Memory Centric Business Analysis and Supply Chain Visibility: One Complementing the Other
Growing data analysis need of business users was evident when a super user expressed the desire to generate an operational report which will not only help him to know how many units of a certain product did a business unit sell in a retail store in Irving, Texas but would also state how much revenue did that product generate during the last four months broken down by individual months, in the south west territory by individual stores, broken down by promotions and demographics, compared to estimates (previous forecast) and compared to the sales of a previous version of a similar product.
The above user request clearly validated that today's decision managers must be able to analyze data along any number of business dimensions, at any level of aggregation, with the capability of viewing results in a variety of ways. They must have the ability to drill down and roll up along the hierarchies of multiple business dimensions.
Memory-centric data management and OLAP (online analytical processing) technologies and tools are the answer. With the cost of memory dropping and speed increasing, the in-memory computing and analytics platform is an efficient way which has helped users carry out complex analysis on enterprise wide data by consolidating, and editing enormous volumes of data across different operational and financial measures.
Memory-centric technologies permits real-time and multidimensional OLAP analysis, with much more speed and computational flexibility as opposed to disk-centric technologies, whose performance is more like "come back after lunch".
The concepts of hyper-cubes and multidimensional domain structure (MDS) presents a special method for representing a data model with more than three conventional business dimensions i.e. product, time and geography. The MDS also is the technological back bone for memory-centric solutions offered in the market place today such as SAP's BI Accelerator/Net Weaver & IBM's Memory-Centric OLAP Platform: COGNOS/TM1-Applix.
These technologies has not only helped users expedite the process of analyzing the voluminous data across product lines, geography, time, demographics, promotions and other business metrics such as direct sales, profit margin, cost of goods sold but also has helped them make key business decisions such as to change their build and buy plans, modify financial and unit forecast and re-position a product inventory to achieve a sell through in another market segment in another part of the world by quickly making an intra-company transfer.
Running multiple cascading what-if simulations, carrying out scenario planning, customer analytics, profitability analysis in a sequence of analysis session using OLAP cubes has helped users accurately and timely gauge the performance of the key metrics of the business and make tactical and strategic decisions, thereby improving visibility across the value chain of a global organization.