How easy is it to get lost in all the jargon that abounds in the business world? One might say easy as pie … but only if one is referring to eating pie, since making one from scratch is actually quite difficult.
How often have you observed people having a conversation about — or, more to the point, around — big data and/or business intelligence, and the more they talk, the more it becomes obvious that, although they are using the same lingo, they mean very different things from each other? Sometimes one person is confused, sometimes both are confused, and sometimes neither are confused — only using the terms to different ends.
Even on social media, the hashtag #BigData is used quite often and if you want a message to be seen by a larger audience you should use it. But we’re always hesitant because most of the information that we share (and most other companies as well) is really about Business Intelligence, not Big Data. And while #BI is a useful tag, there are some other non-business related messages that use #BI as well which are more popular and trendy (we’ll leave that for you to discover).
There is a clear distinction between big data and business intelligence, and it’s important to know the difference because that knowledge could make a big difference to your company as big data and BI become less mere buzzwords and more everyday realities that every business must consider going forward.
We always advise, don’t let #BigData trick you into a solution with #BigCost.
Volume. Velocity. Variety. These are the three main properties defining big data, separating it from the data your company previously generated and could typically manage via old-school toolsets like Excel and Crystal Reports.
Volume. Volume refers to the quantity of data an organization generates or wants to analyze. The term “big” in big data relates to the size or volume of data under consideration. Typically, the volume of data is so massive that traditional data processing applications can’t process it.
Velocity. When it comes to big data, the term “variety” refers to the substantial diversity of data sources and the assortment of data itself (both structured and unstructured data such as emails, videos, and social media).
Variety. In this context, velocity relates to the speed at which data flows in from sources like business processes, networks, and social media sites. The velocity, or flow of data, is massive and constant.
Big data is cool because it represents previously untappable insights that can potentially lead to major business improvements — game-changers for companies and even entire industries.
But big data in and of itself is still just data. There’s a lot of it, of course. Some of it will be relevant. But some of it won’t. And all of it means nothing without the proper analysis.
If big data were a piece of wood, business intelligence might be the ax that cut it and the artist who whittled it down to a figurine.
Business Intelligence is about action. It means engaging with your information, whether regular-size or big data, and making something meaningful happen through it.
BI entails the organization and analysis of raw data to gain valuable business insights. It’s a Rosetta Stone that translates your information from meaningless symbols, strings of zeroes and ones, into a map that leads to business treasure: better decisions, greater efficiencies, and higher profits.
We focus DashboardFox as a self-service business intelligence tool. But with that said, DashboardFox doesn’t have a limit on the size of your data, so it can potentially work in Big Data requirements. We say potentially because it depends on the specific use case.
Typically Big Data environments will use specialized big data technology, such as Hadoop, or more generically a NoSQL database. While there are ODBC drivers for Hadoop and NoSQL databases, and DashboardFox can definitely leverage those and run queries, it’s probably not an ideal use case. For speed and performance, we typically recommend you take data stored in Hadoop, MongoDB, and other NoSQL options and flatten that data into a relational database for reporting purposes. It just makes sense.
DashboardFox also does real-time querying of databases. There is copying data and reorganizing it into a cache or in-memory cube, DashboardFox always gives you the latest and more real-time operational intelligence. When you’re dealing with Big Data you may need some of that caching or OLAP cube building to make the analysis of very large data sets more efficient. On the flip side, if you are simply pulling aggregated or summarized data queries or drill down into filtered transaction entries, DashboardFox will work well against your big data source.
Before you back up the brinks truck and sign that big contract with a more expensive tool thinking you need it for big data purposes, learn more about DashboardFox. Contact us to discuss your requirements and let’s do a demo and a trial to see if the solution will fit your needs.