The world is becoming a digital space. Industrial internet has made the impossible, possible. Brilliant machines are talking, listening and responding. Today we manage, store and share our entire lives online. Thanks to the easy access of technology right at our fingertips. Data is gathered from our devices, computers, and smartphones that collect and transmit information on what we do daily. Every gadget collects and transmits data contributing to a humongous volume that is impossible to be seen by humans, let alone be understood. If we collect all the data from the beginning of time till the year 2000, it will be less than what we create now in a minute!!! This is the buzz
word everyone’s been talking about; Big Data.
In simpler terms, Big Data is the huge volume of data that cannot be stored or processed using the traditional approach within a given time frame. This data need not be in Petabytes or Zettabytes. A 100 MB attachment in context to a limited 25 MB email capacity is Big Data, simply because it cannot be stored or processed conventionally. And to understand it better, this unstructured data is further characterized into the 3Vs, namely Volume, pertaining to how BIG is this Big Data; Velocity gives us an idea of the speed at which the data is generated and processed and Variety, refers to the type and nature of the data. Is it text? In which format, pdf? Or is it an audio or video clip?
Big Data can prove invaluable for businesses. It can now provide a window into the lives of our customers, helping us understand their likes and dislikes, that was hard to imagine previously. But how do we unravel the strands of Big Data and pick out the relevant information? Since data comes from innumerable sources, how do we know where to look, and how do we access it? The crux would be, how do we transform all this information into knowledge and use it to generate revenue?
And this very question paves the way towards the quintessence of our discussions. Collecting, understanding, analyzing and monetizing Big Data is what we call Big Data Analytics.
The Big Data itself is not intelligent, it’s the analytics applied that makes it work smartly. It is the use of math and statistics to derive meaning from data in order to take better business decisions. But do we have a broader perception of what analytics is? Sure, we do and it is even better to understand and apply when further categorized into Descriptive, Diagnostic, Predictive and Prescriptive Analytics.
Putting it forth in a layman language… What is the data telling us and what is it capable of?
#1 What’s happening in my business? This is Descriptive. How comprehensive and accurate is the live data? How effective is its visualization?
#2 Why is it happening? This is Diagnostic. It’s the ability to drill down to the root cause and to isolate all confounding information.
#3 What’s likely to happen? That should be Predictive. How consistent have business strategies remained over time? What are the historical patterns being used to predict specific outcomes using algorithms? Answers to all these questions lead us to the decisions that are automated using algorithms and technology.
#4 Finally, what do I need to do? This is the Prescriptive step. This is where we recommend actions and strategies based on champion – challenger testing strategy outcomes and apply advanced analytical technology that helps us prescribe the best possible algorithm or solution to put data into action.
So we now know applying a math formula on this “not so clear data” would give us knowledge and probably help us understand where and how to use it. Does it? Is it that simple? Well, certainly not.
All these adventures in the data land have given birth to several high-end technologies such as Hadoop, Kafka, Cassandra and much more to cater our needs and unveil trends, gain additional insights and find answers to most pressing business issues.
Leading IT giants such as Facebook, YouTube, LinkedIn, Google+ and Twitter; receive huge amounts of data on a daily basis and are using data analytics to find patterns in the data generated by the users of their
products and services; to analyze it and grow their business.
Predix, another big name in the revolution of Big Data has helped Infosys engineering team to curb the aircraft landing gear problems and flight delays in the aviation industry. At the time of takeoff of any flight, the problems with landing gears can’t be detected until after pushback from the gate while preparing for a landing. The check delays cost the airline between $25,000 and $40,000, which in turn hampers customer satisfaction. Infosys’s team studied these failure modes and identified various locations where sensors could be applied to provide data for early detection of malfunctions. The landing gears have been mounted with 34 sensors such as hydraulic pressure and brake temperature sensors. Data is gathered from these sensors and analyzed to determine the landing gear’s remaining life and how many more landings can it successfully handle.
During take-off and landing, data is collected at an average of once per second from each of the mounted 34 sensors while Predix Analytics Services are used to diagnose anomalies and determine fixes for issues. This Predictive analytics is run on the historical data to determine the remaining life for each subsystem of every landing gear thus helping to prevent unplanned downtime and flight delays.
As quoted in the words of the former chief executive of Hewlett-Packard, Carly Fiorina – “The goal is to turn data into information and information into insights”; so that this ore of Big Data when generated and analysed can help the technology and businesses grow in leaps and bounds.
