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Solve the issue of unstructured data with machine learning

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Were amid a data revolution. The quantity of digital data created next five years will total twice the total amount produced up to now and unstructured data will define this new era of digital experiences.

Unstructured data information that doesnt follow conventional models or match structured database formats represents a lot more than 80% of most new enterprise data. To get ready because of this shift, companies have found innovative methods to manage, analyze and maximize the usage of data in from business analytics to artificial intelligence (AI). But decision-makers may also be running into an age-old problem: How can you maintain and enhance the quality of massive, unwieldy datasets?

With machine learning (ML), thats how. Advancements in ML technology now enable organizations to efficiently process unstructured data and improve quality assurance efforts. With a data revolution happening all over, where does your organization fall? Are you currently saddled with valuable, yet unmanageable datasets or are you currently using data to propel your organization in to the future?

Unstructured data requires greater than a copy and paste

Theres no disputing the worthiness of accurate, timely and consistent data for modern enterprises its as vital as cloud computing and digital apps. Not surprisingly reality, however, poor data quality still costs companies typically $13 million annually.

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To navigate data issues, you might apply statistical solutions to measure data shapes, which enables your computer data teams to track variability, weed out outliers, and reel in data drift. Statistics-based controls remain valuable to guage data quality and regulate how and when you need to turn to datasets prior to making critical decisions. While effective, this statistical approach is normally reserved for structured datasets, which lend themselves to objective, quantitative measurements.

But think about data that doesnt 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

When these kinds of unstructured data are in play, its possible for incomplete or inaccurate information to slide into models. When errors go unnoticed, data issues accumulate and wreak havoc on from quarterly reports to forecasting projections. A straightforward copy and paste approach from structured data to unstructured data isnt enough and will make matters much worse for the business.

The normal adage, garbage in, garbage out, is highly applicable in unstructured datasets. Maybe its time and energy to trash your present data approach.

The dos and donts of applying ML to data quality assurance

When contemplating solutions for unstructured data, ML ought to be near the top of your list. Thats because ML can analyze massive datasets and quickly find patterns on the list of clutter sufficient reason for the proper training, ML models can figure out how to interpret, organize and classify unstructured data types in virtually any amount of forms.

For instance, an ML model can figure out how to recommend rules for data profiling, cleansing and standardization making efforts better 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 for example those on social media marketing or within email records.

As you enhance your data quality efforts through ML, bear in mind several key dos and donts:

  • Do automate: Manual data operations like data decoupling and correction are tedious and time-consuming. Theyre also increasingly outdated tasks given todays automation capabilities, that may undertake mundane, routine operations and release your computer data team to spotlight more important, productive efforts. Incorporate automation in your data pipeline just be sure you have standardized operating procedures and governance models set up to encourage streamlined and predictable processes around any automated activities.
  • Dont ignore human oversight: The intricate nature of data will always need a degree of expertise and context only humans can offer, structured or unstructured. While ML along with other digital solutions certainly aid your computer data team, dont depend 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 see through your technology measures. From there, it is possible to retrain your models predicated on those discrepancies.
  • Do detect root causes: When anomalies or other data errors pop-up, its often not just a singular event. Ignoring deeper issues with collecting and analyzing data puts your organization vulnerable to pervasive quality issues across your complete data pipeline. Even the very best ML programs wont have the ability to solve errors generated upstream again, selective human intervention shores up your current data processes and prevents major errors.
  • Dont assume quality: To investigate data quality longterm, discover a way to measure unstructured data qualitatively instead of making assumptions about data shapes. It is possible to create and test what-if scenarios to build up your personal unique measurement approach, intended outputs and parameters. Running experiments together with your data offers a definitive solution to calculate its quality and performance, and you will automate the measurement of one’s data quality itself. This task ensures quality controls are always on and become a simple feature of one’s data ingest pipeline, never an afterthought.

Your unstructured data is really a treasure trove for new opportunities and insights. Yet only 18% of organizations currently make the most of their unstructured data and data quality is among the top factors holding more businesses back.

As unstructured data becomes more frequent and much more pertinent to everyday business decisions and operations, ML-based quality controls provide much-needed assurance your data is pertinent, accurate, and useful. So when you arent hung through to data quality, it is possible to concentrate on using data to operate a vehicle your organization forward.

Consider concerning the possibilities that arise once you get your computer data in order or even better, let ML look after the work for you personally.

Edgar Honing is senior solutions architect at AHEAD.

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