How to improve your data maturity: a step-by-step guide

Get in touch with Nextant today to discuss how we can help you improve your data maturity and leverage Microsoft technologies to drive business success.

Step 1: Data Maturity Assessment

To improve your data maturity, first, understand where your organization stands. This assessment checks your data governance, data quality, analytics skills, and strategic fit. What is data maturity? Check our last article here

1.1 Key Components of Data Maturity

The assessment should be comprehensive and consider several key areas:

Data Governance: Does your organization have policies in place to ensure data quality, security, privacy, and compliance (e.g., GDPR, CCPA)?

Microsoft tools like Microsoft Purview offer comprehensive data governance solutions to help you manage and govern your data, even a complete data platform such as Microsoft Fabric offers data governance alternatives in place.

In terms of data quality, is your data accurate, consistent, and timely? Poor data quality can derail decision-making. Microsoft Power Query in Power BI or Azure Data Factory can help streamline data cleaning and transformation processes. Last generation data platforms like Microsoft Fabric also include advanced and low code solutions such Dataflow Gen 2 to streamline and simplify data quality and data integrations.

Architecture and Infrastructure: How well do you store and manage your data? In today’s environment, organizations generate data at an unprecedented speed. But storing data is not the same as managing it.

True competitive advantage arises when that data is organized, accessible, and ready to drive decisions. Microsoft Azure offers a robust ecosystem for every need: From Azure SQL Database and Cosmos DB for structured, semi-structured data, and global high availability, to Blob Storage, File Shares, and Data Lake Storage Gen2 for files, objects, and large volumes of unstructured data.

If you need to move, change, or combine data, try Azure Data Factory, Synapse Analytics or Databricks. These tools let you create full workflows, from data ingestion to analytics. Microsoft Fabric takes things to a new level. It is an all-in-one analytics platform. It combines storage, transformation, real-time analysis, and more.

All these features connect through OneLake, the new centralized enterprise data lake. You can centralize files and tables in OneLake. You can also orchestrate Data Factory-style pipelines and run various analytics processes. All this happens within Fabric, so you don’t need to switch platforms or replicate data.

Analytics Capabilities: Is your organization reading reports… or truly understanding the business? In today’s data-driven world, having data is not enough—the real value lies in transforming it into insights that drive smart decisions.

This is where tools like Power BI, Azure, and the new Microsoft Fabric platform make a real impact. With Power BI, you’re not just visualizing data—you’re building dynamic narratives, automating alerts, exploring trends, and identifying anomalies with ease.

From Azure Synapse Analytics to Azure Databricks, you can work with massive data volumes in real time, conduct predictive analytics, and develop advanced machine learning models. And now, with Microsoft Fabric, all of this comes together in a single unified environment. Analysts, engineers, and business leaders can collaborate using a shared source of truth, with integrated visualizations, shared dashboards, and interactive analysis through Power BI, directly on top of the enterprise data lake.

 It’s not just about seeing the data. It’s about anticipating, taking action, and optimizing—with data at the core of your strategy.

Strategic Alignment: Are your data efforts truly aligned with your business goals? To create real value, data efforts should not just produce reports and dashboards. They should match the business strategy. This will aid decision-making across the organization. This means keeping data accurate and easy to access. It should also be organized to match important business priorities.

When data moves easily between departments, silos disappear. This lets everyone—from analysts to executives—work with one clear version of the truth. Strategic alignment makes data an asset. It helps us anticipate challenges, spot opportunities, and create measurable results.

 1.2 Conduct a Data Maturity Assessment

You can use a Data Maturity Assessment Framework to assess your organization’s current state. Leveraging models like Gartner’s Data Maturity Model, helps you determine where your organization stands in terms of data governance, analytics and more. These will help you assess where your data management, analytics, and governance stand.

Questions to Consider:

Do you have a well-defined data strategy aligned with business objectives?

Is your data accessible and used across departments?

How integrated is your data across systems and tools?

What is the quality of your data, and how frequently is it updated?

By honestly answering these questions, you’ll gain a clearer picture of where you stand and what needs to change.

Step 2: Design a Strategy to Improve Data Maturity

Once you’ve assessed your data maturity, the next step is to design a strategy that aligns with your organization’s goals and addresses identified gaps. This strategy will involve several key pillars that build on each other.

