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We’re in the midst of a data revolution. The volume of digital data created within the next five years will total twice the amount produced so far — and unstructured data will define this new era of digital experiences. 

Unstructured data — information that doesn’t follow conventional models or fit into structured database formats — represents more than 80% of all new enterprise data. To prepare for this shift, companies are finding innovative ways to manage, analyze and maximize the use of data in everything from business analytics to artificial intelligence (AI). But decision-makers are also running into an age-old problem: How do you maintain and improve the quality of massive, unwieldy datasets?

With machine learning (ML), that’s how. Advancements in ML technology now enable organizations to efficiently process unstructured data and improve quality assurance efforts. With a data revolution happening all around us, where does your company fall? Are you saddled with valuable, yet unmanageable datasets — or are you using data to propel your business into the future?

There’s no disputing the value of accurate, timely and consistent data for modern enterprises — it’s as vital as cloud computing and digital apps. Despite this reality, however, poor data quality still costs companies an average of $13 million annually. 

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To navigate data issues, you may apply statistical methods to measure data shapes, which enables your data teams to track variability, weed out outliers, and reel in data drift. Statistics-based controls remain valuable to judge data quality and determine how and when you should turn to datasets before making critical decisions. While effective, this statistical approach is typically reserved for structured datasets, which lend themselves to objective, quantitative measurements.

But what about data that doesn’t fit neatly into Microsoft Excel or Google Sheets, including: 

When these types of unstructured data are at play, it’s easy for incomplete or inaccurate information to slip into models. When errors go unnoticed, data issues accumulate and wreak havoc on everything from quarterly reports to forecasting projections. A simple copy and paste approach from structured data to unstructured data isn’t enough — and can actually make matters much worse for your business. 

The common adage, “garbage in, garbage out,” is highly applicable in unstructured datasets. Maybe it’s time to trash your current data approach. 

When considering solutions for unstructured data, ML should be at the top of your list. That’s because ML can analyze massive datasets and quickly find patterns among the clutter — and with the right training, ML models can learn to interpret, organize and classify unstructured data types in any number of forms. 

For example, an ML model can learn to recommend rules for data profiling, cleansing and standardization — making efforts more efficient and precise in industries like healthcare and insurance. Likewise, ML programs can identify and classify text data by topic or sentiment in unstructured feeds, such as those on social media or within email records.

As you improve your data quality efforts through ML, keep in mind a few key do’s and don’ts: 

Your unstructured data is a treasure trove for new opportunities and insights. Yet only 18% of organizations currently take advantage of their unstructured data — and data quality is one of the top factors holding more businesses back. 

As unstructured data becomes more prevalent and more pertinent to everyday business decisions and operations, ML-based quality controls provide much-needed assurance that your data is relevant, accurate, and useful. And when you aren’t hung up on data quality, you can focus on using data to drive your business forward.

Just think about the possibilities that arise when you get your data under control — or better yet, let ML take care of the work for you.

Edgar Honing is senior solutions architect at AHEAD.

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