Advanced Analytics

6 Action Items to Face the Big Data ‘Governance’ Challenge

Story Highlights
  • The ‘How’ of Data Governance
  • How Many Sources?
  • How Good is the Data?
  • How Much Reliable?
  • Have a ‘Blind Spot’?
  • Do You Stress-test?
  • Is Your Data Toxic?

For years, data governance has been a streamlined and controllably defined process managed by a group of select IT professionals. Data analysis and reporting were largely controlled by traditional systems and always kept under the wraps. However, the rise of big data environments introduced better and faster computing abilities and resources. This is turn enabled the data scientists to analyze a variety of data fast and deliver further refined competitive insights.

In this article at Tech Target, George Lawton recommends six actions that will help organizations manage the complexities and challenges of big data analytics.

The ‘How’ of Data Governance

Changes happen at a faster rate in big data environments. To keep pace, one must quickly process and understand the additional application updates, data sources, and analytics procedures that come with every new wave of change. Let’s take a look at how the data scientists can achieve it:

How Many Sources?

As time progresses, the number of data sources also increases. Governing such a variety of data is business-critical since more people get access to it now. There are increasing layers of scrutiny, such as GDPR and California Consumer Privacy Act, that organizations must not ignore. Else they face hefty fines and could even lose their customers’ trust. So, companies must resort to advanced monitoring tools and draw up legal regulations to avoid breaches.

How Good is the Data?

Big data ecosystems receive all sorts of data—processed and unprocessed—from various sources. To ensure proper governance and trust, one must question the source, accuracy, and messaging of the data. The data must be discarded to maintain quality if any of these three metrics ring a warning bell.

How Much Reliable?

Attributes such as accuracy, completeness and consistency define how reliable the data is. Checking data integrity can be challenging due to the presence of diverse operational systems. So, enterprises must employ measurable practices to assess and rate the integrity of various data sources.

Have a ‘Blind Spot’?

Organizations must find ways to monitor the inflow and outflow of all the data within their environment. Else they face a bigger risk of directly moving unstructured data into their data warehouse or data lake. And eventually, that would result in a digital blind spot.

Do You Stress-test?

Data variety must also be governed effectively. Else they add stress to the existing workflow. Validating data variety will help to understand the gaps and where the professionals should focus more. So, the first task of data governance should be to find out what’s working and why.

Is Your Data Toxic?

Some data may be masked to adhere to the privacy norms. But, when combined with other data, they might have toxic results that violate the regulations. Such data combinations can lead to unauthorized identification of individuals. So, due diligence is a must.

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