SAP White Paper Utilities Industry SAP Business Warehouse powered by SAP HANA SAP S/4HANA SAP Business Warehouse Powered by SAP HANA for the Utilities Industry Architecture design for utility-specific processes
Table of Contents 3 Introduction 5 Detailed Architecture Examples 8 Test Results from a Proof of Concept at a Municipal Utility Company 10 Summary 2 / 11
Introduction When it comes to business management information, the utilities industry (and the SAP for Utilities solution portfolio) differentiates between two types of data: that which comes in small amounts, and the kind that accumulates in large or very large volumes. The smaller quantities involve things like disconnections and dunning. The industry also distinguishes between periodic data (daily, for example) and data based on key dates, such as the last day of the month. The purpose of this white paper is to examine processes that generate large quantities of data. This information involves sales statistics, unbilled revenue reporting, and contract accounts receivable and payable (including open and cleared items). The context of unbilled revenue reporting (URR) will also provide an opportunity to delve further into how SAP BW powered by SAP HANA facilitates architecture optimization. URR aids in identifying and evaluating quantities sold and billed, which is necessary for the balance sheet of each balance period to be closed. The business content SAP provides includes a predefined data model for mapping URR in SAP BW. Here, the documents billed (or simulated) are transferred from SAP for Utilities to SAP BW. Fig. 1: Unbilled revenue reporting in SAP BW current status SAP for Utilities Sales statistics Simulation ID Simulation 0UC_SALES_STATS_02 0UC_SIM_ID_ATTR 0UC_SALES_SIMU_01 PSA PSA Actual data Simulation data Invoicing date Simulation ID SAP BW URR MultiCube 3 / 11
Thanks to new InfoProvider types, optimized transformations and data flows, and powerful in-memory technology, SAP BW powered by SAP HANA offers a number of new ways to improve performance in transfers and reporting. This makes it possible, for example, to reduce the time needed to import data into SAP BW by 40 to 80%. In drilling down queries, the amount of time saved can even amount to more than 99% (for further details, please refer to the section Test Results from a Proof of Concept at a Municipal Utility Company, p. 8). Meanwhile, factoring in the enhanced methodology of Layered Scalable Architecture (LSA++) for SAP BW powered by SAP HANA opens the door to more flexible and streamlined architectures customers can use to successfully implement new BI strategies (such as a self-service approach) in a holistic layered architecture. This leaves the core of the enterprise data warehouse (EDW) in question intact while expanding it to cover opera tional reporting through the use of virtual objects and special services. Fig. 2: Layered Scalable Architecture (LSA++) for SAP BW powered by SAP HANA Applications (BPM, BI, and so on) SAP BEx queries SAP BW virtual data mart layer Architected data marts Business transformations EDW propagation layer EDW transformations Corporate memory Flexible data marts, SAP BW workspace LSA++ Data acquisition layer With SAP BW powered by SAP HANA, the following example architectures are possible: Optimized data model in SAP BW powered by SAP HANA with Advanced DataStore Objects (ADSOs) as InfoProviders and CompositeProviders in place of a MultiCube Optimized data model in SAP BW powered by SAP HANA with ADSOs as InfoProviders, CompositeProviders in place of a MultiProvider, and data transfers through Operational Data Provisioning (ODP) Use of the SAP HANA Studio development environment in combination with the option to transfer data through SAP Landscape Transformation Replication Server 4 / 11
Detailed Architecture Examples OPTIMIZED DATA MODEL WITH SAP BW POWERED BY SAP HANA To optimize and exploit the power of SAP HANA, the following data model adjustments are required: Creation of ADSOs for actual and simulation data (with the option to account for semantically partitioned DSOs in the case of very large data quantities) Creation of a CompositeProvider (for SAP BW 7.4 SP5 and later) Adjustment of transformations SAP BW 7.4 SP8 introduced the new Advanced DataStore Object (ADSO), which has replaced the previously used DSOs and InfoCubes. Unlike past implementation options, up to 120 key fields are now possible. They support high-frequency data loading processes, and the modeling takes place in the Eclipse-based SAP BW. CompositeProviders are to replace MultiCubes as a means of merging individual InfoProviders. Along with the usual UNION links, INNER JOIN and LEFT OUTER JOIN are now supported, as well. Fig. 3: Data model optimized with SAP BW powered by SAP HANA SAP for Utilities Sales statistics Simulation ID Simulation 0UC_SALES_STATS_02 0UC_SIM_ID_ATTR 0UC_SALES_SIMU_01 PSA PSA ADSO actual data Invoicing date Simulation ID ADSO simulation data SAP BW CompositeProvider for URR Freely combinable InfoProviders 5 / 11
OPTIMIZED DATA MODEL WITH SAP BW POWERED BY SAP HANA AND OPERATIONAL DATA PROVISIONING (ODP) Among other things, ODP offers a new means of transferring data from a source system into SAP BW. In the process, it adapts the DataSources such that information is written directly to a data target in SAP BW (without the need for a PSA table). The data is stored up to this point in compressed form in the source system s operational delta queue (ODQ). The ODP framework and its new (operational) delta queues handle tasks that were previously covered by the Persistent Staging Area (PSA). In particular, the data transfer process can draw information directly from the (operational) delta queues. This makes info packages and the PSA loading process optional, which in turn leads to significantly improved loading performance. In this context, PSA monitoring is replaced by Delta Queue Monitor. The delta data of the application in question can be stored in the (operational) delta queue for a configurable amount of time and requested again from the data transfer process for the same subsequent period. Fig. 4: Data model optimized with SAP BW powered by SAP HANA and ODP SAP for Utilities ODP source system with DataSources for ODP replication SAP BW Sales statistics Simulation ID Simulation 0UC_SALES_STATS_02 0UC_SIM_ID_ATTR 0UC_SALES_SIMU_01 Operational delta queue Operational delta queue ADSO actual data Invoicing date Simulation ID ADSO simulation data CompositeProvider for URR Freely combinable InfoProviders 6 / 11
