Artificial Intelligence Is Meaningless If The Data Is Not Correct

The consulting companys warns that artificial intelligence implementation processes must have a good basis to be able to develop effectively. For this reason, the data must be coherent from the outset, in order to be able to carry out advanced analytical processes.

Many companies have information models that are not consistent and are based on wrong assumptions. This complicates the implementation process and spends 80% of the effort to debug the information, while only 20% goes to the analytical process.

Incorrect base data

The consulting firms has announced that 77% of companies believe that their final result may be affected by the existence of inaccurate or incomplete data. In addition, 66% of companies lack a consistent and centralized approach based on data quality.

The data must be coherent from the outset, in order to be able to carry out advanced analytical processes

Let’s assume a natural intelligence system. Can you or someone make the right decisions if your information base is wrong? No. Well, the same thing happens with artificial intelligence systems ”says experts.

In this context, as the expert warns, many companies, when launching projects, invest a lot of time and effort without obtaining a good result; This is because their information models are not coherent enough, and not only that, but they perceive that many of the assumptions they made decisions about are incorrect. These types of situations are very common in environments where company integration processes have occurred, there are different reporting or analytical systems/systems with data from different sources.

50% of companies do not have a correct database

Data inconsistency appears when analysts appreciate difficulty comparing data or encounter “holes” in the information. This is why it usually happens in 50% of companies. All of this makes advanced analytics processes very difficult or even masks serious business problems. It is possible that, on many occasions, the results with which they work in sales or marketing are different from those obtained by the financial sector. This situation can cause that in the projects of implantation of advanced reporting systems or predictive analysis, 80% of the effort is dedicated to purifying the information and only 20% to the analytical process.

Savings of companies by purifying information

Businesses can save if they manage to debug information. By simply simplifying the analysis processes, companies can appreciate the savings. Furthermore, in this way the entire organization works under the same principles. And it is that thanks to the appearance of RPA-type tools and advanced information analysis, the information purification processes have improved significantly.