How do I measure my company’s Data Maturity?

Not all companies are prepared to extract business value from data. The first step to Data Fluency is to measure your current Data Maturity level.

We live in a Data dominated world. Both at individual and professional level, we have uncountable daily reminders of how our actions, our decisions and even our feelings are transformed into an ocean of data used in turn to fuel our digital lives and economies. But not all companies are as prepared to extract business value from data.

If you can’t measure it, you can’t improve it.

— Peter Drucker

Paraphrasing a famous quote by Peter Drucker, the only way to improve an organization’s ability to generate value from data is to measure how well it is doing. So the question becomes: how do I measure my company’s Data Maturity?

Data Maturity and your journey to Data Fluency

Data Maturity is a measurement of the company’s ability to embed data in its strategy and decision-making process. Data Fluency represents the highest level of a company’s Data Maturity. When achieved, it can become the foundation for a significant competitive advantage.

To reach Data Fluency, an organization must develop a harmonized set of actions around human resources, processes and technical solutions. It is a very complex transformation journey, but the impact on business performance is so high that it will separate successful companies from those who will be left behind.

In order to analyze and understand a company’s Data Maturity, we’ve developed a framework that defines different Maturity Levels.

Below is a simplified but useful summary:

  1. Data Beginner: Data lives in silos and an excessive amount of time and energy is spent to extract it and prepare it to perform an analysis. Data Analysts explore and summarize data using deductive techniques and basic tools, such as Excel. Insights are presented on hand-crafted PowerPoint presentations.

  2. Data Novice: The company has invested in consolidating all its raw data in data lakes in more specialized tools for data preparation and transformation and started hiring a few data scientists. Still data quality issues and lack of scalable processes generates more frustration than results.

  3. Data Knowledgeable: The company has a clear vision for data analysis. It recognizes Data Science as a core competency for competitive advantage. The Chief Data Officer (CDO) or Chief Analytics Officer role has been introduced to help manage data as a corporate asset. More advanced tools are used to provide automated model deployment to generate business value to the company.

  4. Data Literate: A data-driven approach to decision making is embraced. Data teams are routinely generating value across the organization. Collaboration tools are introduced to enable sharing, modifying, tracking, and handing off data science artifacts (features, models, pipelines etc). Company uses Metadata management tools and predictive models are deployed and monitored in production across heterogeneous systems (on-premise/cloud).

  5. Data Fluent: Prescriptive data analytics is used to empower the company’s business strategy. The process to transform data into strategic and operational decisions is streamlined and automated. The data science professionals are integrated within cross-functional teams to build core products and services. A clear data governance is established company-wide and data-driven decisions blossom as a culture within the organization.

How do I identify my company’s current Data Maturity?

Maturity Models have been around for some time and there are several available from multiple technology vendors. The challenge with these models is that they are typically focused around the areas addressed by the vendor’s products.

At The AI Academy we have treasured our consulting engagements with a number of large enterprises to build a more strategic framework for companies’ Data Maturity. Our Maturity Model offers a 360-degree view on the strategic aspects a company needs to focus on to become Data Fluent.

Data Maturity Model developed by The AI Academy

The Model is built around 3 main pillars, each detailed in 3 dimensions:

  • PILLAR 1: Talent & Competences

  • PILLAR 2: Processes & Governance

  • PILLAR 3: Technology

1. Talent & Competences

Any company transformation journey has to start from people: it won’t matter how well your strategy is designed if you don’t have the talent to execute your vision. To effectively evaluate a company’s Data Maturity from a Talent perspective, we should consider how it is doing in terms of Roles, Organization and Collaboration.


Most companies have data analysts exploring and summarizing data using deductive techniques, but only those who have designated data leaders and well defined career paths for data professionals can truly harvest business value from data. When roles are well defined, the organization has a clear understanding of the competences required to extract value from data and expectations for each role are clearly set. This leads to an efficient execution of data projects with defined responsibilities and effective interactions.


Achieving Data Fluency also requires changes in the organization. Enterprises at the beginning of their transformation journey are characterized by the lack of coordinated efforts towards the adoption of data science best practices. This is sometimes followed by the creation of a centralized team (data Center of Excellence). Only Data Fluent organizations