BI vs. Big BI: What’s the BIGGEST Difference?

Earlier this month, I had the honor of facilitating three separate round table sessions at SIA’s CWS Summit 2019 on the topic of “Business Intelligence: Go BIG or Go Home.” The most interesting takeaway for me at the event was explaining to industry attendees what “BIG” means and the dialogue that then followed.

As a lifelong data practitioner, I have always believed that it’s all about the data. However, I’ve learned over the years the reality is that it’s not just about who has the biggest dataset but rather what they do with the data.

Business Intelligence or Big Data?

At its core, business intelligence, OR BI is the strategies and technologies used by enterprises for the data analysis of business information. In this way we provide historical, current and predictive views of your most critical business operations.

BI covers all types of data, from requisitions, placements, financials, finishes, and much more, from data hosted in Excel spreadsheets to large online databases.

Alternatively, big data, consists of only larger data sets such as Global Benchmarking, BLS, Census, Commodities Exchange, etc.

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Big Data Analytics

The term “big data” simply means large datasets that outgrow databases and data handling architectures. For example, data that cannot be easily handled in Excel spreadsheets may be referred to as big data.

Big data involves various methods of storing, processing and visualizing this data.  When combined properly, we can provide valuable insights from big data assets. That’s big data analytics.

So, what do we mean when we say “Go BIG?” The biggest differentiator is when we combine information outside of a company’s own data sources and serve as a third party resource. In other words, it’s part of your business intelligence but leads you to more comprehensive insights.

For example, GRI’s data experts has built a system of intelligence  from the ground up to handle any data, from anywhere, in any format. Called Envision, it takes in all types of structured data from tables and columns in a company’s VMS to unstructured data (i.e., blobs of text from job descriptions or PDFs of SOWs).

Predict Labor Trends with BIG BI

Modern technology and data science, integrated with big data, such as global contingent labor benchmark data, enables the industry to analyze, visualize, and predict labor trends and outcomes within programs.  GRI’s BIG BI environment enables us to provide clients with the ability to combine and correlate program variables so that they can not only see what has happened within their program, but determine why it happened and what to do next.

That’s BI vs. BIG BI.

Salema Rice

Salema Rice
Salema Rice is chief data officer at Geometric Results Inc. (GRI), leading the company’s evolution to becoming the non-employee labor industry’s extended workforce solutions leader. She can be reached at SRice (at) geometricresultsinc (dot) com.

Salema Rice

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