What is big data vs business intelligence? In short, big data describes massive amounts of data and how it’s processed, while business intelligence involves analyzing business information and data to gain insights. Big data can improve business intelligence by providing organizational leaders with a significant volume of data, leading to a more well-rounded and complex view of their business’ information.
Major developments in the digital consumer landscape have provided companies with more opportunities than ever to acquire consumers’ data and understand their behavior. The advent of the Internet of Things, e-commerce, and social media, for example, have created new forms of digital data that fall under the umbrella of “big data.”
According to PwC, 4.4 zettabytes of data existed in 2013 compared to 44 zettabytes in 2020. IDC anticipates that 463 exabytes of data will be produced daily by 2025. Businesses can expect to increasingly rely on big data and business intelligence to better understand their own consumer bases, as well as develop their brands, products, and services.
What are the main differences between big data and business intelligence? And what is useful for your organization? Though the terms “business intelligence” and “big data” are frequently misused and conflated with one another, they describe two different ideas that can often work together in today’s business operations. Learn more about big data vs business intelligence and how your organization can integrate them effectively into its analytics.
What Is Big Data?
“Big data” is a term that describes two primary ideas:
- The large and complex forms of data that have come with the rise of digitization. This includes huge sets of video, audio, and image files, comments and posts on blogs and social media, etc.
- The powerful technological tools and processes required to compile, organize, analyze, and visualize that same data.
But where does the term “big data” (and its two definitions) come from? We’ll outline both definitions, the differences between big data vs business intelligence, and how they’re connected.
1. Big Data as Information
This first definition requires some background information. From the 1970s and onward, traditional data and its management systems largely consisted of physical files and papers. These could be stored, retrieved, and analyzed with a combination of human labor and some emerging Internet and digital technologies.
However, with the global expansion of Internet technologies and the digitization boom of the early 2000s, new forms of unstructured digital data began to emerge. Photos, videos, audio clips, comments, posts, reactions, and interactions started to appear on social media sites, as well as devices connected to the Internet of Things. Companies saw a chance to harness this colossal amount of digital data to gain new insights about consumer attitudes and behaviors.
As companies integrated more digital technologies into their operations and services, these massive new data sets (referred to as “big data”) were becoming increasingly vital to understanding consumer behavior and company performance in the new digital world. With so much data from so many different sources, organizations have more direct access to the behaviors and attitudes of their own consumers, as well as potential consumer groups, than ever before.
2. Big Data as Technology
The second definition of big data is also related to this history. While technological advancements such as e-commerce, social media, and the Internet of Things were creating big data for more organizations, traditional data management systems were unable to store or analyze this data properly. This left much of it unusable or vulnerable to data breaches.
To make use of these new forms of data, new technologies needed to emerge specifically to handle big data. In this way, big data is as much about these types of complex digital data as it is about the technologies that allow for their storage and analysis.
Big data would be impossible to handle efficiently with human effort or traditional software alone. That’s why artificial intelligence and machine learning have been vital to the development of big data technologies.
Other Characteristics of Big Data
What distinguishes a set of data as “big data”? There are three primary characteristics (known commonly as the Three Vs) that can be used to think about big data:
- Volume: Volume has to do with the size of big data. There is much debate over exactly how much data constitutes big data. But it’s clear that big data involves huge data sets that require higher computing power and more advanced technology to process. Even so, not all big data sets are of the exact same magnitude. Organizations should consider the volume of big data they will be handling when it comes to selecting big data storage and management systems.
- Variety: Variety describes the diversity of data forms present in a big data set. For example, though both the Internet of Things and e-commerce websites can provide an organization with big data sets, the type of data collected from these sources is not the same and can offer different insights.
- Velocity: Big data is not only larger than traditional data, but also it is created at a much faster rate. Because of this, big data technologies often integrate real-time streaming into their systems. This allows them to capture valuable data as it arrives, rather than letting it disappear into the data pipeline.
At first, big data can seem highly abstract, or may only seem relevant to major corporations with monumental technology budgets. However, the artificial intelligence and data fusion technologies that make big data possible are becoming more widely adopted for mainstream purposes. As these technologies proliferate and advance, more and more organizations of all sizes and scopes will be able to take advantage of big data analysis.
What Is Business Intelligence?
Business intelligence (BI) has a more straightforward and easily recognizable definition. It describes the processes and technologies related to analyzing business information and presenting actionable insights for the purposes of an organization’s advancement.
BI is made up of many important analytical tasks, including:
- Data mining
- Predictive and prescriptive analysis
- Event processing
Ultimately, business intelligence depends on both internal data from within the organization about its operations, finances, and staff, as well as external data from your customers, competitors, and industry. It can include data from more traditional sources—such as invoices, surveys, previous analyst reports, or historical data—as well as big data.
