How DIKW Pyramid supports decision makers

Before understanding how this tool can support decision-making, let us take a step back and understand what exactly DIKW Pyramid means. When we talk about pyramid we refer to a diagram that depicts the learning process by placing in a hierarchical relationship and in a linear manner four elements such as: data, information, knowledge, wisdom. This type of model represents the knowledge process as a pyramid consisting of a broad base (the raw data) which, following a process of aggregation-contextualisation (information) and application-experimentation (knowledge), leads to wisdom.

Trying to bring the concept of the DIKW pyramid back to the corporate sphere, it is possible to understand how the data possessed by companies can be managed to obtain information from which to derive knowledge and thus enable decision makers to make more effective and data-driven choices.
In order to become a company that truly leverages data to make winning decisions, both at a strategic and operational level, it is necessary to start with the correct acquisition, management and storage of so-called raw data.

Raw data or raw data are all those data that have not yet been processed. As long as the data is simply collected, but no tools are available to analyze it or derive information from it, it is irrelevant to the company and does not contribute to generating value. It is at this stage that advanced business intelligence software becomes strategically relevant.

We think of the Business Intelligence system as a Data Warehouse and a series of ETL (Extract/Transform/Load) processes, i.e. the collection of data from an unlimited number of sources and their subsequent organization and centralisation in a single repository.
This software is preparatory to the evolution of the data and the process that leads the company to extract useful information for the business and to give an initial meaning to the collected data.

The output of business intelligence work is represented by reports that aggregate huge amounts of data by returning tables that link different information, or graphs and concise dashboards. From these aggregations and representations, decision makers choose the best actions to take. Despite the fact that the use of software for data acquisition and analysis has grown in recent months, even on the Italian scene, there are still few companies that reach this rung of the pyramid because, in order to do so, data and its management must be incorporated into everyday business processes.

After gathering all indications based on the processing of historical data, one can move on to the planning of future actions. The wisdom stage lies in applying the knowledge gained and thus being able to act in the best way for the company. The data at this point become the scaffolding on which the strategy is based and at the same time a yardstick for future evaluation of the project's progress. The focus of this phase is on future actions and how to carry out forecasting analyses and assess the future impact of certain decisions.

The greatest challenge of data analysis in recent years is precisely that of being able to provide tools capable of performing prescriptive analyses and making them comprehensible. Prescriptive analysis involves the use of a variety of statistical techniques such as predictive modeling, machine learning and data mining to analyze historical and real-time data to provide predictions about the future or unknown events. Predictive models search for patterns in historical and transactional data to identify risks and opportunities, find relationships between variables to make risk assessments associated with a correlation of specific variables and thus provide decision makers with the information they need not only to understand how a particular suggestion for action was arrived at, overcoming the limitations of Artificial Intelligence, usually associated with the black box concept, but also explaining how to behave should certain conditions change abruptly.