4 Best practices for managing your unstructured data

Harnessing the true value of unstructured data is one of the biggest challenges that enterprises face today. Chances are if you’re an enterprise company, you’ve been accumulating an increasingly large amount of this data in the form of documents, images and emails and are likely sitting on a gold mine of untapped potential. These unstructured data use cases lead directly to revenue generating initiatives or immense cost savings due to automation.

The volume, variety, and velocity of this information type, however, can be overwhelming which can make it difficult for companies to find the insights that can drive decision-making. n addition, unstructured data quality can typically be lower in quality and needs to be enriched before being usable. With that said, let’s have a look at the four best practices for managing high-volumes of unstructured data that leading data and AI teams have adopted. 

1. Utilize model embeddings and similarity search to speed up getting insights

Employing the latest advances in model embeddings help you quickly uncover high-level patterns and visually similar data from across all your data sets. In machine learning, an embedding, or feature vector, is an array of numbers assigned to an asset by a neural net. Assets that have similar content will also have similar embeddings which allows teams to search across millions and millions of data points in minutes rather than days/weeks.

Leverage workflows such as weak supervision to automatically apply labels, metadata and insights across your unstructured data without needing to always build a model. This can be especially useful for unstructured data such as customer feedback in the form of text or contextual advertising, be sure to utilize all your metadata and text embeddings so that you can automatically add labels at scale and queue them for human review.

2. A clean, thoughtful ontology is critical for understanding your data with minimal errors

Create an ontology for your unstructured data that follows the most logical workflow for your data science and labeling teams. An ontology is the IP for your data and AI products, and contains all of the information to render a set of features and the relationships between them. Ontologies can be reused across different projects and they are required for data labeling, model training, and evaluation. 

Once your initial model is trained, you can leverage comparative analysis between your ground truth and predictions from your AI models to find and fix human labeling errors so that your project is properly labeled. Leading ML teams have typically built in place a strong communication and collaboration workflow so that you can get your data engineers, data scientists and labeling workforces all working in tandem to ensure quality to deliver on this ontology.

3. Tap into publicly available open source algorithms to automate identifying common objects

Data teams no longer need to spend extensive resources and time labeling common objects such as cars, people, and plants when data visualization tools exist that can help you achieve this outcome faster and review your unstructured data. 

To get started faster, we recommend setting up a process to quickly search and visualize all of your unstructured data in one place. With all your data, metadata, labels and predictions at your fingertips, you can make better decisions for streamlining data prioritization and data management.

4. Find ways to prevent duplicate data, which can be crucial as your projects scale to millions of assets

This best practice is accomplished by setting up automation to easily handle data row imports, querying, and deleting duplicate or unnecessary data rows. When it comes to managing unstructured data, we've seen that what teams are missing are explicit tools to explore, organize and curate their unstructured data based on similar attributes.

In the past, this has required painstaking work and high budgets for large teams of people to sequentially catalog large data sets using antiquated techniques like “data entry” or expensive techniques like building AI models. This often required high spend on data before even knowing the data’s true value.

Managing your unstructured data: Best practices applied

As an example of putting these tips into action, let’s walk through an example use case in the retail and e-commerce industry. A leading e-commerce company that focused on making pet products possessed millions of customer survey responses and only a fraction were being categorized for analysis. Product owners were not able to listen to their customers and make data driven decisions, causing them to “fly blind” into customer satisfaction for new releases and investments.

By following the best practices noted above and utilizing a high-volume of text classification, the e-commerce company is now able to track product sentiment across vectors such as durability, engagement, shipping, and product theme.

Data and product teams at the e-commerce company have successfully built a data mart which allows product owners to analyze customer responses and find trends. This has resulted in an increased NPS score and customer retention, as well as increased product scores and lower customer appeasements.

Final thoughts on managing your unstructured data

Fortunately with today’s technology, it’s much simpler to put all of this into action and to better harness unstructured data. While unstructured workloads were primarily the domain of data science teams, with complexity of tooling being a primary barrier to entry, data analysts and BI teams now have access to platforms that enable these key personas to derive benefit from handling unstructured workloads.

By leveraging tools like Labelbox Catalog, data and AI teams can explore areas of value in their unstructured data with a fraction of the work by leveraging tools born in the world of AI that are now packed for easy use by anyone with an interest in their data.