Boost your ROI with data analytics

3 steps to supercharge decision-making

  • Many investments in data analytics fall short of improving decision-making
  • IT leaders can use digital workflows to create analytics “playbooks” that generate actionable insights
  • Layered playbooks can help companies gain visibility and optimize business performance

Corporate investments in data analytics are booming—right along with the explosion in data generated by many enterprises. The global market for data analytics platforms and tools reached $200 billion in 2019 and is expected to hit $274 billion by 2022, according to IDC.

Yet many organizations aren’t seeing the returns they expect from those investments. Some are deploying analytics in isolated use cases but lack the ability to scale them across the enterprise. Others are struggling with a more fundamental problem: They are unsure how to generate actionable intelligence from increasingly large piles of data. Unless acted upon, the value of any stored data is, after all, zero.

The promise of modern analytics, aided by advances in machine learning, is to improve decision-making in support of critical business objectives, from growing the customer base to retaining satisfied, productive employees. To realize that vision, companies must close the gap between knowing and doing—from collecting thousands of data streams and knowing what they contain to applying decision-making frameworks to each of them.

Companies can progress on that journey by acting on data insights through an analytics operation that uses digital workflows as its building blocks and adds layers of intelligence in three phases:

1. Build workflows from data

Digital workflows and the data they generate make every part of a process visible. Take customer behavior, for example. With a data-collection platform in place, analytics teams today can start closing the knowing/doing gap by building workflow “playbooks” triggered by specific insights in the customer journey. At my company, ServiceNow, we use these playbooks in customer operations to track workflows for everything from NPS survey responses to every facet of product usage, 24/7.

The playbooks, in turn, flow into real-time analytics that customer service teams can easily see or be alerted to when customers hit a snag with any aspect of these tracked behaviors. Even better, it doesn’t take a months-long development effort to build analytics playbooks. Using low-code app tools, analysts can create them on their own. They’re highly configurable and translate into easy, intuitive experiences for end users.

2. Apply analytics to workflows

The next step is to apply analytics to those individual data workflows to produce a deeper layer of actionable insights. When a customer signals she wants a product upgrade, for example, playbooks can make timely discoveries, such as: She may not have the latest features enabled (triggering an offer, say, in response) or may have had issues with prior upgrades (triggering prescriptive guidance through the upgrade process).

The dashboard view into data workflows offers relevant information about all sorts of variables—so customer teams are able to act on multiple issues instead of just one.

3. Create a master view of company workflows

Applying analytics to the foundation built with the first two steps can offer executives a potential data gold mine: a highly actionable, digital view into core KPIs and company performance at an enterprise level.

It starts with simple math. Let’s say you have 30,000 customers and you’re tracking 15 workflows per customer per year. These workflows impact an average of 10 types of company employees. The analytics supporting all that is now tracking 15 million tasks or steps in all the workflows you’ve deemed most important to the business.

That’s a 360-degree, data-enabled view into the entirety of work being done—analyzed over any time period. Most companies have only fragmented views. But workflow-powered analytics can give everyone in the C-suite the same kind of dashboard for the business that our customer-facing teams use so successfully.

While no company can pull this off overnight, digital platforms and tools now enable organizations to build intelligent analytics largely by themselves. They can start optimizing the most critical operations and continually convert insight into action.