Unstructured Data Analytics: Using AI to Fuel BI

Data Analytics Using AI

While researchers have been pondering the extraction of unstructured text since the mid-20th century, businesses have struggled to gain value from their scattered data architectures until now.

In recent years, I’ve seen most enterprises deal with unstructured data problems by choosing to ignore this vast resource of untapped information hidden in their systems. The path of least resistance is a simple one but equally damaging when it comes to building a truly agile business that reacts quickly to changes in the market. The raw data trapped in business documents hold valuable insights, and organizations miss out on this critical intelligence when they rely solely on their structured data for business analysis.

Managing unstructured data

With numerous opportunities to leverage unstructured data for business insights, executives may want to tactfully develop data management frameworks to support business intelligence (BI) initiatives. Determining data sources focal to business insights can be a good starting point — you may consider the datasets that will impact your analytics with an objective in mind. The focus should be on vital metrics to produce relevant results. Once you’ve determined your data criteria and requirements, it’s time to look for a solution to structure your unstructured data and import it into a centralized data repository.

Choosing the right destination

Some enterprises use data lakes to handle large volumes of unstructured data. While these solutions allow businesses to funnel raw data with virtually no limit, the purpose of that data is not clearly determined or fixed. As a result, it’s extremely hard to sift through the data and make sense of it since it’s not optimized for querying, and the information isn’t available in a structured format. So, these data lakes can quickly turn into data dumps and become a hotbed for unusable data.

Therefore, you’re better off using a data warehouse. It’s a data repository for structured, filtered data processed for a specific purpose. Unlike data lakes, the storage space of the data warehouse isn’t wasted on data nuggets that may never be utilized for reporting or analytics. But how to bring unstructured data into an enterprise data warehouse?

The need for automation

You must consider automated data extraction to create a truly integrated data ecosystem. A template-based data extraction solution can help you extract information from thousands of documents available in similar layouts in a few seconds.

Leveraging AI capabilities like machine learning and natural language processing, these solutions can identify relevant datasets, including named-entity or tabulated data, and auto-create data extraction templates. Moreover, using the capabilities like job orchestration and scheduling in modern data extraction platforms, users can design workflows to fully automate these activities.

It’s only through automation that businesses can capture relevant data available in unstructured documents and then validate and store it in a structured format for reporting and analytics. It significantly reduces manual intervention and allows users to efficiently process large volumes of documents and power your unstructured data analytics.

Final words

If your business relies on structured data for business analytics , perhaps it’s time to reimagine your BI strategy. Incorporating processed unstructured data will provide richer, more detailed insights resulting in smarter decision-making.

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