A featured contribution from Leadership Perspectives, a curated forum for enterprise technology leaders, nominated by our subscribers and vetted by the CIOApplications Editorial Board.

Chief Data & Analytics Officer at Ualá

Data Monetization: The Role of Self-Service and Analytics

In the current information era, data is the new oil. However, just like oil, data itself has no value until it is refined to unleash its full potential.

To achieve this, it is necessary to build a culture of collaboration and focus on the organization's goals, so that data teams can work together with the business and generate the most suitable solutions.

The objective is to monetize data. This requires collaborative effort from all areas of the organization. Building a Google-like data repository is not achieved with data alone; the effort of each area is necessary.

Secondly, we need to talk about enabling self-service, which allows for the generation of reliable data transformations so that each area has the data it needs in the format it needs. This concept is closely associated with the concept of "thames," which we won't delve into here due to length constraints.

Self-service refers to data processing, not just the creation of dashboards or data storage. We should avoid playing the role of a "translator" as much as possible, as this can create individuals who "love" the work but do not embrace the culture. Additionally, we should promote data literacy.

The third critical point is analytics. A Forbes study found that the last stages of analytical solutions, especially those focused on action based on a model or algorithm, are where solutions often fail. Therefore, it is important to work together to create a plan when addressing a problem and avoid lack of focus or misdirected efforts.

Self-service refers to data processing, not just the creation of dashboards or data storage.

In summary, before creating a model to answer how many passes the people in white made, let's ask ourselves how that information will be used.
The most valuable idea of all is to generate trust and have trust. As an example, we can mention the movie "Moneyball," where a budget-constrained team started buying players based on statistics. Despite going through very difficult times, the team continued to trust and achieved success.

In conclusion, let's never forget the objective, let's work together, and let's create a culture that focuses on the challenges faced by the data team. Having teams with a cultural focus, as I have expressed throughout this article, is essential. Sometimes, it is crucial to give high visibility to everything they do and make the data strategy as transparent and visible as possible to the entire organization.

The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.