MDW recognized as a Microsoft Fabric Featured Partner

MDW has been officially recognized as a Microsoft Fabric Featured Partner, joining a select group of partners worldwide who have demonstrated exceptional expertise and successful customer implementations using Microsoft Fabric.

A certification of excellence

From the very beginning, MDW has focused on helping organizations modernize their data foundations through innovation, governance, and scalability. Therefore, this recognition is awarded by Microsoft to partners that have proven real-world experience in delivering end-to-end Fabric projects. To achieve this status, MDW met Microsoft’s highest standards for:

• Validated production deployments of Microsoft Fabric with measurable business impact.
• Certified technical expertise across Azure, Fabric, and Power BI.
• Governance, architecture, and innovation best practices aligned with Microsoft’s Data & AI frameworks.
• Close collaboration with Microsoft product teams to shape the next generation of analytics capabilities.

Moreover, this certification highlights MDW’s track record in designing and deploying modern data platforms that connect data, AI, and business applications, empowering enterprises to transform raw information into governed, real-time insights.

What it means for our clients

Being a Fabric Featured Partner means our clients benefit from:

• Early access to Microsoft Fabric innovations and roadmap previews.
• Accelerated project delivery through proven methodologies and automation.
• Trusted governance and compliance built directly into their analytics environment.
• Direct collaboration with Microsoft experts for complex enterprise scenarios.

Shaping the future of intelligent data platforms

At the same time, MDW believes Microsoft Fabric represents a new era for data unification, scalability, and AI-driven decision-making. Indeed, this recognition reinforces our mission to help organizations build scalable, intelligent, and trusted analytics foundations, combining the best of Azure AI Studio, Microsoft Fabric, and the Power Platform. Looking ahead, MDW will continue to partner closely with Microsoft across Europe to help organizations modernize their analytics, unlock the potential of generative AI, and accelerate their business transformation. Ultimately, this partnership further strengthens MDW’s role as a trusted advisor for enterprises ready to build the next generation of data intelligence.

About MDW

MDW is a European consulting firm specialized in Data & AI. Recognized as Microsoft Partner of the Year for Azure (2024) and Partner of the Year for Data & AI (2023), MDW helps leading enterprises design, build, and operate data-driven solutions that connect Data, AI, and Business Applications.

We recently hosted a fun and creative contest within our team: designing original superheroes and villains using artificial intelligence. Each participant had the chance to imagine and bring to life a unique character, complete with backstory, attributes, and visual identity.

The results were amazing! From fearless heroes with extraordinary powers to dark and mysterious villains, every creation showcases not only the possibilities of AI but also the imagination and talent of our team.

Below you’ll find the characters we designed, along with their images and key attributes:

Introduction

In our current age of digital information, organizations struggle not because they lack data, but because they can’t find, trust, or manage it effectively. That’s where Microsoft Purview comes into a unified platform for data governance, discovery, classification, and compliance.

But what does that mean to you?

Whether you’re a business analyst, data engineer, compliance officer, or IT admin, Purview helps you do your job better by making data more discoverable, secure, and governed across your entire organization.

What is Microsoft Purview?

Microsoft Purview is a comprehensive data governance platform designed to manage, monitor, and protect organizational data across hybrid and cloud environments. It integrates multiple functionalities to provide a unified system for data oversight, offering tools for data cataloging, policy enforcement, and compliance management. Its flexibility and scalability make it ideal for enterprises of all sizes looking to enhance their data governance strategies.

Key Features of Microsoft Purview

1.Discover and Understand your Data

 “I need to find the right data quickly and trust it.” With Purview’s Data Map and Search, you can:

• Easily search across your entire data estate on-premises, multi-cloud, or SaaS
• See where data comes from and how it’s used (data lineage)
• Understand data through rich metadata and business glossary terms

 Benefit: Less time spent guessing or chasing datasets. You use trusted, approved data faster.

2.  Protect and Classify Sensitive

Data “How do I know where sensitive or regulated data lives?” Purview automatically:

• Scans and classifies data using built-in or custom policies (e.g., credit cards, PII, GDPR terms)
• Labels sensitive fields for security and compliance
• Flags data that requires restricted access

Benefit: Reduce data risk and simplify compliance (GDPR, HIPAA, ISO) without slowing operations.

