Importance of Data Accuracy

Managing data and obtaining useful information can be daunting challenges. Faced with the potential for inaccurate models and erroneous results, it is important to work with standardized rules that help develop the best possible models to better manage data in a meaningful way. Data forms are as numerous as the transactions they represent. Data can be a list of numbers, text characters, locations, product categories, a specific time, financial histories, travel patterns, or any other figure used to represent an event. For nearly any type of valid data, Data Mining can be successfully employed to uncover existing patterns to provide insight and offer predictable indicators for future transactions.  Asking the right questions, however, is the key to discovering meaningful results and useful guidance.

data management processValidity of the results of any analysis technique is essential to provide a clear picture for future projections. Random errors can skew results in unpredictable ways, alter the final analysis and cause significant problems in prediction validity. In any set of complex data, there can be values that are outside the normal criteria that, if not identified, can exaggerate results and provide useless and even damaging results. For this reason, cross-validation and standardized data-management techniques should be employed.

Accuracy and validity begin with a unified approach to data management. Specific standards and practices for data mining professionals have been promoted at professional organizations such as the Association for Computing Machinery's special interest group, Knowledge Discovery in Data, and the IEEE Computer Society's Technical Committee on Data Engineering that publishes "IEEE Transactions on Knowledge and Data Engineering."  Today, professional data- mining specialists must understand rigorous criteria and methods such as these groups identify, in order to successfully bridge the gap between anecdotal summaries and useful analysis. Specific standards govern classic analysis techniques of general statistics clustering through to more sophisticated techniques like decision trees, regression analysis, and network frameworks. For this reason, it is vitally important to partner with knowledgeable data-mining professionals dedicated to accurate, meaningful analysis tailored to your specific needs.