Big Data vs Business Intelligence: The Common Divide
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The analytics platform has come a long way since its inception in the form of decision support system software in the 1970s. And it has continued to evolve rapidly over the past decade to handle the enormous, growing volume of data streaming at analysts from all angles. The rise of self-service BI put data analysis in the hands of every user, ushering in accessible platforms that produce palatable, actionable insights to fuel informed business decisions in every level of the company.
But there is a persisting divide in the majority of existing BI platforms -- they rarely excel at combining business intelligence (BI) capabilities and big data analytics, often focusing on one or the other. The synchronization of big data and business intelligence is what helps businesses truly harness the power of their data and illuminate answers to questions they didn’t even know they wanted to ask. While many platforms are big data versus business intelligence, they should really be big data and business intelligence.
It could be argued that big data is the successor of business intelligence. BI and big data are similar in that they both, combined with the right tools, are used to help decision makers gain insight into the inner workings of their business with the ultimate goal of making better, informed decisions. There are, however, distinct differences that make the combination of the two on a single platform significantly more valuable than one or the other.
Core Differences Between BI vs Big Data
There are three core differences between BI vs big data: volume, velocity, and variety.
- Volume: Big data is presenting us with petabytes to work with - but volume alone is not useful. BI specific technologies such as in-memory and columnar databases can handle big data volumes.
- Velocity: White BI specific technologies such as data warehouse appliances are built mostly using data virtualization or batch-oriented data flows, big data enables us to process data streams in real-time / near real-time.
- Variety: While BI is limited to structured data, big data can handle structured, unstructured, and semi-structured data. BI tools analyze historical data, and big data analytics dashboards analyze both historical and real-time data.
The main purpose of big data is to capture, process, and analyze the data sets. Major benefits include improved insights, forecasting, prediction, identification of trends, implementation of new strategies, fraud detection, and overall improved service. The main goal of business intelligence is to extract information directly from the data source and deliver accurate, comprehensible reports that help improve business processes. Major benefits include faster and more accurate reporting and analysis, improved data quality, and improved operational efficiency.
Business intelligence answers your Where and What, and big data analytics answers your How and Why. The integration of BI and big data analytics significantly improves our ability to study the economic behavior of society, which makes it crucial for businesses to adopt a platform that excels at supporting the harmony of business intelligence and big data in one cohesive, accessible space with striking, consumable data visualizations.
Future of BI Data Solutions
Platforms that support BI data solutions without big data analytics capabilities will soon be defunct, as the volume of data streams we face continues to grow exponentially. Crucial capabilities that are missing from BI-only platforms include: flexible programming language accommodations, statistical algorithms and what-if analysis, streamlined and interactive APIs, and an infrastructure that enables data operations such as, descriptive, diagnostic, predictive, and prescriptive analytics.
HEAVY.AI’s converged analytics platform provides dynamic support for both business intelligence and big data analytics operations in a space that creates interactive data visualizations and enables business users to explore and be at one with their data at the speed of curiosity. For example, energy business intelligence can help organizations make informed decisions on investments.
Heavy Immerse allows you to instantly cross-filter and visually interrogate all of your data, regardless of scale, and expand your use cases beyond the limitations of traditional BI. The open platform allows you to accelerate third-party BI, GIS, and data science platforms with standard ODBC, JDBC, Thrift, and Arrow connectivity. And blazing fast SQL and Python data science integrations of HeavyDB enable significantly deeper analysis, faster and easier feature engineering, and easier ongoing monitoring and maintenance of Machine Learning models.
Modern big data integration and processing tools must integrate with data streaming from a wide variety of sources and networks, such as social media, sensors, network logs, and web clickstreams from around the world. HEAVY.AI can easily ingest this variety and volume of data in real-time and make it readily available for interactive data exploration by business analysts and data scientists.
Interactive real-time analytics is seamless and simple with HEAVY.AI's GPU computing and server-side rendering, standard SQL semantics support, and location intelligence support. HEAVY.AI is at the forefront of location intelligence for big data analytics, combining unprecedented volumes of geospatial and BI data in one interactive analytics experience.
At the heart of data analysis is curiosity, and curiosity flourishes with the proper tools and support. We can tackle modern data challenges with the proper inspiration, and nothing is more inspiring than striking, interactive, real-time visualizations that transform dense, cumbersome business intelligence and big data into illuminating, immersive information. HEAVY.AI's convergence of BI and big data supports exploration at any scale and answers your questions as fast as you can ask them.