3.  Empower the Right People with the Right Access

Through role-based access control and data policies, you can:

• Define who can see, curate, or manage data
• Use collections to group assets by team or function
• Set data access policies based on sensitivity or region

Benefit: Self-service access to governed data, with controls in place. No bottlenecks, no oversharing.

4. Automate and Scale Governance

“I want governance to run in the background, not be a manual headache.”
Purview helps automate:

• Scans (scheduled or triggered)
• Classifications (applied automatically)
• Policy enforcement (based on rules)
• Lineage tracking (from pipelines like Synapse, ADF, Power BI, etc.)

Benefit: Governance becomes part of your data lifecycle, not a separate process.

5. Integrate with Tools You Already Use

Purview integrates with:

• Azure Synapse, Data Factory, Power BI
• Microsoft 365 Compliance Center
• Third-party data sources (Amazon S3, Snowflake, etc.)
• Security and identity via Microsoft Entra (formerly Azure AD)

Benefit: You govern the data where it lives and as it flows — not in isolation.

From “Old Purview” to the Unified Portal

The classic Purview portal (also known as the “Governance Portal”) was the original version of Microsoft Purview, designed to support data scanning, discovery, classification, and lineage. It enabled organizations to curate both structured and unstructured data assets, laying a strong foundation for data governance across on-premises, multi-cloud, and SaaS environments.

The new unified Purview portal (available at purview.microsoft.com) offers a modernized and centralized experience. It’s designed as a single control plane to manage governance, compliance, security, and data policy across the organization, all in one place.

Who Benefits from Microsoft Purview

Feature	Classic Purview	Unified Purview Portal
Scope	One instance per account	Tenant-wide governance
Interface	Legacy, account-specific	Modern, intuitive UI
Access Control	Role-based per account	New tenant-level roles + legacy roles
Capabilities	Scanning, classification, lineage	+ Live view, scan presets, advanced options
Scan Scheduling	Manual or customized	Preset-driven, with templates
Domain Management	Not available	Multiple accounts under one governance model
Private Connectivity	Traditional private endpoints	Supports platform-level private endpoints

Classic vs Unified Portal

1. why migrate

• Centralized data governance: Manage all data assets from a single, unified tenant-level platform.
• Improved user experience: A refreshed interface, real-time data views, scan templates, and simplified asset management.
• Scalability: Easily onboard and consolidate multiple Purview accounts.
• Advanced governance: Introduces more granular role-based access control across your organization.

2. Should You Migrate Now?

Is it worth transitioning now?
Yes. Migration is free, and you immediately gain access to the latest capabilities and improvements — while support for the classic portal will gradually phase out.

Before migrating, consider:

• Ensuring network configurations and firewall rules are in place.
• Informing internal users about role changes and new UI.
• Validating integration points (e.g., Synapse, ADF, Machine Learning lineage).

3. Can You Go Back to Classic?

If you’re using the unified experience (even with the Enterprise version), you can still temporarily switch back to the classic portal using the toggle. However, if you delete the enterprise resource, you’ll lose your configuration and will need to start fresh.

At MDW (My Data Works), we accelerate business transformation by delivering cloud-native solutions that combine data, AI, and business applications. With 5-time recognition as Microsoft Partner of the Year, we’re a trusted advisor to industry leaders. With deep expertise across Microsoft technologies, our team helps you move faster, simplify complexity, and create value from your data.What it means for our clients

How Unity Catalog Simplifies Data Governance in Modern Data Platforms

In today’s data-driven enterprises, ensuring proper governance across complex data and AI ecosystems is no longer optional, it’s critical. Databricks’ Unity Catalog emerges as a unified solution designed to simplify data governance while enabling secure, scalable collaboration. Let’s explore how Unity Catalog supports data governance initiatives and why it’s transforming the way organizations manage their data assets.

The Challenge of Data Governance

Modern organizations face increasing complexity as they manage a variety of assets across data lakes, warehouses, ML models, and BI dashboards. Without a centralized governance framework, they often struggle with:

• Fragmented visibility across data and AI resources.
• Disjointed access control tools, creating security gaps.
• Limited lineage tracking, making audits and compliance harder.
• Manual, error-prone sharing processes that slow innovation.

