In a recent post, we laid out an overview of machine learning: what it is and the various types that are used. Here, we’re delving deeper into exactly what impact machine learning has on Big Data, and how it solves common challenges that enterprises are encountering.
When discussing challenges that come with Big Data, we are usually referring to one of the three Vs:
Big Volume: too much data to manage
Big Velocity: the data is coming at you too fast, and you can’t keep up
Big Variety: the data comes at you from too many places, causing a data integration problem.
Tackling Big Variety
While there are plenty of existing solutions—such as MDM and ETL approaches—that are equipped to handle Big Volume and Big Velocity, they often fall short when it comes to managing the Big Variety challenge. This is because these solutions rely solely on deterministic rules which, although useful in a number of scenarios for data preparation and analysis, are only part of the solution. These approaches simple aren’t sufficient when it comes to the types of large-scale projects that enterprises need to tackle.
The reason is this: a human can easily write a certain number of rules to classify a small dataset and be confident that the results are accurate. Even a few thousand tables or records is theoretically manageable—although extremely slow and tedious—with a strict rules-based approach. But even then, by the time analysts are finished getting data ready to mine or model, the data is often already out of date.
In today’s world, most enterprises are dealing with sets of transactions that number upwards of 20 million. It’s therefore become nearly impossible for humans to write enough rules to handle all of the data, and unify or fix this data manually.
Enterprises need ways to quickly and efficiently make decisions based on hundreds of thousands of datasets stored across different regions and business units. This is where machine learning can help—by providing the scalability needed to tackle the volume, velocity, and variety of Big Data.
How Machine Learning Helps
In many organizations, the challenge of Big Variety is solved by data analysts. Data analysts are pulling data from a variety of sources—databases, data lakes, data files, and relevant information available on the web—to answer a particular question. Once they collect all this data, they have to perform data integration on the resulting datasets. This means that a data analyst’s time is largely spent integrating and cleaning dirty data before they can even begin analysis.
Machine learning helps enterprises manage the mapping, integration and transformation of many datasets into a common data model in a scalable way by:
Greatly reducing the time to add new sources of data
Enabling a small team to manage many data sources
Improving the quality of the data by letting subject matter experts do more
Machine learning is an important technology that is changing so much about our lives—from everyday tasks to how we work. When it comes to data analysis in particular, the sheer volume and variety of data enterprises are tasked with managing has now exceeded a level where humans can easily or manually unify data.
With machine learning, enterprises can unify datasets as they come in. And when algorithms are constantly matching and connecting incoming data to other available datasets, all business units have broader access to the enterprise-wide data asset. This results in faster, more consistent, and scalable analytics.