Tuesday, May 30, 2023

Why Unstructured Data is Important for Business Intelligence

By Swapnil Mishra - September 26, 2022 5 Mins Read


All forms of data play a crucial role in making business decisions. The success of a business is contingent upon its ability to collect, analyze, process, and act on this data.

In today’s digitally-driven world, where the volume of data generated is increasing at a breakneck pace and is available in various formats, it is more complex than ever for businesses to process this data. Unstructured data is the most challenging information for companies to process.

Since unstructured data makes up more than three-quarters of enterprise data, better business intelligence requires more accurate data. Business leaders don’t have to use unstructured data uniformly or holistically across the organization, even though it is pertinent and helpful to various parts of an organization. Instead, they ought to take a targeted approach that fits with the company’s strategic goals when dealing with unstructured data.

According to a recent report by Accenture on Unstructured Data Insights, enterprise data is 80 percent unstructured, and that number will continue to grow.

Unstructured data is expanding at a rate of 55 to 65 percent annually, which is a sharp increase. Companies are losing out on a wealth of knowledge that can help with business intelligence if they don’t have the right tools to analyze this data.

However, it’s critical to understand how to use unstructured data in support of a company’s overarching objectives.

Also Read: Six Business Intelligence Challenges IT Teams Need to Resolve

Difference between unstructured and structured data

There are a few significant differences between structured and unstructured data. Unstructured data is variable, whereas structured data has distinct, repeatable patterns that make it much simpler to search for and extract information from.

Additionally, relational databases house structured data, while NoSQL databases house unstructured data. Structured data such as customer names and transaction dates are stored in the relational database of a customer relationship management (CRM) system.

Companies gain the most by combining structured and unstructured data for the best business intelligence, as opposed to favoring one format over another.

Use cases for unstructured data for enterprises

Customer service, product development, and sales and marketing are all areas where unstructured data can be used.

Sales and marketing

Unstructured data is used by businesses to determine customer purchasing trends and brand perception. One significant advantage specific to unstructured data is sentiment analysis. A company’s sales and marketing performance can be put into context by examining posts on social media, forum discussions, and other media.

Algorithms used in CRM platforms also benefit from unstructured data. The insights produced by predictive analytics teach businesses how to foresee customer needs. For instance, a sales team could use insights to upsell existing customers at the ideal time or make better product or service recommendations for new customers.

Product development

Unstructured data provides businesses with information on how to improve their product or service through sentiment analysis of customer forums, customer service calls, and social media.

Consumer assistance

Automated chatbots support customer service agents by directing customer complaints to the appropriate staff members who can resolve the issue. This information then informs the sentiment analysis mentioned above.

But more importantly, discussions about problems and complaints give the research and development team important insight into which features function well and which don’t. Product development uses this information to determine how to make the product or service better.

Also Read: CIOs’ Common Cloud Strategy Mistakes

Three steps to leveraging unstructured data for business intelligence

To start using unstructured data for better business intelligence, organizations can follow these three steps.

  1. Determine specific uses of unstructured data

The company’s management needs to be clear about the questions it is attempting to answer with external data. The first step in deciding what kind of unstructured data to collect initially is knowing how a company wants to use unstructured data. This will then determine the type of big data business solution(s) to implement.

  1. Simplify the data sources

To create a single source of truth for their data, IT leaders should set up a common data model. It is necessary to construct high-quality data pipelines because unstructured data is sourced from numerous sources and is available in a variety of formats. It guarantees that consistency and timeliness of data delivery remain the same, irrespective of the source.

  1. Create a plan and a fix for the data programs

Employing a vendor that specializes in high-performance, high-quality data integration tools and services are recommended for organizations. Business executives and data analysts must work on the front end after deciding on the back end, specifically the question(s) they are attempting to answer and how they will integrate data sources. As a result, they will need to integrate analytics in a way that enables them to query and visualize the data using common applications.

Why is unstructured data important for business intelligence?

Structured data only partially explains a problem that a business wants to comprehend. The majority of enterprise data, on the other hand, is unstructured and comes from various sources in a variety of formats. As a result, it gives businesses a complete picture to enable them to comprehend and address their problems.

It is true that unstructured data is more challenging to analyze. Unstructured data analytics tools are expensive, but they are well worth the investment because of the wealth of information a business can gather in this way.

In order to improve business intelligence, it is crucial that organizations clearly define the problem or question that unstructured data will be used to solve, integrate and streamline multiple data sources, and use the appropriate tools for analysis and visualization.

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Swapnil Mishra

Swapnil Mishra is a Business News Reporter with over six years of experience in journalism and mass communication. With an impressive track record in the industry, Swapnil has worked with different media outlets and has developed technical expertise in drafting content strategies, executive leadership, business strategy, industry insights, best practices, and thought leadership. Swapnil is a journalism graduate who has a keen eye for editorial detail and a strong sense of language skills. She brings her extensive knowledge of the industry to every article she writes, ensuring that her readers receive the most up-to-date and informative news possible. Swapnil's writing style is clear, concise, and engaging, making her articles accessible to readers of all levels of expertise. Her technical expertise, coupled with her eye for detail, ensures that she produces high-quality content that meets the needs of her readers. She calls herself a plant mom and wants to have her own jungle someday.

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