5 Harvard-Recommended Ways to Improve Data Management for Your Business
Machine learning is what dreams are made of. It can be used to make predictions, uncover insights, and automate processes and still has a host of untapped potential. Today, machine learning makes the perfect power-up for enterprises. However, it's not a simple matter of plug and go (if only). Instead, enterprises must prepare ahead of their machine learning endeavours. In particular, they must prepare their data.
Frankly, you can forget about any of your machine learning initiatives if you haven't prepared your data/don't have any plans to. Without accurate data, how can your machine learning tool(s) make accurate predictions?
Dealing with your data
Thus, it's demonstrably important for enterprises to get their preparation underway. Firstly, you must choose the data set you will be working with. Sounds a bit obvious, right? Not quite. For many, it's tempting to chuck all their data into the mixing bowl, but why do so when you don't even need all those ingredients? Thus, it's important to zero in on only the data that you need. Honestly, it's better to do it in dribs and drabs than using everything available.
The next step is data cleaning. No doubt, there'll be a chorus of groans from data scientists who toil endlessly over data cleaning, but needs must. Once you have done so, you know that your data is in a condition that you can work with.
Then, you must begin the transformation process. At current, your data will likely have conflicting values, quantities, and weight. Therefore, the data must be scaled to a more uniform format. Not only that, see what can be aggregated by common attribute. Therefore, you can work more meaningfully towards the problem you are trying to solve.
Selection, cleansing, and transformation are the make-or-breaks of your data preparation. However, be sure to explore any other avenues and tools you can hone in on. These will all work hand-in-hand to give you machine learning that you can truly rely on.
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