Infs 5024 Enterprise Systems Using Sapm Case Study

In the case of real-time analysis dashboards have become very popular over the past five years they provide a view of key metrics to allow management by exception. Where post transaction data is being analyzed, data warehousing provides the ideal methodology for enhanced forecasting from the data. This also allows the ability to look for improvements in the supply chain, operations, and marketing to adjust processes and refine a message for marketing as part of a continuous improvement program.

Data management is vital to any business as this is a key tool to an organisations business improvement, as you can refer back to data, and compare them against benchmarks. Analysing data can provide evidence for possible future structure such as identify trends, as well as indicate where improvements can be made. However there are strict procedures to be followed when collecting and storing data.

SAP AG, which is the parent company of SAP America, was the world’s fifth largest software firm and the leading producer of real-time, integrated applications software for client-server computing. SAP’s pinnacle product R/3 helped them dominate the enterprise information systems segment of the client-server market. All of this was under the Company and Industry background section.

The state of affairs in the field of data warehousing and offers a variety of approaches to

Enterprise view of data is required to maximize the efficiency of the organization as a whole (csu, n.d.). The sharing of data and maintaining the transparency is achieved by integrating the data within the system. Integrating the data is a complex task as there is always certain information that is sensitive and it is not meant to be shared with each and every department or individual working within or outside the organization. Making enterprise data strategy demands administrators to understand that the data is critical and is an asset to the organization. Wayne Eckerson describes enterprise view of data as the enterprise data strategy built by the organization to have a successful growth rate plan. He also points out that only one in 10 organizations have such sort of strategy as most of them don’t put together the required soft skills that are needed to manage change incurred by the strategy and the investment for data management techniques and tools to certify the delivery of high quality and consistent data supported with business initiatives and strategies (Eckerson, 2011). Most of the organizations face a lot of difficulties in maintaining the data quality. The cost of data quality incurred is much higher and it sometimes contributes to forty percent of the problems related to IT in a corporation this problem occurs because of the inaccurate data (steria, 2012). One major issue when creating an enterprise view

Given the fragmentation, it is no surprise retailer face the challenge to manage marketing, sales, and customer service within the tradition stores, the web, and call centers (Wolter and Haselden, 2006). To conquer this difficulty, the retailer must find a way to leverage single version of customer data, create a holistic view of customer data, and harness the overflow of interactive data through social media(Wolter and Haselden, 2006).

Broadly, there are two types of Analytics i.e. Business/Cube Analytics and Predictive Analytics. Business Analytics is a traditional way where historical measures (revenue, profit, loss etc.) available are extracted, transformed, modeled and stored for analysis. It is about getting insights on events that already happened. E.g. Year-on-Year sales report. In the contrary, Predictive analysis applies data mining and statistics on large volume of data for Forecasting, projecting, and predicting. E.g. KFC giving combo offer based on prediction of consumer’s possible purchase behavior to increase overall sales.

Based on an assessment of the demo applications, tools and videos provided by SAP on their website for this solution, it is apparent that this is an excellent application or analytics, BI and KM tasks throughout enterprises. The ability to quickly analyze terabytes of data and gain insights and intelligence that would not otherwise have been possible makes Crystal Reports and the entire suite of applications very valuable for any business. The case studies of companies with complex value chains and operations that generate literally terabytes of data daily are finding that Crystal Solutions deliver exceptional value in finding patterns in data they would never have been able to find before (Curry, 1999). During difficult economic times the value of the many features and functions of SAP Crystal Solutions is accentuated by the exceptionally higher levels of risk that companies are dealing with (Whiting, 2007). The tools are very powerful and can easily be used for removing significant risk from any decision, while also providing a depth of insight and intelligence into any challenging decision. Combined with the knowledge management (KM) aspects of the SAP suite of analytics applications, it's

HPC’s needed to develop an intelligent business solution for the executives to have a good deal of up-front specific gathered information. They needed access anytime to quarter’s numbers. They also wanted real-time data; Up-to-minute reports, they also needed access on their PC, desktop or via the Web on mobile devices. The goal was to have data shared easily across various business units and to support the companies function, and expansion geographically. This would require a selection process. Honam information system looked over a number of different software, products and vendors. After reviewing HPC selected SAP BusinessObjects dashboards and SAP BusinessObjects Web intelligence as their choice to implement.

b) The Integrated Master Data Management Service is a cross functional initiative composed by business process

Real Time-Low Latency: Unlike other distributions for Hadoop, the MapR architecture is optimized for deployments that depend on high throughput, low latency, high reliability, and no additional administration to ensure production success and significantly lower enterprise data architecture costs. Get faster results on larger data sets to respond more quickly to more complete data. Achieve quicker application responsiveness for an enhanced user experience. Easily load and process high volumes and high velocities of incoming data. Get low 95th and 99th percentile latencies to ensure consistent performance without bottlenecks due to compactions/defragmentation. And get extreme database scalability with millions of columns across billions of rows on one trillion tables.

Abstract—SAP HANA (“High Performance Analytic Appliance”) is doubtlessly a hands’ down winner over Oracle. SAP created Hana for real-time analytics and applications. Oracle released Exadata Database Machine in 2010 to deliver the highest levels of database performance available. Though Oracle claimed that it wants to be the leading applications vendor in the world, it failed to openly accept SAP HANA as its competitor. While HANA is also an in-memory RDBMS like Exadata, it is more of a business platform. It allows customers to see their business in a new

As the market leader in enterprise application software, we help organizations of all sizes and industries combat the damaging effects of complexity, generate new opportunities for innovation and growth, and stay ahead of the competition. SAP© applications and services enable more than 282,000 customers to operate profitably, adapt continuously, and grow sustainably” (SAP SE, 2015).

A PureData system is bundled with server, database and storage unit into a single architectural system. PureData is upgraded by upgrading its hardware unlike other traditional databases wherein software is upgraded. PureData system is capable of providing rapid results claiming to be 10-100 times faster than Oracle and this is because of the PureData system’s asymmetric massively parallel processing (AMPP) architecture along with the efficient use of four level workload management feature it offers. Going through the paper an

SAP HANA is an in-memory technology platform that is deployable as an appliance or in the cloud. Its core is the SAP HANA database, which is built for high performance applications. It makes full use of the main memory and processing power provided by modern hardware. As relevant data is kept in main memory, read operations can run in main memory. SAP HANA is also designed to make full use of multi-core CPUs by parallelization of execution. Research