The world of business intelligence software shifted acutely over the past couple of decades. While the overall goal to achieve smarter, optimized business has not changed, the methods of doing so are like baseball players in the Steroid Era: they’ve grown immensely. Two areas of business intelligence, big data and business analytics, are the very definition of this new world of business data.

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Both demonstrate how the world of BI has changed, one in a very literal sense. Big data, just as a term, personifies the paradigm shift the field underwent. There’s just more data to work with. And because there’s more available, there’s more that can be done with it.

While the two are distinct terms, there is a significant overlap between them. Both are trying to glean insights from data analysis. Big data analytics tools can perform business analytics and has led to an extreme shift in how it is done and what results it can produce. But there are some differences, as well.

First, let’s outline the general definitions of both, then we can start to delineate the similarities and differences of each, what one means for the future of the other, and the skills and tools needed for the implementation of each.

What is Big Data Analytics?

We’ve covered the specifics of big data analytics before here, but we’ll boil it down in this article in the context of the comparison with business analytics.

At its core, big data analytics is the blanket term for processing large quantities of data. For what purpose is irrelevant: it can be used to discover market, customer, social media, traditional media, geospatial and other trends and subsequent outliers. It can focus on internal data or environmental.

It allows for mass aggregation of data and fusing your internal metrics with whatever relevant environmental data you can get your hands on. This helps you reduce costs, make decisions quicker and predict trends.

Big data has four major components, known as the four V’s:

• Volume: the amount of data being processed.
• Variety: the different kinds of data being used.
• Velocity: the speed at which the data is processed and analyzed.
• Veracity: the accuracy of the data.

These are the four major considerations for businesses looking to implement a big data analytics system. You need to be able to process a lot of data from different sources at high speeds, and then have confidence in the reliability of the end result. From there, we can describe the three different data structure classifications when analyzing big data. Here’s a general overview:

• Structured: Highly organized quantitative data. The easiest to digest and use.
• Unstructured: Includes photos, videos, audio files, text, etc. Difficult to scrape information from, but more enriching than structured.
• Semi-structured: A blend of the two. For example, a cell phone photo with attached metadata.

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Understanding the limitations and benefits of the structure of the data you’re working with and what characteristics of the data need to be considered are essential to extracting the most useful information possible.

Sorting out the structure and characteristics of big data opens a whole new realm of analytics and consequential intelligence that isn’t possible without such a volume of information. Some unique benefits of working with big data are listed in this chart:

 

A point that used to fit into that chart but doesn’t as much these days is “develop a competitive advantage.” While using big data analytics software puts your business ahead of the pack that doesn’t, that group at the rear is dwindling in size, almost daily depending on the industry. For some sectors, such as financial services, the use of big data solutions is a prerequisite, not an advantage over your peers.

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What is Business Analytics?

Business analytics, another term we’ve described in detail here, is simply attempting to leverage data and statistics into optimized business practices in the future. It gives users a high-level overview of their business by mashing together all the available pertinent information.

Business analytics software harvests enterprise data, does some fancy magical math stuffs to it, then spits out actionable insights in the form of trends, patterns and discrepancies/outliers. It focuses on predictive analytics, using precedence and historical statistics to forecast future company endeavors. Businesses can develop predictive models with variable inputs to test out projects and concepts and make decisions based on them.

It pulls data from a variety of sources and formats and makes them cooperate to output usable, meaningful and easy to digest information. The full process has several variations from online sources, but the general consensus of how it’s done includes these elements:

1. Identify problem/need/area for improvement
2. Collect enterprise data on the subject
3. Cleanse and process the data
4. Analyze and report the data
5. Model predictive analytics
6. Deploy model
7. Evaluate efficiency

 

The cycle of business analytics utilizes each of the four types of data analytics: diagnostic and descriptive in steps three and four, and predictive and prescriptive in step five for use in step six.

Business analytics boils down to doing statistical analysis to model what future business activities will result in and how to optimize operation.

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Differences Between Them

Big data analytics and business analytics carry a lot of similarities: they both bite off some data, chew it up and spit it out as some new form of cohesive, useful information. But they are distinct concepts with some key differences:

Business analytics focuses primarily on operational statistics and internal analytics. Big data analytics contextualizes operational data in the much larger scope of industry and market data.

Because of the intricacy that comes with the volume and variety of big data it also has a much higher barrier to entry than business analytics. The most simple form can be accomplished with Microsoft Excel and some basic calculus knowledge. The most bare-bones big data analytics, however, requires comparatively sophisticated data science that will almost definitely require a specialist. Utilizing big data analytics requires knowledge of data manipulation, source compatibility (via APIs and other integrations), data translation and interpretation and other complex concepts, just to even get started. We’ll go more into the skillsets for each later.

