For decades machine learning (ML) was slow to evolve because of the complexity of emulating human thought and the difficulty in processing sufficiently large data sets. Today, though, thanks to major advances in software development, computing resources, and the availability of huge, rich data sets, an increasing number of organizations are investing heavily in machine learning.
ML has grown to such an extent that most consumers have come to rely on it for many services they use daily from search engines to recommended systems and social media platforms to name but a few. Likewise, organizations are using it in their client relationship management, communications, management, marketing efforts, and overall, in making better business decisions.
The thing is, successfully implementing machine learning solutions depends on data, and lots of it. One could basically say that data is the lifeblood of any machine learning model. And with huge amounts of data, comes data management. At its core, data management is about preparing data properly. If this fails, machine learning results will surely suffer.
So, why is it vital to prepare data properly and how is it done? This article looks at these questions in more detail and offers a short guide for organizations to get their data management on track.
When faced with a problem that needs solving, raw data is collected based on what the problem is and what prediction the machine learning algorithm will need to make. For example, making predictions on home prices would require a substantial amount of home sales data – including prices and a thorough set of attributes about each home sold.
Once collected, this data can’t be used as is, and the raw data will have to be changed before the organization can use it as a basis to make predictions. The three main reasons why this needs to happen is:
Given that most machine learning models are well established, well understood, and widely used, the key differentiation is the data that’s used to train it. This, ultimately, means that data preparation is crucial and can mean the difference between a successful implementation of a machine learning model or a total failure.
Keeping this in mind, there are some best practices when it comes to data management to make sure that the data is properly prepared, and that the best possible data is available for analysis or to be used for any specific algorithm.
Because many analytics problems are suited for self-learning algorithms, organizations often think that it’s as simple as choosing an algorithm and feeding it the data. In doing this they neglect the question whether the application of a specific algorithm is feasible. In practice, the data available will often dictate which algorithms are best suited for the application or use case. In other words, studying the data will often reveal which algorithm should be used.
Each algorithm requires a data set, its sources, and the frequency with which it will be updated. It’s important to keep in mind that these requirements will vary depending on the algorithm used. For example, where real time analytics are required, the algorithm may require live transactional or clickstream data. Likewise, predictive applications may need historical data to make predictions. In simple terms, it’s vital to assess what is feasible, considering the timelines and budget of the project. This will, ultimately, impact what data is used, how the data set is defined, and how it’s prepared.
Apart from determining what data will be used, the specific use case will also, to a large extent, dictate the preparation steps including the data collection, refinement, and delivery for production analytics. Once the right procedures have been established, procedures to deal with missing values, data profiling and quality measures will need to be established in order to assess false positives and data skew.
