Structured Vs Unstructured Data

Structured Vs Unstructured Data

Data is the raw material that fuels the information age. Today’s most successful enterprises have comprehensive strategies for collecting, storing, and utilizing that data, and this gives them a huge advantage. However, in order to extract that value from our data, we need to turn it into knowledge. 

Artificial intelligence is the tool for the job.

Essentially, machine learning algorithms can crawl through massive data sets to uncover insights that are either hidden to the human eye or that we couldn’t practically discover within any reasonable time frame. By detecting these subtle patterns, businesses transform their raw data into powerful analytics.

Data, however, takes many forms, and our approach to analyzing it likewise needs to adapt. Although data scientists categorize data in many ways, we’re going to narrow in on two general buckets: structured and unstructured data. On the whole, structured data is easier to process than the unstructured variety, and so most big data analytics tools work on it alone.

The problem is that this leaves a huge amount of value on the table. Unstructured data makes up over 80% of enterprise data and is growing at a rate of 55 to 65% per year. This is a big incentive. Not only do we already have these resources at our disposal, but, as we’ll see, unstructured data sets provide unique opportunities.

Now let’s break down the difference between structured and unstructured data before diving into how organizations use AI to solve today’s toughest challenges.

What’s the Difference Between Structured and Unstructured Data?

Let’s say you want to cook the same meal in two different kitchens. They have the same equipment and ingredients, but there’s one key distinction: one of them has a recipe that specifies each step in the process, the ingredients are measured out, and the stove has precise settings. In the other one, none of the ingredients are measured out, the steps are vague and the stove only has a few crude settings: low, medium, and high.

If you’re anything like me, you’d much rather get cooking in the organized kitchen because its structure is going to make life a lot easier, you can repeat the steps in the recipe, it’s going to take less time, and you will have confidence in getting the recipe right. The same principles hold for machine learning models. It’s going to be faster and easier to cook up insights with structured data, but that’s not the whole story.

Sometimes, we don’t have structured data, the outcome we are working towards is based on deriving insights from vast amounts of unstructured data, or the only data available doesn’t have the structure the machine learning model needs to function. After all, if we’re hungry and the messy kitchen is the only one we have, then we’ll roll up our sleeves and do what we have to do.

Let’s make this a little more concrete. Structured data that is stored in a relational database like a SQL database or perhaps even an Excel spreadsheet with predefined structure such as fields, rows, columns, and tables. The metadata associated with the rows, columns, tables and fields (as well as information on how data is organized through schema definitions) is typically represented in structured databases in a separate repository, and can serve as critical raw material, or features, for building machine learning models.

By contrast, unstructured data doesn’t incorporate a comparable rich set of metadata. Although unstructured data includes metadata like date created, file type, and size, there are fewer other pointers for any unsupervised learning to use as input to build or generate a model. Structured data, on the other hand, includes features that are logically organized from the outset.

The main difference between the two isn’t necessarily the data itself; rather, it’s the data model and the predefined logic around the data that are the defining traits. Here are some common examples of unstructured data formats:

  • DOCX or other types of files
  • Emails
  • Text files (social media, blog posts, online communications, etc.)
  • PDFs
  • JPG
  • MP4 video
  • MP3 audio

Solving Business Challenges with AI for Unstructured Data

Since structured data is already organized, it’s relatively straightforward to use it to build a machine learning model. The data already contains plenty of hooks and pointers that the algorithm can latch onto. For instance, a common use case is anomaly detection, which automatically flags outliers in a dataset.

On the flip side, let’s suppose that our business serves customers in Europe and is therefore subject to General Data Protection Regulations (GDPR). If one of our users makes a 'right to be forgotten' request, we’re legally obligated to go through our data and delete any personally identifiable information (PII). This will be easy for our structured data, but how do we comb through the volumes of unstructured data to understand how data elements are related to a single individual?

Or, how can law firms and their clients comb through huge numbers of emails and other communications to determine which communications are covered by privilege when it’s difficult to determine context and relationships?

The answer is the next evolution of artificial intelligence technology: unsupervised learning that’s applied to unstructured data. These algorithms sort through and learn from data on their own - leveraging ways of inferring structure where there is very little, or none, in order to guide the model.

Rather than work backwards from models designed for structured data and try to compensate for the relative lack of organization, these unsupervised learning approaches are built specifically to build connections between data points, and infer structure based on context. These unsupervised learning models can be effectively supplemented with natural language processing (NLP) and other types of semantic analysis to fill in the gaps that metadata description might otherwise provide for the necessary context.

By using these tools, we can uncover and contextualize data that should be protected under privacy regulations like GDPR or the California Consumer Privacy Act (CCPA) in the US, flag privileged communications between attorneys and clients, and enable proactive compliance through automation.

This capability is especially crucial for time-sensitive jobs like responding to a data breach or preparing for litigation. Not only does AI review take a fraction of the time of a manual review, but it can prove to be more accurate than human review - and better mitigate risk.

Conclusion

Ultimately, the difference between structured and unstructured data comes down to how effectively it can be utilized and analyzed at scale. Structured data may be easier to use, but unstructured data brims with even more potential—both for good and for bad. Not having a grasp on your unstructured data brings risk for failed compliance, but effectively processing it can open the door to finding insights that would otherwise remain invisible.

At TextIQ, we use advanced unsupervised learning to process unstructured data at the enterprise scale. Our NLP models go beyond the basics to reveal semantics, code words, and other features that most solutions miss. Want to learn more? Contact us today...or try one of these as your next step:

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