Understanding DataMaps
Understanding DataMaps
In this series of posts, we will be sharing some of our experiences and insights from helping many of our customers develop DataMaps. The posts will cover many different aspects of effectively mapping the data footprint within organizations.
What is a DataMap: It is helpful to understand what a DataMap is and what are some of its critical elements. Equally important to understand is what is not a DataMap.
How can be the DataMap be used and why your organization needs one: There are very interesting ways in which DataMaps are already being used today. However, future use cases and scenarios continue to evolve and broaden.
Working across different teams: DataMaps inherently require and increase collaboration across silos in organizations. They reside at the intersection of Business, Risk, Privacy, IT, and Security. How do you ensure alignment across the different priorities that exist
How to build a DataMap: To effectively build a DataMap, you should understand the different drivers that exist for different stakeholders but ensure there is alignment across the organization on some key common goals. Another question often asked is how much of this can be automated and how?
What types of data should a DataMap include: The short answer is all types of data. But, how you map structured data in a central database might be very different from unstructured data that is streaming from multiple sources and can vary from time to time. What metadata should be captured and how are other key questions
Keeping the DataMap sustaining and evergreen: How do you design an effort to ensure it can sustain. It is common to see multiple attempts in different parts of the organization that stall and eventually be discarded. A well thought out approach is critical
Illustrations of DataMaps and Analogs: Often it is easier to understand something by looking at aspects in more detail or trying to understand the DataMap by something more commonly seen and understood
We will start this series with a post to go over the ubiquitous Google Maps and highlighting some excellent analogs to the DataMap.
