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Figure 1: Moving Decision Making from Sense and Respond to Predict and Act EDW and ETL BI Agile BI Predictive analytics Optimization User engagement Raw data Cleaned data Standard reports Ad hoc reports and OLAP Selfservice BI Agile visualization Generic predictive analysis Predictive modeling Why did it happen? What will happen? What is the best that could happen? Predictive insight What happened? Maturity of analytics capabilities Legend EDW = enterprise data warehouse ETL = extract, transact, load BI = business intelligence OLAP = online analytical processing © 2016 SAP SE or an SAP affiliate <strong>company</strong>. All rights reserved. 4 / 33
The result of predictive analytics is not a BI artifact like a query or a report, but a mathematical representation (a “model”) of the relationships found between different elements in the data. You can then use this model to perform what-if analysis to maximize or minimize a value. Or you can apply the model against entirely new data to predict a future outcome such as “a customer’s probability to purchase” or “projected revenue for the next four quarters.” Making predictions in real time is even more valuable: rather than waiting until the end of the day or end of the week to run an analysis, you can make a decision as soon as data becomes available. This can mean millions of dollars or, in some industries, could be the difference between life and death. However, most organizations have many heterogeneous technologies supporting legacy environments, leading to inefficient data management and significant operational overhead. In addition, many organizational silos are common in larger organizations where multiple teams are responsible for the data infrastructure. This is illustrated in Figure 2. There is an ever-increasing set of technologies designed to make each of these steps faster. However, both the amount of data being generated and the speed at which this data needs to be analyzed is growing exponentially so that traditional methods of analysis will soon no longer be viable. Figure 2: Data Analysis – Multiple Silos Versus In-Memory Computing Transact ETL Analyze ETL Cache Accelerate VERSUS SAP HANA® Separated transactions + analysis + acceleration processes • Redundant data in and across applications • Inherent data latency One in-memory atomic copy of data for transactions + analysis • Elimination of unnecessary complexity and latency • Acceleration through simplification Legend ETL = extract, transact, load © 2016 SAP SE or an SAP affiliate <strong>company</strong>. All rights reserved. 5 / 33
Page 1 and 2: SAP Predictive Analytics with SAP H
Page 3: Big Data, Big Challenges, and Big O
Page 7 and 8: Examining SAP Predictive Analytics
Page 9 and 10: If you are already using SAP HANA,
Page 11 and 12: USING SAP PREDICTIVE ANALYTICS AND
Page 13 and 14: The following sections discuss many
Page 15 and 16: Predictive models executed in SAP H
Page 17 and 18: REAL-TIME AUTOMATED PREDICTIVE ANAL
Page 19 and 20: “Algorithmic” Predictive Capabi
Page 21 and 22: SAP HANA supports this type of side
Page 23 and 24: Figure 14 shows a nonexhaustive lis
Page 25 and 26: “Automated” Predictive Capabili
Page 27 and 28: Operationalizing Automated Predicti
Page 29 and 30: Beyond the basic script-based appro
Page 31 and 32: Comparing R, PAL, and APL Capabilit
Page 33 and 34: Why SAP Predictive Analytics with S

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