The Data Maturity Journey: Preparing Your Organization for AI

In today’s digital economy, data is often referred to as the new currency. But just like any currency, its value depends on its quality and security. As organizations race to adopt artificial intelligence (AI) to gain competitive advantages, the critical factor that often determines success or failure is not the AI technology itself but the maturity of the organization’s data.

The Importance of Data Maturity

Data maturity refers to an organization’s ability to effectively manage, govern, and utilize its data assets. It’s a journey that starts with basic data collection and progresses to a level where data is treated as a strategic asset, driving decisions and innovations through AI. Without a mature data strategy, AI initiatives can quickly fall short, leading to inaccurate insights, security risks, and wasted investments.

A recent study by Gartner revealed that nearly 85% of AI projects fail, often due to issues related to data quality, security, and governance. This statistic underscores the need for a robust data maturity framework, ensuring that data is not just abundant but also reliable, secure, and ready to fuel AI.

End-to-End Customer Journey

The journey toward data maturity encompasses several critical steps, each essential for organizations aiming to be AI-ready. These steps can be categorized into the following key areas:

Data Security:

• For business users, this involves improving role-based security and ensuring robust data security within systems like D365 F&SCM.
• Technical users define roles and data security policies to safeguard information.
• The challenge here is the complexity and time-consuming nature of ensuring security, especially in platforms like D365.

Data Migration:

• Migrating data from legacy systems is a crucial task. Business users need to define migration needs, while technical users handle the setup of integrations.
• The operational team focuses on migrating data into new systems like D365 F&SCM.
• The primary hurdle is the technical complexity, which often requires developer support.

Data Governance:

• Effective data governance is managing master data to drive better business decisions.
• Business users benefit from controlled data management, while technical users set up workflows for data entry and quality checks.
• Operational users are responsible for creating and validating master data entries.
• Inefficient data management can lead to increased costs and complicate decision-making.

Data Integration:

• Enhancing collaboration with trading partners, for example, by improving data integration is vital. It may also be critical to integrate other systems together such as your sales, engineering and supply chain.
• This involves defining integration needs and setting up Electronic Data Interchange (EDI) messages, for example.
• However, complex integration schemas can make customization difficult.

Data Analytics:

• Finally, data analytics monitors business performance using industry-specific KPIs and dashboards.
• Business users monitor performance, while technical users prepare and visualize data.
• The challenge here is dealing with fragmented data visualizations that lack meaningful insights.
• It is also here at this maturity level that you can also turn on AI to help do your job better and faster to be able to leverage your great data to drive decision making and actions.

These steps collectively form the foundation of an organization's journey toward data maturity, each contributing to a more robust, AI-ready data infrastructure.

PS: Note that I do realized that one can use generative AI to drive some efficiencies in the business, such as creating emails, much earlier than this stage. My point here is that to drive real value you need it to be on top of clean data. Moreover, if you want to turn AI on and let it learn all your data, if you dont have the appropiate level of security you are also exposed.

The Data Maturity Journey

Building on the steps outlined above, the journey to data maturity is not a one-size-fits-all process. Organizations must navigate through several stages, each building on the last, to fully leverage AI. Remember that these levels are not strictly linear, and companies may exhibit characteristics from multiple levels simultaneously.

Here is how we see the 4 stages of data maturity:

Stage 1: Basic Level

At this initial stage, organizations focus on collecting data and generating basic reports. However, data is often siloed, inconsistent, and lacks the quality needed for deeper insights. Many companies get stuck here, relying on historical data, without realizing the potential of what lies ahead.

Stage 2: Intermediate Level

Moving beyond basic data management, organizations at this stage start to improve data quality and implement structured storage solutions. Self-service analytics and ad-hoc queries become possible, empowering business users to explore data independently. This is where the foundation for AI is laid, as better data accuracy and reliability open the door to more advanced analytics.

Stage 3: Advanced Level

In the advanced stage, data is treated as a core asset, supported by robust engineering and cloud-based storage solutions. Organizations begin to use predictive modeling, leveraging AI to inform strategic decisions. At this point, data is no longer just a byproduct of operations; it’s driving the business forward.

Stage 4: Expert Level

The final stage of data maturity is marked by excellence in data governance and security. Real-time data access, combined with embedded AI insights, transforms operations. Organizations at this level are not just using AI—they’re integrating it into every aspect of their business, achieving new levels of efficiency and innovation.

Overcoming Challenges and Concerns

Despite the clear benefits, many organizations need help to fully embrace AI due to concerns about data security and quality. A study by McKinsey found that 30% of companies have postponed AI projects because they were unsure of their data’s readiness.

These fears are valid but surmountable. By progressing through the stages of data maturity, organizations can address these concerns head-on. Secure, well-governed data provides the foundation needed for AI to function effectively. It’s about building trust in your data, ensuring that it’s not just abundant but also safe and reliable.

This is why we have create a set of solutions to help you navigate this journey from every step.

Real-World Examples

Take, for example, a leading manufacturing firm that embarks on its data maturity journey with a simple goal: to improve its decision-making processes. Starting at the basic level, they quickly realized the limitations of siloed, inconsistent data. With guidance, they moved to the intermediate level, implementing structured storage and self-service analytics.

As they advanced, they began to see the real power of AI. Predictive models allowed them to optimize their supply chain, reducing costs and improving delivery times. By the time they reached the expert level, AI was embedded in their daily operations, driving decisions in real time and positioning the company as a leader in its industry.

In a world where AI is becoming increasingly essential, the journey to data maturity is more important than ever. It’s not enough to have data; to unlock the full potential of AI, it must be the correct data, managed and governed effectively.

Are you ready?

If you’re wondering where your organization stands on the data maturity journey, I invite you to reach out for a data maturity assessment. Let’s work together to ensure your data is ready to drive AI and your business forward.

Here are some of the results our clients are achieving with us: