Big Data: How to extract value from this ‘buzz word’ with Business Intelligence and analytics
“The Matrix is everywhere. It is all around us. Even now, in this very room. You can see it when you look out your window or when you turn on your television…”
What Morpheus said of the Matrix also holds true for Big Data. It is everywhere. Every click of the mouse, every payWave purchase, every cell phone GPS signal is recorded and contributes to the phenomenon that is Big Data.
All of this information that has never before been available to us is now ready to use. Whether or not we buy into the premise of Big Data will dictate the way we conduct future business operations.
Big Data analytics initiatives are no longer theoretical possibilities or far-flung endeavors to take place in the distant future – plans you’ve put aside for years to come. It may be too late then. Businesses – of varying backgrounds, sizes and pursuits – are already taking advantage of the diverse inputs they have at their disposal, to better understand performance, in order to positively impact future operations and decision-making.
However, a disparity does currently exist between the much hyped promises of Big Data and the tangible outcomes. That is, there is more talk than action. To rectify this current state of Big Data disillusionment, the question needs to be answered: how can organizations derive true value from Big Data initiatives using Business Intelligence (BI) and analytics technologies?
Definition
One of the reasons buying into the Big Data phenomenon so difficult, is that defining Big Data is so difficult. In addition to Big Data’s troublesome definition, grasping the relatively new concept, and differentiating it from current practices, is also particularly problematic.
Why do we only now have Big Data? What have we been working with to date if not Big Data? And is the difference that significant?
All are valid questions, and make the ubiquitous Big Data phenomenon ever more ethereal and inexpressible. Most of these questions, however, can be answered through what the industry calls the “3 V’s”. Which have been covered in the previous piece: Why Big Data and Business Intelligence Are Like One Direction
The definition of Big Data has been debated meticulously amongst industry experts in recent times. Andrew Brust has suggested a tidy definition:
“We can safely say that Big Data is about the technologies and practice of handling data sets so large that conventional database management systems cannot handle them efficiently”. Most can agree that the moving target of Big Data effectively revolves around large sets of data that cannot be processed by traditional tools.
As far as a definition is concerned, perhaps Bill Franks puts it best “The definition of Big Data? “Who cares? It’s what you’re doing with it”.
Just how Big is Big Data?
What constitutes Big Data is a constantly fluctuating variable in itself. This is due to many factors, including the proliferation of existing data types and sources, the emergence of new ones, and the diminishing cost of technology capable of storing and analyzing large quantities of complex information. Ranging from a handful of terabytes to multiple petabytes, data sets are now larger than they have ever been before, and are only getting bigger. A petabyte – for those who aren’t familiar – is a standard unit of measurement that equates to one million gigabytes. A terabyte is 1024 gigabytes. An exabyte is 1,000 petabytes. To put these measurements into perspective:
• 256 MP3 audio files equate to 1 gigabyte
• One petabyte will hold 13.3 years of HD-TV video
• 20 petabytes is the amount of data processed by Google per day
• 50 petabytes would be enough to store the entire written works of mankind from the beginning of recorded history in all languages
To place the accelerated growth of data into perspective, the amount of information that is created is astronomical compared to even 10 years ago. In 2002, 23 exabytes of information were recorded and replicated. We now record and transfer that much information every seven days.
Small business, Big Data
Since the Big Data phenomenon has became more mainstreamed, the composition of companies implementing initiatives has changed dramatically. The benefits have become so profound – and increasingly accessible – that organizations of all sizes, across all industries, are now harnessing the power of Big Data analytics.
A recent Unisphere Research report – 2013 Big Data Opportunities Survey– found 43% of organizations had tailored Big Data initiatives underway. When analyzed by company size, the study substantiated the widespread understanding that Big Data initiatives aren’t just restricted to companies on the fortune 500 list. The report found that when broken down by company size, smaller companies only marginally trail the larger enterprises:
• 37% had 1 to 100 employees
• 43% had 101 to 1,000 employees
• 47% had 1,001 to 10,000 employees
• 53% had 10,000+ employees
Company size no longer solely dictates the ability to implement and attain value from Big Data analytics projects.
So who’s using Big Data?
The implementation of Big Data initiatives is also seen across industries, with a significant number of organizations from most major sectors identifying the ability to leverage Big Data through analytics as critical to business operations. According to Unisphere’s findings, the retail and services sector is leading the charge, narrowly followed by financial services and insurance. Below details the percentage of organizations within each major sector that currently have Big Data initiatives in place:
• Retail/services: 61%
• Financial services/insurance 58%
• IT services/products 45%
• Manufacturing 29%
• Government/ education 27%
From competitive market analysis in the retail sector, to testing and analyzing new products in manufacturing, Big Data has its place within every industry. The opportunities just need to be identified and exploited.
Generating value: Identifying what to analyze
The practical applications for Big Data are near infinite. With more data and more types of data available, organizations are spoilt for choice when it comes to the type of analysis to be implemented.
According to the Unisphere Research survey, the types of Big Data initiatives currently being implemented are as follows:
• 55% Customer analysis / segmentation
• 46% Historical / archived data analysis
• 36% Website monitoring / log analysis
• 36% Competitive / market analysis
• 32% Content management (photos, images, documents)
• 25% Social media analysis
• 25% Testing / analyzing new products / R&D
Different industries can leverage Big Data in various different ways to make better decisions. The retail and services sector, for example, is able to manipulate years of transactional information to generate key insights. Being able to identify your most profitable customers can help generate present day gains via successfully identifying cross-sell opportunities, or future success by helping effectively govern ongoing customer loyalty programs.
Analytics in action
Big Data initiatives have been implemented across the board, paying tremendous dividends to the organizations able to identify its significance – and in which capacity analytics could best ‘put it to work’. Jeff Immelt, CEO of General Electric (GE), said the conglomerate had increased revenue by around $45 billion in the year 2012 through the use of analytics to automate processes and optimize performance.
Multinational professional services firm, PwC, has asserted that Big Data permeates nearly every organization, and is not simply limited to the IT and software industry: “ Data analytics potentially makes every company a software company when it embeds the necessary technology into its products”.
PwC cite the introduction of Nike+, which allows users to measure the distance and pace of their run or walk, as a case in point. Nike+ has generated a great amount of information for Nike, allowing them to more effectively tailor products to consumers needs based on their fitness patterns and levels.
The utilities industry is one particular area that is currently undergoing a digital transformation. The implementation of smart meters, which record energy usage information every 15 minutes, translates to Big Data. The use of smart meters allows energy providers to better monitor energy consumption, identify usage patterns at a macro and household level, reduce power outages, and adapt offerings to individual needs. The implementation of smart meters, and analysis of the resultant information, is expected to help reduce US electricity consumption by three – five percent per household.
The real-world applications of Big Data are only now being translated across all industries. As the bottom line benefits become clearer, the development and adoption of Big Data analytics programs will increase rapidly.
Big Data or big clutter
The differential between Big Data hype and valuable outcomes is quickly disappearing. Organizations that reject the evolution of analytics are quickly being left behind.
How deep is the rabbit hole that is Big Data? Good question. Many businesses have already bought into the phenomenon and have taken the metaphorical red pill. However, with the size of data sources continually growing, and data processing power ever increasing, this may be just the beginning.