By: Mohamed Ali, Consultant (CX), LTIMindtree

These are exciting times–that’s certainly one way of stating it. Almost every major company has prioritized retaining various types of data with the purpose of gathering insights. Thus, the importance of data mining cannot be underestimated. If we take the metaphor of gold, it can be said that data has almost become its equivalent. Now, as cheesy as that may sound, stick with me here, because this metaphor fits when we look at three main factors:

1. Data is constantly being mined for value.
2. When in its rawest form you can still see nuggets of value.
3. When refined, it provides personalized insights that can help achieve goals and objectives.

If we expand further on this premise, we can present the idea that the benefits and importance of internal data mining are numerous, but the insights from data mining really make the difference. This requires the appropriate data processing techniques to extract such insights. These are refined, concise points that essentially lead you in the right direction for improvement.

Strategy vs practicality

Now, as with anything in life, it’s all well and good hoarding all this data, but why are we doing it? If there is no underlying strategy behind data collection and data processing techniques, you may find yourself doing more harm than good by cluttering up the landscape–know what you want, why you want it, and what you need to do to get it. This is certainly the case with data processing. It’s important to have a robust strategy with clear goals and objectives. This lets everyone know what direction to go in when it comes to processing and utilizing data insights.

Another important factor is domain knowledge; we must have the right background knowledge or context to help guide the data strategy. It’s rarely beneficial nowadays to apply blanket principles across industries. If you have no domain knowledge, how will you know what questions to ask? Lack of understanding about the problem you’re trying to solve leaves the answer a distant dream. Accurate insights are best born out of raw data and the right mind to make the connection with what’s happening on the ground. If we summarize these last two points, we can end by suggesting that linking domain knowledge with a viable data strategy is a now pre-requisite.

So, where does this leave us?

In conclusion of the above points, organizations must focus on the importance of data mining and the right data processing technique. While many are treading down this path, the ones doing it successfully know that it’s important to have clear, concise objectives that are identified as catalysts for growth, efficiency, or whatever the goal is. You could mine the data, own the data, understand and put context to the data, but you must know the what and why of doing so before going down the rabbit hole.