by Christian Rodatus, SVP and General Manager, Analytics, SAP

Mature man standing on empty beach, using binoculars, side viewIn today’s connected world, Big Data, people, machines, and processes are interlinked in the “Internet of everything.” One billion Facebook users, four billion YouTube views per day, 15 billion web-enabled devices. And the world’s data doubles every 18 months.

In this environment, the explosion of collectible data about customers, products, and suppliers from web, mobile, social, and machine-generated sources can be overwhelming and led to dark data – information a company collects, but fails to use.Businesses are feeling an urgent need to make sense of Big Data and turn it into actionable insight that drives tangible business value.

For companies that successfully use predictive analytics to tap into vast amounts of Big Data, the opportunity is tremendous: better business decisions and boosted revenue streams. In fact, nearly 90% of organizations surveyed agree that predictive analytic software has given them a competitive advantage. Many companies, for example, use such software to predict customer needs, targeting individual accounts for a retention campaign or cross-sell offer to boost conversions.

Moving from Rearview to Predicting the Future

It’s no surprise, then, that companies want to extend their analytic capabilities. Instead of simply looking at what happened (though standard reports based on historical data) and why it happened (granular data discovery and visualizations), predictive modeling helps companies illuminate what will happen – so they can plan and act for the best possible outcome. In short, “sense and respond” is no longer enough; instead, organizations that want to stay competitive must predict and act.

Predictive Analytics for a Broad Spectrum of Users

One way to extend analytic capabilities is to make advanced analytics usable and accessible by a broader spectrum of users. This is, however, difficult to accomplish because advanced analytics are complex by nature. Predictive models can take even the savviest of statisticians weeks or months to create by hand and these highly-trained resources are not only expensive, but hard to find. Yet, throughout most organizations, there are multiple types of users that could benefit from using advanced analytics.

  1. Accounting for less than one-tenth of 1% are data scientists. These PhDs or individuals with extensive statistic and analytical training utilize statistical libraries to build complex models based on large amounts of data.
  2. The second group, making up about 3% of organizational users, are data analysts. These individuals are analytically savvy, but don’t have an advanced degree in statistics.
  3.  By far the largest group – 97% of analytic users in the organization – are business users and lines of business executives who need actionable insight to do their job but may have never even heard of a predictive model.


The ideal solution allows users of all skillsets and experience to build or consume predictive models while still providing the sophistication needed for today’s data scientists to code with a broader set of statistical libraries such as R.

Prediction + Visualization = a Sophisticated New Level of Reporting

Combine predictive analytics with visualization, and you open up entirely new levels of insight. Rather than constraining visualization tools to discover historical trends, interactive charts and graphs come to life with predictive analytics.

Imagine viewing a chart of quarterly sales by region and then expanding the view to predict sales in future quarters. In another scenario, embedded models in business intelligence tools can identify which attributes or measures are the most predictive, and intelligently suggest charts to include on your dashboard which can illuminate critical business trends to watch.

Predictive Apps

Leveraging our recent acquisition of KXEN, SAP is now able to embed predictive analytics within SAP applications on premise and in the cloud. These automated predictive capabilities can be easily administered by an application administrator or line of business user (e.g. marketer) and set to automatically deliver insights to end users.

Examples include analytics that identify which leads are most likely to convert into qualified opportunities or what is the best offer to extend to a customer while on the customer is on the phone with a call center agent. This delivers tremendous insight across the business which can lead to significant business results.

SAP Helps Deliver Collective Insight

SAP provides an advanced analytics suite which includes best-in-class predictive analytic and visualization solutions which make sophisticated and highly accurate predictions available across the business. Expanding the reach of predictive analytics means that more people throughout the organization can analyze information and confidently anticipate what comes next – guiding better, more profitable decision making.

Get more information on how predictive analytics and agile visualization from SAP can help you make smarter decisions.




VN:F [1.9.22_1171]
Rating: 3.7/5 (3 votes cast)
Making Predictive Analytics Available to a Broad Spectrum of Users, 3.7 out of 5 based on 3 ratings