Business Intelligence Unlike its Military counterpart, Business Intelligence is not an oxymoron. There are many examples of successful implementations of Business Intelligence despite the challenges. This paper explores the purposes for and the complexities of data processing systems that are designed to provide tools for top managers in their task of wading through swamps of unrelated information as they hone in on supports for taking critical business decisions.

A possible confusion of terms comes out of the comparison between Business Intelligence (BI) and Competitive Intelligence (CI). Although it sounds like something that should be conducted at night by operatives in trench coats and hats, CI is an ethical and legal business practice. It is defined as the routine collection and analysis of information about competitors, market trends, new patents and technologies, and changing customer expectations. Although there is substantial overlap, the essential difference between the two activities is that BI is based on information drawn from within the company, whereas CI is knowledge about the outside world in which the company operates. Data experts argue that a company should use both approaches to attempt to assess its position in the market, its customers' demands, and its products' performance relative to the company's business goals and strategies. (Rosencrance, 2011)

Looking back to 2003, Shaku Atre laid out the case for BI systems and provided guidance to help implementers avoid the most common pitfalls. She reckoned that more than half of all BI systems would fail to provide the benefits that were promised at the outset. She visualized that the most common reason for failure would be that IT project managers would not recognize that BI must be a constantly evolving architecture that encompasses the company's vision and strategy and points toward an alignment of an organization's operations with its strategic business goals. (Atre, 2003)

Business Intelligence systems are enterprise wide in their scope and, by their very nature, must cross organizational boundaries. The key to success is to realize that customers and markets, not product managers and manufacturing plants, must be the engines of the business. With top management sponsorship and support, BI demands unusual collaboration among departments of an organization. It also requires integration of proven knowledge about market conditions, individual customers, competitors, vendors, partners, employees and products throughout a hierarchy of levels. (Atre, 2003)

In a more recent report, Patrick Meehan of Gartner estimated that 70 to 80% of all corporate BI projects are failures. According to his research, the failures occur because of poor communication between IT and the business. More specifically, he cited the failure to think about the real needs of the business and to ask the right questions. He observed that IT professionals must stop approaching BI as an engineering solution. Rather, he called for a recognition that they are in the information and communication business. (Meehan, 2011)

EMC Consulting has reported that despite enormous investments by IT departments in the development of BI systems, they have not been successful in getting their business users to engage fully in their use. The central asset of any BI system is the data warehouse, which is a large online depository that brings together an ensemble of various types of data: structured transaction data and unstructured documents (such as a report like this.) The data warehouse must be designed and populated with equipment to satisfy a high volume of read requests of large files with good performance (minimal noticeable delay at the end user work station.) According to EMC's research, many users look to the BI system to access data from the central warehouse and then download it into an Excel spreadsheet for analysis. This indicates a general distrust of the validity of the data and/or the built-in BI analytical tools. Most likely, the reason for this condition is a classic case of technology for technology's sake, and a failure to meet the end-users requirements. The solution to this conundrum is to involve top management and all the end users in the design of the data warehouse and the tools to use it. (EMC, 2011)

Shaku Atre addressed what may be the single most important reason for BI failures. That is "dirty data" or the classic "garbage in -- garbage out." The most frequent problem is that the data warehouse pulls together data that were never expected to be used in concert. Since the data requirements usually extend into the external CI arena as well as multiple internal sources, data merge and standardization must be anticipated at the planning stage of the BI project. Also, millions of dollars are wasted on attempting to process inaccurate and inconsistent data. As a critical step in the planning process, a knowledgeable business analyst must rank the importance of the data elements...

Cleaning up the dirty data is a mandatory step in the BI application process; all critical data must be vetted and its lineage verified. (Atre, 2003)
Danny Bradbury, writing for Silicon.com, has identified the lack of focus on end user adoption as a glaring symptom of a failed implementation. He observes that making the dashboards intuitive for the end user is critical; keep it simple, don't boil the ocean. Starting small, with a pilot project, has many benefits, including user adoption as well as sponsorship from senior management. While starting small is recommended, the overall effort must be planned and controlled in a program management format. Let all stakeholders know of the general direction of the program and keep them informed of the progress. (Bradbury, 2009)

Scott Lowe, CIO of Westminster College, has shared his experiences on the "BI failure fault" question and endorsed the concept of a modest start. Lowe led the effort to implement new BI capabilities and improve business processes to help guide the college's decision making and focus their efforts on critical areas of improvement. Lowe believed that this was the key factor that justifies business intelligence efforts; that is, the ability to dig into weak areas with a surgical instrument rather than relying on gut instinct and scattershot methods. As with most small colleges with modest endowments, Westminster is dependent on tuition, and hitting every enrollment goal is incredibly important, especially in a down economy. The Enrollment Services Division has benefited tremendously from having critical information at their fingertips in the form of new reports and dashboard-style metrics to help that division meet their goals. Lowe followed up the Enrollment project with a deployment of mini-dashboards for the college president containing targeted key performance indicators installed on his desktop. As to the question of whose responsibility it is to make sure that the BI system is implemented correctly and that the data are accurate, Lowe says, everyone's. Business Intelligence is an organization-wide effort most often led by the CIO. However, it's the job of the CEO/President and the senior management team to make BI an organizational priority and to put resources behind it. Lowe was fortunate to gain this support from the college management team and it resulted in a successful implementation. (Lowe, 2009)

The primary goal of this paper is to explore the position of SAP AG, the German business application software giant in the field of BI. SAP's products focus on Enterprise Resource Planning (ERP). The company's main product is SAP ERP, one of the five enterprise applications in SAP's Business Suite. The other four applications are:

Customer Relationship Management (CRM) -- helps companies acquire and retain customers, gain marketing and customer insight

Product Lifecycle Management (PLM) -- helps manufacturers with product-related information

Supply Chain Management (SCM) -- helps companies with the process of resourcing its manufacturing and service processes

Supplier Relationship Management (SRM) -- enables companies to procure from suppliers

In October 2007, SAP AG announced the friendly takeover of the French company Business Objects. This acquisition expanded SAP's Product Suite of Business Intelligence (BI) solutions and increased the customer installed base by 46,000 customers. Business Objects also offers consulting and education services to help customers deploy its business intelligence projects. Other Business Objects toolsets enable universes, and ready-written reports, to be stored centrally and made selectively available to communities of password-protected usernames. (SAP, 2011)

SAP published a paper on BI stressing the importance of having a strategy in place. The paper suggested that the following questions should be answered by a BI strategy:

Do we have sufficient information to make critical business decisions?

What capabilities are required to make information available and useful? What are the dependencies?

Are we using information to identify strategic and competitive advantages?

What should the high-level BI roadmap of initiatives look like?

What is our vision for information accessibility and usage?

What business decisions do we not have sufficient information to make?

What metrics should we use to manage the BI implementation and fulfillment of BI business goals?

What kind of governance model do we need to support BI initiatives?

How should we design the processes, applications, and organization to fulfill our BI vision?

What toolsets should we use to fulfill our BI vision?

SAP's position does not address the crux of the BI matter. Drawing conclusions from the available research, SAP should have suggested the…