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Guidance and results for the data-rich, yet information-poor.

Can Data Mining Be Automated?

automatedComputers relieve us of so many tedious tasks that it can be tempting to think they may do just about everything for us one day. Any task that relies on formulas to compute or decipher seems to lend itself to technology. However, could a process like data mining one day be handled completely automatically? The short answer is ‘no’.

Although collection of data through electronic means is absolutely viable and, with most big data, absolutely essential, the actual mining of data requires clear direction and individual understanding. For instance, as we know, the very first step in mining data is to determine, not only the challenge or problem to be solved, but most crucially, the question to be answered in order to solve it. This, of course, requires an individual to have a clear business understanding. Oftentimes, determining the question may require a data miner to consult business experts in order to define the clear question.

As described by SmartDataCollective.com, what comes next is “understanding the data, the way they have been collected, their particularities… [and deriving] knowledge useful for preparing the data.” Clearly, as this is a task centered on internalizing data and its absolute meaning, a machine is not up to the task, no matter how advanced or orderly.

Data preparation for mining and analytics can, to a degree, be automated. Even in this case, when raw data is combed through to leave only relevant, useful information, outliers and missing fields could easily stump a computer. Detection of issues may be straight-forward enough, but deciding how to assess and move forward is a task for none but a data scientist. Again, the core to all of the decision making within data mining as a whole is genuine business understanding of the problem at hand. How can we expect a computer to manage this task?

Assuming that “that there are enough data, that the choice of the algorithm is not business dependent (which is usually not the case) and that the evaluation criterion is known,” SmartDataCollective.com tells us, “electing a data mining algorithm and tuning its parameters [through careful testing]  is certainly the task that can be the most easily automated.” What follows, however, is the process of evaluating the results and then implementing a solution to the root question, once again requiring business knowledge and deductive reasoning.

It is clear that data mining in its simplest forms can be automated, but for a business owner who intends to make decisions affecting the future of his or her company, a deeper understanding of what the data means is essential. For an amateur data scientist, this can seem a daunting task. Fortunately, The Modeling Agency offers a free webinar to help you grapple the problem. If you’re ready to dive into this exciting challenge further, perhaps it’s time that you learn about TMA’s intensive Training Seminars.

 

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