These challenges highlight the need for a governance layer that spans both structured and unstructured data, across all environments.

What Is Unity Catalog?

Unity Catalog is Databricks’ centralized governance solution that provides a single pane of glass for managing data, AI models, and related assets. It integrates metadata from files, tables, ML models, and dashboards into a unified namespace, making it easier to control and monitor data access across workspaces.

With Unity Catalog, organizations benefit from:

• A single permission model for data, models, and files.
• Centralized access controls with ANSI SQL syntax or UI.
• Automated lineage tracking down to the column level.
• Built-in discovery tools with semantic tags and search.

Key Features That Enable Governance

•  Centralized Access Controls

Unity Catalog allows administrators to grant, modify, and audit access at the catalog, schema, table, or file level, centrally managed and synchronized with identity providers.

• Fine-Grained Security

Features like row-level security and column masking help ensure that sensitive data is only visible to authorized users. Policies can be defined in SQL and reused across tables.

•  Automated Lineage

Unity Catalog captures table and column-level lineage automatically during runtime, providing end-to-end visibility into how data flows across jobs, dashboards, notebooks, and models.

•  Unified Metadata Management

By creating a single metadata layer across structured and unstructured data (including external databases and file systems), Unity Catalog simplifies governance while enhancing discoverability.

•  Governance of All Data Types

From tabular data to unstructured files like images, PDFs, and videos (through Volumes), Unity Catalog brings consistent governance to the entire data estate.

•  Open, Cross-Platform Sharing

With Delta Sharing, organizations can securely share data, models, and notebooks across regions, clouds, or third-party platforms, without replication.

How It Facilitates Data Governance

Unity Catalog addresses key governance needs:

• Compliance: Simplifies meeting regulatory standards through robust auditing, lineage, and security controls.
• Trust: Provides transparency and traceability, helping users trust the data and models they work with.
• Efficiency: Reduces overhead by centralizing policies, automating lineage, and streamlining data sharing.
• Scalability: Supports governance at scale, from small teams to large, multi-cloud enterprises.

Final Thoughts

Unity Catalog is more than just a metadata store; it’s a foundational element for implementing enterprise-grade data governance. By unifying controls, enhancing visibility, and enabling secure collaboration, it helps organizations unlock the full potential of their data and AI assets, without compromising on compliance or security.

If your organization is aiming to modernize its governance strategy, Unity Catalog should be at the top of your list.

How can we help you?

As already mentioned, correctly modeling the CDM folder is one of the main pillars of master data management. To achieve good results and generate a successful project, your company will have to:

1. Create a map of the concerned systems;
2. Democratize this notion of data excellence within your organization;
3. Define roadmap, roles, and responsibilities (governance);
4. Start the implementation of such a project while keeping a holistic view;
5. Have the necessary skills and/or to be accompanied by a specialized partner.

At MDW (My Data Works), we accelerate business transformation by delivering cloud-native solutions that combine data, AI, and business applications. With 5-time recognition as Microsoft Partner of the Year, we’re a trusted advisor to industry leaders. With deep expertise across Microsoft technologies, our team helps you move faster, simplify complexity, and create value from your data.

About MDW

MDW is a European consulting firm specialized in Data & AI. Recognized as Microsoft Partner of the Year for Azure (2024) and Partner of the Year for Data & AI (2023), MDW helps leading enterprises design, build, and operate data-driven solutions that connect Data, AI, and Business Applications.

The Customer 360 View gives businesses a complete, real-time perspective of their clients. By unifying data from multiple touchpoints, organizations can break down silos and build a single source of truth that enhances decision-making. At MDW, we have created a practical guide to show how Dynamics 365 Customer Insights helps companies gain this competitive edge.

1. Enhance Customer Experience:
With a unified Customer 360 View, businesses can understand client behaviors more deeply. Integrated data and real-time segmentation reveal what customers truly need, enabling personalized engagement at scale.

2. Boost Marketing Effectiveness:
AI-driven insights in Dynamics 365 Customer Insights allow marketing teams to refine their strategies. With a clear 360 View, campaigns become more targeted, relevant, and impactful.

3.Increase Operational Efficiency:
Consolidating data into one platform eliminates time wasted on reconciliation. Teams can focus on strategic actions instead of manual processes, improving collaboration across departments.

