Big data has already become a huge business, and it’s only going to get bigger and even much bigger! Gartner expects the market for Big data and Analytics to generate € 2.11 trillion (US$ 2.7 trillion) in products and services, and generate 4.4 million new jobs by 2014. Let’s quickly review the four main areas IBM suggests we should consider when looking at the challenges attached to Big data:
i. Big data volume
Every day we create 2.5 quintillion bytes of data (2.5 x 1018). How do we turn 12 terabytes of daily tweets into improved products sentiment analysis? How do we sort out this massive onslaught of information and filter the relevant Business Intelligence (BI)? How do we use this information and forecast accurately the development of new products and services to an ever more demanding crowd of ubiquitously digitally connected customers? How do we use these 12 terabytes of daily tweeted information to transform our prospects and customers into brand evangelists?
ii. Big data velocity
How do we analyze the 5 million trade events created each day and identify potential fraud in real time and at best before it even happens?
iii. Big data variety
Big data can be presented as structured and unstructured data, such as videos, pictures, files, audio and text. Information can be streamed from the internet but also from surveillance cameras to target points of interest.
iv. Big data veracity
As the sources of data grow on a daily basis, it behooves us to learn how to trust and rely upon the right data we ought to filter. As of 2012, about 2.5 exabytes of data are created each day, and that number is doubling every 40 months or so. However one in three business leaders don’t trust the information they use to make decisions.
What are the three main techniques available today for businesses to harness the richness of this present 21st century Big data tidal wave?
1. Descriptive Analytics for Big data = HINDSIGHT Business Intelligence.
Descriptive Analytics or descriptive data has almost become the antiquated discipline of quantitatively describing the main features of a collection of data. It can also lead to inductive statistics or the process of drawing a conclusion from data that is subject to random variation. As a whole, and as its name clearly spells out, it remains solely “descriptive”. The traditional corporate jokes about Controlling informing Sales about issues Sales had already known about, can serve here as a humorous example of Descriptive Analytics! Descriptive Analytics has a definite sense of finality, but nonetheless grants us the hindsight to elaborate possible business forecast outcomes.
2. Predictive Analytics for Big data = INSIGHT Business Intelligence.
Predictive Analytics encompasses a variety of techniques from statistics and data mining that analyze current and historical facts to make predictions about future events. It includes modeling, machine learning, data mining and game theory, to analyze current and historical facts and make predictions about future events. Predictive Analytics can be used to help analyze customer patterns and thus predict future behavior! Amazon.com “Wish list” is a very good example of predictive analytics allowing Jeff Bezos to gather likes, wishes and interests, thus helping his company to customize specific customer promotions, discounts as well as future product choices.
3. Prescriptive Analytics for Big data = FORESIGHT Business Intelligence
Prescriptive Analytics is the third phase of Business Analytics (BA). It is the discipline of synthesizing Big data to make predictions and then to suggest decision options to take advantage of the predictions. Prescriptive analytics anticipates what will happen, when it will happen and why it will happen. The interesting aspect of prescriptive analytics is the fact that it can help businesses to foresee possible crisis or problems before they even happen, and thus eliminate the possibility of risky business outcomes.
Most companies still rely on the traditional descriptive data analysis process. How are you coming along with predictive and prescriptive data analysis in your enterprise? Looking forward to your insights and comments.
Posted in BI, Big Data, Business Analytics, Business Intelligence, Cloud Computing, IaaS
Tagged BA, BI, Big data, Business Analytics, Business intelligence, Descriptive Analytics, Predictive Analytics, Prescriptive Analytics
The data-mining gold rush goes on, but we are reaching new levels all the time as “big data” brothers grow. Data is everywhere but how do we filter the noise? It is called Business Intelligence“ data mining” with BI clients such as IBM, Teradata, Tableau offering SaaS services and out of he box programs to help us make sense of it all. But make sense of what? Let’s put things into perspective with a couple of facts nicely put together by Business Intelligence vendor DOMO: Every minute of the day, 48 Hours of uploaded YouTube videos, 2.000K Google search queries, 571 websites are created, 700K users share a piece of content on Facebook, 100K Twits sent. Apple receives 47K App downloads … and the list goes on for much more!
Read this carefully: according Birst’s blogue: “By the end of the decade, given the plummeting prices thanks to Moore’s Law, digital sensors will be almost free and embedded in the world in numbers that will make even those consumer product numbers look tiny. . . and all are going to be compiling data on the world and everything taking place in it. This means that, as busy as the digital world is now, by then we’ll likely be spewing out a year’s worth of today’s data in an hour”..
At the time of their IPO process, Facebook made public that they themselves were already moving 20 terabytes of data every day! Marketers and decision makers need specific tools in order to find out, as Market Samurai puts it, the mots significant gold-nuggets and bits of strategic information in order to make the appropriate business decisions don’t you think? But as Birst writes on its blog: “As for the old IBM’s and Oracle’s: if they don’t adapt, they will be reduced to becoming niche, high-end data specialists. Who needs a gold pan, especially one made of real gold, when you have to find four nuggets of gold in an Everest of rock and dirt”.
Being able to find the right BI tool, filter an over-crowded data universe and locate this little “je ne sais quoi” has become a matter of business survival! This tiny gold nugget of data will hopefully be transformed into an invaluable marketing strategy or product novelty, which in turn could change our ever growing business acumen for the better but unfortunately also for the worst!
Which BI tools are you using or considering acquiring for your company or clients? What is your decision making process? How do you pick up a vendor?