To ensure the quality of a data science project, companies need ownership of the data they generate.

Data science strategies are gaining more traction among Spanish companies: a recent report indicates that 96% of business decision-makers believe that the way data is handled and managed has positively impacted their organizations’ performance. Additionally, 64% admitted to achieving higher levels of resilience with the presence of a mature data strategy.

Data is itself the vital input that feeds this entire process, requiring a deep and attentive look from the business. A fundamental question when designing such a strategy is: How much customer history do I have stored? Who owns the data?

At this stage, what I call the “data policy” is central: companies need to own their data and ensure its quality. In most cases, that quality is directly proportional to the quality of the applications or systems in which the data is created.

The second issue is that size does not matter. We can talk about “small data” (surveys, experiments, company data, systematic and structured data) or “big data” (spontaneous, unstructured data generated at high speed from multiple sources), but what is important is finding a way to systematize its exploitation for the business.

When a company gains control over its data and adopts a “data-driven” approach, it means making strategic decisions based on data analysis and interpretation, thereby better serving its customers and consumers. However, initially, a culture of this type must have ownership of that data.

If, in the past, data management was only conceivable for multinational corporations, large corporations, or institutions, the situation has changed significantly today.

I emphasize this last point because it often happens that when starting with a data science strategy and wanting to obtain information, for example, on how consumers use a certain product or where people drop off in the conversion funnel or what the general retention rate is, reports from basic general analytical tools available on the market may seem like a good option.

However, nothing compares to the immense value of being able to perform any business analysis without relying on third-party platforms or applications. What this requires is that the company owns and manages its own database as a strategic decision.

This maturity generally arrives when the questions start to be more in-depth. If the questions being answered with data become more specific, the most common analytical tools often fall short.

Additionally, if a company has not established a basic data governance policy, it is often a problem for all areas that require a close look at that data, not just system and application providers.

Ultimately, managing one’s own data provides a more flexible option to meet all current and future business needs. Data integration, quality, management, and governance are all dimensions that must go hand in hand to achieve a successful data science project.

By Julio Cesar Blanco – July 11, 2022