As a multi-award-winning Microsoft Partner, MDW combines expertise in Data, AI, and Business Applications to help organizations achieve a trusted and actionable Customer 360 View.

Our teams support you to:
1. Implement Dynamics 365 Customer Insights to drive real-time personalization across touchpoints
2. Download our eBook now and make the most of your customer data with Dynamics 365 Customer Insights.
3. Define a data strategy aligned with your customer engagement goals
4. Unify and clean your data from CRM, ERP, marketing tools, and external platforms
5. Design a modern data architecture to support segmentation, AI, and omnichannel activation

The future of advertising with AI is transforming the way brands engage with people. Artificial intelligence is no longer just a supporting tool; it has become the driving force behind modern marketing. By combining data-driven insights with creativity, AI in advertising enables campaigns that feel personal, dynamic, and authentic. From hyper-realistic digital personas to predictive analytics, brands are already experiencing how technology reshapes connections with their audiences.

The Evolution of AI in Advertising

Advertising has always evolved with technology: from print to radio, from television to digital platforms. The rise of artificial intelligence marks the next stage in this evolution. What began with simple programmatic ads is now becoming AI-powered marketing, capable of understanding intent, context, and emotional resonance. Unlike traditional methods, artificial intelligence in advertising adapts in real time. Campaigns are no longer static; they change according to user behavior, location, and even sentiment. This shift enables brands to build stronger relationships while maximizing return on investment (ROI).

Key Use Cases of AI in Advertising

Hyper-realistic digital personas

Advertising has always evolved with technology: from print to radio, from television to digital platforms. The rise of artificial intelligence marks the next stage in this evolution. What began with simple programmatic ads is now becoming AI-powered marketing, capable of understanding intent, context, and emotional resonance.

Enhancing creativity, not replacing it

The future of advertising with AI is not about replacing human creativity. Instead, AI empowers marketers by combining storytelling with data-driven insights. This makes campaigns more adaptable, dynamic, and impactful.

Personalized and dynamic experiences

With AI, advertising is no longer static. Campaigns adjust in real time based on audience behavior, location, or preferences. The result: marketing that feels alive and truly relevant.

Benefits of AI-Powered Marketing

• Stronger connections with audiences through personalization
• Scalable content creation with AI-powered design tools
• Predictive insights that guide campaign strategies
• Authenticity that resonates across digital channels
  

A statement for the future

The future of advertising with AI is a statement of how technology and creativity converge. It redefines engagement, turning bold visions into real campaigns that resonate deeply with audiences.

Add Filter Without Compromising Performance

You’ve built a clean star schema in Power BI. Dimension and fact tables are linked with one-to-many, single-direction relationships. This setup makes DAX calculations efficient and easy to follow.

Then the client asks for a new feature: a slicer that hides categories based on another slicer. For example, they want to see which colors were sold when filtered by size. They also want to avoid empty results when users pick invalid size–color combinations.

The first thing that comes to mind

You might try switching single-direction relationships into bidirectional ones. But this breaks star schema principles for a small feature. Bidirectional filters also demand more processing and may slow down reports. Even worse, they can return unexpected results. Often, you only notice the issue once the numbers are wrong.

Visual-level filtering

A safer option is visual-level filters. They let you control slicer behavior without touching the fact table.
Select the visual, open the filter pane, and apply your measure in “Filters on this visual.”
Create a measure that checks whether the size–color combination exists in the fact table. For example:

Total Price = SUM ( 'FACT Sales'[Price] )

If the result is blank, the combination doesn’t exist. A simpler check is to count rows:

Records = COUNTROWS ( 'FACT Sales' )

Apply the filter with “is not blank.” Now the “Size” slicer reacts to the “Colour” slicer. Sizes not sold in the chosen color disappear from the list.

Modifying relationships at run time

Sometimes the client wants dimension-to-dimension analysis. For example: how many colors were sold for each size? Normally this requires a bidirectional relationship, which can slow down large models.
Instead, you can use DAX to change filter direction only during query time. The CROSSFILTER function helps. It alters relationships only while evaluating the measure:

Different Colours Sold =

CALCULATE (
    DISTINCTCOUNT ( 'DIM Colour'[Colour] ),
    CROSSFILTER (
        'DIM Colour'[Colour],
        'FACT Sales'[Product Colour],
        BOTH
    )
)

This counts colors in the dimension table while making the relationship bidirectional only for the measure. Place it in a table with “Size” and you’ll see how many colors were sold for each one.

