5 tips for excelling at self-service analytics | 7wData

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Data-informed decision-making is a key attribute of the modern digital Business. But experienced data analysts and data scientists can be expensive and difficult to find and retain.

One potential solution to this challenge is to deploy self-service analytics, a type of Business intelligence (BI) that enables business users to perform queries and generate reports on their own with little or no help from IT or data specialists.

Self-service analytics typically involves tools that are easy to use and have basic data analytics capabilities. Business professionals and leaders can leverage these to manipulate data so they can identify market trends and opportunities, for example. They’re not required to have any experience with analytics or background in statistics or other related disciplines.

Given the ongoing gap between the demand for experienced data analysts and the supply of these professionals — and the desire to quickly get valuable business insights into the hands of the users who need it most — it’s easy to see why enterprises would find self-service analytics appealing.

But there are right and wrong ways to deploy and use self-service analytics. Here are some tips for IT leaders looking to make good on the promise of self-service analytics strategies.

Data analytics and analytics tools have gained such a high profile within many businesses that it’s easy to see how they can be overused or inappropriately applied. This is even more of an issue with self-service analytics, because it enables a much larger range and base of people to analyze data.

That’s why it’s important to establish a plan for where and when it makes sense to use analytics, and to have reasonable controls to keep your analytics strategy from becoming a free for all.

“Determine your mission, vision, and questions you need to answer around analytics before even starting,” says Brittany Meiklejohn, a business and sales process analyst at Swagelok, a developer of fluid system products and services for the oil, gas, chemical, and clean energy industries.

“It is extremely easy to get caught up on all the charts and graphs you can create, but that gets overwhelming very quickly,” Meiklejohn says. “Having that roadmap from the start helps to trim down and focus on the actual metrics to create. Have a data governance plan as well to validate and keep the metrics clean. As soon as one metric is not accurate it is hard to get the buy-in again, so routinely confirming accuracy on all analytics is extremely important.”

The analytics plan should emphasize the use of proactive data as much as possible, Meiklejohn says. “Focus [on] data that is actionable and can be implemented back into the business,” she says. “Incorporate learnings to transform processes and decision-making at an organizational scale. It is great to understand the historical side of the business, but it is hard to change if you are only looking at the past.”

At Swagelok, departments are using self-service analytics tools from Domo to determine whether customer orders will be late, schedule production runs, analyze sales performance, and make supply chain decisions.

“We have seen an increase in efficiency; everyone is able to get the data they need to drive decisions much faster than before,” Meiklejohn says. “We are making more responsible data-driven decisions, since each department is using the data for decision-making.”

While it’s important to have a long-range analytics strategy in place, that doesn’t mean organizations should move at a plodding pace with self-service analytics.

“In my previous company, our advanced material business had a saying, ‘Go fast, take risks, and learn,’” says Keith Carey, CIO at Hemlock Semiconductor, a maker of products for the electronic and solar power industries. “That would be my advice for those just getting started [with self-service analytics].