Accelerate Insight with Proactive Data Governance Practices

Accelerate Insight with Proactive Data Governance Practices

Data governance: three steps to success

Becoming a data-driven organization is not exactly getting any easier. Businesses are flooded with ever more data. Although it is true that more data enables more insight, the effort needed to separate the wheat from the chaff grows exponentially. Doing so and truly understanding the data is more important than ever, especially when data privacy regulations are tightening. Especially for multinational organizations, compliance must be enforced and proven not just at a corporate level but also across multiple regional levels, each with their own variations and intricacies. And all this must happen against the backdrop of ever faster changing markets and economic circumstances. More use cases must be deployed to drive more insight and value; more data needs to be made available to more users. The challenges appear diametrically opposed to one another.

Data governance: three steps to success

It is safe to assume that businesses understand the importance of good data governance. Afterall, lack of good data governance practices creates substantial liabilities, from regulatory fines to brand erosion. While the importance and risks are well understood, many businesses find the process of achieving a state of good data governance complex. The reality is, it does not have to be the case: 

Know what data you have

The first step is to understand existing and anticipate future data needs. Data stewards and administrators must know what data is currently collected, from which sources, how it is processed, where it is stored and more. It is equally important to address future needs – which new data sources are in the pipeline, where would data and data workloads reside, which new workloads and use cases such as AI are planned and so on. Understanding the data allows for appropriate labelling and classification. 

Know how data is used

As data travels through an organization, different teams and analytics access, process and transform it so that business insights, or new data, is created. Data classification gleaned in the previous step, an appreciation of data flows between analytics and understanding of which users access it in which context, allows for auditable data access policies to be used to ensure the right users have access to the right data in the right format.  As part of the process, pockets of shadow IT may also be uncovered: analytics procured and managed directly by the business outside of central IT’s control and in reaction to the business’ need for fast and flexible analytics. While the extent to which shadow IT exists may differ from organization to organization, it poses security and compliance risks.  A central data and analytics platform with consistent security and governance throughout allows IT to provide safe and compliant analytics at the speed with which the business requires them.

Know how you implement and prove compliance

Data privacy regulation is now commonplace, directing the treatment as well as placement of personal data. Data sovereignty introduces further complexity, especially when public cloud is put into the mix. Regulatory frameworks like GDPR and CCPA may have been the first few when it comes to region specific data protection rules but they’re definitely not the last. Countries within the APAC region are in the process of defining specific data protection frameworks as well, a few examples of these are India’s Personal Data Protection Bill and Japan’s APPI. In order to prove compliance, organizations will need access to tools that give them control and audit capabilities. In some cases, not only the data but also for the platform components that help manage data may be required to be hosted within the country. 

Better governance for better outcomes

The traditional approach to good data governance has often meant locking away data or shying away from new opportunities because they require new data management practices. However, collecting data is pointless if an organization is unable to put it to good use. and what is the lost opportunity cost of saying no to new use cases, even if these are small experiments?

While there may seem to be a lot of pieces to the data governance puzzle, it is not a difficult puzzle to solve. The primary roadblock to achieving good data governance is that data and data workloads exist in siloed systems that are often spread across infrastructures. This puts the onus on IT to establish consistent security and data governance frameworks on systems that may not be designed to work well together. To solve this problem, IT needs a single data platform that can provide consistent data context across the entire data lifecycle. This is where an enterprise data cloud shines, as it brings visibility, context and control to data management for our customers.

As the data and analytics landscape continues to evolve, good data governance within an organization, at a high level, consists of three things: 

  1. Prepare to deal with more data that is created in new formats and generated from newer data sources. 
  2. Plan to store, manage and process data in a safe, compliant and consistent manner regardless of the type of workload, use-case or infrastructure. 
  3. Open up more data to more users to drive more insight and value by leveraging best-of-breed data governance tools and practices. 

Cloudera Data Platform helps businesses become more data-driven by harnessing quicker and deeper insights from more of their data. To ensure that data governance and security isn’t an afterthought in your data journey,  join us for this upcoming webinar .

Varun Jaitly
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Wim Stoop
Director Product Marketing
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