The Basics of Data Champion Networks

Successful data champion networks are all about value exchange

Data champions, advocates, evangelists, ambassadors - whatever you call them, I've yet to come across a successful data transformation that didn't depend on them. And yet many organisations struggle to maintain data champion networks much beyond year one.

Having maintained and developed a data champions network for five years, I understand the challenge. After year one, the novelty wares off, you've exhausted some of your best ideas, and without a regular stream of success stories, it is hard to continue investing in a programme whose benefits are hard to measure directly.

I've experienced these challenges and won't pretend that the five years I ran a data champions network were plane sailing. They weren't. However, I know the wider data transformation wouldn't have succeeded without a change network. And, after a while, a pattern began to emerge that made running a growing champions network easy.

Sustainable change networks are all about value exchange.

Many people join change networks out of excitement or curiosity but only stay involved if it is worth their time. This isn't cynicism. It is realism. Everyone has busy jobs, and change networks expect people to take on commitments outside their job description. This is only manageable if there is a benefit.

This works both ways. Data champions networks are time-intensive things to run. Central data teams can only spend time running and maintaining a data change or champion network if there is value for them to do so.

Data change networks that offer a fair value exchange (i.e. benefit the champions and the central team) create a flywheel that generates and sustains momentum. To do this, you need to understand what both sides have to offer and what both sides need.

What do central data teams have to offer?

The obvious answer is expertise. However, very few central data engineering/science teams have enough capacity to become effective coaches for data champions elsewhere in the business. And teams around the company quickly develop their technical expertise.

The only thing unique about central data teams is that they are central and are expected to focus on data priorities. Finance data teams are expected to focus on financial priorities, customer data teams on customer priorities etc.  

Whilst this might sound so obvious that it doesn't need saying, this is where central data teams have something to offer and can create value for data champions. They can uniquely focus on the data issues that everyone has, and no one else has time to solve. This includes things like:

• Ensuring there are enough licenses for enterprise data tools;

• Implementing common data access policies to reduce friction in data sharing (e.g. making certain low-sensitivity data available to everyone in an organisation);

• Developing connections with the internal comms teams to make sure that data success stories are well publicised; and

• Working with HR to improve data career paths and training.

What do central data teams need from champions?

While central teams can take on obscure challenges like data access management, they need to show the value that comes from their work. And that's hard because most of the value doesn't come from their work. Most of the value comes from work they have enabled others to do.  

For example, there is no direct value in rolling out a BI and analytics platform. The value comes from all data projects that users across the business get from the new platform. That is very hard to measure.

The key thing, therefore, that central data teams need is success stories. Preferably ones with a measurable value. Data champions can provide these.

The unwritten contract of successful data champion programmes

Data champions report their data success stories to a central data transformation team. In return, they expect some of the blockers on their projects to be removed so that they can deliver more value from data projects faster.

Turning this around, central data teams listen to, prioritise and resolve data champions' blockers on their projects. In return, they expect help capturing the value of the data transformation so that they can get more investment for and accelerate the data transformation programme.  

It is that simple. Much more can be done, from hackathons to internal data conferences and competitions. But if either side fails on their half of the value exchange, the champions programme won't last long, and neither will the data transformation programme.

Getting started

Viewing a data transformation in these simple terms also makes it easy to start a data transformation and champion programme.  

All you need to do is identify volunteers, uncover the issues preventing them from getting more value from data, solve the highest priority issue, document and publicise success stories and repeat.