The Challenge of Extracting Value From Unstructured Data

Unstructured data is growing globally at a rate of about 55-65% each year. This makes sense being that the computer vision industry is literally booming as we speak. Computer vision is fueled by artificial intelligence, a market size that was valued at USD 62.35 billion in 2020. By 2025, global data creation is projected to grow to more than 180 zettabytes. In 2020, the amount of data created and replicated reached a new high. This growth was higher than previously expected due to the COVID-19 pandemic, as more people worked, and learned from home, and used their home entertainment options more frequently.

Forbes

Digital services and applications are flooded with unstructured data of all kinds: images, videos, audio, documents, emails, and many others, with 90% of all digital data being unstructured. Harnessing this data and building cohesive, unified datasets is essential if an enterprise seeks to gain a more accurate understanding of all the information at their fingertips… Yet this is the exact challenge many organizations face. Manually examining unstructured data which is individual pieces of data, is extremely time-consuming and requires a lot of resources. This is where tools and technology become essential to extracting golden nuggets of value from its vast and disorganized digital source.  

Always Start With a Clear Business Objective

“Data is the new oil” is what every kid on the block will say these days and often many companies who already own large amounts of data feel they are sitting on a gold mine, and it’s just a matter of digging deep enough. With close to 90% of AI projects failing, the promise of cheap oil gradually fades away.

It is actually pretty simple to start by asking a simple question: where is the business value? It’s either increasing sales, reducing costs, or improving product satisfaction (competitiveness) where priority always goes from the former to the latter. Whatever the selected business KPI is, you should always test it before going into development. Machine learning is very expensive, so test your hypotheses on real customers or users, generate the outcome manually for small cases (where machine learning is not required) and invest the heavy lifting on a solid offer. In most cases, the data the organization already owns is not enough just by itself, to properly create ML models and additional data is needed either internally or externally to the product. Working on a few examples will also start revealing those missing pieces. Often, data collection has its own challenges and takes time and effort, identifying gaps early on (before development has even started) will greatly increase the solution time to market.

Top Must-Haves When Structuring Your Data

  1. Pattern Recognition Algorithms
    • You can make sense of your unstructured data with pattern recognition algorithms, which leverage machine learning to categorize this unstructured data. These algorithms can quickly tag and categorize large quantities of images, something that would be extremely time-consuming if done manually. Annotating your data takes the general information and actually teaches the machine to see what we see. In addition, automation by means of smart algorithms can significantly reduce labeling time. 
  2. A Data Management & Search Platform
    • What is the point in having all the data if you don’t have easy access to it, in the form of a solid search? Once you wish to turn your unstructured data into structured data, suitable for ML, then the data management platform becomes very handy. This allows you to gain visualization of the data, fully understand it, track issues AND fill in missing gaps you are guaranteed to identify along the way. This is accomplished all by browsing, organizing, and exploring your data from one single interface.  
  3. Metadata Descriptions
    • Metadata presents the context in which this data was collected and therefore has a significant role in both our current and future data management. Metadata will allow you to understand the business context of the data: The customer, product, location, user, etc. Solid metadata will also serve you well when selecting data for labeling by reducing bias and increasing the information capacity of your training sets.  
  4. Human-in-the-Loop
    • As overwhelming as unstructured data can seem, AI and humans working together is a solid recipe for a productive ML approach. Don’t expect AI to outperform humans that easily. This joint human-machine collaboration can streamline the data and make it much easier to extract insights across multiple applications. Working together, humans and machines can transform unstructured data into vital intelligence. By adding human validation into the loop you’re improving the likelihood of success. 

Summed Up

We know 96% of enterprises are struggling with data management, but with the right tools and technologies, you’re capable of extracting value from this plethora of data. 

This is how businesses can grow, by harnessing this data to build cohesive, unified datasets and gain a more accurate understanding, higher quality data, stronger models, and then there’s the added bonus of the results being quicker and cheaper than before. This can be accomplished by automating, visualizing, and combining it with structured data. 

When businesses adopt this approach to tackling their data and truly get the most out of it, then this market won’t just grow, it’ll explode. 

Want to see for yourself how Dataloop can help structure your unstructured data? Set up a 1:1 personalized demo and we’ll walk you through it.

Share this post

Share on facebook
Facebook
Share on twitter
Twitter
Share on linkedin
LinkedIn

Related Articles