What Is the Difference Between Big Data and Business Intelligence?
What is the difference between big data and business intelligence? Big data refers to large data sets that exist typically within organizations. Business intelligence refers to the utilization of this data for analytical purposes from which actionable information can be derived to make more informed business decisions.
Both of these terms are playing an increasingly large role in business operations today, so let’s take a look at both and see what the difference between big data and business intelligence is, how they are used, and what their benefits are for SMBs.
Big data is best described as the information that organizations store—typically in large “sets”, or volumes—that is difficult or in many ways impossible for them to leverage in any meaningful way.
An obvious example of big data would be something like the information that is produced through social media channels—impressions, click-through rates, engagements; all of these indicators add up together to make up what we consider “big data”.
Structured and Unstructured Data
Within big data is structured data and unstructured data.
These two different types of information are important in understanding the importance of big data analysis.
Structured data is what you would typically expect to find in formal databases— and is often understood as quantitative data.
Structured data will exist in things like spreadsheets, with carefully arranged rows and columns which can be easily read and assessed.
Unstructured data refers to virtually everything else, but can be thought of qualitative in nature.
Examples of this kind of data are videos, images, sensor information, call transcripts, and other forms of informal communication like email body text.
Unstructured data collectively accounts for 80-90% or more of all data and is continuing to grow.
Growth of Big Data
While the growth of structured data is already a big challenge for organizations to overcome, the rapid growth of unstructured data is presenting a larger point of contention.
Structured data does at least have the benefit of being relatively simple to decipher—many businesses already use CRMs for example to analyze customer data more effectively to improve their sales process.
It’s the growth of unstructured data that’s giving companies more pause for thought.
The vast majority of big data is unstructured, and this disparity will only continue into the future.
In fact, unstructured data is growing at a rate of around 55–65% per year.
As a result, leveraging tools to utilize this data is now even more important to businesses as effective use of big data becomes the competitive differentiator between organizations.
Business intelligence refers to digital tools which are used to analyze data, both structured and unstructured, into actionable insights to inform decision making.
For most organizations, business intelligence (BI) will be most familiar in the context of structured data, though advances in the use of AI and machine learning mean that unstructured information is more commonly being deciphered for use.
Use of Business Intelligence Within Organizations
It perhaps shouldn’t come as too much of a surprise to learn that many businesses lag behind in their adoption and use of BI tools.
Globally, adoption of BI across all organizations is around 26%.
While over half of all enterprises consider cloud BI to be either “critical” or “very important” to their ongoing and future initiatives, Gartner found that 87% of businesses are considered to have a low level of analytics maturity.
Furthermore, a 2020 executive study found that just 27% of organizations think of their operations as “data-driven”.
So, the present situation is one in which companies understand the importance of using business intelligence for their big data sets, but are showing a low appetite for implementing BI tools into their workflows.
Benefits of Business Intelligence for Business
Why should organizations want to adopt BI solutions?
The answer is perfectly simple in that organizations that implement BI start seeing significant positive results for their productivity and bottom line because they are able to put their big data to use through better-informed decision making.
Related Post: 10 Business Intelligence Stats That Show Its Worth
- 48% of organizations consider cloud BI to be “critical” or “very important” to their future business productivity plans.
- Business intelligence, big data and analytics are the top disruptive technologies Global 2000 organizations are implementing to drive success.
- 84% of enterprises have launched advanced analytics initiatives to bring greater accuracy and accelerate their decision making.
- 56% of organizations leveraging analytics are experiencing faster and more effective decision making.
- 51% of businesses are achieving better financial performance with the introduction of business intelligence.
- 46% of organizations have been able to identify and create new product and revenue streams though their analytics use.
- 45% of brands are currently leveraging analytics to develop new business models.
- Over 90% of sales and marketing teams say that cloud analytics is essential for getting their work done.
- 40% of high-performing companies base their decisions on gut feeling, compared to 70% of less successful businesses.
- 37% on average of company data has the potential for useful analysis.
How Does Business Intelligence Relate to Unstructured Data Sets
As we noted earlier, the proportion of structured data as compared to unstructured data is shrinking at quite a rapid rate.
This means not only that businesses that haven’t already done so should look into a strategy that incorporates BI adoption, but also that the leveraging of unstructured data will become a significant hurdle to overcome—if not now, then most certainly in the future.
Since typical BI tools are meant for structured data, artificial intelligence is used to generate actionable information from unstructured sources, which can then be effectively analyzed.
Take for example a business wants to get a better understanding of their most frequent customer complaints.
Service calls can be transcribed through a solution like Dialpad and this transcription can be assessed with text analysis software to determine commonalities (like words or phrases relating to a particular problem or service) across a wide range of calls.
This data can then be aggregated and structured and analyzed through business intelligence.
That was a very basic example, but the use of AI for analytics purposes in business will be key for organizations going forward.
We began this blog asking what is the difference between big data and business intelligence, but we hope that you took away a broader understanding of the importance of both and the shape of the big data and business intelligence landscape as it stands today.
The rapidly growing volume of big data sets within organizations today presents a challenge and an enormous opportunity.
Frontrunners in BI adoption are seeing benefits in their productivity and competitiveness, while those companies who lag behind recognize the importance of BI implementation.
At the same time, the growth particularly of unstructured data will necessitate the need for more advanced analytics capabilities, especially with regard to artificial intelligence and machine learning engines, which can help breakdown and quantify with information.
Big data is the large amounts of information you store in your organization, business intelligence is the means to make sense of it for the purposes of decision making.
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