Three Mistakes To Avoid For Enterprise Data Quality
Usage of data for organizational operations and growth has moved away from just reporting the performance. Organizations are investing in new tools and technologies that are data hungry to make predictions and prescriptions for managers to take decisions. Sometime algorithms are taking decisions on behalf of mangers. There is also a non stop chase for more and more data sets to feed the analytics that support value creation for the business. The internal datasets within an organization are in ever growing demand because machine learning requires a large amount of training data.
This has shifted the focus and scope of data quality from a small set of data that used to provide reports for internal and external stakeholders.
Poor quality of data can bring immediate and sustained negative impact on organization's decision making and competitiveness. Organizations are investing in tools and processes to set up the adequate governance and measures to improve the quality of enterprise data.
In our experience working with clients here are three mistakes that can contribute to failure or sub-optimal outcome of these initiatives and investments.
Assuming business definition of data to be consistent across the organization
Not having a data profiling and data value interrogation rigor
Viewing technology as the entire solution
What's your experience ? would love to know.