uses a practical, structured framework built around three deceptively simple questions:

  1. Does it truly matter for your business?
    Start with problems worth solving. Together with business and IT stakeholders, we identify the decisions and processes where AI can actually make a difference. Is the goal to reduce manual work, improve forecasting, detect risks earlier or personalise customer interactions?By defining what success looks like – fewer errors, shorter lead times, higher conversion, lower cost – we make sure the initiative is anchored in business priorities, not technology curiosity.
  2. Do you have the data and platform to make it work?
    Next, we assess the current data landscape and platform readiness. Are the required data sources available, reliable and governed? Can they be integrated into your Azure or Fabric environment? Are performance, security and cost manageable?This step covers everything from data models and pipelines to monitoring and lifecycle management. The goal: ensure every use case is grounded in a solid, scalable foundation.
  3. Will people actually use it?
    AI that nobody uses is just an expensive demo. That’s why we consider adoption from day one. Who will use the solution? How will it impact their daily work? What’s needed in terms of transparency, controls and training?We think through user journeys, interfaces (Power BI, Fabric, apps…) and guardrails, so AI becomes part of the process and not something bolted on as an afterthought.

These three questions address business value (viability), data and platform readiness (feasibility) and user adoption (desirability). LACO applies this framework through focused workshops and assessments, always grounded in your existing Microsoft Azure and Microsoft Fabric setup.

The outcome? A shortlist of AI use cases that are technically achievable, strategically relevant and supported by the data and governance to actually succeed.

The results

Organisations that apply this framework move beyond experimentation. Instead of spreading resources across disconnected pilots, they focus on a small number of use cases with real business impact. Each initiative is backed by the right data, a scalable platform and clear outcomes — turning AI from a theoretical exercise into a strategic tool.

By building on existing Microsoft Azure and Microsoft Fabric components, time to value is shortened and adoption becomes easier. Business users are involved from the start, ensuring trust, usability and relevance. The result? A portfolio of AI solutions that deliver measurable value and actually support day-to-day decisions.

Ready to assess your AI readiness?

LACO helps you separate hype from real opportunity by mapping your business priorities, data readiness and Azure / Microsoft Fabric platform capabilities. Together, we identify where AI can create tangible value today — and where it should wait.

AI readiness: are you solving the right problems?2026-01-08T11:51:16+00:00

Microsoft Fabric | Developer bootcamp

In this bootcamp, your team will learn how to design, build and optimise scalable data architectures with Microsoft Fabric, tailored to your organisation’s data landscape. The training focuses on transforming raw data into a high‑performance Lakehouse environment, using practical exercises and real‑world scenarios that reflect your own challenges.

What you’ll learn

  • Understand the key differences and use cases between a Lakehouse and a Warehouse within Microsoft Fabric.
  • Learn how to ingest and transform data using Notebooks with PySpark and Spark SQL.
  • Build and orchestrate robust data pipelines using Data Factory.
  • Implement the Medallion Architecture (Bronze, Silver, Gold) to structure and refine your data layers.
  • Optimize data workflows for performance, scalability, and reusability across projects.
  • Explore best practices for data partitioning, Delta tables, and workload management.

Who should attend

Developers

Data Engineers

Data Scientists

Data Enthusiasts

Location

This training can be held at the LACO office or at your training facilities.

FAQ

What is the required level of prior knowledge or experience for this training?2025-11-26T13:06:45+00:00

No specific prior experience is required. A basic familiarity with general data concepts and terminology (such as data analysis, modelling, or reporting) is helpful, but the bootcamp is designed for both beginners and those with some hands-on experience.

Is lunch, coffee, or catering included in the price?2026-01-12T15:03:42+00:00

When the training is organized at LACO training facilities, lunch and beverages are provided.

Will the training language always be English (or Dutch/French)?2025-11-26T13:08:44+00:00

Yes, the training is delivered in English. On demand, and for specific groups, a Dutch or French session may be arranged. Please let us know your preference upon registration.

What is the duration of the training?2026-01-12T15:02:00+00:00

The duration of the training depends on the content that is taylored for you.

Let’s build your data skills.

Start the conversation.

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Name(Required)
You know your team, we know our training. Tell us who should join, when it suits you, and where you’d like the session – at LACO or on-site. We’ll take it from there.
More information about how we handle your data can be found in our privacy policy.