Wrapping up

SStar schema modeling is best in almost every scenario. Don’t compromise your model. Instead, use virtual relationships with functions like TREATAS, USERELATIONSHIP, and INTERSECT. These allow targeted filtering only where needed, keeping performance high.

If you’re interested in discovering how Microsoft Power BI can benefit your business, schedule a free workshop with our experts now: www.mdw-group.com/contact

Optimize Dynamic KPIs in Power BI with Calculation Groups

Optimize Dynamic KPIs in Power BI with Calculation Groups

Too Many KPIs, Too Many Measures

Have you ever built a report with more than 40 KPIs? If so, you know the pain: each KPI needs its own measure plus extra calculations for year-over-year comparisons, period variations, and more. When you add a new KPI, you repeat the whole process.

Calculation groups solve much of this problem, but they also come with limits. Imagine you want to show three values on one card: the actual KPI, last year’s value, and the difference. Time intelligence calculation groups help with part of this, but Power BI does not let you display multiple calculation items in the same visual.

Why Calculation Groups Matter

Calculation groups were designed to reduce this complexity. They let you define reusable logic instead of duplicating measures. However, they also have limits. Even if you’ve configured time intelligence calculation groups, you can’t apply multiple calculation items to the same visual. That’s a problem for KPI reporting.

The Goal

In this article, we’ll combine calculation groups with conditional logic. This technique allows you to:

• Display dynamic KPIs in one card.
• Reuse the same structure across multiple visuals.
• Add new KPIs easily by writing just one formula.
• Keep consistent business logic across all measures.

Step 1: Create a KPI Table

The foundation of this approach is a dedicated KPI table. It contains every KPI in your report and can also serve as documentation or a dictionary.
A simple structure might include:

• ID and Measure Name.
• Optional fields like Description, Comments, or Format.
• A column for Negative is good to handle conditional colors. For example, a drop in COGS is positive, so you display it in green.

This last column is especially useful. It lets you apply green or red colors dynamically depending on whether the KPI going up or down is good for the business. For example, an increase in Sales should be green, but a decrease in COGS should also be green. Whenever you add a new KPI, just add a row to this table with its name and attributes.

Step 2: Prepare Auxiliary Measures

Next, create placeholder measures. These control how the calculation group logic works.

[DAX CODE] dyn_Actual = BLANK ()
[DAX CODE] dyn_Var_Title = ""
[DAX CODE] dyn_Var_Value = BLANK ()
[DAX CODE] dyn_Var_Detail = BLANK ()
[DAX CODE] dyn_Colour = ""

Each one maps to a section of the card visual:

dyn_Actual → main callout value.
dyn_Var_Title → reference label title.
dyn_Var_Value → reference label value.
dyn_Var_Detail → reference label detail.
dyn_Colour → conditional color for labels.

You also need your base KPI measures. For example, [COGS] and [Sales] as sums of their fact table columns.

Step 3: Build the Card Visual

Create a card visual. Set the title to display the first value of the Measure Name column. Assign the measures:

Set the title to show the first value from Measure Name in the KPI table.

Place dyn_Actual in the callout field.
Place dyn_Var_Value in the reference label.
Place dyn_Var_Title and dyn_Var_Detail in the title and detail fields of the reference label.

At this stage, the values are placeholders. The real logic will come from calculation groups.

Step 4: Create the Calculation Group

Actual Value
First, create a calculation item to select the right measure based on the KPI filter.

valueActual =
SWITCH (
    SELECTEDVALUE ( _Measures[Measure Name] ),
    "COGS", [COGS],
    "Sales", [Sales]
)

When adding a new KPI, extend this SWITCH with the KPI name and its corresponding measure.

