Data Management Track

We believe that every communication and interaction is about data. If you talk to someone, you are sharing data. If you write a letter or an email, you are sharing data. If you put your birth date, credit card number, address, etc. on any IT-system, you are sharing your data. In the end, we create and share data all the time by ourselves in nearly every situation. It is a very usual doing and everybody is used to do it.

In any business there are much more of those processes going on. To be effective on it, it is crucial to manage your data proactively. The greater the amount of data you have, the more important it becomes to know how to handle it correctly. If you don't, you will easily lose a track of where the data is, how it is treated, who has access to it or even how and when to use it.

You might remember a library with a large volume of books. Books consist of a lot of data that readers derive information from. To get useful or valuable information, it is essential to know how to identify and determine which books you need. That is why nobody would expect to go into a library that does not use an appropriate sorting system. Books should be categorised by topic, author, title or in some other way to enable readers to find the desired book easily.

Books should be ordered in a practical manner and location to allow readers to examine and discover them easily. Making a list of previous book users can be also one of the good solution to identify who wrote in, underlined or changed something in the book.

This case is the same with data. Any person or department who wants to conduct analysis to derive business insights or values on a particular topic should know where and how data is stored, what data is available, how and where to access it and what the quality is.

This is what we call the data management: Knowing where your data is, how to access it, checking the data quality, understanding how it is structured and defining or realising who is responsible for data.

To provide you with the clear understanding, we developed our data management track-model:

Our track-model consists of five parts: data processing, data profiling, data modelling, data mapping and data quality.

Data processing and data quality are part of every single step. They build a bracket around the other three parts showing the ongoing performance of the data.

Data Processing
  • Design and understand data processes end-to-end
  • Define responsibilities around the data
  • Understand the purpose of each data evaluation

 

Data Profiling
  • Understand patterns and contents of data
  • Know responsibilities around the data
  • Realise flaws in the data
  • Anticipate the metadata
  • Perceive the data model
Data Modelling
  • Understand business processes and needs end-to-end
  • Create an understandable metadata model
  • Anticipate data quality processes from the profiling task
  • Design a data model on this premise
  • Test, if the data model is fit for a particular purpose
Data Mapping
  • Build mapping rules according to a source and output of data models
  • Integrate data quality rules in the mapping
  • Implement the mapping based on the metadata to anticipate changes in the data more easily and promptly

 

 

Data Quality
  • Define the required quality for each data transformation process
  • Implement data quality check points
  • Design processes for checking data quality