Few organizations recognize that they could be addressing data governance head-on, initiating lasting improvements in their data quality as part of their efforts to gather and prepare data for BDA projects. In this week’s SPIE, Stratecast summarizes the data quality issue, makes the case for elevating data governance alongside other BDA objectives, and discusses several data preparation solutions that could be used to accomplish this.
Introduction Most organizations know that their data resources are less than perfectly accurate or complete, and that poor data quality hampers their efficiency. Data clean-up projects rarely rise to a significant priority, however, as long as the people who interact directly with the data can correct it as needed. Their knowledgeable keystrokes enable purchase orders to be initiated and fulfilled, invoices paid, metrics tracked, and reports rolled up to quantify all these processes and results. Even if the information being utilized is not entirely accurate or complete, it is good enough to maintain the day-to-day operation of the business.
Organizations have had a series of technological opportunities to address their data quality issues over the past 20 years. First came the shift from mainframe computers to client/server implementations, followed by the deployment of data warehouses, and, more recently, the migration of data stores and applications into the cloud. At each of these junctures, organizations might also have undertaken a comprehensive review of their data assets, and implemented policies and procedures that would ensure ongoing data quality and data governance. Instead, most organizations settled for making their data just good enough to accomplish the desired technological upgrade.
The latest technological opportunity to address data quality and improve data governance is at hand today, in the form of Big Data technologies and advanced analytic applications (BDA). Once again, enterprises are in danger of missing the data governance boat. Seeing what data-driven companies like Amazon, Netflix, and Uber have accomplished, many organizations hope that BDA projects will help them to emulate these pioneers. They may suspect that their vast stores of messy data will slow their efforts, but they aren’t making data quality improvements part of their BDA plans. At the same time, data security and privacy concerns are mounting. These also point directly to the need for improved data governance; but, again, most organizations choose to steer around the larger issue—instead, implementing more narrowly-focused compliance solutions that satisfy the minimum of governmental and industry directives.
Table Of Contents
Data Quality - Another Chance to Miss the Boat? Introduction Why Data Quality is Important - and Data Wrangling Is Difficult Historical Approaches to Data Quality and Data Management How BDA Buyers and Sellers Downplay Data Quality Issues Beyond Analytic Insights: Benefits from Better Data Quality New Data Preparation Tools Help Improve Data Quality and Governance Big Data Could Also Be Better Data Stratecast - The Last Word About Stratecast About Frost and Sullivan