Traditional business intelligence is mainly based on descriptive analysis, i.e. based on historical data to obtain conclusions that sought to explain why certain situations had occurred, enabling us to understand our current situation, but doing little to contribute towards decision-making or the defining of future strategies.

Fortunately, descriptive analytics is not the only technique available, since predictive analysis, based on historical information and the use of mathematical and statistical models, enables us to infer what will happen in the future.

Lastly, prescriptive analysis draws on descriptive and predictive techniques to guide us in decision-making and in the defining of new strategies.

Predictive and prescriptive analyses correspond to what is often known as advanced analytics, which involves using and acquiring knowledge to improve the decisions we want to project in the future. 

Much of the success or failure of a data analytics strategy comes from the fact that quality data is available, but the actual fact is that this is not always possible. It is here where two key concepts appear: data governance and quality. Although both concepts are similar, we can distinguish between them primarily by the fact that a data governance strategy considers everything related to the data life cycle, and within this cycle is the data quality.

Data governance is defined as a strategy around data, through which data quality and processing are improved, resulting in enhanced long-term decision-making, maximising information analysis capabilities. A governance strategy should consider at least the following elements:

• Establish and create data governance strategies and policies to understand what information, how, when and who uses it, while ensuring the quality and uniqueness of the data.
• Enable data technologies, tools and architecture through the deployment of an appropriate platform allowing for data acquisition, storage, use, access control, and quality.
• Define the coordination of the data life cycle to appropriately manage the incorporation of new information sources.

Data enrichment is another key element within advanced analytics, because the data we possess often restricts the analytical capability.

Enrichment is understood as a process by which I can expand the value of my data, by incorporating additional external information that can improve the insights generated.

Through enrichment it is possible, for example, not only to have the addresses of my clients but also, based on this information specific to my systems, to add value by incorporating geographical and/or meteorological data, which might be of great use when understanding the behaviour of my clients and, for example, defining bespoke marketing strategies or ad-hoc products in line with their actual needs.