Solve the problem of unstructured data with machine learning

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There is a data revolution going on. This new era of digital experiences will be defined by the amount of data created within the next five years, as well as the amount of data produced.

Unstructured data is information that doesn't follow conventional models or fit into structured database formats. Companies are finding innovative ways to manage, analyze and maximize the use of data to prepare for this shift. There is an old problem of how to maintain and improve the quality of massive, unwieldy datasets.

That is possible with machine learning. Quality assurance efforts can now be improved thanks to the advancement in machine learning technology. Where does your company fall? Do you use data to propel your business into the future or are you saddled with too much data?

Unstructured data requires more than a copy and paste

Accurate, timely and consistent data for modern enterprises is as important as cloud computing and digital apps. Poor data quality costs companies an average of $13 million per year.

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Statistical methods can be used to measure data shapes, which can be used to track variability, weed out outliers, and reel in data drift. Statistics-based controls can still be useful to judge data quality and determine when to turn to data. This approach is usually reserved for structured datasets, which lend themselves to objective, quantitative measurement.

What about data that doesn't fit in a spreadsheet?

• Internet of things (IoT): Sensor data, ticker data and log data 
• Multimedia: Photos, audio and videos
• Rich media: Geospatial data, satellite imagery, weather data and surveillance data
• Documents: Word processing documents, spreadsheets, presentations, emails and communications data

It is easy for incomplete or inaccurate data to slip into models when these types of data are present. Data issues accumulate and wreak havoc when errors are not noticed. A simple copy and paste approach isn't enough, and can actually make matters worse for your business

The adage, "garbage in, garbage out," is very applicable in the data. Maybe it's time to change your approach.

The do’s and don’ts of applying ML to data quality assurance

Machine Learning should be at the top of your list. With the right training, ML models can learn to interpret, organize and classify any type of data.

A model that learns to recommend rules for data profiling, cleansing and standardization can make efforts more efficient and precise. Text data can be identified and categorized by topic or sentiment in unstructured feeds, such as those on social media.

There are a few do's and don'ts when it comes to improving your data quality efforts.

• Do automate: Manual data operations like data decoupling and correction are tedious and time-consuming. They’re also increasingly outdated tasks given today’s automation capabilities, which can take on mundane, routine operations and free up your data team to focus on more important, productive efforts. Incorporate automation as part of your data pipeline — just make sure you have standardized operating procedures and governance models in place to encourage streamlined and predictable processes around any automated activities. 

• Don’t ignore human oversight: The intricate nature of data will always require a level of expertise and context only humans can provide, structured or unstructured. While ML and other digital solutions certainly aid your data team, don’t rely on technology alone. Instead, empower your team to leverage technology while maintaining regular oversight of individual data processes. This balance corrects any data errors that get past your technology measures. From there, you can retrain your models based on those discrepancies. 

• Do detect root causes: When anomalies or other data errors pop up, it’s often not a singular event. Ignoring deeper problems with collecting and analyzing data puts your business at risk of pervasive quality issues across your entire data pipeline. Even the best ML programs won’t be able to solve errors generated upstream — again, selective human intervention shores up your overall data processes and prevents major errors.

• Don’t assume quality: To analyze data quality long term, find a way to measure unstructured data qualitatively rather than making assumptions about data shapes. You can create and test “what-if” scenarios to develop your own unique measurement approach, intended outputs and parameters. Running experiments with your data provides a definitive way to calculate its quality and performance, and you can automate the measurement of your data quality itself. This step ensures quality controls are always on and act as a fundamental feature of your data ingest pipeline, never an afterthought.

Your data is a great place to find new information. One of the top factors holding businesses back is the quality of their data.

Quality controls based on machine learning give assurance that your data is relevant, accurate, and useful as it becomes more prevalent. You can use data to drive your business if you aren't focused on data quality.

When you get your data under control, let the machine learning take care of the work for you.

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