There is a lot of noise these days about data management and specifically what the concepts of big data, machine learning and business intelligence are and are not. It is difficult for many companies to really know what they are made of, how they work and above all what these innovations can really contribute to the business in the short, medium and long term. It is clear that you can draw better conclusions from the data if you know where to start and where to go.

When starting a project, it is important to critically question the return on investment in data science. Against this background, Roger AugustineCEO and co-founder of prenomicpoints out two main lines of action that are sure to give you the results you expect.

Direct effect: more rotation, more clearance

In this case, these are projects with a direct impact on the operating accounts. Here each sector has its specificities, but at the moment and across several sectors we see that it is very interesting to carry out projects in the following areas:

Activation of “sleeping” clients and automation of these actions. Every customer adds up and it’s usually a lot easier to reactivate a customer than it is to get a new one. After an exceptional situation in which customers have changed their shopping behavior due to the pandemic, it is important to remind them that we exist, with the right intensity for each segment.

dynamic pricing. In the sectors that market an asset with limited capacity, as is the case with hotel beds, flights, trains, etc., it will be particularly interesting to make the best use of this asset based on the available capacity at any moment.

Optimize supply (eliminate breakages and emergencies and reduce safety stock). Especially with reasonably regular delivery cycles, this type of action enables costs to be reduced, business opportunities not to be lost due to a lack of stock and the range to be expanded.

Indirect Impacts of Data: Internal Efficiency

In this case, it’s about betting on projects that have a great capacity to empower our most talented people. Most organizations have very talented professionals who spend long hours compiling data and reports in order to analyze them and make decisions. Nowadays it is possible to launch projects that allow data to be automated and made accessible thanks to tools optimized for analysis. Therefore, it is a type of project that usually has very positive indirect effects on internal efficiency.

Choosing where to start… If you have a high level of analytical development in the organization, it probably makes sense to focus resources on the first line of action, which is more efficient if we start from a good base. On the other hand, if you’re considering taking your first steps in data science, it’s probably worth investigating how to build a good knowledge base to help with the second line of action.

Big data, data science, data, data management, machine learning