Business Intelligence and Big Data: What’s the Difference?
Business intelligence and big data sound similar but they are not the same.
In general, business intelligence (BI) refers to structured and readily usable information that impacts profitability and competitive advantage. On the other hand, big data — as you might expect — refers to enormous heaps of digital information scattered all over the place, with practitioners typically focusing more on unstructured data.
Both fields involve crunching information to generate insight and make things happen. But they differ in terms of the volume and nature of information each focuses on, as well as the tools they use to process data. Their specific aims and outcomes sometimes coincide but not always. You don’t need big data to build a decent business intelligence system, for example, but big data dramatically enhances BI capabilities at scale.
In this article we break down what you need to know about business intelligence vs big data.
Scope, formal definitions and benefits: BI vs. big data
Business intelligence helps companies make smart, revenue-boosting decisions. Enterprises purposely use BI to improve process, planning, and profits. Meanwhile, big data can perform the same functions but can do so faster and at scale. Big data also helps organizations achieve many remarkable feats: design the perfect bra, fight cancer, protect national security, enhance athletic performance, and preserve biodiversity. Just to name a few.
Over the years, think tanks and business leaders have attempted to update the meaning of big data and business intelligence as economic and technological contexts evolve. Here are two of the most widely-cited definitions:
“Business intelligence (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.” (Gartner)
“Big data is a term applied to datasets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics — high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media — much of it generated in real time and in a very large scale. (IBM Analytics)
Based on standard definitions, business intelligence and big data refer to two distinct but related disciplines, differentiated primarily by the degree at which each is capable of handling the three V’s of data (volume, velocity, variety).
Business intelligence practitioners generally handle structured data while big data professionals feel at home processing humongous volumes of unstructured data at lightning speeds. Both can provide the fourth and most important V (i.e., value) in the form of descriptive, predictive, and prescriptive analysis/reporting.
Finally, each field uses a different set of enabling technologies, with the data science toolbox generally more sophisticated than the one for BI, although they might share common tools such as SQL and Python.
Business intelligence and big data: benefits
Big data and business intelligence offer distinct value for organizations such that many large enterprises employ both BI analysts and data scientists to mine information and refine it into gold.
Business intelligence covers gathering, monitoring, and processing of raw, but often structured information to detect, develop, or drive opportunities for improving business performance. Organizations leverage BI to support many departments including sales, compliance, recruitment, production, talent management, customer success, and marketing. Using BI tools, companies can generate game-changing insights such as the best pricing model for a specific location or the most effective workflow/staff scheduling for a given manufacturing plant.
On the other hand, big data can deliver even more marvels. Enterprises use big data analytics for similar purposes including cost reductions, faster timelines, anomaly detection, better profit margins, and risk mitigation. Because big data makes a significant difference at scale, governments, financial institutions, large retailers, and telecom giants maintain large and active data science teams.
Tools and technologies
To glean value from information, BI professionals use a wide range of tools including spreadsheets (such as Excel), market insight resources (such as those provided by Thompson, PwC, and LinkedIn), data warehouse services (such as those offered by SAP, Oracle, and Amazon), business analytics software (such as Power BI, Sisense, and Tableau), and database management languages (such as SQL).
On the other hand, big data professionals — who are often mathematicians, statisticians, actuaries, or true-blue data scientists — use highly specialized tools including big data platforms such as Cloudera and Apache Hadoop, cluster programming models such as Apache Spark and MapReduce, and database programs such as MongoDB to navigate and make sense of largely unstructured oceans of data.
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