The Backbone of Machine Learning: Data

The Backbone of Machine Learning: Data

The Backbone of Machine Learning: Data

Sumit Sarkar, Chief Data Evangelist at Progress shares insights on how brands can incorporate machine learning in their digital strategy and source the right data from it to drive business results

Over the past several years, machine learning has started permeating our daily lives, even if people don’t realize it. Sure, they may be familiar with IBM Watson winning “Jeopardy!” but the technology is far more pervasive than that and crucial for any digital business. So now, the question isn’t “will machine learning impact marketing?” Instead, it’s “how does my marketing department need to adapt to accommodate it?”

Many think that all they have to do is start running algorithms for their business to reap the benefits of machine learning, especially when considering how storage and analysis costs have fallen in recent years. But while marketing departments are able to discover trends using years of customer-generated and third-party data sources, machine learning is only as successful as the quality of the data. So before you dive in, there are two critical steps in ensuring your digital business is ready for machine learning: understanding where your data comes from and preparing that data for analysis.

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Where Does Your Data Come From?

Data sources are divided into three main categories:

Now that you have a better idea of where your data comes from, it’s time to talk data preparation and ingestion. Despite all the buzz that seems to imply it’s a new trend, data preparation has been around for almost as long as people have been collecting and using data to gain insights. Data prep is simply the process of pooling data together and ensuring it’s ready for analysis.


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