Solve the Problem of Unstructured Data with Machine Learning
This article was first published by VentureBeat – see the original article here. 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?
Unstructured Data Requires More Than a Copy & PasteThere’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. 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:
- 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
The Do’s & Don’ts of Applying ML to Data Quality AssuranceWhen 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:
- 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.