It’s critical to understand how to use unstructured data in support of a company’s overarching objectives
80% of enterprise data is already unstructured, and that percentage will rise. Unstructured data is expanding at a rate of 55 to 65 percent annually, which is a sharp increase. Companies are losing out on a plethora of knowledge that can help with business intelligence if they don’t have the necessary tools to examine this data. However, it’s critical to understand how to use unstructured data in support of a company’s overarching objectives.
Applications of Unstructured Data
Unstructured data provides businesses with information on how to enhance their service or product through sentiment examination of consumer forums, customer support calls, and social media.
Sales and Marketing
Unstructured data is used by businesses to determine client purchasing trends and brand perception. One significant advantage specific to unstructured data is sentiment assessment. The performance of a company’s sales and marketing can be put into context by examining posts on social media, forum conversations, 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 wants. For instance, a sales staff could use analytics to upsell existing customers at the ideal time or make better product or service suggestions to new customers.
Automated chatbots support customer care agents by directing client complaints to the appropriate staff members who can resolve the issue. The sentiment analysis indicated above is then informed by this information.
But more crucially, discussions about problems and complaints give the research & design team important insight into which features function effectively and which don’t. Product development uses this information to determine how to make the product or service better.
How to Leverage Unstructured Data for BI?
To start using unstructured data for greater business intelligence, Freiberg outlines three steps.
Determine the specific use(s) for unstructured data
“Be precise with what issues your organization is aiming to solve with external data,” Freiberg encourages management.
The first step in deciding what kind of unstructured data to collect initially is knowing how a corporation wishes to use unstructured data. The type of big data business solution(s) to employ will then be determined by this.
Streamline the data source (s)
To “create a set of truth in your data,” Freiberg advises establishing a “shared data model.” Freiberg underlines the necessity to “create quality data streams to ensure that irrespective of the source consistency and reliability of data delivery stays the same.” This is because unstructured data is retrieved from a range of sources and in a wide variety of formats.
Create a plan and a fix for your data program(s)
Work with a supplier that specializes in high-performance, high-quality apps, and resources for data integration. A company can take advantage of Crux’s hundreds of datasets, for instance, by integrating them into the data pipelines it built in step 2.
Business executives and data analysts must work on the front end after deciding on the backend, specifically the question(s) they are attempting to answer and how they will connect data sources. As a result, they will need to integrate analytics in a way that enables them to query and view the data using common apps.
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