Big Data Analytics



“You can’t mange what you don’t measure” (W. Edwards & Perter Drucker)

To my understanding, since the enbracement of  Big data analytics, this world became a better place to live and  businesses became more interesting. Organizations now have a better understanding of their businesses and can quickly react to any situation that might give them setbacks, they can now draw more insight from their big data and complex datasets to predict future customer behaviours, trends and outcomes.

Looking at the definition of Big Data:

“Big Data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” (Gartner, 2014)

Big data is the data that exceeds the processing capacity of conventional database systems.  These high Speed Volume and Varities of  Structured, unstructured and semi-structured data are being generated from data collected from social networks, web logs, traffic flow sensors, satellite imagery, broadcast audio streams, MP3s of rock music , web pages content, scans of Government documents, GPS trails, Telementry from automobiles, Banking transactions, financial market data etc.

Volume : Scale of data, the amount of data, the mass quantities of data that organizations are trying to harness to improve decision-making across the enterprise. Data volumes continue to increase at an unprecedented rate

Velocity : Analysis of Streaming  data, data in motion, the speed at which data is generated, processed and analyzed continues to skyrocket, and combined with the real-time nature of how these data is generated.

Variety : Refers to the  complex multiple data types and forms of different sources, which are structured, semi-structured and unstructured from a complex array of both traditional and non-traditional information sources, from within and outside the enterprise.

Big data falls into two categories ; Analytics and enabling new products.

Big data analytics is the application of advanced analytic techniques to very large, diverse data sets that often include varied data types and streaming data. (TDWI)

These analytics explores the granular details of business operations and customer interactions that seldom find their way into a data warehouse or standard report, with unstructured data coming from sensors, devices, Web applications, and social media which were mostly sourced in real time on a large scale. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, businesses can study big data to understand the current state of the business and track evolving aspects such as customer behavior with tools like Hadoop and MapReduce.

Business analytics enable businesses to identify and visualize trends and patterns in areas, such as customer analysis that can have a profound effect on business performance. They can compare scenarios, anticipate potential threats and opportunities, better plan, budget and forecast resources, balance risks against expected returns and work to meet regulatory requirements.
Big data analytics can reveal hidden patterns such as peer influence by customers, shoppers transactions, social and geographical data.


As an enabler of new products and services, by combining a large number of signals of user’s action and those of their friends, Facebook was able to craft a highly personalised user experience and create a new advertising business.

Looking at the story of Amazon which stated as a startup and with the help of big data was able to transform a startup into a massive industry.
Amazon was able to achieve this with the help of big data analytics; by tracking what customers bought, what they show interest in, how they navigate the web and was able to predict what customer is likely to buy next.
“The traditional bookstore had no chance to access this evidently valuable information (MacAfee et al 2012).”

With the help of Big data analytics provided to Dublin City by IBM, the city was able to identify and solve the root causes of traffic in its public bus network, which improved traffic flow and better mobility for commuters.
Integrating data from network of sensors with geospatial data made it easier for the city official to better monitor and manage its fleet of buses in real-time.

The road and traffic department was able to combine Big Data streaming in from an array of sources – bus timetables, inductive-loop traffic detectors, rain gauges and closed-circuit television cameras, GPS updates that each of the city’s 1,000 buses transmits every 20 seconds – and build a digital map of the city overlaid with the real-time positions of Dublin’s buses using stream computing and geospatial data.

With the big data analytics, traffic controllers could now see the current status of the entire bus network at a glance and rapidly spot and drill down into a detailed visualization of areas of the network that are experiencing delay. These insights and the interface allow visualization of the data that gave them an opportunity to identify the cause of the delay as it is emerging and before it moves further downstream. Hence, they were able to accelerate the decision-making process to clear congestion more swiftly. Using advanced analytics on data collected on each bus’ journey in real-time, they were able to quickly hone in on network issues as a result of analyzing Big Data and respond faster.

In conclusion,

Applying this to my current college situation, coming to college has been so easy, in the sense that, I leave my house at the appropriate time, instead of having to stay in the cold weather  waiting for bus that I am not even  sure of its arrival time,  it saves my time and energy and made it possible for me to be punctual, not having too much to worry about is good for someone’s health. Real-time bus time table provided by Dublin bus made life easier  for commuter like me.

As a small business owner, one can now enjoy the derived potentials of big data that was formerly available to statisticians and multinational enterprises only. SMEs now have access to  cost efficient, useful data driven tools and analytical systems to gain meaningful insight on market, competitors and thereby able to measure their business performance and also discover new product and services. Organization can now have greater insight  of their business and are able to predict consumer preferences. Big data can play a significant economic role to the benefit of not only private commerce but also of national economies and their citizens.


Is Big Data too Big for SMEs?
MS&E-238: Leading Trends in Information Technology Stanford University,Summer.2014. Extracted 9:13pm Aug,. 2015.
Stephen p. Robbins. David A. DeCenzo. Mary Coulter
Fundamental of Management 8th ed. p 154.
Big data: The next frontier for innovation, competition, and productivity.Extracted 9:25pm 20/8/2015.

IBM news room 2013. Big data helps city of Dublin improve its public bus transportation network and reduce congestion


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Digital Marketing Strategist

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