2018 Trends in Data Governance: Heightened Expectations

Organizations will contend with an abundance of trends impacting data governance in the coming year. The data landscape has effectively become decentralized, producing more data, quicker, than it ever has before. Ventures in the Internet of Things and Artificial Intelligence are reinforcing these trends, escalating the need for consistent data governance. Increasing regulatory mandates such as the General Data Protection Regulation (GDPR) compound this reality.

Other than regulations, the most dominant trend affecting data governance in the new year involves customer experience. The demand to reassure consumers that organizations have effective, secure protocols in place to safely govern their data has never been higher in the wake of numerous security breaches.

According to Stibo Systems Chief Marketing Officer Prashant Bhatia, “Our expectations, both as individuals as well as from a B2B standpoint, are only getting higher. In order for companies to keep up, they’ve got to have [governance] policies in place. And, consumers want to know that whatever data they share with a third party is trusted and secure.”

The distributed nature of consumer experience—and the heightened expectations predicated on it—is just one of the many drivers for homogeneous governance throughout a heterogeneous data environment. Governing that data in a centralized fashion may be the best way of satisfying the decentralized necessities of contemporary data processes because, according to Bhatia:

“Now you’re able to look at all of those different types of data and data attributes across domains and be able to centralize that, cleanse it, get it to the point where it’s usable for the rest of the enterprise, and then share that data out across the systems that need it regardless of where they are.”

Metadata Management Best Practices
The three preeminent aspects of a centralized approach to governing data are the deployment of a common data model, common taxonomies, and “how you communicate that data for…integration,” Bhatia added. Whether integrating (or aggregating) data between different sources either within or outside of the enterprise, metatdata management plays a crucial role in doing so effectually. The primary advantage metadata yields in this regards is in contextualizing the underlying data to clarify both their meaning and utility. “Metadata is a critical set of attributes that helps provide that overall context as to why a piece of data matters, and how it may or may not be used,” Bhatia acknowledged. Thus, in instances in which organizations need to map to a global taxonomy—such as for inter-organizational transmissions between supply chain partners or to receive data from global repositories established between companies—involving metadata is of considerable benefit.

According to Bhatia, metadata “has to be accounted for in the overall mapping because ultimately it needs to be used or associated with throughout any other business process that happens within the enterprise. It’s absolutely critical because metadata just gives you that much more information for contextualization.” When attempting to integrate or aggregate various decentralized sources, such an approach is also useful. Mapping between varying taxonomies and data models becomes essential when utilizing sources from decentralized environments into a centralized one, as does involving metadata in these efforts. Mapping metadata is so advantageous because “the more data you can have, the more context you can have, the more accurate it is, [and] the better you’re going to be able to use it within a… business process going forward,” Bhatia mentioned.

Regulatory Austerity
Forrester’s 2018 predictions identify the GDPR as one of the fundamental challenges organizations will contend with in the coming year. The GDPR issue is so prominent because it exists at the juncture between a number of data governance trends. It represents the greater need to satisfy consumer expectations as part of governance, alludes to the nexus between governance and security for privacy concerns, and illustrates the overarching importance of regulations in governance programs. The European Union’s GDPR creates stringent mandates about how consumer information is stored and what rights people have regarding data about them. Its penalties are some of the more convincing drivers for formalizing governance practices.

“Once the regulation is in place, you no longer have a choice,” Bhatia remarked about the GDPR. “Whether you are a European company or you have European interactions, the fact of the matter is you’ve got to put governance in place because the integrity of what you’re sending, what you’re receiving, when you’re doing it, and how you’re doing it…All those things no longer becomes a ‘do I need to’, but now ‘I have to’.” Furthermore, the spring 2018 implementation of GDPR highlights the ascending trend towards regulatory compliance—and stiff penalties—associated with numerous vertical industries. Centralized governance measures are a solution for providing greater utility for the data stewardship and data lineage required for compliance.

Data Stewardship
The focus on regulations and distributed computing environments only serves to swell the overall complexity of data stewardship in 2018. However, dealing with decentralized data sources in a centralized manner abets the role of a data steward in a number of ways. Stewards primarily exist to implement and maintain the policies begat from governance councils. Centralizing data management and its governance via the plethora of means available for doing so today (including Master Data Management, data lakes, enterprise data fabrics and others) enable the enterprise to “cultivate the data stewardship aspect into something that’s executable,” Bhatia said. “If you don’t have the tools to actually execute and formalize a governance process, then all you have is a process.” Conversely, the stewardship role is so pivotal because it supervises those processes at the point in which they converge with technological action. “If you don’t have the process and the rules of engagement to allow the tools to do what they need to do, all you have is the technology,” Bhatia reflected. “You don’t have a solution.”

Data Lineage
One of the foremost ways in which data stewards can positively impact centralized data governance—as opposed to parochial, business unit or use case-based governance—is by facilitating data provenance. Doing so may actually be the most valuable part of data stewardship, especially when one considers the impact of data provenance on regulatory compliance. According to Bhatia, provenance factors into “ensuring that what was expected to happen did happen” in accordance to governance mandates. Tracing how data was used, stored, transformed, and analyzed can deliver insight vital to regulatory reporting. Evaluating data lineage is a facet of stewardship that “measures the results and the accuracy [of governance measures] by which we can determine have we remained compliant and have we followed the letter of the law,” commented Bhatia. Without this information gleaned from data provenance capabilities, organizations “have a flawed process in place,” Bhatia observed.

As such, there is a triad between regulations, stewardship, and data provenance. Addressing one of these realms of governance will have significant effects on the other two, especially when leveraging centralized means of effecting the governance of distributed resources. “The ability to have a history of where data came from, where it might have been cleansed and how it might emerge, who it was shared with and when it was shared, all these different transactions and engagements are absolutely critical from a governance and compliance standpoint,” Bhatia revealed.

Governance Complexities
The complexities attending data governance in the next couple of years show few signs of decreasing. Organizations are encountering more data than ever before from a decentralized paradigm characterized by an array of on-premise and cloud architectures that complicate various facets of governance hallmarks such as data modeling, data quality, metadata management, and data lineage. Moreover, data is produced much more celeritously than before with an assortment of machine-generated streaming options. When one considers the expanding list of regulatory demands and soaring consumer expectations for governance accountability, the pressures on this element of data management become even more pronounced. Turning to a holistic, centralized means of mitigating the complexities of today’s data sphere may be the most viable means of effecting data governance.

“As more data gets created the need, which was already high, for having a centralized platform to share data and push it back out, only becomes more important,” Bhatia said.

And, with an assortment of consumers, regulators, and C-level executives evincing a vested interest in this process, organizations won’t have many chances to do so correctly.

Originally Posted at: 2018 Trends in Data Governance: Heightened Expectations by jelaniharper

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