Last Year Value
Next, reference the actual value to calculate last year’s result:

[DAX CODE] valueLastYear =
CALCULATE (
    SELECTEDMEASURE (),
    'Smart KPIs'[Calculation] = "valueActual",
    SAMEPERIODLASTYEAR ( 'Calendar'[Date] )
)

Absolute Variation
Now calculate the absolute difference between current and last year:

[DAX CODE] valueVariationAbs =
VAR __VALUE_ACTUAL =
    CALCULATE (
        SELECTEDMEASURE (),
        'Smart KPIs'[Calculation] = "valueActual"
    )
VAR __VALUE_LAST_YEAR =
    CALCULATE (
        SELECTEDMEASURE (),
        'Smart KPIs'[Calculation] = "valueLastYear"
    )
RETURN
    __VALUE_ACTUAL - __VALUE_LAST_YEAR

Percentage Variation
Finally, compute the percentage difference:

[DAX CODE] valueVariationPerc =
VAR __VALUE_ACTUAL =
    CALCULATE (
        SELECTEDMEASURE (),
        'Smart KPIs'[Calculation] = "valueActual"
    )
VAR __VALUE_LAST_YEAR =
    CALCULATE (
        SELECTEDMEASURE (),
        'Smart KPIs'[Calculation] = "valueLastYear"
    )
RETURN
    DIVIDE (
        __VALUE_ACTUAL - __VALUE_LAST_YEAR,
        __VALUE_LAST_YEAR
    )

Step 5: Control Card Output

As you can see in the code above, SELECTEDMEASURE() will always reference the original dyn_Actual or other blank DAX measures. This is not important, as when Power BI applies the valueActual calculation item, it will override the blank value for the corresponding measure that we are indicating with a filter. Finally, we can create a new calculation item that references all of the previous items and controls which one is used in each section of the card visual.

[DAX CODE] Card =
VAR __VALUE_VAR_ABS =
    CALCULATE ( SELECTEDMEASURE (), 'Smart KPIs'[Calculation] = "valueVariationAbs" )
RETURN
    SWITCH (
        TRUE (),
        ISSELECTEDMEASURE ( [dyn_Actual] ),
            CALCULATE ( SELECTEDMEASURE (), 'Smart KPIs'[Calculation] = "valueActual" ),
        ISSELECTEDMEASURE ( [dyn_Var_Title] ),
            SWITCH (
                TRUE (),
                __VALUE_VAR_ABS < 0, "⠀▼ ",
                __VALUE_VAR_ABS = 0, "⠀",
                __VALUE_VAR_ABS > 0, "⠀▲ "
            ),
        ISSELECTEDMEASURE ( [dyn_Var_Value] ),
            __VALUE_VAR_ABS,
        ISSELECTEDMEASURE ( [dyn_Var_Detail] ),
            CALCULATE ( SELECTEDMEASURE (), 'Smart KPIs'[Calculation] = "valueVariationPerc" ),
        ISSELECTEDMEASURE ( [dyn_Color] ),
            VAR __VALUE_ADJUSTED =
                __VALUE_VAR_ABS
                * POWER ( -1, INT ( SELECTEDVALUE ( _Measures[Negative is good] ) ) )
            RETURN
                SWITCH (
                    TRUE (),
                    __VALUE_ADJUSTED < 0, "#D64550",    -- Red
                    __VALUE_ADJUSTED = 0, "#000000",    -- Black
                    __VALUE_ADJUSTED > 0, "#6F9F2F"     -- Green
                )
    )

ISSELECTEDMEASURE is the key to making the card dynamic. It tells Power BI which placeholder measure is running and applies the right logic: actual value, arrow, variation, percentage, or color. With this single calculation item, you can control every part of the card in a consistent way. Once the logic is in place, move to the visual. Add a few filters to the card, and its behavior becomes dynamic and fully automated.

‘Calculation’ is the name of the calculation group column. The card already looks good, but the formatting still needs improvement. We want both the callout value and the reference label value displayed in millions of euros. Negative symbols are unnecessary, because arrows and dynamic colors already show the trend. Removing those symbols avoids confusing users with redundant cues.

For that, we can use custom format strings in the field settings.

• Callout value: Use ##0,,.0 M€. This divides the number by one million and adds “M€” at the end.
• Reference label value: Use ##0,,.00 M€;##0,,.00 M€;0 €. Both positive and negative values appear the same. If the value is zero, the “M” for millions is hidden.
• Reference label detail: Use (0.0%);(0.0%);0%. Percentages appear in parentheses, without negative signs, and with one decimal place so they don’t take up too much space.

