Business intelligence combined with big data and analytics has shown it can have a transformative effect on how companies conduct their operations and strategic planning. However, with the power and capabilities of the technology comes a problem. It’s too easy for companies to focus too much on the tech and too little on the company’s issues.
Years ago, the problem used to have the nickname of “management by magazine.” Executives would read about the capabilities of the newest computerized function and then ask their CIOs why they weren’t using the latest wonder. At the same time, often technologists taken with their expertise, or vendors looking to make a buck, would press forward on the same front. The net result was to focus overly on the tool and not necessarily on the business problem.
Putting the tool first has the danger that companies will make decisions about software without understanding exactly what they need — or if any software might be an answer in search of a problem.
The same issue can appear when considering BI and data analysis tools. Author Scott Diehl, who is with a custom software developer, offered an example of a company concerned that low quality data would badly affect its ability to gain insights and to make decisions. The company then decides to buy software to improve data and, therefore BI, but in the process forgets to ask some basic questions.
Here are some of the issues that could develop:
- What are the quality standards in the company?
- Are there common data standards and definitions so that what appears to be similar data actually is?
- What about data that is used from outside of the company? What are the quality issues and definitions inherent in that material?
- Data for some analyses doesn’t reside in a central repository but rather on a departmental server or even on an individual computer. How will data quality management extend to these areas?
- Could mistakes be created when managers develop their own spreadsheets and reports without necessarily understanding the variety of issues with any data they use? For example, might they use a different definition for a specific data label other than the central one and, as a result, create misinterpretations?
- How will the company enforce compliance with central standards?
- Does the tool that is being considered work in a decentralized environment?
Before considering the purchase of any tool, make sure the analysis of business needs, requirements, and limitations is foremost.
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