SAP by sennchi

Predictive Analytics And Machine Learning Solutions

SAP draws a straight-line from predictive models to business applications. 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. the solution has plenty of room to grow into its existing applications customer base, but its dependence on sAp’s HAnA data platform will limit its attractiveness to non-sAp customers.

SAP

SAP is based in Walldorf, Germany. It has yet again rebranded its platform: SAP Business Objects Predictive Analytics is now simply SAP Predictive Analytics (PA). This platform has a number of components, such as Data Manager for dataset preparation and feature engineering, Automated Modeler for citizen data scientists, Predictive Composer for more sophisticated machine learning, and Predictive Factory for operationalization. SAP Leonardo Machine Learning and other components of the SAP Leonardo ecosystem did not contribute to SAP's Ability to Execute position in this Magic Quadrant.

Over the past year, SAP has made good progress in several respects, but still lags behind in others. It is a Niche Player due to low customer satisfaction scores, a lack of mind share, a fragmented toolchain, and significant technological weak spots (in relation to the cloud, deep learning, Python and notebooks, for example), relative to others.

STRENGTHS

• **Collaboration across roles: **SAP has increased the integration of its two core machine-learning environments (Automated Modeler and Predictive Composer), to enable use by both expert and novice data scientists. The improved integration also encourages collaboration between roles.

• **Business-integrated machine learning: **SAP's vision, evident in SAP Leonardo, of a unified machine-learning fabric across all its applications, is unique. SAP's new Predictive Analytics Integrator (PAI) is a good start. The first Leonardo applications demonstrated were SAP Fraud Management and SAP Customer Retention, but it remains to be seen whether they will scale and integrate with potential machine-learning deployment points (such as SAP SCM and SAP Forecasting and Replenishment).

• **Some strong product capabilities: **SAP PA is especially good at automating many tasks and deploying across a range of business applications. It can also scale to handle very large datasets, especially via its tight integration with SAP HANA. The size of the datasets processed determines the licensing cost — a great simplification.

CAUTIONS

• **Customer experience and mind share: **SAP has one of the lowest overall customer satisfaction scores in this Magic Quadrant. Its reference customers indicated that their overall experience with SAP was poor, and that the ability of its products to meet their needs was low. SAP continues to struggle to gain mind share for PA across its traditional customer base. SAP is one of the most infrequently considered vendors, relative to other vendors in the Magic Quadrant, by those choosing a data science and machine-learning platform.

• **Fragmented and ambiguous toolchain: **Multiple tools contribute to the SAP data science and machine-learning experience. Various role-based tools create a machine-learning pipeline or implement different sections of the process flow at different levels (for example, to implement and push down data preparation and data quality preprocessing to the database source in SAP HANA). SAP PA Automated Modeler and Expert Analytics target different roles. SAP HANA offers pipeline development and scripting capabilities to database developers or data scientists enabled to work in-database. This fragmentation results in confusion and cumbersome version management. In addition, the UI is cluttered and difficult to use.

• **Technology delays: **SAP is lagging behind in key technology areas, such as capabilities for cognitive computing (in relation to vision, text, audio and video) and "deploy anywhere" cloud capabilities for its core machine-learning pipeline. SAP was one of the last vendors to integrate with Python and deep-learning capabilities, although it announced TensorFlow integration in August 2017. Integration with Python came with PA 3.3 in November 2017, after the cutoff date for evaluation in this Magic Quadrant. Additionally, it is still early days for SAP's Leonardo Machine Learning activities, and reference customers' feedback on SAP PAI was unavailable at the time of writing. Furthermore, SAP's vision for Leonardo Machine Learning seems rather decoupled from that for PA.