As companies continue to generate and store unprecedented amounts of big data, regulators are increasingly crafting and imposing new rules that define standards for how information systems must be designed and maintained.
Regulators have concluded that monitoring the systems is the only means of assuring big data content assets are to be trusted as authentic records of a company's actions and proof of its compliance with relevant process and documentation requirements. As a result, when it comes to regulating big data, information governance professionals must now influence the design and engineering of complex, new big data systems.
This, of course, also elevates compliance costs, a further burden on information governance managers' efforts to secure the funding required to achieve other business objectives. Companies' information governance teams must make sure their approach to data system engineering pulls double duty by making sure that the processes for regulating big data gains revenue for the company and also keeps up with the evolving compliance mandates.
Leverage big data appetites
For big data analytics tools to be effective, they must have the ability to review immense amounts of electronic corporate records. Many records and data are actually not capable of being ingested into these analytics engines, however, because their content assets do not conform to the governance rules that allow it to be properly classified and therefore be thoroughly evaluated for business gain.
For example, when data does not conform to information governance rules, the system engines embargo, quarantine, or totally reject the inbound information. This leads to less data being input into analytics engines, lessening the potential for useful insights into the company's operations. The entire economic model for why big data analytics can be effective at creating new wealth is compromised when there is insufficient quality data to be ingested.
But when information governance professionals are involved in the front end of engineering and design decisions for regulating big data, their experience with classifying, indexing and managing the data assets becomes even more valuable. These professionals know how to navigate the new regulations in order to achieve compliance for the company's data assets.
Information governance allows stored data to have a much higher functional value in big data analytics. That functional value puts big data economic models back where they belong as priority tools in the quest for creating new wealth. When an organization's digital content assets are created and maintained pursuant to well-defined, and properly enforced, rules, the analytic engines can ingest the data at higher volumes to make resulting reports more useful to future business planning. In addition, front-end information governance design requires fewer downstream resources to be devoted to the data quality validation necessary to regulatory compliance. Results can be relied upon more quickly, enabling more time be devoted to planning and front-line business governance.
By following these strategies for regulating big data, information governance will no longer just enable compliance but also demonstrate its economic value to the company as a whole.
Incorporate data provenance standards
For information governance professionals today, the exploding volumes of digital assets that confront them often have origins that are novel to many data management programs. Computers and their applications are engineered to produce records of their performance. Every process, no matter how small, generates event data. The resulting primary content now connects to hundreds and thousands of event logs documenting how the content has evolved from the first keystrokes to its present state.
That data is the provenance of the content: records about the records that thoroughly document the systems, devices, applications, users and actions taken to create and maintain the primary content. In effect, provenance records document effective information governance.
In big data analytics, the provenance data becomes as valued as the primary content. Useful insights can be gained by closely evaluating this data. Provenance data is also essential to assuring the primary content is available when required for routine operations. Every request for stored data will always require validation that the content being delivered has not been altered, and provenance data provides that validation.
The good news for information governance and compliance is that new standards are evolving for how provenance data can be engineered so that it can be transported across and between different systems. When information uses the evolving standards to engineer consistent provenance, both that data and the primary content are more useful to big data analytics.
Useful content creates new wealth. Therefore, by engineering information governance so it not only helps in regulating big data but also better analyzes content and its provenance data, it helps create new revenue capabilities for an organization. Information then becomes a business process that generates wealth instead of an obligatory service to deliver mandatory compliance.
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