What good looks like

Analytics can be turned into action with digital workflows

Analytics without action is purposeless. That’s where digital workflows on an enterprise platform comes in.

While housing all data on one platform can be a tough decision to make, the simplicity of powering all desired capabilities through a single source is essential to modern analytics.

With one data architecture, one version of truth, and one master data source to power it all, you achieve the single pane of glass view of your organization—and then can make better decisions, automate actions, and monitor results so that you can continue to optimize. Using one common data platform, all decision-making capabilities can be built to scale.

While a singular data platform might get less attention than, say, a beautiful data visualization, it’s the critical component that brings everything together.

[Read also: Analytics translators: The new heroes of data science]

Here’s how this looks in action: An account owner receives an alert that a customer needs help. If the account owner requires assistance, the workflow routes to the customer success advocate and executes a playbook to resolve the customer issue. Using machine learning and analytics, the workflow improves over time, automatically adjusting bottlenecks and optimizing the response path.

“Once analytics and workflow come together, they live happily ever after,” says Vijay Kotu, vice president of analytics at ServiceNow, leading implementation of large-scale data platforms and services to support the company’s enterprise business. “One can never be complete without the other.”

One of ServiceNow’s customers saw this in action at KAR Auction Services, which provides remarketing solutions for wholesale used vehicle industry.

KAR was relying on manual processes to measure service performance. Individual teams laboriously extracted and analyzed their own data, creating significant overhead and delays. This disconnected approach also created trust issues, since everyone was looking at the data from different angles.

“We had no centralized ownership of metrics, so there was no consistency. For example, we had multiple ways of defining when an incident was resolved or closed. That’s a major issue—when people don’t trust data, they won’t act on it. And, when we tried to pull everything together, we ended up with a 90-page PowerPoint presentation that was weeks out of date and hard to understand. You can’t deliver consistent support or drive service improvement that way,” says Jason Hagen, process manager at KAR.

“People now collaborate instead of working in silos. That shared understanding lets you optimize your services by setting unambiguous objectives, driving aligned actions, and accurately measuring your results,” he said.

In the words of Vijay Kotu, “Once analytics and workflow come together, they live happily ever after. One can never be complete without the other.”