Today, data analytics should not be the sole domain of data scientists. In fact, employees from all kinds of backgrounds and expertise must be able to use, share and work with data.
Why? According to NewVantage Partners’ Data and AI Leadership Executive Survey 2022, 92% of organizations are investing in data and AI, but only 19% have successfully established a data culture. You can see a disconnect: Agencies can invest heavily in analytics but not see data-driven outcomes agencywide.
To make investments that produce mission-centered results, analytics need to be in the hands of technical and non-technical data workers.
“Your data team is not just your data scientists,” said Andy MacIsaac at Alteryx, a unified data analytics platform provider that is designed to make analytics accessible to everyone. “It’s the entry-level analysts, the caseworkers, the logistics person, the procurement team. Whatever their role, they should be viewed as part of the data team and able to participate in the analytic process.”
There are three key technology approaches that can help. You may have heard these analytics terms before, but here’s what each means.
What Is ‘Democratized Analytics’?
Democratized analytics is the technological capability that enables data workers of various technical skill levels to leverage data and share its insights with other employees of various skill levels. Put simply, it’s analytics accessible for and inclusive to all.
“You never know where the answer or solution is going to come from,” MacIsaac said.
That’s why democratized analytics can make a difference. Data and analytics will only play a more integral role to government services and operations in the future. You want to bring a diversity of thought to analytic processes so that you have the best and brightest pool of solutions to draw from.
What Is ‘Unified Analytics’?
Unified analytics is technology that allows data workers to perform the entire analytic life cycle in one place. From data prep and blend, which identifies and combines data for descriptive, predictive and prescriptive analytics, to machine learning, an advanced form of AI that gets smarter over time — unified analytics allows a range of data transformation processes to be done in a single location, no matter the data source or type.
What Is ‘No-Code/Low-Code Analytics’?
No-code/low-code analytics is analytics that does not require coding skills to prep, clean, analyze and share data. And we’re not talking spreadsheets here.
Unfortunately, a lot of analytic tools that organizations invest in require coding or data science skills. If you don’t have them, you’re often left with a decades-old desktop tool that is time-consuming and limits the potential growth of your data skills. A no-code/low-code approach frees employees from these barriers to unlock analytic insights for their team and organization.
“It’s one thing to be able to do analytics, but the real value of it is the insights it creates. Can you share that insight easily and effectively?” MacIsaac said. “And can you enable everyone to get the data they need, apply their domain expertise and create the insight to solve problems?”
This article is an excerpt from GovLoop’s guide “Your Data Literacy Guide to Everyday Collaboration.”