Banco Ripley

Data has become the center of debate in many business conversations, where access to and understanding of data has become one of the technological, procedural, and cultural challenges for companies of all sizes and industries in the last 10 years.

Like many who opted for a technology-related career, I began developing software or ad-hoc applications. During this development stage, I had the opportunity to interact with different technologies, understand different businesses (manufacturing, healthcare, telecommunications, financial services, government, among many others), and make multiple deployments. However, something that was always constant regardless of technology, industry, or business case was the data and how it should be exploited and used for decision-making.

With the birth of the so-called digital age, the need to obtain, store, transform, and give meaning to data to make decisions with an impact on the commercial, financial, or operational results of companies increased exponentially. We began to segregate roles in data-focused teams, creating roles oriented exclusively to technical development – cloud architects, data engineers, DevOps – and others more oriented to business – business intelligence (BI) analysts and data scientists.

  With the birth of the so-called digital age, the need to obtain, store, transform, and give meaning to data to make decisions with an impact on the commercial, financial, or operational results of companies increased exponentially.  

I have had to form and lead both technical and business teams focused on the use of data, and one of the biggest challenges has always been to break down the barriers, often self-imposed, between technical knowledge for data exploitation by BI or data scientist teams and its application by business stakeholders – commercial areas, marketing, products, services.

To break this gap between technical and business, (1) data decentralization, (2) bringing technology closer to business users, and (3) forging a data-driven culture must be generated.

(1) Data decentralization – the ability for business teams to access, manipulate, and generate analyses that allow them to make impactful decisions largely depends on having a robust data governance and how the data ecosystem is implemented technologically. To explain this at a very high level, it is necessary to have a robust data lineage and catalog, a business-aligned semantic layer, and a data architecture that allows workspace delivery to business areas in which they have the necessary data and tools to manipulate and analyze it.

(2) Technology for business users – historically, data analysis has been done using, in most cases but not exclusively, database engines that mostly work with SQL. The new generation of No-Code tools will allow business-oriented roles to analyze large volumes of data without the need for deep technical knowledge. It is important to understand that technology does not replace the analytical capacity that must be given in the interpretation of data.

(3) Data-driven culture – undoubtedly, the most complicated aspect of this equation because when we talk about cultural changes, it means breaking paradigms in the way in which a team or teams within an organization have historically worked. If we could place it in an equation, the weight of this variable would be 80 percent, with the remaining 20 percent being technology and its availability.

Business challenges change according to market dynamics and the birth of new competitors, technology continues to innovate and be disruptive to serve as an enabler in the development of products and services, but data remains constant and increasingly relevant to know where to direct the limited resources that organizations have.