It may be the next “big thing” and yet it has been around for quite some time.

Insights & Perspectives

Wednesday, October 16, 2013
Tim Bankes | View Posts by Tim Bankes

It may be the next “big thing” and yet it has been around for quite some time.


 “Big data” refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, search, share, transfer, communicate, aggregate, and analyze.  It may be the next “big thing” and yet it has been around for quite some time.  More generally, most people refer to big data in terms of all the data being created and captured on the Internet.  The mainstream definition has been defined using the 3 V’s or, alternatively, the 4 V’s, which are:

  1. Volume – how large is the data
  2. Velocity – how quickly is the data being generated
  3. Variety – how many formats of data exist
  4. Veracity – how accurate is the data being generated

Companies, including your company, already use data and data modeling to manage your business, your business processes, and strategies.  It is likely that your business has already seen the benefits of business intelligence and data mining on a smaller scale.  You already have data in rows, columns, charts, graphs, databases, spreadsheets, paper reports, financials, etc. It goes without saying that larger data sets lead to increasingly accurate analysis, which leads to more accurate and confident decision making, which leads to greater operational efficiencies, productivity, profitability all while reducing risk and anxiety.   However, achieving this outcome is not so simple.  Big data is, after all, complex.  It’s crunching large collections of information, analyzing it very quickly (or instantly), and hopefully determining outcomes and predicting accurate conclusions.  Nevertheless, big data will quickly become a key basis of understanding your industry, your competition, the market and the primary drivers of productivity, innovation, and growth.  

Digital data is everywhere.  “Data breadcrumbs” are being created and captured at a staggering rate.  Your organization needs to prepare itself for the ability to store, aggregate, and combine this data and then use the results to perform deep analyses in order to improve innovation and productivity.

How important, relevant, and realistic is this to my industry and my company?  In short, the holy grail of big data is simple: can big data predict future behavior of my clients, my suppliers, my competitors, my employees, or my industry?  Indeed it can.  The McKinsey Global Institute predicts:

  • 40 % projected growth in global data generated per year vs. 5 % growth in global IT spending
  • $600 billion potential annual consumer surplus from using personal location data globally
  • 60 % potential increase in retailers’ operating margins possible with big data


Big data may unearth epiphanies that you never considered or it may reinforce hunches you have suspected all along.  Regardless, it promises to provide a level of insight that you have never had the ability to see before.

However, the next big thing is not a single big thing at all – but a whole lot of little things.  This is also referred to the Internet of Things and it has already well established.  The Internet of Things (proposed by Kevin Ashton in 1999) refers to uniquely identifiable objects and their virtual representations in an Internet-like structure.  The increasing volume of data and the increasing detail of information tracked and captured is obvious.  After all, it’s all around us.  If you own a smartphone, participate in social media, search on the Internet, download songs, watch videos, download books, etc.; you already create or utilize big data on a personal level.  However, it’s more than that.  Its credit card purchases, smart energy meters, phone calls, e-Commerce, transactions, industrial machines, etc.  Other examples of sources of big data are Lexis, Hoovers, search engines, web sites, digitization and datafication projects, and existing business applications. Technically, big data can be structured or unstructured but precisely defining big data is not required to understand its importance.

So, how big can big data get?  The answer is gargantuan.  To forecast the growth of big data, we can’t allow ourselves to make the mistake of looking backward (to analyze trends) when trying to look forward (to predict growth).  The reality is that the growth of data (big data) is not linear, it’s not cubic; it’s exponential.

  • From the beginning of recorded time until 2003, we created 5 billion gigabytes of data
  • In 2011, the same amount of data was created every 2 days
  • In 2013, the same amount of data is created every 10 minutes
  • IBM reports that every day we create 2.5 quintillion bytes of data

Understanding this fact is key and should be a primary motivator to understanding and utilizing big data for your organization.


Once you have access to the big data, data mining provides data insights.  Big data is a big deal and your company’s ability to utilize it will make a big impact on your ability to compete and remain successful.  You are already using data to make decisions in your workplace.  Your competitors most certainly are.  So, it is only logical that big data is the next step to creating data-driven strategies to innovate, compete, and excel by analyzing robust, thorough and real-time information.

“Data are becoming the new raw material of business: an economic input almost on a par with capital and labor. ‘Every day I wake up and ask, “how can I flow data better, manage data better, analyze data better?” - Rollin Ford, the CIO of Wal-Mart.”


The challenge is not acquiring large amounts of data.  That’s becoming increasingly easy.  What do you do with that knowledge?  “Datafication” is required make data actionable.  Datafication is the process of making digitized information usable through text, metadata (data that describes other data) and indexing.

Big data can help manufacturers reduce product development time by 20 to 50 percent and eliminate defects prior to production through simulation and testing.

Some examples of how your organization can use that data, analytics, and knowledge are:

  1. Improve transparency, cost reductions of analyzing failures, issues, defects, etc.
  2. Provide time reductions in development, delivery routes, assignments, etc.
  3. Reduce the time in making business decisions
  4. Improve the development of the next generation of products and services or enhance existing ones
  5. Better inventory management
  6. Manage demand planning across extended enterprises and global supply chains, while reducing defects and rework within production plants
  7. Better tracking of market trends and customer purchase trends
  8. Better visibility to gaps, segmentation of high-quality customers and problem customers
  9. Smarter business decisions based on more accurate business models
  10. Make the data (and information you can learn from it) transparent, valuable, relevant and available for the entire organization
  11. Support (or even replace) human decision-making, thus leading to more accurate data on all aspects of business which lead to better decisions
  12. More precise information creates precisely tailored products or services that can better match what a customer needs


Your organization will need to locate vendors, technologies (like Hadoop, HBase, Cassandra, among many others), and techniques that can help you get a better understanding of big data and utilize big data opportunities.  Some other steps might include:

  1. Set clear, specific, transparent business objectives and data policies
  2. Create a culture open to big data and an organizational structure in alignment with the big data strategy
  3. Utilize big data to improve your business intelligence, decision-making, performance, etc.
  4. Utilize big data to provide predictive analytics, trends, and predict business and market outcomes and set a plan to achieve those goals
  5. Test these strategies, hypothesis and continually make adjustments
  6. Focus on strategies for managing data, reporting on analytics, creating reports with relevant data visualizations
  7. Modify strategies to improve operations, efficiency, quality, customer service, and bottom line results
  8. Modify strategies to personalize product, services, distribution, marketing, sales, etc.

There are significant challenges to overcome as well.  Big data requires increased collaboration between IT and business.   There are issues regarding data ownership, security, and privacy.  Effectively utilizing big data will require hiring big data specialists, analysts, and consultants.  By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.  There specialists will be paramount to helping your organization answer the questions of where do you store that data, what do you analyze and how often, what data is relevant, and how do you use the results to your advantage.

The outcome of big data will change how we look at business, privacy, growth, opportunities, and more.  In order to effectively utilize the opportunities created by big data, organizations will need to partner with progressive companies specializing in big data consulting, utilizing high-performance technologies to accurately analyze big data trends and provide meaningful analysis.  Meaningful analysis can only be accomplished by analyzing meaningful data.  Fortunately, the price of storage is inexpensive (and still dropping), processor technology and speed continues to increase, big data solutions, platforms, and cloud-based resources are becoming increasingly more available.


McKinsey Global Institute Report, May 2012
McKinsey Global Institute Big data: The next frontier for innovation, competition, and productivity, June 2012

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