Smarter business systems know your next move: are you ready?

Profile picture for user danbeck By Dan Beck March 25, 2015
Enterprise applications are getting smarter. Built-in business analytics will improve performance by recommending what to do next, writes Workday's Dan Beck

Dan Beck, Workday
Dan Beck, Workday

In the world of enterprise applications, the most exciting and innovative work is happening in analytics. Business analytics are moving beyond views into the past and predictions about the future to include a third critical step: recommendations on what to do next. This step wouldn’t be possible without the emergence in the past 10 years of new ways to manage and make sense of data. Innovations in data classification, machine learning, and unstructured data access will enable even smarter enterprise applications that will deliver a new wave of understanding about our organizations.

This third step in analytics, however, isn’t entirely new. Like many other enterprise technology innovations, the roots of analytics-based recommendations are in the consumer world. When we receive an online recommendation for a book, movie, or driving route, it’s because past user data had been analyzed, future outcomes had been predicted, and finally, a recommendation was delivered.

But in business the stakes are much higher than whether you’ll purchase a book or movie. Analytics that follow through with recommendations can be applied to a range of questions and scenarios, such as how to identify and stop talent and revenue losses within the company.

These smarter analytics will be deployed across multiple functions and delivered straight to business managers, without assistance from in-house data experts. They’re going to allow business leaders to put even more focus on the future rather than the past, with insights that can deeply impact the way they solve problems today and plan for what’s next. What’s more, these smarter analytics will be within the business applications themselves, a big advancement from the archaic, bolted-on business intelligence systems approach.

Demand is clearly growing for this new era of analytics. Gartner has identified “advanced, persuasive, and invisible analytics” as among the top 10 strategic technology trends for 2015. “Every app now needs to be an analytic app,” said Gartner Vice President and Fellow David Cearley in a report on the trends.

IDC, in a report on worldwide trends for big data and analytics in 2015, states:

Growth in applications incorporating advanced and predictive analytics, including machine learning, will accelerate in 2015. These apps will grow 65 percent faster than apps without predictive functionality.

Why a modern system approach matters

compass blue sky cloud © WavebreakMediaMicro -
So what has stopped the enterprise software world from reaching this point in analytics in the past? The answer lies in the limitations of traditional ERP systems, which are designed to capture transactions and not to perform analytics. As a result the data — often living in many individual modules running on separate servers and databases — needs to be moved into a separate business intelligence system and reconciled before even the basic steps of analytics can take place.

Innovations in the past 10 years have opened the door to much greater possibilities for analytics. Hybrid transaction/analytical processing (HTAP) system architectures with in-memory computing (IMC) (which Gartner discusses in its “2015 Strategic Road Map for Postmodern ERP” report) have defined a new way for architecting applications — a fundamental requirement for getting  immediate access to real-time data.

The options for analytics open up further if all of the critical information, such as workforce and finance data, is unified in one system. The options expand further still if you can marry other operational data with your core workforce and financial data leveraging technologies like Hadoop, and increasingly, in-memory processing enabled by Apache Spark. With all the data in one place,  intelligent data classification and machine learning methodologies can now be used, which are critical to refining the prediction and recommendation stages of analytics.

On top of all that data science, add a friendly, consumer-like interface, and truly actionable insights can be easily delivered to business managers. Managers could be alerted to the potential flight risk of highly valued employees, for example, and then receive recommendations on how to retain those individuals, all based on data analysis related to each individual and his or her role in the organization. (Read this short Workday Q&A for more on how organizations might use analytics that incorporate recommendations.)

So get ready. Analytics keep getting smarter in ways that will impact the performance of our work, the health of our businesses, and the success of our customers.

Image credits: Sky compass © WavebreakMediaMicro -; headshot by Workday.