I read an interesting article by Louis Colombus in Forbes on how big data is in the top 5 most disruptive innovations. Based on his research, he quoted that
- making it core to the future of digital factories.
- 36% expect mobile technologies and applications to improve their company’s financial performance today and in the future.
- 49% expect advanced analytics to reduce operational costs and utilise assets efficiently
Working in the ecommerce sphere, I tend to agree with Louis’s view and here are the reasons why.
“You can’t manage what you can’t measure”
There’s much wisdom in that saying, which has been attributed to both W. Edwards Deming and Peter Drucker, and it explains why the recent explosion of digital data is so important. Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.
The familiarity of the Amazon story almost masks its power. We expect companies that were born digital to accomplish things that business executives could only dream of a generation or few years ago. But in fact the use of big data has the potential to transform traditional businesses as well. It may offer them even greater opportunities for competitive advantage (online businesses have always known that they were competing on how well they understood their data). As we’ll discuss in more detail, the big data of this revolution is far more powerful than the analytics that were used in the past. We can measure and therefore manage more precisely than ever before. We can make better predictions and smarter decisions. We can target more-effective interventions, and can do so in areas that so far have been dominated by gut and intuition rather than by data and rigor.
An HBR article written by Andrew McAfee and Erik Brynjolfsson states that as the tools and philosophies of big data spread, they will change long-standing ideas about the value of experience, the nature of expertise, and the practice of management. Smart leaders across industries will see using big data for what it is: a management revolution. But as with any other major change in business, the challenges of becoming a big data–enabled organization can be enormous and require hands-on—or in some cases hands-off—leadership. Nevertheless, it’s a transition that executives need to engage with today.
1. What is big data analytics?
According to SAS, big data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions. With big data analytics, data scientists and others can analyse huge volumes of data that conventional analytics and business intelligence solutions can’t touch.
I have not worked in a business that is not obsessed with analysing data, whether it is customer data, web data, infrastructure data etc. In fact i know that most businesses do because data analyst are very hard to find, and if you are 16 or 18 years old with a good math degree I would seriously consider adventuring myself into this type of role!
2. What has changed in the past 3 years?
As of 2012, about 2.5 exabytes of data are created each day, and that number is doubling every 40 months or so. More data cross the internet every second than were stored in the entire internet just 20 years ago. This gives companies an opportunity to work with many petabyes of data in a single data set—and not just from the internet. For instance, it is estimated that Walmart collects more than 2.5 petabytes of data every hour from its customer transactions. A petabyte is one quadrillion bytes, or the equivalent of about 20 million filing cabinets’ worth of text. An exabyte is 1,000 times that amount, or one billion gigabytes.
For many applications, the speed of data creation is even more important than the volume. Real-time or nearly real-time information makes it possible for a company to be much more agile than its competitors. Now this is where I feel even more progress will be made. Our systems and human nature is to get things faster and faster and faster!
Big data takes the form of messages, updates, and images posted to social networks; readings from sensors; GPS signals from cell phones, and more. Many of the most important sources of big data are relatively new. The structured databases that stored most corporate information until recently are ill suited to storing and processing big data. At the same time, the steadily declining costs of all the elements of computing—storage, memory, processing, bandwidth, and so on—mean that previously expensive data-intensive approaches are quickly becoming economical.
As more and more business activity is digitised, new sources of information and ever-cheaper equipment combine to bring us into a new era: one in which large amounts of digital information exist on virtually any topic of interest to a business. Mobile phones, online shopping, social networks, electronic communication, GPS, and instrumented machinery all produce torrents of data as a by-product of their ordinary operations. Each of us is now a walking data generator. I work in retail and we are not at the forefront of new technology but we are getting there and beacons are an example of that.
3. Why is big data important? Benefits and challenges
A report from McKinsey Global Institute estimates that Big Data could generate an additional $3 trillion in value every year in just seven industries. Of this, $1.3 trillion would benefit the United States. The report also estimated that over half of this value would go to customers in forms such as fewer traffic jams, easier price comparisons, and better matching between educational institutions and students. Note that some of these benefits do not affect GDP or personal income as we measure them. They do, however, imply a better quality of life.
