Data quality is about assessing the quality of corporate data and improving its accuracy using the data profiling method. Corporate data is increasingly important as companies continue to find new ways to use it.
Data Quality Management is essential to CIOs, CFOs, or CEOs who need to understand, correct, or prevent data quality issues in their organization. There are proven approaches to identifying, warehousing, and analyzing data errors the first step in any data quality program. Master techniques in:
Data profiling and gathering metadata
Identifying, designing, and implementing data quality rules
Organizing rule and error catalogues
Ensuring accuracy and completeness of the data quality assessment
Constructing the dimensional data quality scorecard
Executing a recurrent data quality assessment
Get the most out of your Quality Management system in SAP
From QM (Quality Management) configuration to business process management to working in the system, SAP provides you will all you need to get a 360-degree view of the Data Quality Management, and set up essential master data. Quality Management works tightly with other components such as supply chain, and also workflow tools like the Classification System and Engineering Change Management.
Chief Data Officer Role
Whether or not you actually have somebody called a CDO in your organization, or whether someone is just acting as the data transformation strategy lead in the organization, the chief data officer role is rising, and it's becoming more formal.
If companies know that they need to become a digital business or if they know that data is going to be the center of everything that they're doing, the people at the top are asking if the company is ready, and if there's a tipping point to redesign their data transformation strategy! There's also a lot of interest in the digitalization of resources, as well, with data as the new asset. It's something that everyone in the organization can use now and wrangle it; it's not just the people around the executive boardrooms. With all this data that's coming in, organizations have to ask if they are going to openly share that data or are they going to monetize that data.
Data Ethics Policy
When it comes to big data ethics, contextual errors are the tip of the iceberg. The bigger concern is what such blind faith in the data will lead to -- as opposed to scrubbing the analysis with scrutiny or critical thinking.
Future of Data Quality and Chief Data Officers
It is a bold prediction that the next big data trend would be lawyers. Applying ethical codes to big data stewardship and the importance of not ignoring the debate over privacy and big data!