As a result of digital transformation, companies and organizations have now become aware that data is not a by-product of digital processes, but itself represents a significant corporate value. Data analyses make it possible to identify patterns and trends in companies and are therefore an indispensable basis for management decisions.
It is therefore high time to find a set of rules for controlling internal company data. The easiest way to do this is by using the term 'Data Governance Summarize, something like that 'Control system for handling data'.
As part of the General Data Protection Regulation, the first sets of rules were created in companies due to the impending penalties, but that is by no means enough. This is because the data-based company value is not limited to personal data. Data is generated during projects, sales or by manufacturing machines, servers in the IT landscape constantly generate log data, there are access systems, telephone systems, logbooks, website visits, personnel changes and much more.
So the first most important step is to create a Overview of existing data to invest. The next step is to qualify the data sets: Is the data created once or repeatedly? What influence does the data have on? What is the quality of the data? What can I use it for? Who should have access to it and who shouldn't?
For the Evaluation and definition of data quality Is it necessary for different areas of the company to work together: For accounting, an address without a VAT number may be unusable, but for sales, on the other hand, it requires a telephone number or email address so that the contact has value. A common denominator must therefore be found. Only then will the data be gladly used by all departments, as credibility and transparency are ensured and this makes it possible to trust the data. It also determines which people have access to the data and to what extent.
Systematic handling of data saves enormous amounts of time and money.
Finally, the structured merging data More efficiency and overview from different systems: Different people and departments are no longer accessing differently structured data sets in parallel at considerable additional cost - and in doing so usually generate new data and even misinterpretations. Data preparation and preparation typically requires 80 percent for data projects and the actual activity, namely data analysis, requires only 20 percent.
It is therefore high time to save time through data governance and the To significantly increase efficiency in data projects.
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