We stand with Ukraine

The Data Discovery Challenge (Part 1)

Expert.ai Team - 15 November 2022

This is Part 1 of a 3-part blog series on how to utilize taxonomies to leverage the value of unstructured language data in your organization.  

In your business, insight is what drives innovation. It’s the combination of data and analysis that allows you to better understand the markets and customers you serve and to discover where you can deliver a competitive advantage or increase efficiency.    

Data discovery is the process of uncovering insights lying dormant in your enterprise – creating value out of unstructured data and getting it to the business users who need it, when they need it. Data discovery is a high stakes activity: content that is difficult to find can lead users to have less confidence in the relevance and usefulness of the content itself, defeating the purpose of your content in the first place. The bottom line is this: making your data easy to access is an enterprise imperative. This helps build what we call actionable intelligence, which is information you can leverage to support decision making. 

However, not all of the data in your organization is easy to access. If you’re like most organizations, you probably have a process to leverage your structured data (think: sales and financial metrics), but this is just a small portion of ALL the data you manage. So, what about all the other data? Emails, documents, reports, transcripts, PDFs, reviews, social media content—all of the non-numerical data that doesn’t fit neatly into rows and columns. This is unstructured data, and according to Forrester, it constitutes as much as 80% of the data that enterprises process and continues to grow two times as fast as structured data.  

Because of the nature of this unstructured information, being higher volume and the less leveraged portion of data within the enterprise, you need a strategy for managing it. But before we get to that, let’s look at three broader considerations, that will help inform your strategy. 

1. Why Data Discovery Matters 

Organizations contain a huge amount of knowledge that they depend on to do business, and much of it is contained in those unstructured sources we mentioned above. We don’t have to imagine the critical need for researchers and decision makers to have actionable insights about the spread of infectious diseases or climate change, or for one business to know what its customers are saying about its products and services. There is no shortage of examples that apply to any organization.    

2. Key Aspects to Manage Data Discovery 

Domain knowledge. Your domain knowledge is the result of all the experience and expertise your organization has acquired over time. It’s the R&D information that guides product development; it’s the customer data that helps you communicate with them better and provide the products and services they need; it’s the identification of risk factors that helps a business navigate market forces and avoid harmful impacts. Domain knowledge is what a company provides to customers and partners, as well as to internal operations that depend on this information to do their jobs. Together, it helps form your competitive edge.   

Audience engagement. Digital transformation has led everyone—your internal and external users/customer and information consumers in general—to expect the same speed of information access and response from the organizations they do business with as they get with the average internet search. Whether it’s users who need to find product information or subscribers who rely on your content for news or investigative purposes, you want all of the audiences you serve to be able to easily access the information they need, as quickly as possible. This keeps your customers coming back, again and again.   

Efficiency. Efficiency in accessing information is important, whether it applies to your team or to your data processes. Process efficiency equals faster time to market, which is a key differentiator in a digital environment. Even if you have a large team to help you assemble data, if you want to scale to more sources, more information, more organized intel, you will need to go faster.  

3. Obstacles to Data Discovery  

So, why do organizations struggle to drive insights from their data? There are several aspects of information that make data discovery so challenging: 

Lack of data structure. Content that is not organized in clearly divided categories makes it difficult to navigate through search or other discovery methods. In addition, data that has not been standardized or normalized, as well as documents that come from multiple sources, in multiple languages, only compound the challenge of being able to find exactly what you need when you need it.    

Siloed content. When content is split between different data sources and spread across the enterprise, it’s difficult to connect the variety of data sources with an enterprise-wide strategy. Inconsistency across the data structures, as well as the unknown of what is contained in your data archives only adds to the challenge. 

Inefficient information access. This happens when your data discovery/search operations return non-relevant content or so much content that you’re unable to get exactly the information you need. Simply put, the content is not geared to your needs or preferences.     

So, now that we have seen how data discovery will leverage your unstructured data and provide the actionable intelligence that can help your organization make better decisions and act on them more quickly, how do you get started on your journey?   

The solution to rising to the data discovery challenge starts with a taxonomy.  

In our next post, we’ll look into Why Taxonomies are the Answer.