Getting started with Artificial Intelligence (AI)
Help and support in applying AI to data-driven research and policy

Businesses and companies have a lot of data that is all nicely stored - but what then?

Data can no longer be ignored in today's society. They are continuously generated, sorted and stored. In fact, there are often too much data to be handled by humans. Smart machines and self-learning algorithms can help us with this; they are not only useful, but often necessary to provide us with key insights that are normally invisible. Moreover, sometimes even complex relationships can be discovered by coupling multiple data sources.

Many companies collect data in a targeted manner to get to know their customers better and provide better service. Other organizations manage data that is necessary (or even mandatory) to realize their services.

But how do you deal with data? How do you make the step from data to data-driven research? Which steps are necessary and which things should you pay attention to? Are there any bottlenecks? Consequently, there can be all kinds of questions when initiating a data-driven research.

Even in advanced analysis, sometimes we forget some important aspects of data-driven research or miss some crucial details. A manual with a clear overview of various aspects, tailored to a specific project, research, or topic, can help your organization on its way. As a result, data may suddenly play the leading role in policy-based solutions.

AI manual and roadmap

At CentERdata we offer customized AI manuals. We also supplement the manual with a detailed step-by-step plan to make data-driven research even more accessible for your cause or organization. In addition, we offer feedback moments for more detailed explanation and consultation.

Example project
AI manual and roadmap for the Municipality of The Hague

For the Municipality of The Hague, we have drafted a comprehensive AI manual for data-driven research on helping people on welfare to find work faster. Using data-driven research, the outflow of people on welfare to work is predicted using predictive modeling and clustering techniques. By providing specific advice for the case of The Hague and by means of a detailed five step plan, advanced analyses and predictions have been made possible to be used in policy decisions.

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