Predictive Analytics: 5 Considerations for Data Miners

Data mining and analytics is the process of extracting and analysing data from systems databases to discover relationships; these patterns are then used to indicate future performance.

Simple as it may sound, the process goes beyond making tables in Microsoft Excel and representing numbers in graphs. The importance of data mining and analytics is best seen when the following considerations are kept in mind to execute the process –

1. Objective

Research and analysis are always good to have for reference but their true power can only be unleashed when they are brought in for a specific purpose. For effective data mining it is critical that the objective of the study is clear.

It is possible to conduct a large amount of analytics for a given set of data so in order to create the most predictive reports, knowing the objective is imperative.

2. Scope of functions / processes

Most businesses have functions that overlap with one another to ensure seamless flow. It is possible that the processes supporting the functions may not all be required for analysis. Sometimes when managers look at the extracted data, they begin to think of a lot more things that they can do with it, losing sight of the present objective.

Studies show that the best results are achieved when business managers target a specific activity or process, not by exhaustively examining every piece of data available.

3. Key parameters with frequency

As a subset of the scope, if possible, parameters of measurement and consideration should be defined so that data can be mined only for the specific items.

Also, it is important to keep in mind what frequency of data would best suit the subsequent analytics. Sometimes results taken over a short period can vary substantially from those taken over longer durations.

4. Tools and software

Depending on the complexity, impact and importance of the data mining and analytics exercise, it is worthwhile to consider the usage of third party tools. These are software applications that are available in the market and can quickly turn around with the analysis.

One needs to be cautious when opting for an off the shelf tool as significant customization may be required to achieve the desired results.

5. Facts and indicators

The use of data mining varies as per the audience. While some individuals choose to use the information as historical evidence to gauge how performance has been, others develop models which use the data to project future outcomes.

Data points have always proved to be the most concrete way to judge efficiency. When data is coupled with intelligent analytics, businesses can develop smart algorithms and models to prepare for future contingencies. As with any instrument, the importance of data mining and analytics relies heavily on the expertise of the individual employing it.

This post was first published on our CSO’s blog.

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Omar is MD & Chief Strategy Officer at Sandstorm Digital. His experience includes 10 years in traditional marketing and advertising in the Middle East and a further 10 years at two of the largest media agencies in the UK. Follow Omar on Twitter for updates on the latest in digital, branding, advertising and marketing.

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