Analytical Maturity: Lay the Foundation

The concept of ‘maturity’ applies in many of life’s arenas as a marker of the attainment of a specific, highly regarded level of status. Not surprisingly, it’s applicable in many areas of the digital realm, too, as emerging processes elevate and improve on their predecessors. In many cases, the achievement of maturity is something that just happens as an organization follows its business courses along their likely and logical paths. In other cases, however, attaining a truly ‘mature’ status requires insight, intention, and investment.

 
In an ‘analytically mature’ enterprise, the insights of its C-Suite places a high value on the analytics investment, which led to the intentional pursuit of that elevated status. Research indicates, however, that many companies struggle with the processes of becoming analytically mature and have yet to actually attain ‘analytical maturity,’ however. If you intend for your organization to become analytically mature, now’s the time to act on that intention by gaining intelligent insights and planning intelligent investments in the process.

Defining the ‘Analytically Mature’ Enterprise

Like every maturation process, becoming analytically mature begins as an idea upon which procedures are built, practices are developed, and, eventually, progress is made.

Foundations

The path to analytical maturity begins by developing a series of foundations that will support the changes to come. These foundations are company-wide and inform every element of the enterprise. Missing just one can doom the success of the entire project:

• A forward-thinking corporate culture sets the fundamental platform for overall maturation project success. That culture ensures that all project participants (including everyone who works in the company) are amenable to the changes driven by the introduction of advanced technological systems. Smaller organizations should find this to be an easier task than larger ones; in many cases, workforce reluctance to embrace new ideas impedes the speed of the maturity project. Fewer workers should mean fewer barriers.
• The corporate prioritization of ‘data literacy’ is another key to success. Every employee should be as invested as the C-Suite in prioritizing data as their primary occupational tool, so they understand how the technology improves their work and how important it is to improving the company’s fortunes as a whole.
• Prioritizing talent and data are another critical step toward analytical maturity. The role of the ‘data scientist‘ continues to evolve, but even the earliest interpretations of the job were beyond the essential work of the IT department. These technological wizards can make sense of the vast volumes of messy and varied data formats to identify emerging trends and relevant connections that are usually not visible to the untrained eye. They are also experts at ensuring data quality, so leaders don’t make decisions using obsolete, erroneous, or incomplete information.

 
Getting these foundational elements in place and operative is, in itself, a major project and a critical step in your organization’s digital maturation progress.

Drivers

In tandem with developing an amenable corporate culture, encouraging data literacy, and building your talent team, as a leader, you should also be thinking about what, exactly, ‘analytical maturity’ means to your organization. Often overlooked by people who are early in the process is the fact that a fully mature enterprise consists of many ‘mature’ departments, all of which work harmoniously to drive the company forward. Leaders in these enterprises have parsed out what ‘maturity’ looks like in each of those departments, then combined the efforts of those several sub-entities into a single, uniform, highly functioning whole.

As you get started on this project, consider finding answers to these questions to inform your decisions and next steps:

• How do your departments find and use their data? Your human resources department, as an example, needs to see different data than the product research and development department.
• Do you know if each department has access to all the data it needs? Many legacy systems are limited by their smaller and obsolete programming that can’t interact with today’s more sophisticated and complex software and programs.
• Do you know how the data from the different departments relate to each other? Is there a process followed that connects relevant elements to each other so all departments are informed by the efforts of the others? Are there dashboards available to all so that the entire organization works off a single understanding of ‘truth’?

 
Fact-finding for these concerns will help you map out how the various corporate offices interrelate and how structuring their data sharing can improve their metrics.

You should also be considering the status of your corporation’s digital systems. Many companies continue to operate on legacy programming that has been cobbled together over time as needs arose.

However, today’s digital arena is global, connected, and diverse, and too many legacy systems lack the tools to fully engage with the evolved programming. You’ll need to figure out how and where your organization’s data currently exists to determine how to configure it in the analytically mature model:

• Do you know where and how data relevant to all departments is stored?
• Do you know who can access them and why they have that authority?
• Do you know the identities and practices of your third-party vendors? Do you have or need processes in place to ensure their data arrives clean, safe, and relevant?
• Do you know if your organization currently utilizes cloud analytics assets, and, if not, do you know why not? If earlier leaders made this decision more than two or three years ago, it might now be obsolete. Your company may be losing market share because your on-prem systems can’t keep up with the speed and comprehensiveness of today’s cloud computing forces.

 
All this work is foundational to the analytical maturity project, and decisions regarding further investments in the project shouldn’t be made until this step is complete.

Datavail’s data management professionals can help you work your way through these fundamentals, so your analytics project moves forward as a well-designed plan. We can also help you define and develop your organization’s ultimate ‘mature’ analytics position and take the steps necessary to achieve it. Feel free to contact us to discuss your organization’s analytics goals and needs.

To learn more about how cloud analytics can benefit your organization, read our white paper “Journey to Cloud Analytics: Using the Cloud to Solve Your Analytics Challenges”.