Data preparation for AI

25.03.2021

Data preparation for AI

Artificial intelligence for marketing? Bring it on. AI makes processes possible that were unthinkable just a short time ago. Consistent digitization with machine learning ensures more sales with modest effort — but only with intelligent data preparation. Find out how to make your data fit for AI here.

The quality of the data is decisive

Machine learning uses data to identify structures and correlations. Using this as a basis, AI programs identify new solutions to deal with specific problems. But without sufficient input, there is no good output. Software based on artificial intelligence therefore needs data that is

  • available in large quantities,
  • complete
  • and of good quality.

These three properties are the basis for the successful use of AI. In most cases, this means that the existing data must be verified. This is particularly important for Big Data from the Cloud. Generally speaking, there are three factors that stand for good data preparation: storage, compatibility and scope.

1. Storage

Backing up data at all times is fundamental. Of course, this means that programs for customer relationship management, including all marketing tools, must always be up to date. Companies use the Cloud for this — also as a security plus for in-house storage. This ensures that the most important KPIs or other data used for artificial intelligence are not lost.

Important: If only parts of the relevant key figures for your business are lost, an AI system can draw the wrong conclusions. You should therefore ensure that your data is complete by storing it consistently.

2. Compatibility

You must be able to export the existing data. If you develop your own AI model for your company, you will need a smooth export. It is important to select a specific system at an early stage; it must have as many interfaces to powerful machine learning programs from other providers as possible. This will significantly speed up the work of AI systems.

3. Scope

Is less more? This wise saying does not apply to AI, at least not in terms of source material. When the quality and relevance of data from different vendors is accurate, the motto is: Do things in a big way. KPIs, for example, become more informative the further back they go in time. This reveals the historical development of processes, from which AI can draw lucrative conclusions. Even supposedly outdated information can offer great added value.

How does data preparation work?

Preparing data for AI tools often accounts for up to 80 percent of the total workload involved in implementing AI systems. The more fragmented the data, or the more unstructured it is, the greater the time and effort required for the two steps involved in data preparation: Exporting and cleansing.

Data export

The source of the problem is well known, especially in marketing, where data from different providers is available from a wide variety of sources. For instance:

  • Social media channels
  • Websites
  • Mobile applications
  • CRM
  • Mailings

You can extract valuable data from all these tools and analyze it using artificial intelligence — as long as the providers of the programs offer effective options for data export. Automated interfaces (APIs) are the basis for clean and effective data export.

Data cleansing

Data cleansing ensures that the data is suitable for training a system. This process is primarily concerned with eliminating incorrect or erroneous data. Data cleaning also involves finding similar or contradictory data sets. In this case, logarithms can be used to normalize data. This transforms the data.

Data cleansing can be used as a solution to the following problems, for instance:

  • Large amounts of data are available, but they do not cover the entire spectrum. There is for example no data regarding pre-sorted objects. But these are especially important for AI training and for insightful analytics.
  • But even a large spectrum does not guarantee data quality per se. This is because the respective rules of different data sets can ultimately reduce the amount of data in such a way that too little remains for artificial intelligence in the end.
  • While the different use of classes and hierarchies may be effective for users in a previously treated dataset, it can distort the data in the background. So even AI produces incorrect findings.
Optimize your AI training data – clickworker supports you in data preparation; evaluates, categorizes and labels existing data sets.

Companies in service and industry that rely on AI for the first time are often surprised. Data that is otherwise useless suddenly becomes important in the context of machine learning. That is why every successful AI project starts with the analysis of existing data.

Summary

Digitization often fails because of inadequate data preparation, rather than because of insufficient AI tools. Therefore, preparing data is not an end in itself. When used in sensitive areas, for example in industry, AI tools require high-quality data — if only for security reasons. Therefore, before implementing a training system, it is important to check

  • where the data are secured,
  • that they are exportable,
  • that they are consistent
  • and that the quality of the data is high.

Having too few data sets will not be enough to generate effective results. Especially in the case of KPIs, it is more reasonable in case of doubt, to make all existing historical data available to the AI. Even if they seem outdated at first glance.

 

Dieser Artikel wurde am 25.March 2021 von humangrid geschrieben.

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