In many cases, regulatory pressures are used to justify investments in data governance.

This is because of regulations such as:

• King IV – for corporate governance
• PoPIA – for protection of personal information
• FICA – for anti-money laundering (AML) and sanctions
• BCBS 239 – for risk management

that are tightly linked to enterprise information management outcomes.

Data governance about more than regulatory pressures

Yet, in practice, data governance is about much more than regulatory pressures.

Many of our customers see regulatory pressures as a sideshow – important, yes, but not the key driver.

In many cases, the requirement is summarised as “We want to use our data more effectively.”

As one of my customers put it – “We cannot rely on our brand alone to continue to be competitive. We have to change the way we use data – to improve our ability to make decisions and to improve the way that we engage our customers.”

Data Governance is critical for Customer Experience

This perspective, that data governance is critical for customer experience success, was evaluated in a Forrester Research whitepaper.

In the whitepaper, Forrester suggests that customer experience ecosystems have shifted towards value chain ecosystems. As a result, data governance must shift from a focus on rules and constraints, “to one of customer experience enablement as data links to the outcomes of customer engagement.” – Forrester Research Customer Ecosystems Demand Outcome-Oriented Data Governance

The whitepaper picks up on another technology trend – the desire to use big data and advanced analytics to better understand our customers buying patterns and preferences. Once again, data governance has a critical role.

Data governance must support the delivery of relevant and analytics-ready customer data – not  a trivial exercise in the large, modern enterprise

CX was the key data governance driver for Norwegian bank, DNB, as they implemented data governance to support their digital investments without losing customers.

In many organisations, data governance initiatives are started and fail, many times.

Data governance is often confused with other data management competencies, such as metadata management.

We may, for example, embark on an exercise to define business terms (such as Customer or Account).

This kind of metadata is very useful to understand our data and reporting landscape, but is not in itself data governance. And, without governance (business accountability and prioritisation) these metadata exercises may fail to attract business support and become unsustainable.

The DNB Case Study continues to provide some tips and guidance towards successfully delivering a data governance capability to support an improved customer experience.

Forrester suggests “shifting toward a performance management approach to data where business priorities and the data principles connect and align”

This is very much the approach that we propose when talking to our customers and prospects.

Data Governance is critical to CX’s success

Data governance must be linked to business outcomes, and the tactical EIM tasks that are completed and governed must be linked to the delivery of a business task or goal.

We must remember that data governance is not about governing data, but about governing the behaviour that we want from our customer relationship managers and other stakeholders, when they are dealing with customers and their data.

#DataGovernance is not about governing data, but about governing behaviour

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