This data model requires the following activities: Creation of ADSOs for actual and simulation data (with the option to account for semantically partitioned DSOs in the case of very large data quantities) Creation of a CompositeProvider (for SAP BW 7.4 SP5 and later) Data transfer transition to ODP (ODP source system with ODP-ready DataSources) ADVANTAGES OF SAP BW POWERED BY SAP HANA AND TABLE REPLICATION OF MASTER DATA More virtualization, less persistency: power meets convenience Combining SAP HANA s tremendous speed and aggregative power with the convenience and integration capacity of SAP BW makes it easy to implement innovative analytics scenarios in short order with a single tool: SAP HANA Studio. This makes it possible to conjoin a native SAP HANA model (CalculationViews) designed for rapid big-data processing of sales statistics, for example, with a SAP BW CompositeProvider. The computing aspect is pushed down to SAP HANA DBServices, while SAP BW handles areas like authorization control and administration. This all results in fewer redundant data layers and better computing performance. Fewer data flows, lower costs In scenarios involving multiple clients (corporate groups, holdings, and so on), replicating master data tables with the help of SAP Landscape Replication Transformation Server significantly lessens costs throughout the ETL process. Instead of having a data flow for each individual client s master data as before, the server reduces them all to a single flow. The master data table is extracted in its entirety (including the client in the key) into SAP BW. The data can then be replicated further for each particular client based on the authorizations at hand in subsequent SAP BW updates. Comparison of data models Data model Optimized data model Optimized data model with ODP Table replication Advantages Easy to integrate into an existing data model Fewer redundant data layers, simpler data flow, short loading times Fewer redundant data layers, reduced data flows when dealing with large numbers of clients 7 / 11
Test Results from a Proof of Concept at a Municipal Utility Company Even without detailed knowledge of the data models and the data quantities actually selected for loading and queries, the figures below clearly show how SAP BW powered by SAP HANA can significantly accelerate unbilled revenue reporting. In all of the aforementioned examples, this does not include the mass invoicing and simulation runs in the SAP for Utilities source system. Loading results SAP BW on DB2 SAP BW powered by SAP HANA Differential Sales statistics Init 27:19 h 12:43 h -53.4% Sales statistics Delta 1:25 h 0:26 h -69.4% Contract accounts receivable/payable Contract accounts receivable/payable Init 45:41 h 10:20 h -77.4% Delta 0:48 h 0:08 h -83.3% Unbilled revenue reporting Init 23:29 h 13:42 h -41.7% Unbilled revenue reporting Delta 10:42 h 3:20 h -68.8% Total 109:24 h 40:39 h -62.8% Results by process type SAP BW on DB2 SAP BW powered by SAP HANA Differential DTPs 48:30 h 38:20 h -21.0% DB actions 0:29 h 0:01 h -96.6% DSO activation 50:44 h 1:53 h -96.3% Compression 9:41 h 0:24 h -95.9% 8 / 11
Reporting results Sales and distribution SAP BW on DB2 SAP BW powered by SAP HANA Differential Sales statistics 8:51 min 1:50 min -79.3% Unbilled revenue reporting 5:31 min 0:02 min -99.4% 81:07 min 1:43 min -97.9% Grid Sales statistics 8:18 min 0:05 min -99.0% Unbilled revenue reporting 29:17 min 2:12 min -92.5% Total 133:04 min 5:52 min -95.6% 9 / 11
Summary Future SAP developments are poised to focus on the use and benefits of SAP HANA s in-memory technology as part of both SAP Business Suite and SAP BW. Based on open CDS views, analytical models in SAP BW powered by SAP HANA will supplement virtual data models used in connection with SAP S/4HANA. This will form a truly comprehensive source of information capable of handling practically any reporting-related issue by uniting operational data with historical analyses and supporting strategic decision-making in real time. Fig. 5 and 6: SAP S/4HANA and SAP Business Warehouse SAP S/4HANA (incl. virtual data model) SAP NetWeaver Multichannel unified user experience SAP SAP NetWeaver SAP BW New SAP apps Partner apps SAP Business Warehouse powered by SAP HANA Strategic and tactical Integration, harmonization, crosssystem consistency Consumption Planning Multisourced data Preconfigured content Data life cycle Data governance Full-blown analytical suite SAP HANA PLATFORM Application services Database services (including SAP HANA views) Synchronization services SAP S/4HANA Operational data Real-time Lightweight modeling and consumption Extensible Uniform Basis for multiple embedded use cases Model reuse in analytical applications BI clients and other UIs SAP S/4HANA SAP HANA SAP S/4HANA Analytics CDS (VDM) Virtual access through SAP HANA Smart Data Access SAP BW/OLAP Data staging SAP HANA SQL runtime views Optimized for SAP HANA Other data marts Transactional tables SAP BW data SAP HANA tables Real-time replication or data staging 10 / 11
FURTHER INFORMATION Layered Scalable Architecture (LSA++) for Data Warehouses with the SAP HANA Database https://help.sap.com/saphelp_nw73ehp1/helpdata/en/22 /ad6fad96b547c199adb588107e6412/frameset.htm Operational Data Provisioning http://help.sap.com/saphelp_nw73ehp1/helpdata/en/d9 /0da4dd2c4743948e3f018c90a235d7/frameset.htm 11 / 11 Studio SAP 40113enUS (15/10)
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