Self-service is both a hot trend and a looming goal for those in the data space, but implementing a successful program is still a work in progress at many enterprises.
A 2020 survey by IDG Research found that 51% of responding data professionals — scientists, architects, and more — cited self-service as an essential capability in the effort to generate quick time-to-insights.
Nevertheless, at many companies, a majority of non-analytical employees do not have direct access to the data or tools required to conduct meaningful analysis. The same IDG survey reported that over half of the participants’ lines of business rely on data teams or IT to build dashboards.
Though the process of piloting self-service is no small task, agile analytics remains a key component in the push toward becoming a more data-driven organization.
As a data strategy leader, how do you provide employees with the ability to rapidly obtain the data they need without waiting on overburdened data or IT teams, or compromising governance?
Here are some best practices for empowering a self-service culture from senior data strategy leaders who have made it work for their companies.
Determine what self-service will mean to your enterprise
Data self-service is a bit of a buzzword, so it’s important to determine what self-service will look like for your organization. The parameters and tools you utilize can vary depending on the enterprise industry and the number of users.
USAA Chief Data Officer Chris Gifford recently shared how their enterprise is democratizing data to allow for self-directed analytics during a recent Data Board panel discussion on leading a data cultural transformation.
As a basic principle, Chris says data as a product needs to exist in a well-curated, maintained, and documented environment — particularly within a highly regulated industry such as financial services.
Typically this data is exposed through BI solutions, however, certain business units desire a more hands-on analysis than what’s made available through BI tools. In that case, Chris says he has leveraged pre-built rapid analytics and visualizations, or RAV, tables to display larger data sets for those less analytical enterprise teams.
Chris advises striking the right balance between enabling team members to leverage data to make smarter business decisions and not creating a potential for risk.
“You want to give them the ability to run their business on data, but you don’t want to give them the ability to run with scissors. That’s really the kind of balance that we seek to deliver in this democratization space,” Chris says.
During the same panel discussion, Jennifer Battista, Vice President of Data Strategy at Shaw Communications, spoke to the critical importance of self-service at her organization, which operates in the telecommunications industry.
Jennifer says her goal as a data strategy leader is to empower every member of the company to actively use data to create the story of their business — not just report on it. Due to the vast amount of users at Shaw, what self-service looks like can vary greatly across the enterprise.
“We have a range of users from a very basic end user who works in Excel or can maybe do something in Tableau, to your data analysis and data scientists who are very capable,” she says.
As a result, the organization utilizes a range of tools to support each user with the goal to “meet them where they’re at.”
How does Jennifer know these tools are meeting users’ needs? The organization leverages a team called the Data Empowerment Team, which works with individual users and groups to help them become more self-sufficient.
Keep governance and data protection in mind
As you work to scale up self-service at your enterprise, staying on top of governance and compliance is paramount. How do you ensure that your companies’ data is not misused or misreported?
Craig Brabec, Vice President and Chief Data and Analytics Officer at McDonald’s, says it’s essential to provide members access to utilization, as well as appropriate training. Still, he said business units will always be more creative with the use of data than what you initially anticipated.
During the Data Board panel discussion, Craig played on Chris’ “running with scissors” analogy to speak on the difference between usage and appropriate usage.
Your household pair of scissors were designed to cut — but how many times have you used them to dig something out of a crevice or in another creative capacity? Perhaps, Craig says, they’re only appropriately used as scissors a minority of the time.
“People are creative with the tools once they have the tools,” Craig says. “It’s making sure we’re not violating data protection — we’re following the ethics and guides of the company and what our policies are without stifling innovation.”
Even with proper training, teams may still slip up, and according to Craig, letting them make mistakes can be part of the process.
Different data will require different levels of protection. Of course, financial information or personal data within HR should be more closely safeguarded than data used to identify business opportunities. It can be helpful to leverage an adaptive data governance policy.
Leverage lessons learned from your industry peers
As more enterprise data becomes available, both internally and externally, more individuals want to benefit from it. Based on that factor, Craig says self-service is here to stay.
“Everybody’s got a data component to their programs, which to me is great. You need the strong data scientists, but you also need the knowledge workers upping their capabilities and skills.”
The process can be slow and painful so it’s always helpful to hear lessons learned from a community of leaders like yourself.
There are a number of webinars and leadership discussions focused on self-service, including Dataversity’s webinar From Spreadsheets to Workflows, The Journey to Self-Service Analytics, and the Data Board’s panel on Leading a Data Cultural Transformation.