2.1 Establish Clear Goals for Data Maturity

Before diving into the tools and technology, you need to define clear objectives. This includes:

Improving Data Quality: Aiming for higher accuracy and consistency across data sources.

Enhancing Data Accessibility: Ensuring that data is easily accessible for decision-makers across the business.

Optimizing Analytics Capabilities: Using advanced analytics and machine learning to gain actionable insights.

Example Goal: “Enhance data accessibility by integrating our various data silos and providing real-time reporting capabilities through Power BI.”

2.2 Identify the Right Tools and Technologies

Microsoft provides an integrated ecosystem of tools to support data maturity across all stages. Here are some key tools that can help you achieve your goals:

Microsoft Azure Data Services: Azure offers a powerful ecosystem to manage data at scale. Use Azure Synapse Analytics to integrate and analyze massive volumes of data in real time, Azure Data Lake for scalable, secure storage of structured and unstructured data, and Azure Machine Learning to build and operationalize advanced ML models that unlock predictive insights and drive strategic decisions.

Power BI for Data Visualization: Power BI goes beyond traditional reporting—empowering users to explore data intuitively, uncover trends, automate insights, and create interactive dashboards that foster a culture of data-driven decision-making across the organization.

Microsoft Purview for Data Governance: With Microsoft Purview, organizations gain a unified solution for end-to-end data governance. It provides the visibility, control, and compliance capabilities needed to manage and protect your data estate—ensuring trust, transparency, and regulatory alignment.

Azure Data Factory for Data Integration: Azure Data Factory enables seamless integration and automation of workflows across systems. It ensures that data flows efficiently, tasks are streamlined, and business processes become more agile and responsive.

Microsoft Fabric: Microsoft Fabric is an all-in-one analytics platform. It brings together data engineering, data integration, real-time analytics, and business intelligence all in one simple space. Built on OneLake, a centralized enterprise data lake, Fabric eliminates data silos and enables teams to collaborate using a shared source of truth.

Fabric has tools for data transformation, advanced analytics, and visual storytelling. This helps organizations change raw data into useful insights quickly and efficiently.

Microsoft’s Ecosystem for Data Maturity

Microsoft Azure Synapse Analytics: Integrated analytics platform. Learn More

Microsoft Fabric: What is Microsoft Fabric? | Microsoft Fabric 

Power BI for Reporting & Insights: Empowering https://www.nextant.com/analytics-and-business-insights. Explore Power BI

Microsoft Purview for Data Governance: Protecting and governing data across the business. Discover Microsoft Purview

2.3 Set Key Performance Indicators (KPIs)

It’s important to track the effectiveness of your data maturity strategy. Some KPIs to monitor include:

Data Quality Improvement: Measure the percentage of clean, accurate, and up-to-date data.

User Adoption Rates: Track how widely data tools like Power BI and Purview are being used within the organization.

Time-to-Insight: Measure the time it takes from data collection to actionable insights.

Cost Savings from Automation: Monitor how automation tools like Power Automate reduce manual work and improve efficiency.

2.4 Build a Data Governance Framework

To manage your data responsibly, a solid governance framework is essential. Microsoft Purview helps you manage your data. It keeps your data clean, secure, and compliant with rules. Here’s how you can build your governance framework:

Define Data Ownership: Clearly assign responsibilities for different data sources.

Implement Security and Compliance Controls: Use Microsoft Information Protection to ensure data privacy and compliance.

Establish Data Quality Standards: Use Power Query and Azure Data Factory to enforce data quality standards during data integration and transformation processes.[JG5] 

Step 3: Build a Roadmap for Improving Data Maturity

A detailed roadmap is essential for systematically improving your data maturity over time. Here’s how you can design a roadmap that moves your organization through the various stages of data maturity.

3.1 Prioritize Immediate Needs

Start by addressing the most urgent data challenges identified during your assessment. This may include:

Unified Data Platform Centralization: Data silos are one of the biggest barriers to achieving data maturity. Centralizing data into a unified and scalable platform allows teams across the business to access consistent, reliable data

Data quality issues: Start using tools like Power Query in PBI, Azure Data Factory or Dataflow Gen 2 in Fabric to clean and standardize your data.

Data accessibility: Begin integrating disparate data sources into a unified platform using Azure Synapse Analytics.