Out of 100s of ideas, McKinsey believes big data analytics is one of the top 5 catalysts that can increase US productivity and raise thee GDP in the next 7 years. For the retail sector, big data applications covered three areas—supply chain, operations, and merchandising. By creating greater performance transparency, these companies can optimize inventory, transportation, returns, labor, assortments, and more. They estimate that this sector will gain $30-55 billion in GDP through use of big data. In our previous article on 20+ big data examples, we provided links to stories about how Walmart, Sears, Kmart, and Amazon are using big data. McKinsey’s quote that will make my CFO and CEO listen is 60% potential increase in retailers’ operating margins possible with Big Data.
5 key benefits of big data:
1. Big Data can unlock significant value by making information transparent. There is still a significant amount of information that is not yet captured in digital form, e.g., data that are on paper, or not made easily accessible and searchable through networks. We found that up to 25 percent of the effort in some knowledge worker workgroups consists of searching for data and then transferring them to another (sometimes virtual) location. This effort represents a significant source of inefficiency.
2. As organisations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days and therefore expose variability and boost performance. In fact, some leading companies are using their ability to collect and analyse big data to conduct controlled experiments to make better management decisions.
3. Big Data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services.
4. Sophisticated analytics can substantially improve decision-making, minimise risks, and unearth valuable insights that would otherwise remain hidden.
5. Big Data can be used to develop the next generation of products and services. For instance, manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance to avoid failures in new products.
However, not all is that simple and McAfee and Brynjolfsson identified 5 key challenges to big data, which are:
1. Leadership: Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions. Big data’s power does not erase the need for vision or human insight. On the contrary, we still must have business leaders who can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings, articulate a compelling vision, persuade people to embrace it and work hard to realize it, and deal effectively with customers, employees, stockholders, and other stakeholders. The successful companies of the next decade will be the ones whose leaders can do all that while changing the way their organisations make many decisions.
2. Talent Management: As data become cheaper, the complements to data become more valuable. Some of the most crucial of these are data scientists and other professionals skilled at working with large quantities of information. Statistics are important, but many of the key techniques for using big data are rarely taught in traditional statistics courses. Perhaps even more important are skills in cleaning and organizing large data sets; the new kinds of data rarely come in structured formats. Visualization tools and techniques are also increasing in value. Along with the data scientists, a new generation of computer scientists are bringing to bear techniques for working with very large data sets. Expertise in the design of experiments can help cross the gap between correlation and causation.
3. Technology: The tools available to handle the volume, velocity, and variety of big data have improved greatly in recent years. In general, these technologies are not prohibitively expensive, and much of the software is open source. Hadoop, the most commonly used framework, combines commodity hardware with open-source software. It takes incoming streams of data and distributes them onto cheap disks; it also provides tools for analyzing the data. However, these technologies do require a skill set that is new to most IT departments, which will need to work hard to integrate all the relevant internal and external sources of data. Although attention to technology isn’t sufficient, it is always a necessary component of a big data strategy.
4. Decision making: An effective organisation puts information and the relevant decision rights in the same location. In the big data era, information is created and transferred, and expertise is often not where it used to be. The artful leader will create an organization flexible enough to minimize the “not invented here” syndrome and maximize cross-functional cooperation. People who understand the problems need to be brought together with the right data, but also with the people who have problem-solving techniques that can effectively exploit them.
5. Company culture: The first question a data-driven organisation asks itself is not “What do we think?” but “What do we know?” This requires a move away from acting solely on hunches and instinct. It also requires breaking a bad habit we’ve noticed in many organizations: pretending to be more data-driven than they actually are. Too often, we saw executives who spiced up their reports with lots of data that supported decisions they had already made using the traditional HiPPO approach. Only afterward were underlings dispatched to find the numbers that would justify the decision.Without question, many barriers to success remain. There are too few data scientists to go around. The technologies are new and in some cases exotic. It’s too easy to mistake correlation for causation and to find misleading patterns in the data. The cultural challenges are enormous, and, of course, privacy concerns are only going to become more significant. But the underlying trends, both in the technology and in the business payoff, are unmistakable.
Convinced that Big Data should be part of your business strategy for the next 5 years? If not, you might be heading down the well and your business with it!