Brands have made data their #1 priority. It’s the key to digital transformation. Yet research shows that while most brands understand the power of data, they believe they have a long way to go to succeed with data the way they need to.

91% have already seen revenue increases with data, but less than half of companies (44%) see themselves as advanced or leaders in data and analytics. So what’s the problem? Brands report that the #1 obstacle is their current technology.

The many people in every department of every enterprise who would benefit from integrating data better into their day-to-day activities can’t because the systems and tools used today have serious limitations.

What Data Tech Should Achieve

Meeting the needs of increasingly demanding customers and thriving in an environment that grows more competitive each day with disruptive models and products requires continuous automated improvement.

You need to wire your entire enterprise so that every part of it improves automatically 24/7. You need to become a responsive enterprise, automatically sensing opportunities to adapt and improve and acting on them with perfect execution.

You get there by continuously converting data into the actions—the next-best-actions—your teams and your systems can put into play in real-time to improve performance.

What a Responsive Service Company Looks Like

A services company has a team of project managers whose limited time and attention is split between the thousands of projects they are trying to manage, and make successful, simultaneously.

A project is considered successful if it’s on-time and on-budget, so the company has established key performance metrics like time in each project phase, overtime, and changes in project managers to measure performance.

Their data analysis technology pulls in data from across their disparate project and resource management systems and converts it to evidence-based recommendations—a list of projects that should be called on to ensure they aren’t delayed and don’t experience a cost overrun—that makes it easy for project managers to ensure they’re spending their time in the best way, maximizing productivity, project success, and ultimately profitable revenue.

What a Responsive Telco Looks Like

A global telco customer acquisition cost is high so customer satisfaction is incredibly important, but challenging to maintain, because they have millions of customers. The company tracks performance metrics that support customer satisfaction like contract length, renewals, and beyond.

Their data analysis solution scans data from several siloed enterprise systems and converts it into a variety of recommended actions, including a daily list of top 50 customers that should receive high-touch contact because patterns in the data indicate they are likely to become dissatisfied or disloyal without proper intervention.

Where Does Current Tech Fall Short?

Most data tech today does not convert all your data to a constant stream of next-best-actions your people and systems need. It does not deliver continuous, automated improvement.

Data tech today (spreadsheets, R/SAS, BI and reporting) is flawed because it still requires human effort—frequently analysts and scientists with specialized data skill sets—that is labor-intensive, costly, and takes time.

Because it’s largely a human effort, it’s also subject to human bias (what people can think to query) and therefore does not result in comprehensive findings.

It gathers some of your data (clean, numeric data from some systems), but not all (data that’s text and numbers, dirty or missing, from cloud and on-premise systems, and from every business function.)

Also because of the human element, the analysis isn’t instant and real-time and continuously updating.

Frequently the outputs of analysis are charts and dashboards that a human must convert into “insight.” This insight step where humans makes sense of the charts and data presented them and decide what to do about it is often frustrating, difficult, and doesn’t produce the best results.

 The Solution is Emerging Tech

But there is new data tech available that solves all these problems and more.

The emerging technologies that are fast becoming hallmarks of the digital transformation because of their unique ability to rise to the challenges of the modern landscape are automation and cognitive technologies (think artificial intelligence, machine learning, smart machines).

Both automation and cognitive technologies transfer manual or mental work that’s better accomplished by a computer—“better” being faster, higher quality, lower cost—from humans to computers, producing better results for the customer and business while freeing humans up to add the value they’re uniquely suited to deliver (tasks requiring creativity, relationship-building).

The solution is for enterprises to augment their current data technologies with emerging tech.

This means automating your data discovery and data analysis, or transferring a significant bulk of the manual and mental work of discovery and analysis from data workers (analysts, scientists, IT) with their spreadsheets, queries, coding, and modeling, to machines with exponential computing power that do the work for them, better than they can do it.

The end result is immense value for the business through continuous automated improvement.

And continuous automated improvement delivers customer experiences that live up to the expectations of today’s demanding customer, operations that capitalize on brand new efficiencies and opportunities, increasing sales and revenue in the face of escalating competition, and previously occupied data workers with time to do the immensely valuable work that only they do best.

Radhika Subramanian


Radhika helps executives adopt breakthrough technologies to outperform the competition. A serial entrepreneur, Radhika has been taking leading brands to new heights with groundbreaking applications of math and data for decades.

How is automation transforming analysis? Download Redefining Analytics to find out.