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Big Data: Data Mining and the Business Process
The preceding discussion of data mining can be helpful in establishing a working equipment asset management environment driven by information and knowledge. However, the organization itself is a critical element to the success of such an undertaking. Very often the drive to delve into the valuable data stores of an organization comes from an analyst, an engineer or other technical person working on very specific objectives and with a limited view of the data required and the information deliverables to be created. Individuals in these situations often find themselves working on their own. They do not, or are not able, to gain the support of the information technologies / services organizations. In addition they often only have the support of the first funding signature above them and are completely unknown to the highest levels of management.
This situation is never a formula for success and long term return on investment. The development of the data mining process and distribution of the resulting information deliverables is dependent on the energy and skill of one or a few people who are challenged to work across the organizational boundaries to seek support and provide their services. Such projects often whither and fade away over time.
Clearly a high level champion for the concept of management by information is essential to success. The project does not have to be a major corporate initiative from the start. It can be a focused application of affordable technology and techniques to address a specific organizational need. It should however be done with the goal of eventually increasing the value of all of the corporate data sources through data quality monitoring and improvement.
Once the mandate to improve the corporate data assets is known by the collective organization a host of inexpensive means for ongoing data quality improvement can be developed and implemented. That assumes that there is a central focus that can act as the arbiter for what constitutes "data quality improvement". The significant challenge is establishing the means of clearly relating data in each data source to that in others. The improvement of the data accuracy by data owners (i.e. the people who collect and enter test or transactional data) is relatively straight forward. However, before they invest their time and energy there should be a determination that better accuracy for a particular data element will be of value to the company (e.g. updating the color of each asset in the CMMS asset catalog may not be of great value where as accurate model number and ratings information could be of great value).
Another aspect of creating a data mining effort is identifying the team that will be at the heart of the effort. A large team is not necessary, but a core focal point is. Especially in todays business environment existing staff have been stretched to or beyond their limits. To expect them to also, in their "spare time", be responsible for a data mining endeavor is really unrealistic. So, a core staff is needed. A core that is dedicated to the integration of as much of the corporate data assets as practical and the mining of the valuable information it contains.
Lastly, there is an aspect of the introduction of a data mining process that may not be in the forefront of everyones minds. That is the effort required to prepare the rest of the organization to be contributors to the quality data pool and to be recipients of improved information for decision support. People who have struggled all of their careers to get what should be the simplest of answers today are not always ready for a influx of targeted information that forces them to rethink their entire decision making process. They have been the keepers of very important assets for much of their careers and have made the best decisions possible with the information they had. Often that was not very much information. A publicity campaign is needed throughout the organization to let everyone know that a better process is coming. Those with well defined information needs that they would like supported should be encouraged to contact the core data mining group. They, as satisfied customers, can become the best sales people for the expansion of management by information that a company can have.
This point was emphasized in the research for this paper in a statement from a large insurance firm implementing data mining. One of their big surprises was the need to rebuild workflow and other processes to be able to make effective use of all the new information that data mining yields. The quote was "The ability to operationalize the use of derived data is not simple. In most cases, people have not had the type of information we're now able to supply."
So, be prepared not only to engage the IT / IS and engineering personnel if you set out to embrace data mining, as every company should, but to engage all of your staff to find ways to further improve the business process, based on having better decision support information available to everyone.
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