What increases success for data-based decision support?

by Daniel J. Power

Data-based decision support development is a company-wide effort and requires many resources, including people, money, and technologies. Data-based decision making or data-driven decision making refers to an ongoing process of collecting and analyzing different types of data to aid in decision making. Building an effective enterprise-wide decision support capability is challenging and hard work. Providing company-wide decision support requires creating a sophisticated information technology architecture of computing assets.

Creating and managing a modern computing architecture requires a mix of people skills, technologies, and managerial procedures that are often difficult to assemble 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. Also, some companies must expand and improve the network infrastructures.

MIS/IT 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 decision support 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 decision support analytics project? A number of authors have suggested some lessons learned from implementing Data Warehouse and Decision Support Systems (DSS) projects. After evaluating the suggestions, the following recommendations seem reasonable:

The first recommendation 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 an analytics and decision support team. 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 decision support projects. Many times the technology to accomplish some of the desired decision support tasks is not currently available or is not easily implemented. Unforeseen problems and frustrations will occur. Building any decision support capability, 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 decision support capability. Managers need to know how much it costs to develop, access and analyze decision support data.

Fourth, 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 system. From the start, get users in the habit of "testing" complex questions or queries.

Finally, market and promote the new decision support capability to the managers you want to use the system.

The decision support architecture should be documented with a detailed description of the various information processing assets including hardware, processes and software. Decision support involves ongoing innovation and refinement. As decisions become more complex and as data increases in quantity and variety, systems must be refined and enhanced.

Last update: 2017-07-09 07:56
Author: Daniel Power

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