Create self-service analytics tools: enabling self-serve analytics helps foster a data-driven culture. Power BI or Fabric real-time analytics are two powerful tools.

3.2 Set Short- and Long-Term Milestones

Short-Term Goals (1-3 months):

Implement Power BI dashboards for real-time reporting.

Clean up data using Power Query (for small datasets) or Azure Data Factory

Integrate data from key systems into Azure Data Lake.

Long-Term Goals (6-12 months):

Establish a comprehensive data governance framework with Microsoft Purview.

Scale AI and machine learning models using Azure Machine Learning.

Enable data-driven decision-making across all business units.

3.3 Foster a Data Culture through training and collaboration

Building and improving data maturity requires investing in employee training and education. Establishing a culture of continuous learning and collaboration is key. Microsoft offers extensive training on Power BI, Azure, and more via the Microsoft Learn platform. This helps your team quickly get up to speed and empowers them to interpret and act on data, bridging the gap between tools and business outcomes.

3.4 Continuous Monitoring and Iteration

Once your roadmap is in place, it’s important to continuously monitor progress and iterate based on feedback; data maturity is not a one-time effort, optimizing systems, costs and performance ensures long-term value. Microsoft’s Power BI, Azure Monitor, and Fabric Monitoring Hub provide valuable insights into performance metrics and help you adjust your strategy as needed.

Step 4: Leverage Advanced Analytics and Machine Learning

Once a solid data foundation is in place, organizations can take their data maturity by introducing AI and machine learning into their operations. Tools like Fabric Data Science and Azure Machine Learning provide tools to operationalize ML and AI models with governance and monitoring. Adopting AI enhances decision-making, automates complex processes and helps uncover hidden insights.

Conclusion: How Nextant Can Help You Improve Your Data Maturity

Boosting your data maturity takes careful planning and the right tools. It also needs a promise to keep improving. Microsoft offers tools like Azure, Power BI, Microsoft Purview, and Fabric. These technologies help you create a data-driven organization.

At Nextant, we understand that data maturity is a journey, and we’re here to support you every step of the way. We can help you assess your current state, design a strategic roadmap, or implement the right technologies. We’re here to guide you in becoming a data-driven enterprise. 

Get in touch with Nextant today to discuss how we can help you improve your data maturity and leverage Microsoft technologies to drive business success.

Frequently Asked Questions (FAQ)

What is data maturity, and why does it matter?

A. Data maturity is how well an organization manages, governs, analyzes, and uses data. High data maturity helps make better decisions. It also improves operational efficiency and boosts competitive advantage.

How do I assess my organization’s current data maturity?

A. Assess your maturity by looking at these key areas:
a. Data governance
b. Quality
c.  Management
d.  Analytics
e.  Strategic alignment
Tools such as the Microsoft Data Maturity Assessment and Gartner’s maturity models can guide this process.

What Microsoft tools support data maturity improvement? 

A. Key tools are:
a. Power BI (visualization)
b. Azure Synapse Analytics (large-scale data integration and analysis)
c. Microsoft Purview (governance)
d. Azure Data Factory and Power Automate (integration)
e. Microsoft Fabric (unified analytics)

How can I improve my organization’s data quality? 

A. Enhance data quality by cleaning and transforming it with Power Query or Azure Data Factory. Use Dataflow Gen 2 in Microsoft Fabric. It helps enforce data standards and automate processes.

What are some KPIs to track data maturity progress?

A. Monitor progress with KPIs like:
a. Data accuracy
b. User adoption of tools like Power BI
c. Reduced time-to-insight
d. Cost savings from automation

How do I align data efforts with business goals? 

A. Link your data strategy to business goals. Set clear, measurable targets. Involve stakeholders and use tools like Power BI and Azure. This will boost data transparency and teamwork.

What does a data maturity roadmap look like?

A. A roadmap should include:
a. Short-term goals, like deploying dashboards and cleaning data.
b. Long-term goals, such as implementing machine learning and full governance.
c. Clear KPIs.
d. A plan for ongoing training and improvements.

References & Resources:

Gartner: Data Maturity Models

Microsoft Azure: Azure Synapse Analytics

Forbes: The Importance of Data Governance

Microsoft Learn: Data Analytics & Power BI Learning

Microsoft Purview: Manage and Govern Your Data