In the same vein, business analytics is very human-focused, while big data analytics requires too much processing and attention to be conducted without automation processes. The latter requires help from machines at essentially every step of the process: from extract, transform, load to analysis to visualization to modeling predictive analytics. Business analytics, for the majority of its history and modern use, has been constituted and continues to be constituted by human inferences drawn from data. This, however, is changing, which we’ll also get to.

How They Interact

To say big data analytics has had a huge impact on business analytics is an understatement. Just like every conceivable topic having anything to do with anything, business analytics has exploded in depth, complexity, reach, applications and accessibility since the dawn of the internet age. The ability to stream and access copious amounts of data plays no small part.

Businesses can now crawl huge datasets from social media, sales, customer experience and environmental sources both internally and from their competitors. They can completely overhaul their shipping operations. They can develop customer personas based on thousands of personalized datasets with auxiliary semantic information that allows them to understand exactly why a customer chooses their product over a competitor’s and vice versa.

It’s also helped to level the playing field between multi-billion dollar corporations and small, single-office competitors. Access to public information is fairly universal, with fewer data sources hidden behind paywalls that smaller businesses can’t afford. The development of platforms like Hadoop and Apache means that the little guys can afford to invest in big data without having to commit resources to extensive in-house computing abilities.

Directly, the actual analytics has simply gotten better. Increased processing speeds and data access and more desire for the insights all lead to extreme advances in the field. Big data analytics enhances capabilities at each step of the business analytics life cycle, and each of the four kinds of analytics. When you have more data, the trends become more representative and accurate.

The large sums enable better contextualization of data. Users mash together internal and environmental data to figure out where their business is positioned among its competitors. This can apply to direct KPI measurement if applicable, or more abstract things like public perception through scrubbing of social media and reviews.

Big data also enables data forecasting and projection. This happens both through the summation of large quantities of data and the ability to stream data in real time. A strong continuous data flow from creation to storage lets trends be discovered and decisions to be made off them immediately.

This same data flow uniformly organizes data and stores it autonomously. This increases data governance and makes all of an enterprise’s data more accessible for further processing later. It simplifies use and reuse of data.

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Machine Learning

All that about autonomously mashing data together and projecting out future actions? That’s where machine learning comes in.

Machine learning is the term for AI that utilizes statistics and data input to autonomously improve on its own methodology for processing and analyzing data. All the user inputs are the data and the question they want to be answered. The computer handles the rest.

It allows for big data analytics, and then subsequent improvement on the process. It is essential for tasks like sentiment analysis on social media, customer engagement through chatbots and many predictive analysis efforts.

The system takes in whatever data is available, produces its models, accounts for real-life results, takes in more data, and adjusts future projections. For real-time data streaming, it is constantly evolving and producing insights through calculations that are impossible to understand and produce by humans.

The analysis happens in what is called a “black box,” an area of the program that is difficult to interpret by humans. The produced insights are so sophisticated, that humans don’t know how we got there. It has opened a new door in the world of big data processing.

Leveraging Them

Enhancing your business analytics with big data requires a very high-level skillset from data scientists. The skills of big data analytics and business analytics need to be blended together.

The biggest difference between the two is knowledge of R and/or Python, the two top data manipulation programming languages. When working with large quantities of data, optimizing the code used to process it is essential, and those languages have emerged as the top dogs in the analytics world. This is in addition to the normal coding skills needed by professionals, such as SQL.

A big differentiator between big data analytics for business analytics and simple techniques is industry experience. Having that background knowledge helps analysts determine which datasets are useful and which aren’t.

Implementing big data requires a series of tools, as well. Hadoop and similar platforms allow for data processing and storing distribution without buying more hardware, letting software scale up their analytical abilities.

Access to relational databases and other data sources allow internal data to have more context and create more accurate predictions and models.

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Final Thoughts

Big data analytics and business analytics share a few similarities but are distinct categories of software. Big data analytics can also be used to enrich business analytics.

In this article, we’ve discussed the differences between the two, their similarities, and how big data analytics has forced an evolution in the business analytics world. We dipped our toes into the waters of implementing BA-based big data analytics, and what tools are necessary to make it all work.

If you’re looking to use advanced data analysis to make your business smarter, we have requirements checklists for both big data analytics and business analytics. We offer customizable scorecards for one and the other, so you can decide which product(s) might work best for you.

Did we miss anything in this story? How has big data analytics differed from business anlaytics in your experience? How have you used both? Please feel free to reach out to us in the comments section below.