Estimates are that the digital capacity of this resource is growing at a rate of 30% annually, and yet for the most part few business interests are capable of handling such an opportunity (Cultureofperformance, 2010). Google and other forward-planning companies have identified this capacity as a resource explosion worthy of investing in for at least two recognizable reasons that are directly tied to its master plan for popular engagement (Payne, 2010)
Cindy McCann, Vice President of Investor Relations noted in her May 4, 2011 press release that share prices were on the rise, saying: "These are the strongest overall results we have reported in the past five years. Our solid execution is generating consistent cash flow, and with our long-term debt now fully repaid, we are considering other uses for our growing cash balance, including accelerating our growth, raising our dividend and repurchasing stock" (McCann, 2011)
Their use in operations and manufacturing planning and execution, including integration to Enterprise Resource Planning (ERP) systems, continues to gain greater use as BI tools can now be used in conjunction with lean manufacturing and Six Sigma quality initiatives (Subramoniam, Tounsi, Krishnankutty, 2009). This however is a recent development and one that has led researchers in the field of analytics and BI integration to ERP and fulfillment systems to label with the term process intelligence (Bosilj-Vuksic, Indihar-Stemberger, Kovacic, 2008)
This nascent area is one that seeks to capture quantitative information on the performance of a given series of complex processes and create more effective workflows using Business Process Re-engineering (BPR) techniques (Subramoniam, Tounsi, Krishnankutty, 2009). The origination point of process intelligence can be tied to the optimization of workflow management systems and the use of Business Process Management (BPM) and BPR techniques in conjunction with PERT-based optimization logic and operations management techniques (Fakas, Karakostas,1999)
There is an element of truth to that criticism, as often SAP and other enterprise software vendors will send in six to ten sales reps, professional services experts and process re-engineering teams to sell their services once an application has been purchased, all at an incremental cost to the user (Chussil, 2005). Organizations purchasing enterprise software often get mired in the minutiae of implementing the expensive software they bought and less on staying on track to solving the problems they had in the first place (Ariyachandra, Frolick, 2008)
Adoption was immediate, accurate and the company, within 120 days, was already seeing the results. This story illustrates a fundamental concept of any successful change management strategy, and that is executive leadership must be vested, completely committed to the change for it to be successful (Srica, 2008)
This paper analyzes the reasons why BI and analytics projects succeed or fail, and it often has little if anything to do with what an enterprise software tells a company during a sales cycle, providing insights into what SAP fails to tell customers when they purchase software. SAP Never Tells A Client That Aligning to a Clear Business Need or Objective is Critical For BI Applications to Deliver Value Ironically the need that often drives enterprises to seek out a BI, analytics or EPM solutions during the evaluation process take a secondary role relative to the system and process-based integration points that take the majority of time to complete (Werther, 1999)
Business Intelligence What SAP didn't tell you about creating and using business intelligence, and why Creating value through the use of information systems to gain critical insights into how a business is functioning, meeting the needs of customers or not, coordinating with suppliers and managing costs is one of the greatest values of Business intelligence (BI) systems. Intuitively, BI and analytics are seen as a means to significantly reduce risk while increasing the profitability of a business (Yeoh, Koronios, 2010)
Analytics and Business Intelligence Assessing the Impact of Analytics and Business Intelligence The pervasive adoption of analytics to mitigate risk has accelerated due to greater uncertainties in economic conditions, the accelerating pace of change in markets, and a reliance on quantified measurements of performance vs. qualitatively based (Hopkins, LaValle, Balboni, Shockley, Kruschwitz, 2010)
Business Intelligence Project Plan and Requirements for a Data Warehouse Requirements Analysis In defining the data warehouse requirements for the university data warehouse, the process areas most often relied on to serve the students, faculty, suppliers and broader community need to first be taken into account. The effectiveness of a data warehouse is directly related to the level of accuracy, clarity and precision it contributes to each role in an organization (Bara, Botha, Diaconita, Lungu, Velicanu, Velicanu, 2009)
$500 for the dashboard module, $2,500 for 10 user report and database starter package). Reasonably, a small business could be up and running with a premium software package for $5-10,000, saving a great deal of time, accountant and IT fees, all the while supporting the overall strategic goal of the company through information management (Peck, 2008)
A data base and statistical analysis over time of customer comments). Competitive intelligence is a key element in the marketing mix, in fact, so vital, that in many cases it is simply assumed (Bangs, 2002)
Second, consumers are increasingly wary of providing personal information about themselves over the Internet for fear of cybercrime, misuse of data and even companies purporting to be market research and collecting information for something entirely different. Thus, the trust and accuracy level diminishes on both sides when someone is not physically present -- even though the amount of data collected and the cost per collection is quite low (Lee, 2001, 12-14)
It is further argued that BI only really takes place when such "information" is generated. Analytics in the Modern Environment In the globalized world, many firms find it challenging, if not impossible, to effectively differentiate their products (Davenport, 2005)
However, once the scenarios that these systems are running in changes then can quickly become obsolete. The problem lies in determining when the systems are no longer effectively representing reality (Smild, 2011)
Data does not equal Information It has been recommended that the BI initiatives be broken down into smaller categories to better define the steps in the process. The three sub-categories are as follows (Staples, 2009): 1
This provided Vodafone to quickly assess, analyze and develop marketing plans based on the insights gained from analyzing their legacy and current customer data. Vodafone was able to successfully create a highly integrated EDW through the use of these tools and their aligning them to the most critical strategic information and customer needs they had (Kimball, 2008)
Teradata's EDW platform is specifically designed to ensure a high degree of integration to legacy analytics, CRM, service and pricing data (Solomon, 2005). It is also one of the most effective for cross-channel and multi-channel marketing analysis (Nayak, 2006)
Vodafone went with Teradata for their EDW platform. Teradata's EDW platform is specifically designed to ensure a high degree of integration to legacy analytics, CRM, service and pricing data (Solomon, 2005)
The widening use of BI enables greater performance. Integrating clinical and operational performance can empower Healthcare Providers and organizational executives to make informed, timely decisions based on accurate, information that can impact the financial viability of their units and organizations (Jackson, 2009)