Data-Driven DSS development is a company-wide effort and requires many resources, including people, money, and technologies. Building an effective enterprise-wide DSS is hard work. Providing company-wide decision support requires creating a sophisticated information technology architecture. Creating such architecture requires a mix of people skills, technologies, and managerial procedures that are often difficult to find and implement. For example, storing a large quantity of decision support data is likely to require purchasing the latest hardware and software. Most companies need to purchase high-end servers with multiple processors, advanced database systems, and very large capacity storage units. Some companies need to expand and improve their network infrastructures.
MIS staff needs to develop detailed procedures to manage the flow of data from the transaction databases to the data store. Data flow control includes data extraction, validation, and integration. To implement and support the DSS architecture we also need people with advanced database design and data management skills.
How can managers increase the chances of completing a successful Data-Driven DSS project? A number of authors have suggested some lessons they have learned from implementing their Data Warehouse and DSS projects. After evaluating the suggestions, the following recommendations seem reasonable.
The first recommendation that makes sense is to identify an influential project champion. The project champion must be a senior manager. A project champion can deal with political issues and help insure that everyone realizes they are part of a DSS team. All managers need to stay focused on a company’s decision support development goals.
Second, managers should be prepared for technology shortfalls. Technology problems are inevitable with Data-Driven DSS projects. Many times the technology to accomplish some of the desired DSS tasks is not currently available or is not easily implemented. Unforeseen problems and frustrations will occur. Building any DSS, whether it is Data-Driven or Model-Driven, requires patience and perseverance.
A third recommendation is to tell everyone as much as you can about the costs of creating and using the proposed Data-Driven DSS. Managers need to know how much it costs to develop, access and analyze DSS data.
Next, be sure to invest in training. Set aside adequate resources, both time and money, so users can learn to access and manipulate the data in the new Data-Driven DSS. From the start, get users in the habit of "testing" complex questions or queries.
Finally, market and promote the new Data-Driven Decision Support System to the managers you want to use the system.
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