As the data industry continues on a trajectory of self-service, data enablement, and analytics empowerment, organizations need to change the way they think about data professionals.
Too often, we think “data professionals” but hear “data scientists,” “business analysts,” and “power users.” And while those data professionals certainly need to be empowered with better tools, they still only make up a minority (~20%) of the people within the organization who already work with data. What about the other 80%?
User Experience Is Everything
Empowering every data user starts with reassessing how we think people work with data. There is a misconception that data is scary and that people don’t know how to find, analyze, and use data in decision making. This is simply untrue. People use everyday data to make decisions routinely. They know how to find data online (search) and how to collaborate and share findings (texting and posting). Consider the process of perusing the ratings and review sections of sites such as Amazon and Yelp (trusting data) to make informed purchase decisions or select where to have lunch (taking action). These are simple choices, requiring simple analysis. But there are larger, more complex evaluations that necessitate larger, more complex analysis—selecting and buying a vehicle, researching all the variables surrounding the purchase of a new home, and so on—and people use analysis to make these decisions too. Making data approachable isn’t the problem. We need to give emergent data professionals the data they need within an environment designed to make them successful so that the task of analysis isn’t a chore—it’s an experience.
User experience is everything. Enabling data professionals requires providing an environment to work with data in a natural, intuitive format. Too many tools are designed for the power user with a developer’s mindset. While the analytics capability is certainly there, merely updating the user interface doesn’t make a tool user-friendly any more than watering down bloated enterprise software makes it “self-service.” Aesthetics do not equal usability—?or, to put it plainly, just because something is pretty doesn’t mean anyone will want to work with it.
Participation and Collaboration
Stop thinking about tools and start thinking about platforms designed to encourage participation, collaboration, and ease of use. A new generation of vendors is bridging this gap, and while these tools will likely find themselves part of an enterprise ecosystem, they offer instant value due to their familiarity. With solutions such as Datameer Spotlight for data prep or Grid.is for amping up Excel, even the most basic data user can begin providing benefits back to the business.
Next, while organizations should look to enable every business user to work with data, they should also enable IT to support data users. Data management is IT’s responsibility, but so is providing access to data. If people are intimidated by IT systems and processes, this presents a barrier. Think of businesspeople making decisions with data as water flowing down a river: If you put in an obstacle, they will flow right around it. Telling an eager data user they can’t work with a dataset and/or can’t have access to a database just means they’ll find a way to do it anyway—and likely away from the watchful gaze of IT. As gatekeepers, IT team members aren’t just custodians of data; they are there to enable the organization to work with its data—to provide secure access, management structure, and governance safety.
Evaluating Experience
Shifting perspectives and adopting new organizational cultures doesn’t happen overnight. However, we can make data-?informed, analytics-driven decisions to measure data enablement. While metrics for the impact of “self-service” are still somewhat nebulous, we can measure the efficacy of end-user-?designed experience for data work by simply changing the way we evaluate its adoption. Rather than counting how many users are engaging with a tool or platform (the number of licenses in use), we should consider how often they are using, sharing, and publishing it (how often and how it is being used). It’s “screen time” analysis for organizational self-service.
Ultimately, the success of a data-enabled organization will be largely dependent on its ability to support self-service users with the platforms they need to work with data and the data with which they want (and need) to work. This means we must empower all data users and consider the needs of internal customers as much as external customers. Productive data users are happy data users and role models, and happiness starts from within.