Machine Learning involves algorithms that learn from and make predictions on data and, generally speaking, more data means better predictions. Combine that with the vast amounts of data that most organizations are now generating, and the transformational potential of Machine Learning is nothing short of amazing.

It’s no surprise that Predictive Analytics and Machine Learning are two of the hottest areas in analytics today, as organizations see their potential to help with Digital Transformation. In fact, Forrester Research forecasts compound annual growth rate of 15% in this space for the next four years. Enterprises are investing heavily with the hope of reaping big business benefits from smarter business processes & better decisions that improve their Return on Investment.

Let’s be honest though, many of us don’t understand how the complex algorithms that make Machine Learning work translate into measureable business results, and there is so much hype that it’s difficult to separate great marketing from great products. This need to separate fact from fiction is why we rely on Yelp for restaurant reviews and TripAdvisor for choosing a great hotel. It’s also why many of us rely on research from independent firms such as Forrester for help evaluating the field of companies in a technology area…separating great marketing from great products that deliver business results.

I am thrilled that Forrester Research has ranked SAP a “Leader” in  “The Forrester™ Wave: Predictive Analytics and Machine Learning Solutions, Q1 2017” published this week. In our view, Forrester’s evaluation criteria does an excellent job of setting aside hype and marketing to focus on the practical application and resulting business benefits of Machine Learning.

 “SAP draws a straight-line from predictive models to business applications” Forrester Research

Here are some highlights from Forrester’s research.

Forrester Research Attributes for Predictive Analytics and Machine Learning

Forrester identifies seven attributes that a predictive analytics and machine learning solution should have, and I’d like to share my perspective on how SAP delivers in each of these:

  • “Drive data scientist productivity.” With the best automation tools in the industry—from data preparation, model development, testing, deployment, and model management—SAP BusinessObjects Predictive Analytics ensures data scientists can focus on the science and its application to business problems. This allows small teams to deliver big results.

 

  • “Accommodate citizen data scientists.” Guided authoring tools in SAP BusinessObjects Predictive Analytics ensure that enterprises can expand the number of users able to create predictive models that deliver business value, helping to drive wider adoption of predictive insights and enabling data scientists to focus on the business challenges where they can have the greatest impact.

 

  • “Include multiple model deployment methods.” SAP provides not one, but multiple ‘straight lines’ to deploying models directly into enterprise applications and business processes. First, users can score and deploy models directly into SAP HANA for in-memory processing, which is at the core of modern-day enterprise applications. And for users working with Big Data, SAP BusinessObjects Predictive Analytics can build and score models inside Apache Spark. But it doesn’t stop there. Users can centrally score, deploy, and update models into a wide range of third-party database systems for in-database processing and it even allows users to export models as code for common software development languages (like Java, C, C++, SQL, Web Services) so that users can harness predictive analytics in virtually any application.

 

  • “Provide sophisticated model management.” Models require continuous retraining and scoring as the business and underlying data changes. With Predictive Factory in SAP BusinessObjects Predictive Analytics, you can easily build, deploy, and manage thousands of models across your organization while still complying with your IT department’s governance structure. And when models become out-of-date, Predictive Factory can automatically retrain, score and apply these models to new data.

 

  • “Expand to Apache Spark.” Even better. With SAP BusinessObjects Predictive Analytics, you can use its full suite of tools to build models natively inside Apache Spark, ensuring you don’t have to transport massive volumes of data in order to build and test your models.

 

  • “Allow polyglot programming.” SAP provides data scientists with a wide range of programming languages to carefully script models, including deep R support in both SAP HANA and SAP BusinessObjects Predictive Analytics. You can build simple to complex predictive workflows using R, SAP HANA – PAL & APL and you can even write your own custom algorithms using R.

 

  • “Build the foundation for AI and invest in deep learning.” Though SAP has a strong predictive analytics and machine learning foundation with SAP BusinessObjects Predictive Analytics and SAP HANA, we’re not resting on our laurels. SAP is investing heavily in its portfolio of machine learning technologies, ensuring organizations who partner with SAP will have the latest capabilities that can be integrated into their business applications and processes. Expect a wide range of innovations coming from SAP in the next 12 months!

According to Forrester, “SAP offers comprehensive data science tools to build models, but it is also the biggest enterprise application company on the planet. This puts SAP in a unique position to create tools that allow business users with no data science knowledge to use data-scientist-created models in applications. SAP’s solution offers the data tools that enterprise data scientists expect, but it also offers distinguished automation tools to train models.”

With a long list of great companies focused in this area, it is truly an honor for SAP to be recognized as a leader in Machine Learning and Predictive Analytics. I would like to thank our team for their incredible dedication and hard work, and our customers for their continued partnership and co-innovation.

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