Digitization and the widespread adoption of e-commerce and mobile service sites (such as online banking services) have greatly expanded the number of data points that business intelligence must take into account. This is where big data often intersects with BI.
Business intelligence requires business analysts to understand how these various data sets work together to create a comprehensive image of a company’s current state and performance. Together, this data helps executives, managers, and other business leaders make informed business decisions that will benefit the development and growth of their organization.
The best modern BI practices rely on a strong network of data technologies that allow for the collection, storage, organization, analysis, and visualization of newer data forms, namely big data. On top of managing these new data sets, businesses still need ways to properly process and store invoices, quotations, and client information, while also following federal and global compliance mandates.
Big Data vs Business Intelligence: How They Work Together
Traditionally, business intelligence practices have involved business analysts formulating questions that they needed answered. From there, analysts and IT teams had to sort through, compile, analyze, and visualize relevant data for their reports.
Big data has radically expanded the scope of today’s business intelligence operations. It enables business analysts to answer big-picture questions first, then dig into more relevant and detailed questions about valuable business data.
While it can be useful to understand when you’re utilizing big data vs business intelligence, in many cases big data will be integrated into BI. Big data analysis works with business intelligence in 4 major steps:
Learn more about big data vs. business intelligence below! Please feel free to share our infographic on social media, or copy and paste the code below to embed it on your website:
<img src="http://bit.ly/bigdatavsbusinessintelligence"> <p>How Big Data and Business Intelligence Work Together: an infographic by the team at <a href="https://www.entrustsolutions.com/">Entrust Solutions</a></p>
Big data management software collects large amounts of data from a variety of sources. Different industries often have different big data sources to consider.
For example, healthcare companies may benefit greatly from remote device monitoring in order to improve patient care. By contrast, retail companies may be more interested in collecting data from smart sensor devices that track consumers’ physical behaviors in store locations.
From here, the management software can determine which data will be essential or inessential to providing your organization with important insights.
First, artificial intelligence sorts big data into sets based on type (structured, unstructured, or semi-structured) in more permanent data management systems. Machine learning processes allow this software to become better at determining data as essential or inessential over time.
Once the data is compiled and organized, data analysis software can then develop insights into these data sets. These insights can come in the form of statistics, lists, predictive analysis, or further questions for analysts to consider when reviewing the data set.
Data analysis software can also provide analysts with graphic visualizations based on these insights. This can make it easier for analysts to identify the most useful data points available in a given set.
With time and feedback from analyst usage, AI-driven big data analysis software can pinpoint which data refinements are most useful to its users.
Based on the refined data sets provided by big data analysis software, analysts are able to formulate business intelligence reports that integrate complex questions and findings from the start. In turn, business leaders are able to make informed company decisions faster and based on more detailed analysis.
With big data and its related technologies, an organization’s leaders and analysts can stay ahead of market trends, while better tailoring their products or services to the needs of their current and target customers.
How Big Data Can Advance Business Intelligence Analytics
Now that you understand how big data and business intelligence work together, what are the benefits of this union?
Ultimately, big data helps create BI processes in which analysts and leaders can get important questions answered faster than ever before. As of 2017, Forbes reported that 53% of businesses were already integrating big data technologies into their analytics.
Organizational leaders ought to consider the positive impact that big data could bring to their BI operations—or else risk getting left behind. There are 3 key ways that big data is reshaping business intelligence for the better:
1. Deeper Insights into Consumer Behavior
Instead of relying on results from long-term consumer surveys, studies, or clinical trials to predict how customers will react to a product or service, big data can provide business intelligence analysts with more direct access to consumer behaviors and attitudes.
Organizations can use this data to uncover new customer bases to target or even develop new products and services based on specific customer needs. Notably, this tactic has been employed successfully by e-commerce giant Amazon, which has used big data to tailor consumer experiences with buying anything from groceries to electronics.
2. Real-Time Data Capturing
Traditional business intelligence takes a retroactive approach to gauging the success or failure of a product or service. Big data collection and analytics allow for a real-time approach to analytics.
For example, as a company’s post, article, video, or marketing campaign gains comments and views from audiences, big data analysis allows these reactions to be collected and analyzed on the spot. Big data can provide immediate insights into consumers’ reactions to a company’s marketing tactics, allowing for nimble adjustments and optimizations.
3. Stronger Predictive Analytics
Not only can big data provide business analysts with more meaningful company insights, it also can strengthen a company’s ability to predict the speed of its own operations.
For example, the software platform FourKites uses big data to provide manufacturing and shipping clients with predictive analytics related to supply chain operations. In turn, these analytics allow manufacturing and shipping companies to more accurately estimate shipping and delivery times for their clients, as well as implement more efficient operational protocols that can improve shipping times.
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