Any data scientist will tell you – data cleaning is often the most important step in machine learning.Įven in the most basic quantitative data analysis, if you have hundreds of records and data sets, but much of it is irrelevant or just noise, your resulting analysis will probably have no relationship whatsoever to your actual needs.

In fact, better data is even more important than more powerful algorithms.

In machine learning, for example, if you build or train a model based on bad records, the resulting machine learning model will provide poor predictions. This goes for both quantitative (structured) and qualitative (unstructured) data.Įssentially, it makes more sense to invest $1 in prevention, than to spend $10 on correction or $100 on fixing a problem after failure. The more bad data you use, and the longer you use it, the more it will cost your company – both in dollars and wasted time. No one wants to be making “trash” data-driven decisions, which could affect your company’s trajectory for years. Raúl Garreta, CEO and Co-Founder of MonkeyLearn, says “If your downstream process receives garbage as input data, the quality of your results will also be bad”.īasically, if you work with dirty data, not only will you not get the most accurate results possible, but they may be skewed to the point that they’re actually detrimental. “Dirty” data can actually do your business more harm than good. But that only really works when you use clean data from the outset. We often hear about the power of data and the need for data-driven decision-making in business. Successful data cleaning measures will ensure that your analysis results are accurate and consistent.

And certain tools can take a lot of the pain out of the procedure. There are a number of different data cleaning techniques you can use, depending on the type of data at your disposal and the type of analysis you wish to do.ĭata cleaning can be a tedious process, but it’s absolutely necessary to get proper results and truly great insights. This way, you will analyze only relevant data, and your results will be more accurate. Data cleaning (also known as data cleansing or data scrubbing) is the process of correcting or removing corrupt, incorrect, or unnecessary data from a data set (or group of datasets) before data analysis.

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