Data Maturity Model: Data Analytics & Business Intelligence
As we progress through the data maturity model, our overall vision for an operationalized AI and machine learning solution takes shape. While the earlier stages of our model set the foundation for how data is collected, processed, and organized, the next two stages – Data Analytics and Business Intelligence – is where businesses begin to truly experience the power of data. It’s where raw information is transformed into insights that inform decisions, shape strategies, and drive meaningful action.
Maturity Level 4. Data Analytics
Now that your data is structured and stored properly, it’s time to start unlocking the full potential of the dataset using machine learning and AI. Before full-AI, or even Business Intelligence, a great interim step is operationalizing a data analytics solution. These types of solutions use your data in an organized and intelligent way, and can also provide more immediate value to you and your business.
Data Analytics involves designing, building, and implementing solutions that allow brands to export information in an intelligent and structured way. Rather than sifting through endless rows of numbers, stakeholders can now query their data, identify patterns, and uncover opportunities or risks that might otherwise be hidden.
When organizations harness analytics effectively, they move away from instinct-driven decisions and toward evidence-based strategies. Operational efficiency often improves as bottlenecks are identified and addressed. Customer behaviors and sentiment become easier to understand, enabling you to tailor your messaging and marketing approach with greater precision. And, perhaps most importantly, leadership gains confidence that decisions are backed by credible data-driven insights and patterns, rather than guesswork and instinct.
Everything that can be accomplished in this stage (and beyond) is made possible as a result of the foundational steps that were taken. In ensuring the quality of the data, and its adequate structure, organization, and storage, we have enabled a scalable and powerful data solution that can grow alongside the business.
Maturity Level 5. Business Intelligence (BI)
While Data Analytics equips organizations with the ability to uncover insights, Business Intelligence takes the next step – making those insights accessible, understandable, and actionable across all facets of the business. At this stage, data evolves into stories. Through dashboards, reports and visualizations, complex datasets are transformed into narratives that can be understood not only by those close to the data – e.g. analysts and data scientists – but also by executives, managers, and business teams.
The value of Business Intelligence lies in its ability to align strategy and execution. Leaders and employees across departments can view the same set of metrics, ensuring a shared understanding of goals and performance. Additionally, decision making becomes more proactive, as predictive analytics allows organizations to anticipate outcomes rather than react to them. With the right Business Intelligence frameworks in place, your brand can create feedback loops where data continuously informs and optimizes operations.
When navigating Business Intelligence, visualizations should remain clear and purposeful, avoiding overcrowded dashboards that dilute the impact of meaningful insights. Operating around a clear strategic approach – and solid north star – are imperative as your brand shifts towards using data in a more intelligent manner.
Together, Data Analytics and Business Intelligence represent the turning point in the data maturity journey. Analytics enables organizations to generate insights, while Business Intelligence ensures those insights are shared and acted upon in a meaningful way. These two stages bridge the gap between simply having data and using it as a strategic advantage.
Self-Examination
Are you using data-driven insights to optimize your business operations? How about your customer experience? Here are a few self-evaluation questions to see how you stack up with latest set of data maturity levels.
Do you and your organization use data to inform your decision making?
Do you have processes in place that help you derive customer insights from your data?
Do you have processes and mechanisms in place that use your data to identify operational deficiencies? Can (or do you) act on these optimization opportunities?
Do you use Business Intelligence to transform datasets into stories and narratives?
Does your brand leverage dashboards to visualize data-driven insights and patterns?
Is the output of your data efforts solely consumed by your Data Scientists and Analysts? Or are the outputs easily shared to other facets of the business?