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Self-Serve Data Prep for Machine Learning

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Machine learning

By Alex Ziko, Data Analyst

Machine learning is fast becoming a popular form of business analytics. With advancements in data collection, as well as innovative tools that allow ease of access to that data, robust models can cultivate information on newly minted datasets that help business analysts make strategic decisions based on the data that they have. Machine learning consists of multiple computer models that train themselves with datasets. The objective of a machine learning model is to predict a future outcome.

Machine learning concepts can quickly become complicated, especially when you start to get into the complexities of how they work. However, a simple thing to understand is that there are two kinds of machine learning models – supervised and unsupervised. Supervised machine learning means that the data you use to train your model is labeled. You know what the data points are and how they differ from one another. Unsupervised machine learning means that your data points are not labeled. Unsupervised learning requires the model to organize and identify patterns for you and then tells you how they relate (through data clustering), while supervised learning takes what is already known and helps classify it further. Supervised learning requires more attention since the data must be labeled and accurate. That is where easy and reliable data prep comes in.

Make Data Prep Easy

It has been said that data prep takes up about 80% of a data scientist’s time and is the least enjoyable part of the job. There are a number of reasons for this and a few major hurdles that must be cleared. First, select the data. Is the data coming from multiple flat files that need to be pushed to a server, like SQL? Are you working with multiple servers of information, but need to blend them together to access all the data points needed to train your model? Do you need to derive new data points from existing ones? After these questions are answered, the next hurdle is processing and data cleaning. This is a common headache for anyone who has been tasked with data work. Issues of mismatched data types, removing values, identifying missing values, duplicating or anonymizing values, or removing or identifying outliers all need to be taken into account – and these are just the most common data prep tasks.

Move to Machine Learning

Once your data is cleaned there is still more work that needs to be done with scaling data points (like creating conversion ratios), or splitting data into multiple columns, as well as creating aggregations. If you are a player in the machine learning data prep game, then you usually have a utility belt with scripts and macros that took hours to build and need to be carefully orchestrated to use. If you are new to the machine learning data prep game, then my explanation above probably has you feeling overwhelmed and looking for the exit signs. Luckily, Rapid Insight has a self-serve data prep tool that can do all of the work I mentioned, plus more strategies to ease pain points. Data prep for machine learning doesn’t need to be a headache. Get accurate data prep results, customize your machine learning training data, and fast-track your business innovation.

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