Last week I attended a partner sales event and was dismayed (unsurprised) to learn that the confusion associated with the definition of cloud computing is logarithmically accelerated when it comes to Big Data. In many ways, I was not surprised. Big Data is not a term used in colloquial public discourse; it is one of those words buried underneath complex technology and esoteric business imperatives. You can ask the average Joe what “cloud computing” means and get one of a few dozen answers. Ask the average Joe about Big Data and you’re as likely to get a blank stare as you are a vituperative response laden with both confusion and derision.
Unsurprisingly, sales guys typically hate me; I’m too nerdy and I ask too many questions. But at this event, they couldn’t get enough of me. I’d like to attribute that to my good looks or my new haircut, but it was more a function of my Big Data knowledge than it was any physical attribute. I was mobbed and bombarded with questions. A sample of the inquiries I entertained:
“What the hell is Big Data?”
“What’s the difference between Big Data and BI?”
“My client has Big Data. How can I help him?”
“My client doesn’t want to be behind the curve when it comes to Big Data. What should I pitch?”
“Is there a product or a solution I can sell when it comes to Big Data?”
“What problems can I fix with Big Data?”
“Wait, who do you work for?”
My experiences at this event led me to two conclusions. One, no one really knows what Big Data is, and two, no one knows the right way to position Big Data as a solution. This blog is going to address both, but facing the wrong direction. I want to tell you what Big Data isn’t, in the hopes that it will clarify what Big Data is.
1) Big Data isn’t a simple and efficient fix for complex problems
Fundamentally, Big Data is a mechanism to attribute correlation to dissimilar pieces of data.The premise here is that the analysis of multiple streams of disparate data will lend itself to analytical processes that will ultimately drive some sort of value to the organization. This process is not simple or efficient. It requires investment of both time and money. However, if leveraged properly, it can give you a give you insights and potential solutions to problems that you didn’t even know existed. It can illuminate buying patterns of customers, operational weaknesses in your manufacturing process, and a multitude of other possibilities too numerous to mention.
2) Big Data isn’t a solution you can lead with
Driving value out of Big Data is a journey that can take you down many different paths. If your client is asking you for a Big Data solution, then you’re already in the wrong place. You need to think about your value proposition to the client; the real value of Big Data is not in answers, but rather in the questions it will raise. In addition, Big Data is something that you must mature to; the path to Big Data includes BI, data normalization, data management, the ability to recognize value from existing data, and a keen sense of perspective that suggests that there’s something missing that you’d like to know. If you are going to pitch Big Data, your client needs to have a good handle on data management and business analytics. If they are not already using these powerful tools to make their organizations more efficient or profitable, a Big Data proposition is an impossibility. Big Data is a generational step-change from BI, but you can’t run before you know how to walk.
3) Big Data isn’t “BI on steroids”
Despite what you may have heard, it’s not about just a lot of data. Ten petabytes of subsurface data in and of itself is not Big Data; an analysis of a single source of truth is analytics and nothing more. This is analytics associated with a large data set; although very valuable, it cannot be considered Big Data. Big Data is not a function of a single data set; it is a function of multiple data sets coming from multiple sources. Running analytics across a massive data set is BI on steroids; running it against multiple, disparate data sets is Big Data. A Big Data model would include information from subsurface data as well as information from public GIS records, emails from geophysicists, white papers from Gartner or Forrester, chat session between drilling engineers, and RSS feeds from this blog and others like it.
4) Big Data isn’t “a solution”
There is no solution when it comes to Big Data. Solutions are not a function of business strategies; they are a subset of organizational demands. You can’t expect Big Data to solve existing challenges; rather, it is a way of looking at institutional issues with an institutional perspective. Just as you can’t solve global challenges with regional thinking, and you can’t use Big Data to solve compartmentalized problems. The solutions it will posit are outside the bounds of traditional perspective. These solutions are not designed to address what you know; they are designed to address causations that you did not know existed. If you’re looking for use cases, you’re probably barking up the wrong tree. Not always. But probably.
5) Big Data doesn’t lend itself well to “low hanging fruit”
This is the single most pervasive reason that funding a Big Data initiative doesn’t get off the ground. Most companies look for results within a quarter or two. These results may be financial or operational; either you’re making/saving money, or you’re speeding something up. Most companies want to see a return on their investment so that projects can be justified. If you’re looking for low hanging fruit, your best bet is to attack a problem using analytics and BI. That, as I alluded to earlier, is where you should start. Big Data is a normative and evolutionary step for companies that want to exploit the data that they have collected. Big Data is not problem solving as much as it is pattern recognition, predictive modeling, and ultimately decision support.
Note that I didn’t use velocity, veracity, volume, or variety once in this blog. Not because they aren’t relevant; it is because they are what you need to start thinking about once you have established that a Big Data pursuit is in the best interests of your client. This is not always the case. Your client must understand the value of data and realize that the financial value of asymmetries associated with proprietary data are a thing of the past. It’s no longer about having the data; it’s about what kind of knowledge you can glean from it.
Getting into Big Data is not a function of investing in high end tools or top-down reporting practices. It is not a technology solution; it is a services practice. You’re not going to save your client a billion dollars by implementing a Big Data platform, irrespective of what Cloudera or IBM claim. At least, you won’t be able to until and unless certain criteria have been met.
Those criteria will be the topic of my next blog.
Jamal is a regular commentator on the Big Data industry. He is an executive and entrepreneur with over 15 years of experience driving strategy for Fortune 500 companies. In addition to technology strategy, his concentrations include digital oil fields, the geo-mechanics of multilateral drilling, well-site operations and completions, integrated workflows, reservoir stimulation, and extraction techniques. He has held leadership positions in Technology, Sales and Marketing, R&D, and M&A in some of the largest corporations in the world. He is currently a senior manager at Wipro where he focuses on emerging technologies.
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