How to make your (Big) Data fit for Artificial Intelligence?

Of course, data is the basis for any Artificial Intelligence – what should algorithms analyze if there is no data?
Often, there is no need for big data, in order to optimize business and technical processes in a predictive way. However, you need a) the right data, b) in the right quality, c) fast enough.
That sounds easier than it is, but data analytics and good data interpretation depends on it.

Based on many years of experience in Artificial Intelligence projects, we have developed procedures how existing data can be made “fit for AI”. Therefore, before we get started with AI analytics, we establish the following quality and performance procedures, also using AI algorithms.

• Data availability incl. problem analysis
  Sensor, connectivity or database problems can lead to missing data, again and again. Reliable AI analytics might not be possible anymore, depending on missing data volume and time periods.
  Therefore, people in charge are informed directly which data is missing, also giving information for possible causes. In this way, problems can be solved quickly.
• Data quality thanks to semantic checks
  Processes are complex and heterogeneous, i.e. due to differently equipped production lines or product diversity. Therefore, case-specific semantic checks are required. For example, assembly values differentiate according to production lines, products, …. Thus, production data has to be considered individually for each part.
  Automatic semantic checks discover problems at once and indicate root cause.
• Performance thanks to transformation
  Independent of the fact if your data is available in a company-wide data lake or in a local data “pond”: It is important that relevant data is available fast. Even in high data complexity, target-oriented transformations realize very fast queries and analytics, even on large time periods.

We will be pleased to assess your data landscape if it is “fit for AI” and analyze what must be done to reach AI fitness.