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CIO Central Guest

He Who Rules The Data, Rules The World: A Brief History Of Data Governance

Data rules the world, but who rules the data? The companies that collect it? The servers that store it? The cables and satellites that transmit it? Or the laws that keep it flowing into the right hands – and away from the wrong ones?

Welcome to the world of data governance. It’s one of the most important (and overlooked) practices driving business intelligence today, but to return maximal value, it relies on one thing: smart master data management.

The Origins of Data Governance

At its core, data governance is a set of processes that allows an enterprise to formally manage important data assets. When IT applies logical and flexible controls to those assets, the enterprise trusts that the right information is flowing to the right person at the right time.

If the process sounds simple, it’s because of years of savvy back-end systems development. A decade ago, data governance was an emerging trend in enterprise information management – unsurprising, considering the time period. E-commerce had just taken off, social media was in its infancy, and Apple was introducing the iPhone. Data was only starting to be everywhere, but it was already disruptive.

Big Data and BI

Today data is a buzzword, though the difference between “big data” and Big Data is starting to blur. Two-thirds of businesses have undergone, or are preparing to undergo, a full digital transformation, enabling them to capture every bit of customer commerce and communication. When information from marketing campaigns, customer service inquiries, and purchase histories can be tagged and encoded, data is an enterprise’s lifeblood.

What do thriving businesses do with data? Harness it for Business Intelligence (BI). According to a recent study, 81% of 400-plus senior executives from industries across the globe have had “significant” or “very significant” success with their BI programs.

Yet the executives have one big worry: that data quality and inconsistencies will impede further BI insights. When running predictive analytics, for instance, projections must be based on complete and correct data. If the data is not complete and correct then projections will only provide limited value, and can actually create negative value by falsely amplifying results across all data sets.

Master Data Management and Data Governance

As the study suggests, the solution is stronger data governance. Data governance systematizes different rules for different data sets, giving different capabilities to different departments. It determines permissions to access, change, and analyze data. Rather than restrict data stewards, permissions empower them to obtain a more nuanced, proactive view of the nexus of data and BI. When stewards have a high contextual understanding of data, they can prevent incomplete and inconsistent data assets from contaminating BI.

The key to strong data governance is master data management (MDM). Often confused as separate ideas, MDM and data governance are two sides of the same coin: the latter describes the practices and the former describes the discipline and technology. Moreover, MDM enables the most important part of governance – the enforcement of data policies and procedures.

Defining those policies and procedures is like having a rulebook; but without a way to enforce them, the process is merely academic. In the past, conventional MDM solutions posited a dictatorial approach to governance. But as big data came into play, traditional MDM choked on volume, speed, and flexibility requirements. New, agile, intelligent MDM systems give data shape and purpose across the enterprise by providing clear, logical tools to define and enforce governance.

Collaborative Data Governance

Just as royal families tend to no longer govern countries, the practice of having a single person or group to arbitrate data governance has fallen out of favor. No longer the domain of IT, data runs up and down the employee hierarchy, from the C-suite to junior employees in all departments. Input on how it should be managed, and on what rules it should be given, can come from anywhere.

Creating a data democracy, where all employees can access data at scale – as Facebook did – means employees don’t have to wait to execute on projects that can add value. It also means that data hiccups and deeper data problems are more likely to be discovered and corrected. This is all the more important in an environment where 85% of organizations’ information is redundant, obsolete, and trivial (ROT), and 41% of all stored data hasn’t been touched in three years.

Conclusion

Just because data governance has been an afterthought in the age of big data doesn’t mean you should hastily dust it off and start making assignations and permissions. Instead, apply it incrementally so you don’t disrupt other processes. Use it to test business projects for their ability to produce real value. After all, you can clean a million data sets, publish their schema, assign them a data steward, and layer them on an open API – but if no one uses it, you’ve only incurred costs at no benefit.

Big data is here to stay. Master and govern it with intent.

 

This article was written by CIO Central Guest from Forbes and was legally licensed through the NewsCred publisher network. Please direct all licensing questions to legal@newscred.com.