It is nothing new to say that data and how it is managed is the oil of the 21st century. In fact, it is estimated that more data will be generated in the next five years than in the previous 5,000 years. For many years now, the vast majority of industries and companies have been investing a significant part of their budgets in integrating the most advanced technologies that allow them to work with and exploit information sources to obtain real value from data. But in this constantly evolving ecosystem, one of the aspects that can make the difference between success or failure is the ability to identify the analytical and digital needs of the organization.

To this end, there are models that help measure maturity in terms of data management and make it possible to determine a company’s current situation, its position against competitors and the main gaps compared to a realistic aspirational model. Allowing to design a roadmap that marks the right way to become, in a progressive way, a Data Driven Company. Being models that help to identify the real needs of the company, the level of maturity in analytical environments and how prepared its employees are for these new working models.

Currently, there are many studies that can help a company to know its level of data maturity. From academic models focused on evaluating the more structural aspects such as processes and control of data and information, to more business models that seek to measure the more “human” aspects of companies. From there, hybrid models are born that are able to evaluate the more theoretical aspects of the company with the development of employee skills.

Kepleer has designed a model that includes the most significant points of the previous models and introduces a new variant in the equation for measuring data maturity. This axis is those attitudinal aspects of both the company and the employees that are part of the company’s DNA and have always been more complicated to quantify. And this is possibly where the key to success lies, in knowing what to measure and how to evaluate it from the different possible points of view. 

To do this, it is essential to define the pillars of analysis that allow the evaluation of the company’s strategy and culture, work processes, technology, available data and its use, as well as the analytical capabilities of employees. In addition to defining the tests to evaluate the different dimensions of each of the pillars. 

The maturity levels range from a situation of zero maturity or even denial, to the stages of being a leader or benchmark in the field. Thus, as companies increase their level of maturity, they manage to increase the credibility of their data and improve the governance and quality of the information. Moving from descriptive analysis with past data to collaborative environments with integrated information that allows the generation of predictive and prescriptive analysis.

Analytic maturity analyses are very valuable tools for any organization seeking to evolve and improve its performance by identifying areas of opportunity in process optimization, improving operational efficiency or increasing profitability by assessing its current ability to understand, use and act on data.

While the outcome of these analyses is important, what really makes them relevant is the ability they offer to set and define a roadmap with a clear vision and an idea of how quickly you want to move forward on the analytical opportunities identified. 

Regardless of the specific objectives of the companies, there is a common aspect in all cases, and that is that a correct analysis of analytical maturity and data management has to serve to increase the value provided to the business through data. This means that an organization can make decisions based on the information available to it in a much more reliable, agile and robust way.

Author

  • Miguel Sáez

    Data Management Consultancy at Keepler. "I like to think that all the beautiful stories of our lives are data told with a bit of soul. That's why I'm passionate about what I do; helping to ensure that this data is well managed and of high quality."