Run a company more intelligently by managing data real-time

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By Suraj Pai   Nearly all companies realize the way to gain a competitive advantage is to obtain better data, interpret them quickly, and distribute them in easier-to-use formats. But there are many obstacles to the effective use of data, resulting in unused corporate data in many companies. As companies grow in revenue and volume, they churn out a huge volume of transactional data usually in mega and terrabytes of customer, partner and operational information. All this corporate wealth of information can either be stored for future use or be analyzed immediately and applied into existing information management processes. Either way, the volume of data being created can have a massive, negative effect on how it needs to be utilized or maintained by the company?s decision makers. The questions often asked among executives are ?How can I get access to my sales data now??, ?How are we going to keep up with this much volume of data?? ?How are we going to use it for our operations?? and perhaps the most difficult question to answer, ?How much will it cost us to use this data?? Times are gradually changing in the world of business analytics and traditional methods of data management are being challenged given the volume, variety and velocity of data pouring in from employees, customers, partners etc. through a variety of sources like enterprise systems, social media sites, smartphones, tablets and other consumer devices including PCs and laptops. Keeping track of all these file types, where they go, and how to use them is a very daunting task especially for companies that are data-intensive, such as those in the power sector, financial services, telecom, food and beverage, and even information technology. In recent years, development in in-memory database has put forth some novel but very promising functions that revolutionize database management systems. By definition, in-memory database refers to a database management system that utilizes main memory (RAM) of a computing infrastructure to store data. Compared to traditional disk storage processes, in-memory database is immediately captured and processed or analyzed in real-time, thus reducing the need to wait for certain results to be received by the user. In-memory database as a game changer Why is in-memory database important when processing big data? It?s simply because in-memory database cuts down the entire process of analyzing data that may be important the moment it?s received. In addition, by having an in-memory database process, company executives can start reviewing other types of data that they never thought they had. In which case, they could either improve their overall business operations or create new services, or even both. SAP already saw the qualities of in-memory some time ago. The company acquired Transact In Memory in 2005 which was already doing commercial memory-centric relational database solutions. Under SAP, the eventual technology developed was named HANA, short for high-performance analytical appliance. It is both a revolution and evolution in database management as it utilizes in-memory-capable hardware with the database management functions of SAP software. This type of technology can be such a game-changer for companies who rely on data-intensive analysis especially if they have volumes of customer data that arrive every second. Attesting to this use of in-memory database are companies like Nongfu Spring in China and Nomura Research Institute from Japan, both of which have tried, tested, and succeeded in implementing in-memory database systems. A tale of two companies: Nongfu Spring and Nomura Research Institute Nongfu Spring is a leading distributor of water and fruit drinks in China, and is currently holding the largest share of the market. The company faced many challenges such as, its sales team was collecting point-of-sale terminal data in different ways. The large volume of data meant it would take a day to run ETL (extract, transform and load data) and another day to receive these channels and POS (point-of-sale) data. ?This hampered our executives? ability to make informed decisions and take action,? said Patrick Hoo, CIO, NongFu Spring. Nongfu Spring successfully went live with SAP HANA last year, and upon implementation, they found that it met the three goals they had set out to achieve. The first goal was to accelerate its displaying reports. ?We did the same report, running on the same script, in both HANA and Oracle. We found that the same script was 200 to 300 times faster on SAP HANA than on PL SQL on Oracle. This result was consistent over all 150 reports,? Hoo said. The second goal was to produce highly efficient business logic calculations. ?We transferred over 50 stored procedures and functions from Oracle?s Data Mart to SAP HANA and compared the calculation speed,? Hoo said. Using the example of a freights report, Hoo said what took 24 hours on Oracle only took 37 seconds after it was optimised on SAP HANA. The third goal was to achieve real time data replication and data synchronization. ?Before, captured data was displayed 24 hours after ETL. But now, real time data is displayed instantly,? he said. The SAP system, he says, minimizes the possibility of making mistakes, as well as reduces Nongfu Spring?s maintenance costs. ?SAP HANA is a comprehensive and sophisticated data solution that we will continue rolling out at NongFu Spring,? he said. The company intends to introduce a mobile distribution system, leveraging HANA. As one of Japan?s leading consulting firms, Nomura Research Institute (NRI) is a driving force in the economy of the country. In finance, logistics, industry, and the public sector, this highly professional institute has developed a reputation for cutting-edge solutions and quality, high-end services. According to NRI, ?looking ahead to the big data era when data more finely-tuned to our needs than today?s mere chatter will become available at an instant thanks to alliances with social networking services such as Twitter and Facebook, there is no doubt that the volumes of data we need to process will grow at an exponential rate. We must, therefore, be able to process even more data in real-time.? NRI develops mobile phone navigation solutions for individual users. That said, however, the service itself ? Zenryoku Annai! ? is very well known and well adopted. This service helps subscribers receive superior navigation solutions like the shortest route, ETA (estimated time of arrival), etcetera. In order to provide this information at a higher degree of accuracy and precision than the competition with high-performing real-time processing of massive volumes of traffic information, this department conducted testing to verify the performance of the SAP HANA in-memory analysis appliance. The results of these tests confirmed that 336-million items of data acquired from approximately 13,000 taxis and by Zenryoku Annai! subscribers (who have given permission for their locations to be used) could be analyzed in just over one second. In other words, what would normally take several minutes in a RDBMS (relational database management system) environment could be completed in less than a second. In real terms, the search speed was increased by a factor of more than 1,800. In both cases of Nongfu Spring and Nomura Research Institute, the potential of in-memory database is not just in streamlining operations and cutting down cost but also in introducing new types of services that could not have otherwise been made available to customers. Taking away the mechanical functions of database management and analytics to make it more adaptable to real-time use makes all the difference between remaining a mediocre provider of services to becoming a thriving and competitive company in any industry. It is of utmost belief that in-memory database will create a huge impact on traditional data management. What remains to be seen is how receptive industries would be to having such a new way of looking at their data. As a company, SAP will ensure that new technologies to improve database management are utilized to their fullest while keeping customers happy. The author is the vice president for database and technology at SAP Southeast Asia]]>

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