The concept of good governance is key to any high functioning society and economy. It helps establish and maintain trust between constituents and their representatives, along with setting the stage for people and organizations to operate within a well-defined framework of predictable rules. In short: good governance makes life easier, more productive, and way less chaotic.
Just like society at large, data also needs to be governed, or everything falls apart. That’s why every organization that depends on data, from startups to NGOs, to large enterprises, needs a data governance strategy. This need for data governance is increasing in step with a combination of factors, including:
- Increased data volumes, velocities and new data types that can cause big inconsistencies, leading to decisions based on bad data sets
- More self-serve analytics users from across the business who need real-time access to clean, consistent data
- Increased regulatory requirements (like GDPR and CCPA) pushing companies to identify and classify sensitive data in their systems, such as PII data, including where it lives, how long it is kept, and how it is shared
Data is a valuable resource – but it needs refining
The concept of data can be easily compared with another valuable and in-demand resource – crude oil. Oil companies extract it from numerous sources every day. But for it to be usable, it must be refined into various petroleum products.
The same goes for data from all your sources. All that raw data flooding your system like clickstream data from websites, streaming data from IoT devices and customer info from CRMs has inherent value, to be sure. But for all these data types to play well together, while being reliable, consistent and adding the most value to your business, they too need refining through a strong master data management (MDM) strategy encompassing data governance best practices. That means looking at all your options to ensure your data is usable and secure through regular data cleansing, de-duplication, and other maintenance.
But data governance can be a huge chore. For business users who just want to get things done, it can even conjure images of needless bureaucracy and irritating slowdowns. And even though it’s a core element of business intelligence (BI) and analytics, it’s also something most data analysts or scientists want nothing to do with – after all, cleaning data sets isn’t nearly as fun as presenting game-changing insights to the CEO.
5 reasons why data governance is a must
For those insights to be correct, however, they must be underpinned by strong data governance. So if you don’t already have one in place, here are five big reasons why implementing a data governance strategy should top your IT to-do list in 2020:
- Data governance ensures data quality and availability. The rise of self-serve BI and analytics has big implications for data governance, where the difference between a business user looking like a star or not in the boardroom can entirely depend on the quality of data on hand. Data governance as part of an organization-wide MDM strategy helps eliminate data silos between groups and departments, making quality data more available for users across the organization. Organizations can also apply schemas and metadata to stored data, so data scientists and business users can more easily “find, understand and join data of interest” for analysis purposes.
This goes for data lakes, too – even though these repositories of unstructured data don’t necessarily need the same diligence as a data warehouse, some level of governance is still required to ensure your data lake doesn’t end up a mess. If there’s one thing that will irritate both business users and data scientists to no end, it’s having to work with obviously bad data.
- Data governance ensures consistency. We’ve already mentioned the importance of ridding your organization of data silos while working from a “single version of the truth” – anything else is courting disaster. For an enterprise-wide data program to truly work, consistency across groups and departments is key in terms of both data quality and how data is understood. To keep things consistent, organizations should appoint a data steward (which can mean anything from a Chief Data Officer to a group of business users tasked with overseeing your governance program) to establish consistent data definitions, processes and terminology that can be applied across the business.
- Data governance helps you sell stuff more efficiently. At the end of the day it all comes down to sales, right? There are a few reasons why well-governed data can help your sales team do their jobs better:
a. Bad or inconsistent customer data makes customer analytics a near-impossible task, in turn making it tough to upsell, cross-sell, or gauge customer experience.
b. Siloed data between groups means your sales team isn’t working from a single version of the truth, which can lead to mistaken assumptions.
c. Bad data may cause sales teams to outreach to customers or prospects at the wrong times – or, maybe even worse, to the wrong people altogether.
d. Many customers are now very aware of their data rights, and will quickly lose faith in organizations that don’t manage data well or that get breached.
- Data governance keeps you secure and compliant. The number of data privacy laws coming online in the last few years – including the EU’s GDPR, California’s CCPA, and Japan’s APPI, with even more on the way – along with the growing number of malicious threats mean organizations must stay on top of their data more than ever. Complying with ever-more-stringent regulations, with the power to levy massive fines, requires organizations to have a firm handle on their data and where it resides. And from a security perspective, badly governed data makes it difficult for systems or security experts to spot anomalies that could actually be threats.
- Data governance helps you let go of old, unnecessary data. A strong data governance program doesn’t just help you integrate and make available consistent data across the entire enterprise. It also helps you identify what data you don’t need anymore. ROT (redundant, obsolete, or trivial) data is everywhere – accounting for around one-third of the average organization’s data – yet without a data governance strategy, it’s not easy to identify or find. This helps you determine which data you can or should destroy, and which you should consider putting on ice in cold storage. That means you’re paying less for data storage, while business users don’t have to wade through troves of useless, outdated information to get their insights.
At this point, I hope you see the immense value a well-planned data governance program can bring. But we get it – it’s not easy. You’re already dealing with way too much data to tackle this problem manually, or through ad hoc efforts. And most of your people almost certainly don’t want to spend their days cleaning data.
That’s why cloud-native enterprise data platforms (EDPs) can help organizations achieve data governance nirvana (or, as we like to call it, Data Integration Zen). EDPs take advantage of automated processes that clean, de-duplicate, organize and integrate data for both business users and machine learning applications, keeping your users happy and fed with accurate insights – while also ensuring everyone in the organization works from the same version of the truth.
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