Confusion Kills Big Data

Confusion Killer: Big Data Analytics vs. Business Intelligence Demystified

What’s the difference between big data analytics and business intelligence? Is there a difference? The answers to these questions may vary a great deal depending on who you ask. In a world where many terms are used interchangeably and others seem to have taken on all new meaning, keeping up with the lingo can be challenging for the most knowledgeable of IT scholars. Let’s take a closer look at these two terms to see if we can determine just how they fit into the data universe.

Business Intelligence and Big Data Analtyics Defined

Based on entries in data-driven dictionaries across the web, business intelligence (BI) is best described as a collection of technologies, tools, skills, and practices that help organizations gain a better understanding of information and its business value. A BI application may perform a wide variety of functions, including collecting and reporting data, data and text mining, analytics, and predictive analysis. Most BI solutions are designed so organizations can see their data as prestigious nuggets rather than mere numbers and characters.

Entries for big data analytics define the process of how companies go about collecting and interpreting large volumes of information from multiple sources. The analytics part itself often entails using extensive statistical analysis, predictive modeling, and other strategies to extract the value of data. In this case, the aim is leveraging data to make informed decisions and optimize specific areas of business. What’s interesting about all this is that when examining these definitions, you may come out viewing big data analytics as a form of business intelligence – and you wouldn’t necessarily be wrong.

Effective Apart, Better Together

Traditionally, business intelligence tools have functioned by helping organizations find answers that sit in website traffic logs, videos, social media conversations, and other sources. Analytics has traditionally taken the solutions-oriented approach of generating value by transforming raw data into knowledge. Despite being effective in their own unique way, both tactics have been limited by databases that were only equipped to handle so much in the way of data storage, processing, and management capabilities. Times have certainly changed for the better.

Today, it is not uncommon to find analytics and business intelligence living under one unified roof. These all-in-one platforms constitute what big data is all about by giving companies the ability to handle larger sets of diverse data – from structured data stores to unstructured email archives, they offer the databases and processing frameworks to handle it all. While the integrated solutions tend to be marginally more expensive in terms of upfront fees, they earn their keep by helping organizations uncover new business opportunities, reduce operational expenses, and enhance their decision making capabilities.

Although they can live independently, big data analytics and business intelligence are usually better as a team. Apparently practitioners agree because both are driving a high rate of adoption and spending. According to a report by Gartner, analytics and business intelligence software generated $14.4 billion in revenue in 2013, which was 8 percent higher than the $13.3 billion generated in 2012. So when you go shopping for a big data analytics solution, keep in mind that it may benefit to have some BI tools in the package as well.




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