Building Robust Data Governance Practices

In his session, Bill gives practical advice on how to: 

• Key features successful data governance programs have in common 
• 3 phase approach to structuring data governance excellence 
• 5 key steps to consider moving forward

Trainer Bill Hoffman highlighted how vital governance and organisation are to successful data leadership.  The session began with the importance of defining just what data governance is, and Bill himself regards data governance as encompassing the people, process and information technology required to facilitate the correct handling of data.  It helps to remove silos and provides clarity regarding who owns and is responsible for data throughout the business. 

For data governance to be successful, it’s vital that there is an objective third-party with ultimate ownership across the business.  Only in this way can you get the single version of the truth that is the bedrock of good data governance.  

Bill revealed that various things have historically worked well, including creating fast decisions on specific proposals; C-suite exposure to key decisions; and ensuring the right people are in the room and continuously engaged.  

Data governance efforts can struggle with securing accountability, particularly to specific financial targets, while they can also get bogged down in very high-level conversations when more operational matters are really key.  

Example data governance framework from a leading Las Vegas casino  

Companies that really do data governance well tend to do four things well: 

1. They drive management rhythms and establish a clear cadence for decision making that is centered on the customer. 

1. They facilitate rapid decision making and ensure the right information sits behind those decisions. 

1. They increase transparency and focus their efforts on the most critical customer metrics for the business. 

1. They communicate the case for change and share success stories widely. 

Of these four, the delegates thought they were strongest in increasing transparency, with the lowest score for driving management rhythms.  

Unified analytics  

The boundaries between data and analytics is increasingly porous, with the more advanced data-driven companies unifying the two.  This enables them to innovate faster by integrating big data and ensuring intersections become natural.  It also drives improvements in work quality and talent retention as it provides a single version of the truth across the business.  This helps to make data a profit center, not a cost center, and really drive differentiation for the business.  

This integration also helps to make it easier to connect the work the data teams are doing and the revenue and shareholder growth of the business.  This helps to secure buy-in from senior executives as it makes it clear “what’s in it for them”.  

Building excellence over time  

The best companies typically follow a 3-phase approach to data governance excellence:  

Phase 1 sees them use the insights team as a store, with positive ROI generated in the first year and a handful of clear use cases showcasing positive ROI.  

Phase 2 sees the insights team owning the single version of the truth across the business.  They’re capable of showing clear improvements on customer experience scores and the customer value proposition.  

Phase 3 sees insights move out of the central team and become a toolkit for deployment across the business.  The work is capable of generating over $5 million in incremental revenue and has clear executive buy-in.  

Structuring for data excellence  

While each of these have pros and cons, Bill believed that the best approach would be the hybrid structure as you gain the benefits of the centralized and decentralized model.  Among the delegates, 57% have this structure in their own organization.  

The reality for whatever structure you use is that the relationship with the rest of the business, and indeed the way the data team defines itself in relation to the rest of the business is crucial.  

Another way to look at the structure you take is to understand how important data is to the strategy of the business.  This allows you to understand the areas you want to focus on in the year ahead, whether that’s better communicating the power of data or developing a common understanding of the key customer metrics.  This development of the analytics agenda helps to position data in the mind of executives in terms of its importance in driving the business.  

Key steps to consider 

• Identify the key elements of governance to focus on 
• Consider the main elements of a data governance charter for your company 
• Begin to map out the data governance advisory board 
• Explore how you can unify the analytics functions within your business 
• Build your insights agenda and priority areas for the year ahead 

Our members can see the full presentation on our Hub. Get in touch if you would like to join.