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Big Data: Equipment Asset Management Data Mining
The catalog of possible useful information that can be produced with even our bounded definition of data mining is very large. To date the limitation has been a combination of processing power limits and data source limits. Ongoing hardware advances and cost reductions have all but eliminated the financial barrier to the processing power limitations. The adoption of electronic information systems for everything from predictive equipment analysis to work management to inventory management have established a "raw" data source more than sufficient to support profitable data mining.
Data mining and the automation of the delivery of information derived from data mining discoveries can be a simple matter or it can be a painstaking and challenging project. Some very valuable deliverables can be created and automated in a day or less. Others may require weeks of struggle to gain access to and then tie crucial data from a variety of systems together. The number and diversity of the data sources and the degree to which key values are cleanly used in all data sources may indicate how difficult the data mining creation effort will be. However, surprises and opportunities to learn are not uncommon in these undertakings. Sometimes one must accept timely delivery of a useful product, even if it is not the complete fulfillment of the original goal, then work over time to achieve the ultimate decision support information.
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