4 Ways to Improve Analytics Accessibility for Your Workforce
Data is only as valuable as it is usable. Simply having lots of data on hand means nothing unless organizations are able to turn it into valuable insights. And turning stored data into actionable intelligence capable of boosting business outcomes starts with employee accessibility, as these are the people who need data access to help fuel informed decision-making.
It’s worth evaluating: How accessible to non-data specialists is your analytics platform? How can you make it more accessible, helping users across the company glean the most value from data for the least hassle?
Consider these four ways to improve analytics accessibility for your entire workforce.
Empower Employees to Run Ad Hoc Queries
Perhaps the largest gulf between employees and data emerges when the average, non-technical user is unable to run ad hoc queries themselves on an as-needed basis.
Legacy business intelligence (BI) has traditionally made data analytics the domain of data and IT teams, who are then in charge of querying data and creating reports on behalf of other employees. As you can imagine, this setup gives employees little control over how soon they’re able to get these insights—and if they have follow-up questions on static reports, they’ll have to repeat the process. Throw the high potentiality for reporting backlogs into the mix and you have an inefficient, somewhat frustrating approach to data on your hands.
If this sounds like your business, you’re probably wondering how you can possibly break down these silos and empower employees to run their own ad hoc queries in real-time.
Data today can be more democratic than ever before thanks to the recent wave of BI tools prioritizing user-friendliness for all. The very definition of who can partake in data science is changing, as today “employees both with and without data science backgrounds… learn the business context and work with tools and data to prepare to produce analyses” that would have required professional data experience in years past.
To put it succinctly: Give business decision-makers direct access to data tools, regardless of their roles. This helps them answer questions quickly and act on insights faster, too.
Embed Analytics into Existing Workflows
Accessibility has to do with not only who can access data tools, but where they can do so. The farther removed your data analytics tools are from normal workflows, the less useful they’ll be to employees. For this reason, embedded analytics from platforms like ThoughtSpot inserts analytics tools themselves, plus interactive data visualization models other users have already created, directly into business apps, company portals, and workflows.
There’s a reason people say: “Location, location, location.” Employees are more apt to access BI tools and glean insights from interactive data visualization models if they’re located directly in their line of sight.
Provide Data Literacy Training
Gartner predicts by 2020, half of the organizations will lack sufficient AI and data literacy skills to achieve business value. The firm likens this to every department within an enterprise speaking a different language, but none a second language. It becomes difficult to communicate about data or find commonality if employees lack the context to do so.
Data literacy training will help all employees “speak data” whether they’re in marketing, HR, sales, leadership, etc.
Ensure C-Suite Leads by Example
Last but not least, make sure leaders are, well, leading improved data initiatives by example. Accessibility depends on tools and training, yes, but it also depends on an organization’s overall attitude toward data.
Getting leaders to buy into your accessible data strategy is positive because it encourages everyone to incorporate BI into decision-making, of course. But it also acts as a “test drive” for your system. If non-technical executives can get the insights they need with little hassle, it’s a good bet the rest of your company can, too.
Improving analytics accessibility for your workforce is key if you want to derive genuine value from your stored data.