Microsoft Fabric | Developer bootcamp2026-02-16T08:43:00+00:00

Microsoft Fabric | Analyst bootcamp

In this one‑day bootcamp, your team learns how to connect, integrate and model data across the full Microsoft Fabric ecosystem, tailored to your organisation’s context. You will explore how to use Fabric’s end‑to‑end capabilities to deliver robust analytics, clear dashboards and better‑informed decision making.

What you’ll learn

  • Use Dataflows to centralize and transform raw data into structured, high quality datasets
  • Build reliable data models for scalable analysis
  • Write DAX expressions to solve analytical challenges
  • Design and publish insightful reports and dashboards in Power BI and Fabric
  • Apply best practices for secure data sharing within your organisation

Who should attend

Data Analysts

BI Professionals

Data Scientists

Finance professionals

Business Analyst

General IT

Location

This training can be held at the LACO office or at your training facilities.

FAQ

What is the required level of prior knowledge or experience for this training?2025-11-26T13:06:45+00:00

No specific prior experience is required. A basic familiarity with general data concepts and terminology (such as data analysis, modelling, or reporting) is helpful, but the bootcamp is designed for both beginners and those with some hands-on experience.

Is lunch, coffee, or catering included in the price?2026-01-12T15:03:42+00:00

When the training is organized at LACO training facilities, lunch and beverages are provided.

Will the training language always be English (or Dutch/French)?2025-11-26T13:08:44+00:00

Yes, the training is delivered in English. On demand, and for specific groups, a Dutch or French session may be arranged. Please let us know your preference upon registration.

What is the duration of the training?2026-01-12T15:02:00+00:00

The duration of the training depends on the content that is taylored for you.

Bring this data knowledge to your team.

Talk to us.

This field is for validation purposes and should be left unchanged.
Name(Required)
You know your team, we know our training. Tell us who should join, when it suits you, and where you’d like the session – at LACO or on-site. We’ll take it from there.
More information about how we handle your data can be found in our privacy policy.

Microsoft Fabric | Analyst bootcamp2026-02-16T08:43:07+00:00

Compliance and operational reporting: from fragmented data to trusted insight

Compliance and operational reporting are becoming more demanding as regulators, auditors and boards expect timely, consistent and explainable numbers, supported by strong risk data aggregation and governance. Organisations must show not only what they report, but also how figures are derived, aggregated and controlled across systems – reflecting principles found in BCBS 239 and broader RDARR guidelines.

At the same time, many reporting landscapes are still built on a mix of legacy platforms, local extracts and spreadsheets, making it difficult to guarantee data quality, lineage and governance end to end when supervisors or internal audit start asking detailed questions.

The data challenge

Behind every compliance report sits a data problem:

  • Critical metrics (financial, risk, ESG, operational KPIs, customer or product metrics) are sourced from different systems, with overlapping or conflicting definitions, leading to inconsistencies between regulatory, risk and management reports.

  • Data moves through multiple steps – ingestion, transformation, aggregation – without consistent documentation or automated controls, so it is hard to trace how a figure in a report links back to the original transaction, which BCBS 239‑style principles explicitly expect.

  • Reporting teams depend on manual reconciliations and ad hoc SQL or Excel logic that only a handful of people fully understand, increasing key person risk and making it harder to evidence robust risk data aggregation.

As reporting requirements grow in volume and granularity under RDARR‑inspired expectations, these data issues become more visible. Organisations need reporting that is faster and more flexible, but also demonstrably governed: complete, accurate, consistent and explainable to internal and external stakeholders.

The solution: a governed data and reporting layer

LACO helps organisations redesign their operational and compliance reporting around a governed data foundation, using modern cloud technologies such as Microsoft Azure, Microsoft Fabric, Power BI and Azure Databricks.

The goal is to create a single, reliable layer where critical data is integrated, modelled and controlled, and from which both day‑to‑day operational reports and BCBS 239 / RDARR‑aligned compliance reports can be served.

Concretely, this means:

  • Data integration: ingesting source data from core systems into a central, secure data platform (for example using Azure Data Lake, Azure Data Factory or Synapse pipelines), with clear ownership and access controls that support regulatory expectations on data governance.

  • Semantic and modelling layer: building governed data models that standardise key definitions – such as exposures, limits, revenue, cost, ESG indicators or operational risk metrics – so the same trusted data feeds BCBS 239 reports, RDARR‑driven risk dashboards and management reporting.

  • Reporting and visualisation with Power BI: exposing governed datasets to business and compliance users via Power BI, with role‑based access, row‑level security and reusable report templates for recurring regulatory and internal reporting cycles.

