| Marketing strategies often propel the BI decision-support initiatives at organizations because of the constant challenge to keep up with the competition and to retain market share. To a large degree, it is the marketing focus that drives the impetus for more knowledge about the business, in particular about its customers. Marketing strategies have had an impact on the evolution of decision-support systems since the early days of IT. Figure 4.1 shows the effect this evolution has had on increasing the decision-support value of customer-centric applications. -
Traditional decision-support systems focused on product- related operational processes of the organization. Decision-support capabilities were limited, and marketing efforts revolved around products, not customers. -
Customer information files were the first attempt to aggregate all customer-related data from dozens, if not hundreds, of disparate operational systems into one central file. Decision-support focus started to shift from products to customers. -
House-holding databases contained customer hierarchies in order to help business managers understand customer-to-customer relationships. These databases also contained organizational hierarchies in order to help business executives understand organizational and regional profitability. House-holding was the rudimentary precursor of customer relationship management (CRM). -
Data warehousing was the first ambitious undertaking of cross-organizational integration of data for decision-support purposes, such as sales reporting, key performance indicators, performance trend analysis, and so on. Due to the enormous effort involved, a wave of new tools started to flood the market, with extract/transform/load (ETL) and OLAP tools leading the pack. -
Customer relationship management focuses on customer-product (sales) relationships, as well as on customer service, customer buying behavior, and other knowledge about customers. The goal is to improve customer sales and services through personalization and mass customization. -
Business intelligence is a more holistic and sophisticated approach to cross-organizational decision-support needs. It uses data mining to gain hidden (nonexplicit) knowledge about customers, general market conditions, and competitive products. The goal is to "predict" the future by analyzing the present, thereby gaining a competitive edge. Figure 4.1. Increasing Decision-Support Value  Besides marketing, other departments in the organization are also keen to take advantage of today's technologies to solve their business needs. Such departments include finance, product management, portfolio management, customer service, engineering, and inventory management, to name a few. Interviewees for General Business Requirements Determining the general business needs of the organization requires interviewing individuals at every level of the organizational hierarchy, both on the business side and on the IT side. -
Business executives are the visionaries. They know which direction the organization should move and how to achieve new goals. They also know what the organization's business pains are. The business executives' requirements will be focused around strategic information from the BI decision-support environment. -
IT managers support the operational systems of the business areas. They know the deficiencies of these systems very well, and they are aware of the backlog for decision-support requirements. Their input can be helpful in identifying unfulfilled decision-support requirements and in determining how the BI application can improve the workload for IT. -
IT staff work directly with the business staff. The IT staff have firsthand knowledge of the unfulfilled requirements. They also know the technical skills of the business people with whom they work. This information will become valuable input for access and analysis tool selection. -
Line-of-business managers are responsible for the smooth operations of the organization. They focus on tactical decisions on a daily basis. Their requirements frequently include a mixture of strategic information and operational information. -
Subject matter experts are the senior business analysts with the 10,000- foot view of a department or a division. Sometimes they are the "power users"; other times they act as internal business consultants . In addition to having an overall business view, subject matter experts are usually very familiar with detailed operational data and can give a lot of insights into current data quality problems. Data Quality Requirements Data quality must be discussed with all interviewees. Questions to ask fall into three categories: existing data quality, desired data quality, and prioritization for data cleansing. -
Existing data quality: Different interviewees might have a different perspective of what is clean and what is not. They will also have a different perspective of what should be cleansed and what can remain "dirty." -
Desired data quality: Knowledge workers "in the trenches" typically have a higher tolerance for dirty data than business executives do, mainly because the knowledge workers have learned over the years how to decipher and interpret their bad data. -
Prioritization for data cleansing: Critical and important data must be sorted out from insignificant data. Business executives and line-of-business managers should make that decision. Data quality affects business people in all critical business areas of an organization, especially strategic decision- makers , business operations staff, customer support staff, and marketing staff. -
Strategic decision-makers: Probably more than anyone else, strategic decision-makers (the business executives of the organizations) are affected by poor-quality data. The decisions they make have an effect on the organizational lifeline. -
Business operations staff: Line-of-business managers and their staff could be much more efficient if they did not have to constantly resolve errors and waste time on rework . -
Customer support staff: The customer representatives and the sales force have direct contact with the organizations' customers. Poor-quality data puts a tremendous burden on this group to keep the customers satisfied and to prevent them from leaving. -
Marketing staff: Managers and knowledge workers in the marketing department do not want to waste millions of dollars by soliciting customers who are not worth soliciting, by sending marketing materials to customers who have moved, or by pursuing dissatisfied customers who have defected to the competition. Business Requirements Report The deliverable from a high-level business requirements activity is a report on the findings, issues, opportunities, recommendations, and next steps, as shown in Figure 4.2. -
Findings: The compilation of all requirements from the interviewees should be sorted by topic. Each finding should be associated with the interviewees and the interview dates. -
Issues: A separate list should highlight critical business issues, so that these issues can be addressed immediately. Not all business issues require a BI solution. -
Opportunities: Obvious business opportunities should also be extracted and highlighted from the findings. Again, not all business opportunities will translate into BI requirements. -
Recommendations: After analyzing the findings, issues, and opportunities, a list of recommendations should be added. These can be recommendations for correcting a problem on the existing systems or for building a new BI solution. -
Next steps: Certain recommended actions are more critical than others, and some recommended actions may depend on the completion of others. This section of the report should list the prioritized sequence of actions to be taken toward implementing a BI solution. Figure 4.2. Business Requirements Report Content  This report is not listed as a deliverable for a BI project because it occurs outside of an already approved BI project. It may be used in lieu of a business case assessment report, if the business case assessment is high-level and not specific to a BI application.  |
