The idea of using data to drive business insight isn’t new —the term ‘business intelligence’ in the context of computers systems dates back to the 1950s. For the most part, its focus has been on analysing past data to make educated decisions about the future. But what if we could take business intelligence a step further, and get recommendations on what to do next? That’s already happening, in fact, in our everyday lives.
Consumer websites and applications, for example, make recommendations based on user data—such as Amazon suggesting a book we might like based on our purchase history, or Netflix predicting a film based on what we’ve viewed in the past. These recommendations are delivered to us as a very user-friendly experience—we don’t need to be data experts to understand them.
Yet while these smart analytics live behind many of our interactions as consumers, it is not typical to have specific recommendations about business decisions delivered to us—as business leaders—from our enterprise applications.
This may not be altogether surprising when considering that business challenges are much more complex than knowing which film you might want to watch next. The stakes are much higher for analytics to answer questions that impact business survival: How can we improve staff retention? How do we increase revenue?
But now, we are entering an era where the same approaches and ease-of-use can be applied to decision-making in the enterprise. Where once these important decisions would have been based on limited information and even gut feel, we will now get recommendations based on accurate and current data that help us make confident and informed decisions about how to grow our businesses.
The end of instinct-only decision-making
There are three broad subsections of analytics: descriptive (provides insights into what happened), predictive (predicts what might happen in the future), and prescriptive (delivers a recommended course of action to deliver optimal results). The latter category—in which recommendations are provided as to what steps to take next—is the newest and most advanced and also the most interesting for business users.
Modern business solutions combine all three, using advanced data science and machine learning algorithms to provide leaders with insights, predictions and recommendations, leading to smarter financial and workforce decisions.
What kind of decisions could benefit from prescriptive analytics? Consider retention risk. Knowing when an employee might be ready to leave is often based solely on the instincts of his or her manager. Millennials are making up an ever greater proportion of the workforce, and their propensity to change jobs more frequently than previous generations means retention is becoming an even bigger problem for businesses today.
It can cost tens of thousands of pounds to replace an employee, representing a significant cost to the bottom line as well as a business risk, since work quality and customer satisfaction could be impacted negatively by attrition.
All companies need to find new approaches to increase retention, and the most innovative ones are looking at that third step of prescriptive analytics to revolutionise their strategies.
The future is prescriptive
The most innovative solutions deliver interactive dashboards that enable businesses to quickly identify and understand retention risk for the organisation as a whole, through to specific lines of business or departments. Available metrics include the number of top performers at high risk of leaving in the next twelve months, as well as the projected cost of replacing them.
Data can also be used to define the top risk factors driving staff turnover, such as time spent in current role, number of job functions held, or time between promotions, as well as highlighting which departments, job types, or teams are at the highest risk of turnover.
This insight is already here, but the next step is even more exciting: solutions will also offer actual recommendations based on data to ensure good business decisions that will help organisations to reach their goals. And like the simplicity of Amazon or Netflix recommendations, all of these recommendations will be easily delivered to managers in a simple interface – without the assistance of IT or data analysts.
When it all sounds so simple, it’s tempting to ask why this hasn’t been possible before. The answer lies in the proliferation of traditional ERP systems that are spread across multiple servers and databases, with separate processes and systems. In these instances there is no single, unified view of an organisation’s data. What’s more, any data needs to be moved into a separate data warehouse or business intelligence application before even the basic steps of analytics can be performed.
The past decade of technology innovation has broadened the horizons of what analytics can deliver. A critical step has been the ability to change how we architect enterprise applications, with a combination of hybrid transaction/analytical processing (HTAP) system architectures with in-memory computing (IMC), enabling new approaches and immediate access to real-time data.
When real-time workforce and finance data are in memory and unified in a single system, then the most advanced capabilities in analytics become unlocked. At that point, we can leverage technologies such as Hadoop, and increasingly, in-memory processing enabled by Apache Spark. Then, add on to that intelligent data classification and machine learning methodologies to refine the prediction and recommendation stages of analytics.
Companies rely on analytics to grow their businesses, because most decisions are too important to be made on instinct. With prescriptive analytics forming the next chapter, analytics can be used to provide recommendations on the best course of action to achieve the desired results.
But all of this intelligence is pointless if it isn’t available to the managers who need it. Modern analytics solutions are proving that smarter doesn’t have to mean more complicated. And perhaps this simplicity and accessibility is the most exciting thing of all.
Sourced from Annrai O’Toole, CTO, Europe, Workday