Enterprise data is 80 percent unstructured, and that number will continue to grow. The rate of unstructured data’s growth is climbing rapidly at a rate of 55 to 65 percent each year. Without the right tools to analyze this data, companies are missing out on a wealth of information that informs business intelligence. However, it is important to know how to leverage unstructured data in service of a business’s broader goals.
What is unstructured data?
Unstructured data is information that is highly variable and does not follow any clear patterns. Because of this, unstructured data is difficult to label, organize, search, and ultimately analyze.
Examples of unstructured data include:
- Analyst reports
- Audio files
- Event logs
- Geospatial data
- Images
- Meeting minutes
- Multimedia data
- Social media posts
- Video files
- User-interaction data
What is the difference between unstructured and structured data?
Unstructured data is different from structured data in a couple of key ways. While unstructured data is variable, structured data consists of clearly defined, consistent, and predictable patterns that make structured data much easier to search for and get information from.
Examples of structured data include phone numbers, dates, or names and are used in sales transactions and online booking systems for restaurants, airlines, and other reservable services.
Also, unstructured data is in applications or NoSQL databases, while structured data lives in relational databases. For example, unstructured data in the form of meeting minutes is found in an app, such as Notes or Microsoft Word or a social media post lives in Instagram. Customer names and transaction dates, for instance, are structured data that live in the relational database of a customer relationship management (CRM) system.
Rather than choosing one format over the other, companies benefit the most by leveraging structured and unstructured data together for optimal business intelligence.
Who benefits from unstructured data and how?
Benefits of using unstructured data include:
- Variety of information sources to draw from
- Quick accumulation of rich datasets
- Vast storage capacity in data lakes
- More accurate data for better business intelligence, since unstructured data makes up more than three quarters of enterprise data
According to Will Freiberg, CEO of Crux, “all levels of an organization can benefit from proper management of unstructured data, as it contains a wealth of insights that can benefit a variety of internal consumers.“
For instance, Freiberg explains that unstructured data “provides a better understanding of sources of supply chain risk, measures consumer sentiment and buying behaviors, and improves operational processes.”
Though unstructured data is relevant and advantageous to various parts of an organization, business leaders don’t need to use it holistically or evenly across the organization. Instead, they should approach unstructured data in a targeted manner that aligns with the business’s strategic objectives.
Use cases for unstructured data
Unstructured data use cases apply to sales and marketing, product development, and customer service.
Sales and marketing
Businesses use unstructured data to identify customer purchasing patterns and sentiment toward the brand. Sentiment analysis is a major benefit unique to unstructured data. Analyzing social media posts, forum discussions, and other media provides context for a business’s sales and marketing performance.
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Unstructured data also informs algorithms in CRM platforms. Predictive analytics generates insights that inform companies on how to anticipate customer needs. A sales team could, for example, act on insights to make better product or service recommendations to new clients or know the right time to upsell existing clients.
Product development
Through sentiment analysis of customer forums, calls to customer service, and social media, unstructured data informs businesses in terms of how to improve their product or service.
Customer service
Automated chatbots augment customer service representatives by routing customer concerns to the proper personnel who can address the issue. This information, in turn, informs the sentiment analysis mentioned above.
More importantly, however, complaints and troubleshooting conversations provide useful information to the research and development team about what features work well and which do not. This data informs product development on how to improve the product or service.
Read more: Machine Learning Chatbots Explained: How Chatbots use ML
3 steps to leveraging unstructured data for business intelligence
Freiberg breaks down three steps to start taking advantage of unstructured data for better business intelligence.
1. Determine specific use(s) of unstructured data
Freiberg advises management to “be clear with what questions your company is trying to answer with external data.”
Knowing how a company wants to use unstructured data is the first step in determining what
kind of unstructured data to collect in the first place. This in turn will inform what kind of big data business solution(s) to implement.
Read more: Examining the Big Data Explosion
2. Streamline data source(s)
Freiberg recommends setting up a “common data model” in order to “establish a set of truth in your data.”
Since unstructured data gets pulled from a variety of sources and in a variety of formats, Freiberg emphasizes the need to “build quality data pipelines to ensure that—regardless of the source—consistency and timeliness of data delivery remain the same.”
3. Develop a roadmap and solution for your data program(s)
Work with a vendor that specializes in high-performance quality data integration tools and services. Crux, for example, has thousands of datasets that an organization can integrate into its data pipelines created in step 2 and use to its advantage.
Once business leaders and data analysts determine the backend—namely, the question(s) they are trying to answer and how they’ll integrate data sources—they need to work on the front end. This means they will need to embed analytics in a way that will allow them to visualize and query the data through everyday applications.
Unstructured data tool providers
The insights generated from unstructured data do not just magically appear. Rather, it takes special tools to process the data before it produces any meaningful knowledge. Popular unstructured data tool providers on the market include:
- Accenture
- Azure
- Crux
- DynamoDB
- Hadoop
- MongoDB
Read more: Best Data Management System Software 2022
Why is unstructured data important for business intelligence?
Structured data only tells a fraction of the story about a problem that a business seeks to understand–only 20 percent, to be exact.
Unstructured data, on the other hand, makes up 80 percent of enterprise data and comes from a variety of sources in several different formats. It thus paints a fuller picture for businesses to help them better understand and solve their challenges.
Unstructured data is indeed more difficult to analyze. However, the wealth of information that a business acquires by doing so makes unstructured data analytics tools well worth the investment.
It’s therefore critical that businesses define the question or challenge they seek to answer through unstructured data, integrate and streamline varied data sources, and use the right tools to help analyze and visualize it for better business intelligence.
Read next: 12 Key Steps for Unstructured Data Analysis
