Solve unstructured data problems with machine learning

Translator | Bugatti

Reviewer | Sun Shujuan

The data revolution is in full swing. The total amount of digital data created in the next five years will be double the amount of data generated to date, and unstructured data will define this new era of digital experiences.

Unstructured data refers to information that does not follow traditional models or is not suitable for structured database formats, accounting for more than 80% of all new enterprise data. To prepare for this shift, many companies are looking for innovative ways to manage, analyze and make the most of all the data available in a variety of tools, including business analytics and artificial intelligence. But policymakers also face an old problem: How to maintain and improve the quality of large, unwieldy data sets?

Machine learning is the solution. Advances in machine learning technology now enable organizations to efficiently process unstructured data and improve quality assurance efforts. With the data revolution just around the corner, where is your company struggling? Faced with a trove of valuable but unmanageable data sets, or using data to drive your business forward?

Unstructured data requires more than just copy-pasting

The value of accurate, timely, and consistent data to modern businesses is indisputable, and it is as important as cloud computing and digital applications. Still, poor data quality costs companies an average of $13 million per year.

To solve data problems, you use statistical methods to measure the shape of the data, which enables data teams to track changes, weed out outliers, and eliminate data drift. Controls based on statistical methods remain valuable for judging data quality and determining how and when data sets should be used before critical decisions are made. Although this statistical method is effective, it is generally reserved for structured data sets, which are suitable for objective and quantitative measurements.

But what about data that doesn’t quite fit in Microsoft Excel or Google Sheets? Includes:

• Internet of Things: sensor data, stock data, and log data
• Multimedia: photos, audio, and video
• Rich media: geospatial data, satellite imagery , weather data, and surveillance data
• Documents: word processing documents, spreadsheets, presentations, email, and communication data

When these types of unstructured data come into play, Incomplete or inaccurate information can easily enter the model. If errors go unnoticed, data problems can accumulate, wreaking havoc on everything from quarterly reporting to forecasting and forecasting. A simple copy-and-paste approach from structured to unstructured data is not enough and may actually make the business worse.

The often said "garbage in, garbage out" applies very well to unstructured data sets. Maybe it's time to ditch your current approach to data.

Things to note when using machine learning to ensure data quality

When considering solutions for unstructured data, machine learning should be the first choice. This is because machine learning can analyze massive data sets and quickly find patterns in messy data. With the right training, machine learning models can learn to interpret, organize, and classify any form of unstructured data type.

For example, machine learning models can learn to recommend rules for data analysis, cleansing, and scaling, making work in industries such as healthcare and insurance more efficient and precise. Likewise, machine learning programs can identify and classify text data by topic or sentiment in unstructured data sources, such as those found on social media or in email records.

As you improve your data quality efforts through machine learning, keep a few key considerations in mind:

• Automate: Manual data operations such as data decoupling and correction are tedious and time-consuming. They are also increasingly obsolete operations given today's automation capabilities, which take care of tedious day-to-day operations and allow data teams to focus on more important, more efficient work. To incorporate automation into your data pipeline, simply ensure that standardized operating procedures and governance models are in place to encourage streamlined, predictable processes around any automation activities.
• Don’t overlook human oversight: The complexity of data will always require a level of expertise and context that only humans can provide, whether it’s structured or unstructured data. While machine learning and other digital solutions will help data teams, don’t rely on technology alone. Instead, empower teams to leverage technology while providing regular oversight of individual data processes. This compromise can correct data errors that cannot be handled by any existing technical measures. Later, the model can be retrained based on these differences.
• Detect root cause: When an exception or other data error occurs, it is often not a single event. If you ignore deeper issues when collecting and analyzing data, your organization risks pervasive quality issues throughout your data pipeline. Even the best machine learning initiatives cannot address errors generated upstream, and selective human intervention can again solidify the overall data flow and prevent significant errors.
• Don’t make assumptions about quality: To analyze data quality over the long term, find ways 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 methods, expected outputs, and parameters. Running experiments with your data provides a deterministic way to calculate data quality and performance, and you can automatically measure the data quality itself. This step ensures that quality control is always in place and serves as an essential feature of the data ingestion pipeline, rather than an afterthought.

Unstructured data is a treasure trove of new opportunities and insights. However, only 18% of organizations currently leverage their unstructured data, and data quality is one of the main factors holding back more businesses.

As unstructured data becomes more popular and more relevant to daily business decisions and operations, machine learning-based quality control provides much-needed assurance that your data is relevant and accurate ,useful. If you're not stuck on data quality, you can focus on using data to move your company forward.

Think of the opportunities that arise when you take control of your data or, better yet, let machine learning do the work for you.

Original title: ​​Solve the problem of unstructured data with machine learning​​​ , Author: Edgar Honing​

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