The Analytics Maturity Model
These days, all businesses know that they should be working with data. The promise of becoming ‘data-driven’ involves your team gaining tremendous insights that will improve all elements of business and, ultimately, increase profitability. However, this promise remains elusive for many organizations. Without a clear roadmap, companies aren’t sure which projects offer the best returns or are even feasible. Teams may try to launch a data project only to have it fail due to a lack of infrastructure or clear requirements. Worse still, teams may feel paralyzed and not pursue any data projects at all, slowly watching their competitive advantage slip away. Here at DAS42, we help companies understand their data and develop their own data-driven culture so, in turn, they can make smart decisions and maximize their bottom line.
We often use a framework known as the Analytics Maturity Model to help our clients understand how to best make use of their data. In short, the Analytics Maturity Model tells us that an organization can best develop a successful data-driven culture by maturing through a progression of tools, techniques, and capabilities. The model can be used to guide a company’s next steps in developing their ability to work with data, or to help a company avoid the pitfalls of trying to skip ahead several steps. If your team is feeling lost in a sea of big data, being handed a navigational chart can be incredibly beneficial.
Most companies start their data journey working with simple ad-hoc analytics tools and small pieces of raw data. This is the world of 1000 excel spreadsheets. For many small organizations, this offers some advantages – it’s fast, flexible, and inexpensive. Very soon, however, the limitations of such an approach become apparent. People rely on business logic, data, and reports that are stored on local machines. There is no transparency and little collaboration. This means data is disorganized, disconnected, and can easily be lost. Many of these issues can be alleviated by implementing a data warehouse solution – a database system designed for analytics. Analysts can write SQL queries to generate regular reports and KPIs can be established and monitored. While this is a big step up there are still limitations. Generally, only Analysts who are comfortable with SQL can create reports, which means other members of your organization have to submit requests for a report and then wait. And wait. And wait. Additionally, analysts often define business logic on the fly, creating reports that don’t match up with each other.
This is where a good BI tool can make a real difference. These tools sit on top of a data warehouse and provide an easy-to-use interface that allows any member of the organization to explore the data and build reports. Modern BI tools also allow for the rapid development and deployment of dashboards that can be easily shared across the organization. This empowers all members of your team to answer any question they can think of with data – a key step to fostering an organization-wide data-driven culture. It also provides an organization-wide single source of truth, which can help answer questions and improve cross-team communications.
This also frees up your analysts to make the leap from standard reports to more advanced analytics. Up until now all points on the Maturity curve have been focused on answering the questions “What happened?” or “What’s happening.” With advanced and predictive analytics your organization can begin to ask “What’s going to happen?” Sophisticated analysis includes supervised learning techniques such as classification and regression models as well as unsupervised techniques such as clustering and dimensionality reduction. Your team will be able to segment your customers, predict CLV of leads based on patterns of behavior, determine how likely a customer is to respond to a particular marketing campaign and more.
Finally, your team will be ready to move to the last stage of the Maturity curve: Optimization. In this step, your team asks “What is the best possible outcome and how can we make it happen?” This requires a tremendous amount of expertise and an organization-wide commitment to data-driven decision making, but the benefits can be enormous. Your team will be able to use techniques such as Monte-Carlo simulations, A/B testing, linear/integer programming and more sophisticated machine learning to examine specific business functions and learn how to tweak them to maximize their effectiveness.
Moving up the Maturity curve can be done in as little as a few months, but could also take many years, depending on the technical skills of a team, the level of buy-in from all key stakeholders, an organization-wide culture of data-driven decision-making, and adherence to best-practices. Here at DAS42, we have helped many diverse organizations effectively move up the Maturity curve. Drop us a line if you’d like to learn more about how we could help your team make the most out of your data.