  • Built‑in data quality, reconciliation and lineage: embedding checks, reconciliations and metadata so teams can trace any reported figure back to its sources and transformation logic, and can demonstrate that data is complete, accurate and consistent – core BCBS 239‑style requirements.

By placing this governed layer at the centre, work shifts from rebuilding logic in each reporting tool to modelling and governing data once and reusing it many times – for regulatory risk reporting, RDARR‑aligned aggregation, internal risk dashboards and operational steering.

Result: explainable, BCBS 239 / RDARR‑ready reporting

Compliance and operational reporting become more repeatable, explainable and resilient, and better aligned with BCBS 239‑ and RDARR‑style expectations.

Reporting teams work with a single set of validated data and definitions, reducing inconsistencies between reports and limiting discussions about which number is the “right” one, both internally and with supervisors.

Business, risk and compliance users gain access to controlled data through modern tools, without bypassing the underlying governance, quality checks or lineage.

Organisations can adapt more easily to new reporting requirements or additional disclosures, because the underlying data architecture and technology stack are already designed for scalability, governance and reuse – creating a reporting landscape that not only supports today’s BCBS 239 / RDARR‑inspired demands, but is also ready for further digitalisation, stricter data rules and new forms of analytics and AI.

The reporting transformation becomes an engine for agility and trust, ready to support future regulatory change.

Ready to strengthen your compliance reporting?

LACO helps you move from fragmented data and manual reconciliations to a governed, Azure‑based reporting platform with clear lineage, consistent definitions and BCBS 239 / RDARR‑ready insight for your stakeholders.

Compliance and operational reporting: from fragmented data to trusted insight2026-01-16T09:24:54+00:00

Integrating SAS with Microsoft Azure

Many organisations rely on SAS as a trusted engine for analytics, reporting and modelling. At the same time, business users increasingly expect the modern flexibility of Microsoft Fabric and Azure Databricks. They want interactive dashboards, faster access to insights and a unified view across teams. This creates a gap between what the organisation already depends on and what the business now requires.

By integrating SAS with Microsoft’s cloud ecosystem, organisations gain the best of both worlds: a governed analytics engine on one side and a streamlined approach that minimises migration investment and accelerates change adoption on the other.

The challenge

SAS remains a powerful platform for processing and modelling, yet it was not built for today’s expectations around real time insights, cloud scalability and self service analytics. As a result, organisations end up switching between a central data warehouse (SAS DI) and end-user compute (SAS EG), manually exporting data and recreating reports. This leads to inconsistent versions, slow refresh cycles and a clear divide between technical teams and business users.

The challenge is not choosing one platform over the other. It is creating a landscape where they reinforce each other.

The solution

LACO helps organisations build a seamless bridge between SAS, Databricks and Microsoft Fabric.

  • The journey begins with a thorough scan of the existing SAS environment to understand dependencies, data sources and reporting processes.
  • Once there is clarity, we design an hybrid architecture where SAS outputs land securely and automatically in Databricks and Microsoft Fabric as certified datasets. These datasets follow shared governance and metadata principles so that access rules, terminology and lineage remain consistent across platforms.
  • We then automate the data flows to ensure that business users always work with up to date information. Manual exports disappear and data refreshes run on predictable schedules. Throughout this process, analysts and business users receive practical training so they can explore SAS outputs in Power BI with confidence.

The (gradual) transition becomes smooth, governed and supported by clear communication.

Results

The organisation gains one connected data landscape instead of two separate tools.

SAS continues to provide the analytical strength and validated outputs that teams rely on, while Power BI and Synapse deliver the flexibility and speed business users expect. Duplicate work disappears because data is prepared once and reused across the entire ecosystem. Reports refresh faster, users adopt the new environment more easily and IT teams spend far less time supporting manual tasks.

Most importantly, insights become both governed and accessible. Business users explore information in real time without recreating models or manipulating data manually, and leadership gains a trusted, consistent and audit ready view of the organisation. By connecting SAS with Microsoft’s cloud platform organisations modernise without replacing what still works and create a future ready foundation for analytics, AI and decision making.

Want to connect SAS and Microsoft Azure in a single data landscape?

We help you build the bridge — safely, efficiently and at your own pace.


Keep the strength of SAS. Add the flexibility of Microsoft Azure. And bring everyone onto the same page.

Integrating SAS with Microsoft Azure2026-01-08T09:54:32+00:00
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