Data Governance: Top Trends Influencing Data Driven Business Success
Big data has come of age. We are at a turning point in analytics, as business leaders find new ways to drive meaningful results with data. Companies are going beyond the tactical benefits of old-school BI and finding new ways to drive strategic value.
As the business environment remains steeped in uncertainty, business agility and automation continue to be top priorities. Advanced analytics are shifting from speculative projects to practical real-world initiatives that provide measurable ROI. At the same time, analytics are moving away from their position as a relatively narrow domain, now expanding to a very broad user base. Data democratization is proving valuable, as business leaders are putting analytics into the hands of more and more users throughout their organizations.
The higher volume of data, a proliferation of available data sources, increased regulation, and broader usage across the enterprise all point to a stronger need for data governance.
In this second article of this two-part series, we will cover the key data trends identified by 451 Research in their recent webinar presentation entitled “Top trends influencing data driven business success in 2022.” This time, we will look at those trends through the lens of data governance, exploring the impact that data quality, compliance, and other factors play into the current tendency toward the deeper and broader scope of data initiatives in play among today’s enterprises.
In 451 Research’s survey cycles, 2021 was the first year in which cloud deployment outranked on-premise, non-cloud options for analytics. This is driving a broader shift toward hybrid IT, which Paige Bartley describes as “more than just public and private clouds.” She continues: “It is the collection of tools, products, and services that allow enterprises to choose the best location for each workload’s particular requirements.”
This has very important ramifications with respect to data governance. Data sovereignty regulations, including those contained in Europe’s General Data Protection Regulation (GDPR), often dictate the physical location of servers containing protected information. Most notably, this includes consumer data, which according to recent court cases, may not be stored on servers located in countries that do not have adequate measures in place to ensure privacy and security of personally identifiable information (PII).
Aside from GDPR, there are numerous other regulations in place or under consideration which will only increase the level of care companies must take to protect PII. California’s Consumer Privacy Act (CCPA) as been in effect since early 2020, and will likely evolve as litigation makes its way through the courts.
Real-time analytics are finding their way into more and more real-world use cases. In some instances, delays of a few minutes may be entirely acceptable. In others, though, immediacy matters. Fraud detection, dynamic pricing, and supply chain applications provide mission-critical insights to drive business decisions in which lost time translates to lost money.
As companies struggle to unify and harmonize data from multiple sources, many are encountering challenges with data quality at scale. In the past, data engineers might have viewed data quality as a matter of periodic cleanup or ongoing data cleansing. In the case of real-time data analytics, data quality must also be addressed in real time.
