Beyond Bank Data Exec Talks Governance Realities

Senior Manager for Data, Governance and Strategy Krista Bell says data expertise should sit with the heads of business

Data governance remains an important topic of discussion among data leaders, and an area they want business decision makers and the heads of companies to understand acutely.

Evangelising the importance of data governance to business decision makers isn’t just done for the sake of making the chief data officer’s life easier though.

Beyond Bank Senior Manager for Data, Governance and Strategy, Krista Bell, who spoke at CDAO Perth in October, says companies that demand data products before the data is fit for purpose are asking for trouble. 

Bell asserts that the data maturity of an organisation will determine how hands-on a company should be with its data, and how much it can get out of using its existing data to enrich other findings or insights.

“For an organisation that has low maturity, it’s important when rolling out a data management and data governance framework that they are addressing key capabilities needed to uplift that data maturity,” she says.

“You need to make sure that you have the ownership, the roles, and the accountabilities in the organisation. Someone has to own the critical data. From my perspective when we talk about that, the focus is on, in most cases, customer, product and employee data – data which is critical to business operation and integrated across its value chain.

“It’s critical to ensure that you have, for instance, your chief marketing officer or your chief customer experience officer fully aware of what they own.

“Then you can start to cascade that out and look at the teams that report into that function. That gives you visibility of the reports and the data that they’re pulling and that’s when you can start to generate more maturity around data usage with respect to accountabilities and roles.

“It’s likely that data literacy will have to become a program too, so that people understand what they are looking for when examining data quality.”

Standing up Stewards

Data stewards (data savvy members of other business units) are beneficial to the roll out of data governance, and they help to enlarge and proliferate data culture.  

In building the foundations of a data-driven business, Bell says once the roles and responsibilities concerning data are in place, having data stewards sitting within lines of business is the next step, providing data leaders with additional sets of eyes and ears.

“Data stewards are the ones that you’ll potentially provide more significant training and uplift to so that they can be the ambassador for their business unit around the data, they’re also that first point of contact back to a BI function for reporting and remediation of quality,” she says.

“From there is when you can look at those data quality programs and actively find the opportunity to dovetail into other pieces of work, like getting some proof of concepts going within the larger transformation programs.

“Building up maturity in those areas will flow into opportunities to enrich your data. For example, you might take data from a marketing analytics campaign and get insights that let you predict what your customers are going to do at a certain point in their customer journey. “That’s when people start to understand what the data can do, but again it only happens once there is a built-up maturity level.”

 Accountability

With data and analytics all the rage right now, there might be a lot of enthusiasm for teams to get stuck into exciting data projects.

This eagerness is not uncommon, but Bell says without the described groundwork being put in, projects will often fail to deliver. And when this happens, it won’t be the data team’s fault.

“Quite often the business will be asking the business intelligence team and the reporting team to tell them about the data and what it means. But the fact is that data expertise should sit with the business,” she says.

“It’s the technical team’s job and skillset to clean, reformat and model the data, visualise that data in a dashboard or a report that will help the business understand something, make a decision or reach an outcome, but the business owns that data and it’s their responsibility.

“If the business reviews a report and suggests its quality is suboptimal, the reporting team is in a position to push back across the lines of business to those who look after the data and hold them accountable for doing data quality checks and business logic.”

With that said, data leaders, particularly data governance leaders, can’t be on the sidelines, Bell says.

“You live in everyone else’s arena. That’s the thing about data governance. You’re building in approaches and practices and you’re building out the support so that each person and each team will be able to understand what a data quality threshold is,” she says.

“They will then by their own determination and through their stewardship function, be able to recognise that they can not provide a report if there is incomplete or inaccurate data. “You’re enabling the people in the organisation to walk in that space of data governance, and data management and data quality, but you have to smuggle yourself into everyone else’s world. That’s how it normally works.”

Collaborative Approach

Data management frameworks play a big role in setting the direction of how data should be handled within an organisation’s various functions.

Policies, procedures and standards that cover data quality and the thresholds that are required to get good insights should be put in place. However, Bell says a good approach is often to develop these frameworks in conjunction with existing ways of working, rather than trying to wipe the slate clean and start anew.

“I don’t think it’s recommended to go into a greenfields environment and say ‘Here are all the procedures and the processes that we’re going to apply and this is the data quality standards that we’re going to use’,” she says.

“I personally think it’s better to come in and gravitate and gain traction with the team, with your stewards, so that you’re faster and you’re more engaging.

“Then when you are working away in the background and you’re creating these artefacts, per se, you can then go to business functions and data stewards and work out where it is that you need to address some of the data quality or data governance issues.

“You can also ask for their input on it. Maybe you really need to understand how they’re working with CRM before you can address their issues about incomplete data and how to create the right flags.

“However, most importantly, you need to ask those key data questions of: ‘What do you want to know?, ‘Do we have correct, accurate and reliable data?’ and ‘What decisions do you intend to make with this insight?’.”