With the addition of the dyn_Color measure in the conditional formatting, we have finally created the ‘smart KPI card’.

The Result: A Smart KPI Card

You now have a dynamic card that adapts to any KPI. To add another, simply copy the card and change the Measure Name filter. The logic automatically adjusts, and slicers interact smoothly.
The arrows and colors respond dynamically:

• Green for positive outcomes.
• Red for negative.
• Black for neutral.

And the “Negative is good” flag ensures the color logic matches the KPI’s business meaning.

Wrapping up

This approach creates a building block for KPI reporting. Instead of building dozens of custom visuals, you design once and reuse. The technique is highly customizable:

• Extend the KPI table with more metadata like formats or descriptions.
• Use calculation group format strings for dynamic rounding (units, thousands, or millions).
• Keep all formatting and logic centralized in one place.

Mastering this method saves hours of repetitive work and ensures consistency across all reports.

If you’re interested in discovering how Microsoft Power BI can benefit your business, schedule a free workshop with our experts now: www.mdw-group.com/contact/

About MDW

MDW is a European consulting firm specialized in Data & AI. Recognized as Microsoft Partner of the Year for Azure (2024) and Partner of the Year for Data & AI (2023), MDW helps leading enterprises design, build, and operate data-driven solutions that connect Data, AI, and Business Applications.

Une présence remarquée sur le plateau

Paris, France – le 24 juin 2024 – MDW, acteur majeur du conseil en intelligence artificielle et gestion de données, a été mis en avant dans l’émission Le Grand Entretien, présentée par Michel Denisot.

Lors de cette émission, Maxime Gouffé, CEO de MDW, et Amandine Clauzon, Marketing & Partnership Lead, ont souligné l’importance de l’intelligence artificielle comme moteur de croissance pour les entreprises européennes.

Une collaboration avec les leaders du secteur

MDW s’appuie sur des partenariats solides avec Microsoft et Databricks. L’entreprise propose un accompagnement complet, allant du conseil initial à la mise en œuvre et au support continu.

“Faire appel à MDW pour des solutions en data et intelligence artificielle permet à nos clients de bénéficier d’un accompagnement de A à Z”, a expliqué Maxime Gouffé.

De plus, grâce à cette approche intégrée, MDW garantit un retour sur investissement mesurable, qui peut atteindre plusieurs millions d’euros selon la taille et le secteur de l’entreprise.

Maintenir la compétitivité grâce à l’innovation

Amandine Clauzon, Marketing & Partnership Lead, a ajouté : “Nous aidons les entreprises à rester compétitives en adoptant des technologies de pointe.”

Ainsi, MDW confirme son engagement à fournir des solutions technologiques avancées. Ces projets permettent aux entreprises de naviguer efficacement dans un paysage commercial en constante évolution.

Pour en savoir plus, consultez l’article complet sur Le Point : MDW, le pionnier des solutions de données et d’IA (lepoint.fr).

A propos de MDW

Présente en Suisse, en France et en Espagne, MDW accompagne plus de 50 clients internationaux. L’entreprise est spécialisée dans :

• la gestion de données,
• l’intelligence artificielle,
• les applications métiers.

Notre entreprise est reconnue pour avoir accompagné de nombreuses organisations dans la mise en œuvre de plateformes de données hautement scalables et de projets d’applications métiers déployés à grande échelle.

MDW fait aussi partie des tout premiers partenaires au monde à avoir lancé des projets utilisant les services Azure OpenAI, dès leur mise à disposition.

Cette avance a renforcé notre réputation : une approche orientée résultats, une expertise sectorielle solide et un engagement constant envers l’innovation. Nous travaillons en étroite collaboration avec nos clients afin de bien comprendre leurs besoins et leurs défis. En parallèle, nous restons à la pointe des dernières avancées technologiques et des tendances du marché.

Une histoire forte avec Microsoft

MDW travaille exclusivement avec Microsoft. Tous les projets de l’entreprise reposent sur l’écosystème Cloud de l’éditeur. Cette expertise a valu à MDW plusieurs récompenses décernées par Microsoft. Par ailleurs, l’obtention de trois spécialisations avancées consolide encore sa position de partenaire de confiance et de leader sur le marché.

