INTERACTIVE SESSION TECHNOLOGY Kraft Heinz Finds a New Recipe for Analyzing Its Data When the Kraft Foods Group and Heinz finalized opportunities for improving efficiency, self-service their merger in July 2015, it was the marriage of two reporting, and real-time analytics. giants. The new Kraft Heinz Company became the SAP BW Accelerator was not suitable for these fifth-largest consumer-packaged food and beverage tasks. It could optimize query runtime (the period organization in the world. The combined company of time when a query program is running) only has more than 200 global brands, $26.5 billion in for a specific subset of data in the warehouse, and revenue, and over 40,000 employees. Eight of the was limited to reporting on selected views of the brands each have annual revenue exceeding S1 data. It could not deal with data load and calcu- billion: Heinz, Maxwell House, Kraft Lunchables, lation performance and required replication of Planters, Velveeta, Philadelphia, and Oscar Mayer. Business Warehouse data in a separate accelerator. Running these companies required huge amounts With mushrooming data on the merged company's of data from all of these brands. This is clearly the sales, logistics, and manufacturing, the warehouse world of big data. was too overtaxed to generate timely reports for To remain profitable, enterprises in the fast- decision makers. Moreover, Kraft Heinz's complex moving consumer goods industry require very lean data model made building new reports very time- operations. The uncertain global economy has damp-consuming-it could take as much as six months to ened consumer spending, so companies such as complete. Kraft Heinz needed a solution that would Kraft Heinz must constantly identify opportunities deliver more detailed reports more quickly without for improving operational efficiencies to protect their affecting the performance of underlying operational profit margins, Kraft Heinz decided to deal with this systems challenge by focusing on optimizing its supply chain, Kraft Heinz business users had been build- manufacturing optimal quantities of each of its prod- ing some of their own reports using SAP ucts, and delivering them to retailers at the best time BusinessObjects Analysis edition for Microsoft and least cost to capitalize on consumer demand. Office, which integrates with Microsoft Excel and Managing a supply chain as large as that of Kraft PowerPoint. This tool allows ad hoc multidimen- Heinz requires timely and accurate data on sales sional analysis. What these users needed was to be forecasts, manufacturing plans, and logistics, often able to build self-service reports from a single source from multiple sources. To ensure that Kraft Heinz of data and find an efficient way to collate data from would be able to use all of its enterprise business multiple sources to obtain an enterprise-wide view data effectively, management decided to split the of what was going on, data among two large SAP enterprise resource plan- Kraft Heinz decided to migrate its data warehouse ning (ERP) systems, one for North American busi- from its legacy database to SAP BW powered by SAP ness and the other for all other global business. HANA, SAP's in-memory database platform, which The combined company also had to rethink its data dramatically improves the efficiency at which data warehouse. can be loaded and processed, calculations can be Before the merger, the North America business computed, and queries and reports can be run. The had maintained nearly 18 terabytes of data in a SAP new data warehouse would be able to integrate with Business Warehouse and was using SAP Business existing SAP ERP applications driving day-to-day Warehouse Accelerator to facilitate operational re- business operations. The company worked with IBM porting. SAP Business Warehouse is SAP's data ware- Global Services consultants to cleanse and stream- house software for consolidating organizational data line its existing databases. It archived and purged and supporting data analytics and reporting. The unwanted or unused data, with the IT department SAP Business Warehouse (BW) Accelerator is used working closely with business professionals to jointly to speed up database queries. Kraft Heinz manage- determine what was essential, what was still being ment wanted decision makers to obtain more fine- used, and what data thought to be unused had been grained views of the data that would reveal new moved to a different functional area of the company. Chapter 6 Foundations of Business Intelligence: Databases and Information Management 231 Cleansing and streamlining data reduced the data base size almost 50 percent, to 9 terabytes. According to Sundar Dittakavi, Kraft Heinz Group Leader of Global Business Intelligence, in addition to providing better insights, the new data warehouse environment has achieved a 98 percent improve- ment in the production of standard reports. This is due to the 83 percent reduction in load time to exe- cution time to make the data available, and reduction in execution time to complete the analysis. Global key performance indicators for the Kraft side of the business are built into SAP HANA. Kraft Heinz can now accommodate exploding volumes of data and database queries easily, while maintaining enough processing power to handle un- expected issues. The company is also able to build new reports much faster and the flexibility of SAP HANA makes it much easier to change the com- pany's data model. Now Kraft Heinz can produce new reports for business users in weeks instead of months and give decision makers the insights they need to boost efficiency and lower operating costs. Sources: Ken Murphy, The Kraft-Heinz Company Unilocks Recipe for Strategic Business Insight,' SAP Basvler Profiles, January 25, 2017; The Kraft Heinz Company Migrates SAP Business Warehouse to the Lightning-Fast SAP HANA Database, IBM Corp. and SAP SE 2016 and www.krafthein company.com, accessed February 15, 2018. CASE STUDY QUESTIONS 1. Identify the problem in this case study. To what extent was it a technology problem? Were any management and organizational fac- tors involved? 2. How was information technology affecting busi- ness performance at Kraft Heinz? 3. How did new technology provide a solution to the problem? How effective was the solution? 4. Identify the management, organizational, and technology factors that had to be addressed in se- lecting and implementing Kraft-Heinz's new data warehouse solution. Analytic platforms also include in-memory systems and NoSQL non-relational database management systems and are now available as cloud services. Figure 6.13 illustrates a contemporary business intelligence technology in- frastructure using the technologies we have just described. Current and histori- cal data are extracted from multiple operational systems along with web data, social media data, Internet of Things (IoT) machine-generated data, unstruc- tured audio/visual data, and other data from external sources. Some companies are starting to pour all of these types of data into a data lake. A data lake is a re- pository for raw unstructured data or structured data that for the most part has not yet been analyzed, and the data can be accessed in many ways. The data lake stores these data in their native format until they are needed. The Hadoop Distributed File System (HDFS) is often used to store the data lake contents across a set of clustered computer nodes, and Hadoop clusters may be used to pre-process some of these data for use in the data warehouse, data marts, or an analytic platform, or for direct querying by power users. Outputs include re- ports and dashboards as well as query results. Chapter 12 discusses the various types of BI users and BI reporting in greater detail. Analytical Tools: Relationships, Patterns, Trends Once data have been captured and organized using the business intelligence technologies we have just described, they are available for further analysis using software for database querying and reporting, multidimensional data analysis (OLAP), and data mining. This section will introduce you to these tools, with more detail about business intelligence analytics and applications in Chapter 12