4 Keys to Improving Data Quality
The hidden barriers to becoming data-driven
It is reasonable to expect that data-driven decision making would follow the usual pattern of development and adoption: early adopters start out with centralized, carefully managed projects which eventually move into limited production. Then as the value of the technology becomes clear, implementations begin to proliferate, resulting in more distributed use.
What drives this ‘standard’ technology adoption pattern? Trust. Or more correctly, the lack thereof. Concerns about the complexity and cost of the required technologies and business processes undoubtedly play a part in slowing the adoption of data-driven decision making. But efforts to become a more data-driven organization actually suffer from an even deeper layer of mistrust. The problem here is not just the technology and systems involved. It is the deeply rooted lack of trust in the data itself.
At this point, the science and technologies for data analysis are already quite powerful and well proven. In fact, the current trend is rapid advancement of Business Intelligence (BI) and analytics into the realms of Artificial Intelligence (AI) and Machine Learning (ML). But it is exactly such advancements which are now making the underlying problem of poor data quality painfully obvious. When you apply advanced analytics, and especially when you unleash AI and machine learning, the impacts of bad data are magnified.
Data quality issues have often been considered unavoidable and uncontrollable, and so living with bad data simply became normalized as just another cost of business. But the unavoidable reality for any organization looking to adopt data-driven decision making is that it cannot succeed in doing so without fully and actively addressing the age-old scourge of poor data quality.
This eBook will guide and inform you regarding how to overcome the root problems of data quality, not just identifying specific types and patterns of quality problems, but also clarifying the roles of data quality management and data governance in resolving them.