About MDW

MDW is a European consulting firm specialized in Data & AI. Recognized as Microsoft Partner of the Year for Azure (2024) and Partner of the Year for Data & AI (2023), MDW helps leading enterprises design, build, and operate data-driven solutions that connect Data, AI, and Business Applications.

Automating Infrastructure the Smart Way

If you work with cloud infrastructure, you’ve probably heard about Pulumi and Azure DevOps. When used together, these tools can transform how you build, manage, and deploy infrastructure. In this article, we’ll explain what they are and how they complement each other.

Pulumi is an Infrastructure as Code (IaC) platform. Instead of configuring servers or networks manually, you can define them using programming languages such as TypeScript, Python, C#, or Go. Once the code is written, Pulumi provisions and manages the resources in the cloud on Azure, AWS, or GCP.

Some key advantages:

• Developers use real programming languages, not just templates.
• Infrastructure benefits from modern coding practices like testing and automation.
• Teams reuse existing skills to manage both applications and infrastructure.

Azure DevOps is Microsoft’s suite of tools for managing the entire application lifecycle. For infrastructure management, two services stand out:

• Azure Repos: A version control system for storing and managing code with history and collaboration features.
• Azure Pipelines: A CI/CD tool that automates building, testing, and deploying applications and infrastructure on any platform.

By combining these services, organizations gain a streamlined and automated workflow for software and infrastructure delivery.

Using Pulumi with Azure DevOps offers clear advantages:

• CI/CD Automation: With Azure Pipelines, every change to your Pulumi code goes through a consistent validation and deployment process. As a result, errors are reduced and efficiency improves.
• Infrastructure Lifecycle Management: Azure Repos versions your infrastructure code just like application code. This makes it easier to track changes, revert if needed, and collaborate with teammates.
• Improved Collaboration: Azure DevOps provides a single platform that combines version control, CI/CD, and project management. Together with Pulumi, it helps development and operations teams work more effectively.

Now that we know both tools, let’s see how we can start working with them. The first thing to do is to have the Pulumi code in Azure Repos, so that it can later be used in Azure Pipelines. Once we have the code in the Azure repository, we can start creating the pipeline that will continuously deploy the infrastructure.

Before setting up your pipeline, you’ll need:

Pulumi extension for Azure DevOps → Install the Pulumi Azure Pipelines Task extension.

Azure Storage → Store Pulumi state in an Azure Storage Blob to track infrastructure changes.

Azure Key Vault → Safely store and access keys or secrets needed by Pulumi.

Azure Pipelines Library → Manage configuration variables and link secrets from Key Vault.

Building the Pipeline

With everything prepared, you can create a pipeline in Azure DevOps to deploy infrastructure automatically.
Two main tasks are needed:

For example, if your Pulumi project is in TypeScript, use pnpm to install dependencies inside the infrastructure folder.

• cwd → folder containing Pulumi code.
• createStack → creates a stack if one doesn’t already exist.
• args → include --yes to bypass confirmation, plus configuration variables stored in Azure Libraries.
• azureSubscription → service connection for the deployment.
• command → Pulumi command, typically up.
• loginArgs → connection details for Azure Storage (where the Pulumi state is stored).
• stack → name of the target stack.

• Add these environment variables:

AZURE_STORAGE_ACCOUNT → storage account name.

AZURE_STORAGE_KEY → key for the storage account.

PULUMI_CONFIG_PASSPHRASE → encryption key for configuration variables.

With these steps, the pipeline can continuously deploy your Pulumi-defined infrastructure.

Wrapping up 

Integrating Pulumi with Azure DevOps creates a modern and reliable way to manage infrastructure as code. With Azure Repos for version control, Azure Pipelines for automation, Azure Storage for state management, and Azure Key Vault for secret handling, you get a workflow that is both secure and efficient.

This approach speeds up deployment, reduces human error, and fosters stronger collaboration across teams. Ultimately, Pulumi and Azure DevOps make it easier to scale your infrastructure with confidence.

About MDW

MDW is a European consulting firm specialized in Data & AI. Recognized as Microsoft Partner of the Year for Azure (2024) and Partner of the Year for Data & AI (2023), MDW helps leading enterprises design, build, and operate data-driven solutions that connect Data, AI, and Business Applications.