Ask the Manufacturing Execution Systems (MES) Expert: Scheduling Assistance
 Asked on May 25 2007 4:29:54:000PM | Q | I have been reading about the importance of modeling in the BI space. Can you explain why a C-level executive or plant manager would care about how the data is structured? John O'Donnell, Royersford, PA |
| A | This is a great, and important, question. Conversations between IT professionals and senior business and operations managers on the subject of data management are a bit like explaining the facts of life to your children; it's difficult to explain, your audience is always uneasy about the topic, and after the conversation, the reaction is often one of indifference. Twenty years ago, companies had whole layers of middle management that sifted through a mass of data to find a nugget of knowledge. Those layers are eliminated now due to corporate downsizing and technology advances. Like it or not, business people have to become more familiar with data, because so much of the company's fortune depends on making the right decisions based on an accurate view of business activity. With that in mind, business executives should satisfy themselves that the right data and information are being collected to make business decisions. Much of it can be contextual in nature, and, used in the wrong way, may lead to incorrect conclusions. That is why it also must be clear in the manager's mind what the relationship of the data collected is to the analysis at hand. Data collected from different applications often also gives a view of business activity at different points of time. It is the manager's responsibility to determine whether this is acceptable. Finally, an executive must ask if the results of analysis tie out, and can be reconciled numerically, with transactional systems such as a general ledger or a sales database. Managers are becoming increasingly aware of the importance of data and how it is used. The growing interest in master data management by both IT and business executives is an example. I once saw a study that estimated that 30% of spreadsheet models used in business had errors in the logic, data, or the calculations used. If the truth is anywhere close to that, think of the risk you are running by not verifying the integrity of much more complex business analytical models. |
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