Introduction to SAP HANA Modeling for SAP Business

Introduction to SAP HANA Modeling for SAP Business One: Modeling Business Analytics INTE RNA L

Objectives At the end of this unit, you will be able to: Understand the

Business Analytics are used in business to accomplish two main goals: • Acknowledge the business insight about what was and is happening in the business by analyzing the historic business data. • Predict trends of business about what will happen, to support the business to make faster, better and smarter decisions. © 2015 SAP AG. All rights reserved. 3

Questions that need answers © 2015 SAP AG. All rights reserved. § How are sales going for this month, quarter, and year? § Who are the best customers? § What are the best selling products? § Who are the high-performing sales employees? § How do we keep the sales budget on track? § How will sales trend next quarter? § What would be the best promotion scheme for an upcoming campaign? 4

How to answer the questions § How are sales going for this month, quarter, and year? § Sales Revenue and Gross Profit measured by months, quarters or fiscal year § Who are the best customers? What’s the definition for the best customers? § Best customers of the year are those who contribute the most sales revenue or profit. , so use Sales Revenue and Profit measured by Customer § What are the best selling products? How do we define the best products? § Best selling products contribute the most to sales revenue and profit, so rank these by product. § Who are the high-performing or under-performing sales employees? § Sales staffs are ranked by contribution to sales revenue and profit, so measure these key figures by employee. © 2015 SAP AG. All rights reserved. 5

Sales Analysis Cube Product Dimension Customer Product Employee Date Sales Employee Dimension Sales Revenue Gross Profit Time Dimension Cube = Fact Table + Dimensions © 2015 SAP AG. All rights reserved. 6

Basic Concepts Doc. Num Customer Employee Doc Total Gross Profit 1 C 001 S 001 100. 00 2 C 002 S 002 150. 00 32. 00 Measure Attribute q Referenced master data and underlying tables/attributes q Attributes in transaction row q Can group on any attribute © 2015 SAP AG. All rights reserved. q Values in transaction row for aggregation q Calculated totals 7

Sales Analysis Model Product Dimension View Customer Product Employee Date Sales Employee View Sales Revenue Gross Profit Time Dimension View Cube for Sales Analysis © 2015 SAP AG. All rights reserved. 8

Modeling Steps Step 1: Analyze business requirements Step 2: Determine models needed Step 3:

Models provided by SAP provides prebuilt semantic layers: • Made up of views that model business processes • Define elements of common transactions and master data • Allow users to easily build queries and dashboards © 2015 SAP AG. All rights reserved. 10

Query Views and Reuse Views Query View • Used to run queries. • Consumed directly by analytics Re-use view • Used as components in other views • Well structured and consistent Both are designed with the goal of reducing development effort for writing reports and dashboards © 2015 SAP AG. All rights reserved. 11

Reuse views as dimensions in sales analysis cube Product Dimension Customer Product Employee Date Sales Employee © 2015 SAP AG. All rights reserved. Sales Revenue Gross Profit Reuse View Time Dimension 12

Example Step 1 The sales manager at OEC Computers wants to analyze sales totals in an interactive way. He wants to be able to see invoice amounts summarized by customer information and date. Transactions and Dimensions Needed: q Transaction: A/R invoice q Measure(s): document total q Dimensions: Customer Group, Customer, Posting Period q Potential additional measures and dimensions: gross profit, status, territory, sales employee, item group, etc. © 2015 SAP AG. All rights reserved. 13

Step 1: Analyze business requirements Talk with potential users: q Objectives q Use of analysis q Tools Think about what to include in the models: q Business transactions q Measures q Dimensions Consider efficiency and flexibility © 2015 SAP AG. All rights reserved. 14

Step 2: Determine models needed Can you use existing models? Can you copy and extend existing models? What views or tables will be included? What will be the basis for the data foundation for your view? Which measures and attributes will be included in each view? What table joins are needed? © 2015 SAP AG. All rights reserved. 15

Step 2: Example Customers Revenue Distributors 45, 000 ADA Technologies 11, 800, 000 Mashina Corporation 33, 200, 000 High Tech Parameter Technology 32, 400, 000 Cube with Star Schema Customer Dimension Data Foundation: Invoice Number Customer Group Invoice Date Customer Name Customer Group Invoice Total © 2015 SAP AG. All rights reserved. 16

Step 2: Example Details We decide to build an view for sales totals with a business partner dimension and a time dimension. Data foundation (fact table) with transaction and measures: q A/R invoice – OINV: Document Total, Tax Amount, Business Partner, Posting Date Data sources for our dimensions: q BP Master Data – OCRD: BP Code, BP Name, BP Group q BP Group – OCRG: BP Group, BP Group Name q Posting Date – M_TIME_DIMENSION: Posting Date, Posting Month, Posting Year © 2015 SAP AG. All rights reserved. 17

Steps 3 & 4: Model then activate and preview Our example: Step 1: Analyze business requirements Step 2: Determine models needed Step 3: Model view for BP Step 3 - Model the view Step 4 - Activate & preview the view Steps 3 and 4 are repeated for each model Step 4: Activate and preview BP view Step 3: Model view for time dimension Step 4: Activate and preview time dimension Step 3: Model star schema view for transactional data Step 4: Activate view and preview data © 2015 SAP AG. All rights reserved. 18

View Types Attribute views Analytic views © 2015 SAP AG. All rights reserved. This view type can model dimensions by joining related master data tables. This view type joins one transactional table with master data dimensions. Most flexible view type: Calculation views § Can model dimensions or cubes with star joins. § Can contain multiple transactional tables. § Used for even more complex modeling, for example, when you need a union between tables. § Build graphically or with script 19

Views and Engines • Semantic layer has models for relationships – no data. • Data is moved between engines if a model mixes different types of views. • Best practice: Use calculation view for all models. Calculation Views • When run query, engines pull data from HANA database and calculate results. Calculation Engine Analytic Views © 2015 SAP AG. All rights reserved. OLAP Engine Join Engine Attribute Views 20

Calculation View types q Dimensions for Master Data q Cube. Data with. Foundation Star Schema for fact tables q Date Dimension Cube with Year Star Schema Cubes for unions or calculations from multiple data sources Quarter Month Date Time Stamp Data Foundation Invoice Date Customer Dimension Invoice Number Customer Invoice Total Customer Name Gross Profit Customer Group © 2015 SAP AG. All rights reserved. Customer Group Name 21

Best practices Ø Reuse predefined SAP models by copying and extending them. Ø Use consistent naming conventions. Ø Models for reporting should end in QUERY. Example: XXX_CUBE_FINANCIAL_CONSOLIDATION_QUERY Cubes with Star Schema XXX_CUBE_. . . Master Data Dimension XXX_DIM Calculation View for Union XXX_UNION_. . . Calculated Measure XXX_CM_. . . Restricted Measure XXX_RM_. . . Analytical Privilege XXX_AP_. . . © 2015 SAP AG. All rights reserved. 22

Summary Key points: Analytics are used to accomplish two main goals: analyze historical data and predict trends. Cubes are a traditional way to model business analytics. A cube consists of a fact table and dimensions. The fact table will contain key figures and the dimensions contain tables of attributes Because of its shape, a cube is also known as a star schema In SAP HANA, we can model our cube as an calculation view that defines a relationship between a fact table and dimensions. The dimensions of the cube are designed as reuse views that model master data attributes. © 2015 SAP AG. All rights reserved. 23

Thank you This concludes the topic on Modeling Business Analytics. Thank you for your

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