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

Data science strategies are gaining more and more ground among Spanish companies: a recent report points out that 96% of business decision-makers affirm that they are sure that the way in which data is handled and managed has had a positive impact on the performance of their organizations. On the other hand, a 64% admitted to having obtained higher levels of resilience with the presence of a mature data strategy.

Data is itself the vital input that feeds back into this entire process, which is why it requires a deep and careful look on the part of the business. Faced with the design of a strategy of this type, a fundamental query is: How much customer history do I have saved? Who is the owner of the data?

In this instance, what I call the “data policy” is central, that is: companies need to be the owners of their data and ensure their quality. In the vast majority of 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 the important thing is not the size. 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 the important thing is to achieve a way of systematize its exploitation for the business.

 When a company achieves mastery over its data and adopts a "data-driven" approach, it means that it makes strategic decisions based on data analysis and interpretation, and thus manages to better serve its customers and consumers. However, in the first instance such a culture must have ownership of that data.

If long ago the "Management of data" was only conceivable in the case of multinational companies, large corporations or institutions, today the situation has changed significantly.

I reinforce the latter because it often happens that if you are just starting with the data science strategy and you want to obtain information, for example, on how consumers use a certain product or to know where people leave the conversion funnel or what the conversion rate is general retention, reports from basic general analysis tools available in the market may seem like a good option. 

However, nothing compares to the enormous value of being able to perform any business analysis without having to rely 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 usually comes when the questions start to get deeper. If the questions that are trying to be answered with the data become more specific, the most common analytics tools often fail.

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

In short, the administration of the data itself provides a more flexible option to respond to all the present and future needs of the business. Data integration, quality, management, and governance are all dimensions that definitely must go together for a successful data science project.

Julio Cesar Blanco – July 11, 2022