Solving the problem of unstructured data with machine learning

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We are in the midst of a data revolution. The volume of digital data created over the next five years will total double the amount produced so far – and unstructured data will define this new era of digital experiences.

Unstructured data (information that does not follow conventional patterns or fit into structured database formats) accounts for more than 80% of all new business data. To prepare for this change, companies are finding innovative ways to manage, analyze and maximize the use of data in everything from business analytics to artificial intelligence (AI). But policymakers also face an age-old problem: how to maintain and improve the quality of massive, unwieldy datasets?

With machine learning (ML), that’s how it is. Advances in ML technology now allow organizations to efficiently process unstructured data and improve quality assurance efforts. With a data revolution happening all around us, where does your business fit in? Are you struggling with valuable but unmanageable datasets, or are you using data to propel your business into the future?

Unstructured Data Requires More Than Copy-Paste

It’s undeniable that the value of accurate, timely, and consistent data to modern businesses is as vital as cloud computing and digital applications. Despite this reality, poor data quality still costs businesses an average of $13 million per year.

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To solve data problems, you can apply statistical methods to measure data shapes, allowing your data teams to track variability, eliminate outliers, and catch data drift. Statistics-based checks remain valuable for judging data quality and determining how and when you should turn to datasets before making critical decisions. Although effective, this statistical approach is generally reserved for structured data sets, which lend themselves to objective quantitative measurements.

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

  • Internet of Things (IoT): sensor data, ticker data, and log data
  • Multimedia: Photos, audio and videos
  • Rich media: geospatial data, satellite imagery, meteorological data and monitoring data
  • Documents: word processing documents, spreadsheets, presentations, emails and communication data

When these types of unstructured data are in play, it’s easy for incomplete or inaccurate information to creep into the models. When errors go unnoticed, data problems accumulate and wreak havoc on everything from quarterly reports to forecast projections. A simple copy-and-paste approach from structured data to unstructured data is not enough and can actually make things worse for your business.

The common adage, “garbage in, garbage out”, applies perfectly to unstructured datasets. It may be time to trash your current approach to data.

The Do’s and Don’ts of Applying ML to Data Quality Assurance

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

For example, an ML model can learn to recommend rules for profiling, cleaning, and normalizing data, making efforts more efficient and accurate in industries like healthcare and insurance. Similarly, ML programs can identify and classify textual data by topic or sentiment in unstructured streams, such as those on social media or in email records.

When improving your data quality efforts with ML, keep a few do’s and don’ts in mind:

  • Automate: Manual data operations such as decoupling and data correction are cumbersome and time-consuming. They’re also increasingly obsolete tasks given today’s automation capabilities, which can take over mundane, routine operations and free up your data team to focus on productive efforts. more important. Integrate automation into your data pipeline. Just make sure you have standardized operating procedures and governance models in place to encourage streamlined and predictable processes around all automated activities.
  • Don’t Ignore Human Oversight: The complex nature of data will always require a level of expertise and context that only humans can provide, structured or unstructured. While ML and other digital solutions certainly help your data team, don’t rely solely on technology. Instead, empower your team to take advantage of technology while maintaining regular monitoring of individual data processes. This balance corrects any data errors that exceed your technology measurements. From there, you can retrain your models based on these deviations.
  • Detect root causes: When anomalies or other data errors appear, it is often not a singular event. Ignoring deeper issues with data collection and analysis exposes your business to widespread quality issues across your entire data pipeline. Even the best ML programs won’t be able to resolve errors generated upstream. Again, selective human intervention strengthens your overall data processes and avoids major errors.
  • Don’t assume quality: To analyze long-term data quality, find a way to qualitatively measure unstructured data rather than making assumptions about the shape of the data. You can create and test “what if” scenarios to develop your own unique measurement approach, expected outputs and parameters. Running experiments with your data provides a definitive way to calculate its quality and performance, and you can automate measuring the quality of your data itself. This step ensures that quality checks are always enabled and act as a fundamental feature of your data ingestion pipeline, never an afterthought.

Your unstructured data is a treasure trove for new opportunities and insights. Yet only 18% of organizations are currently leveraging their unstructured data, and data quality is one of the biggest factors holding back more businesses.

As unstructured data becomes more pervasive and more relevant to day-to-day business decisions and operations, ML-based quality checks provide much-needed assurance that your data is relevant, accurate, and useful. And when you’re not obsessed with data quality, you can focus on using data to drive your business forward.

Just think about the possibilities that arise when you have control over your data – or better yet, let ML do the work for you.

Edgar Honing is a Senior Solutions Architect at AHEAD.

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