Data Governance

As your organisation grows, so does the overhead of managing and controling the data. In large organisations, it is one of the most complex and time consuming activities, requiring teams of specialists to oversee and manage. Data governance is the framework of policies, processes, standards, and responsibilities that ensure your organization’s data is accurate, secure, consistent, and used appropriately. Think of it as the “rules of the road” for data.

You hear of horror stoires when organisations get this wrong. For example, payroll sending a report containing the salaries of all staff to an external entity. Sending an email campaign to 50,000 people who all unsubscribed 3 years ago. Internally giving staff access to highly confidential information they are not permitted to see. Without controls and governance, this happens. When used properly, we can answer the following, critical questions:

• Why are we collecting this information?
• Who owns this data?
• Who can access it?
• What does “customer” actually mean?
• Where is our sensitive data stored?
• How do we ensure data quality?

Then there regulations that impose further controls on the data. GDPR, CCPA, HIPAA, SOX – the alphabet soup of data regulations isn’t going away. Data governance helps you know:

• How long you’re keeping it
• What personal data you’re collecting
• Where it’s stored
• Who has access

Risk controls defined in BCBS-239 add to this and ensure organisations are able to observe trends in risks, issues and incidents, whilst simultaneously managing the controls and tests that define this space.

Garbage In = Garbage Out

When data entry is inconsistent (Is it “Street” or “St.”? “USA” or “United States”?), your analytics suffer. Data governance establishes standards and quality checks that keep your data clean.

Data Catalog

A searchable inventory of your data assets. Think of it as a library catalog, but for datasets. It answers:

“Where can I find customer churn data?”

or

“What does the ‘status’ field in the orders table actually mean?”

The space occupied by data catalogs includes a few key players, each with their own pros and cons. Fundamentally the tool is only ever as good as the poeple using it. Get the wrong team involved and it really doesn’t matter what tool you use, it’s going to fail in the long run. From experience I’ve seen these capabilities turn into dumping grounds for metadata. The more the merrier. Vendor specific terms, incorect labels and frankly baffling descriptions. They detract from the underlying reasons the tools exist in the first place and once you’ve committed to a single tool its then a multi-year journey to unpick the chaos and start again.