Similar to last week’s Data Quality Study Guide, we wanted to continue to take advantage of the slower summer season to review the latest in data governance. Take a break from the heat and spend a few moments to get yourself caught up.
New to Data Governance?
Data Governance + Data Quality = Trust
Data governance requires data quality because ensuring data quality is the only way to be certain that your data governance policies are consistently followed and enforced. It’s likely that is why both data governance and data quality were top of mind at this year’s Collibra Data Citizens event.
At this year’s Data Governance and Information Quality Conference (DGIQ), our own Keith Kohl lead the session about how data governance and data quality are intrinsically linked, and as the strategic importance of data grows in an organization, the intersection of these practices grows in importance, too.
During her Enterprise Data World presentation, Laura Sebastian-Coleman of the Data Quality Center of Excellence Lead for Cigna, noted specifically that data quality depends on fitness for purpose, representational effectiveness and data knowledge. And, without this knowledge, which depends on the data context, our data lakes or even our data warehouses are doomed to become “data graveyards.”
As our new eBook “The New Rules for Your Data Landscape” points out, data is shifting from IT to the business. The result is a new data supply chain which impacts data movement, manipulation and cleansing.
Today’s business leaders rely on Big Data analytics to make informed decisions. But according to figures presented at the recent Gartner Data and Analytics Summit, C-Level executives believe that 33% of their data is inaccurate.
It appears there is an abundance of data, but a scarcity of trust, and the need for data literacy. It’s important to understand what your data MEANS to your organization. Defining data’s value wedge may be key to developing confidence in your enterprise data.
For more information about the data value wedge, watch this educational webcast hosted by ASG and Trillium Software. The recorded discussion explores the importance – and challenge – of determining what data MEANS to your organization, as well as solutions to empower both your technical (IS) and business users (DOES) to collaborate in an efficient, zero-gap-lineage user interface.
Data Governance for Hadoop
Keeping track of data, data security, data access, and regulatory compliance are more critical and more challenging than ever before. Data governance in Hadoop — including auditing, lineage, and metadata management — requires a scalable approach that is easy to interoperate across multiple platforms.
In 2015, Syncsort joined Cloudera to provide a unified foundation for open metadata and end-to-end visibility for governance, effectively bridging the gap between mainframe and Hadoop.
Just last year, Hortonworks CTO Scott Gnau recognized that data governance in Hadoop was still in early development, but definitely a priority at his organization.
At this year’s DataWorks Summit, Gnau made a joint appearance on theCUBE with Syncsort CTO Tendü Yoğurtçu. Gnau was bullish on Hortonworks’ partnership with Syncsort, pointing out that it is built on the foundation of accelerating joint customers time to value and leveraging our mutual strengths.
Syncsort’s Focus on Data Governance
Also during her DataWorks theCUBE appearance, Yoğurtçu explained how the Trillium Software acquisition has been transformative for Syncsort, allowing the organization to deliver joint solutions from data integration and data quality & profiling portfolios. She shared that recent first steps have been focused on data governance use cases leveraging Trillium’s solutions.
Yoğurtçu also touched on the recent announcement of Syncsort’s partnership with Collibra, noting the importance of making business rules and technical metadata available thru dashboards for data scientists.
For more information on how data governance is changing to match the new flow of data delivery, download our new eBook: The New Rules for Your Data Landscape