The business technology landscape has been shifting so fast lately that if you turn your head for a moment and then turn back you may not recognize what you’re seeing. Advances in the markets for mobile software and cloud services just in the last few years have opened the way for an entirely new type of relationship between IT and business users. One result of this rapid change is that old boundaries between areas and practices are beginning to blur. Then there are the completely new categories no one had heard of even five years ago. To help you navigate the terrain of business data concepts, we’re going to give you a basic summary of what some of the most common terms refer to and how they relate to each other.
One caveat: not everyone will be a hundred percent in agreement with these definitions. But some level of consensus does seem to be setting in. Still, it’s probably a good idea to press prospective vendors or partners to explain how they’re using the words and have them give an example or two.
This is the broadest category and encompasses the other three terms here (at least as they’re used in a business IT context). BI is data-driven decision-making. It includes the generation, aggregation, analysis, and visualization of data to inform and facilitate business management and strategizing. All the other terms refer to some aspect of how information is gathered or crunched, while BI goes beyond the data to include what business leaders actually do with the insights they glean from it. BI therefore is not strictly technological; it involves the processes and procedures that support data collection, sharing, and reporting, all in the service of making better decisions. One of the trends in recent years has been away from systems that rely on IT staff to provide reports and graphs for decision-makers toward what’s called self-service BI—tools that allow business users to generate their own reports and visualizations to share with colleagues and help everyone choose what course to take.
This is all the ways you can break down the data, assess trends over time, and compare one sector or measurement to another. It can also include the various ways the data is visualized to make the trends and relationships intuitive at a glance. If BI is about making decisions, analytics is about asking questions: How did sales for the new model compare to sales for the old one last month? How did one salesperson do compared to another? Are certain products selling better in certain locations? You can even ask questions about the future with systems that perform Predictive Analytics. Some companies treat analytics and BI as synonymous—or simply rely on one to the exclusion of the other. But analytics is generally the data crunching, question-answering phase leading up to the decision-making phase in the overall Business Intelligence process.
This is the technology that stores and processes data from sources both internal and external to your company. Big Data usually refers to the immense volumes of data available online and in the cloud, which requires ever more computing power to gather and analyze. Because the sources are so diverse, the data is often completely raw and unstructured. Since you’ll probably be using this data for purposes it wasn’t originally intended to serve, you’ll have to clean it up a bit before you can garner any useful insights from it. The systems you put in place internally to track KPIs are obviously the main source you turn to when you need to answer a question about your business, but Big Data makes available almost limitless amounts of information you can sift through for insights related to your industry, your business, your prospective customers. Big Data is the library you visit when the information to answer your questions isn’t readily at hand. And like a real library it allows you to look for answers to questions you didn’t even know you had.
Finding answers you didn’t know you were looking for beforehand is what Data Mining is all about. With so much information available, you can never be sure you’re not overlooking some key fact pointing the way to better performance. Data Mining is the practice of sifting through all the evidence in search of previously unrecognized patterns. Some companies are even hiring Data Scientists, experts in statistics and computer science who know all the tricks for finding the signals hidden in the noise. Data Mining probably fits within the category of analytics, but most analytics is based on data from systems set up to track known KPIs—so it’s usually more measuring than mining.
One of the difficulties in keeping all the terms straight is that there are tools that bring together elements from all of the categories. Power BI, for instance, is obviously a BI tool, but it allows business users to analyze, visualize, and share data in a multitude of ways. You can also use the analysis and visualization functions with information you pull in from the cloud, so it’s an example of Big Data. In the end, though, it’s not as important that we apply the proper labels to everything as it is that you have an effective way to gather and use information to keep your business growing and thriving.
I'm Aptera's Content Strategist. I've been writing about tech and marketing for 5 years and have certifications from HubSpot and The Content Marketing Institute. A big science and literature geek, I taught college rhetoric and composition while I was still busy going to school for way too long, earning bachelor's degrees in anthropology and psychology, along with a master's in British and American literature. Look me up on LinkedIn.