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Big Data is about People and Behavior

February 25, 2013


With all of the hype surrounding big data, we should remind ourselves that what is most important is how it can be used to help us better understand ourselves, the decisions we make, and the actions we take. Much of the data that is currently being collected comes from mobile devices. “To say mobility is huge – gargantuan, even – would be an understatement,” writes Chelsi Nakano. As a result, she notes, a lot of attention has been given to the hardware (i.e., smartphones and tablets). She believes that focus is changing. “At today’s intersection of intelligence, computing and massive amounts of ubiquitous and networked data,” she writes, “mobility is no longer about the hardware – it’s driving an entire shift in human behavior.” [“Mobility: It’s About Behavior, Not Devices,” Conspire, 4 February 2013] Marketers obviously want to understand that behavior so they can cater to changing tastes. Nakano continues:

“Lisa Weinstein, President, Global Digital, Data and Analytics at Starcom MediaVest Group, had a similar comment at CES this year: … ‘I actually think that we have to take off the channel lens [from] mobile as a device, and think more about the consumer and behavior, and I think when you do that there’s some really, really interesting implications about the way that consumers are interacting with different types of experiences in places – whether that be at retail or with friends in a social type of environment through their personal device. And so I very much believe that the device is important because of the personalization, but the behavior that it drives is actually a much deeper opportunity for how brands can intersect with consumers in new and different ways. … Because the focus is now on our actions rather than the tools we use to carry them out, we’re going to see even more objects – not just phones, tablets or computers – connected to the Internet, further augmenting this behavior and providing even more touch points for data and information exchange.”

Nakano believes that all of this connectivity (i.e., the Internet of Things) “will not only cause a widespread demand for better ways to obtain data, but also highlight the importance of getting it to the right people in the right forms.” In other words, the Internet of Things will only increase the need for technologies that turn data into actionable intelligence — once again connecting data to people. Last fall Jim Stikeleather, Executive Strategist for Innovation at Dell Services, also stressed the importance of the human connection with big data. “Machines don’t make the essential and important connections among data and they don’t create information,” he wrote. “Humans do.” [“Big Data’s Human Component,” HBR Blog Network, 17 September 2012] He continued:

“Tools have the power to make work easier and solve problems. A tool is an enabler, facilitator, accelerator and magnifier of human capability, not its replacement or surrogate — though artificial intelligence engines like Watson and WolframAlpha (or more likely their descendants) might someday change that. That’s what the software architect Grady Booch had in mind when he uttered that famous phrase: ‘A fool with a tool is still a fool.’ We often forget about the human component in the excitement over data tools. Consider how we talk about Big Data. We forget that it is not about the data; it is about our customers having a deep, engaging, insightful, meaningful conversation with us — if we only learn how to listen.”

Stikeleather offered a few other insights about the human component and its connection to big data. The first insight is that “expertise is more important than the tool. Otherwise the tool will be used incorrectly and generate nonsense (logical, properly processed nonsense, but nonsense nonetheless).” Cliff Cate, Senior VP of Customer Success for GoodData, believes that analytical expertise needs to be employed primarily by firms that offer analytical solutions not by the companies that use those solutions. “Companies need solutions that enable them to use and customize their data easily,” he writes, “because it is the whole team, not just the individual analyst, that knows the business best.” [“Data Scientists Not Required: Big Data Is About Business Users,” SmartData Collective, 5 February 2013] He continues:

“By offering business users intuitive data solutions, we bypass the need for the data scientist, who works in isolation. In fact, most data scientists are associated with the old school of business intelligence, where systems were so complicated that they needed someone with a data science background to run and get value from them. The new generation of solutions, on the other hand, is making it easy for business users to engage big data. An interdisciplinary team will see and use the visuals provided, and collaborate on the best decisions on a regular basis.”

Stikeleather agrees with Cate when it comes to the importance of visualization. He writes:

“Humans are better at seeing the connections than any software is, though humans often need software to help. … We have eons of evolution generating a biological information processing capability that is different and in ways better than that of our digital servants. We’re missing opportunities and risking mistakes if we do not understand and operationalize this ability. Edward Tufte, the former Yale professor and leading thinker on information design and visual literacy, has been pushing this insight for years. He encourages the use of data-rich illustrations with all the available data presented. When examined closely, every data point has value, he says. And when seen overall, trends and patterns can be observed via the human ‘intuition’ that comes from that biological information processing capability of our brain. We lose opportunities when we fail to take advantage of this human capability. And we make mistakes.”

Stikeleather asserts that “there are many other risks in failing to think about Big Data as part of a human-driven discovery and management process.” He provides examples of insensitivity and unexpected consequences when big data tools are “over-automated.” He believes that keeping the human component in mind is central to turning data into information and insight. He explains:

“Although data does give rise to information and insight, they are not the same. Data’s value to business relies on human intelligence, on how well managers and leaders formulate questions and interpret results. More data doesn’t mean you will get ‘proportionately’ more information. In fact, the more data you have, the less information you gain as a proportion of the data (concepts of marginal utility, signal to noise and diminishing returns). Understanding how to use the data we already have is what’s going to matter most.”

The staff at the Social Media Observatory agrees that value of data relies on what they call “the human algorithm.” [“The Human Algorithm: Redefining the Value of Data,” Social Media Observatory, 11 December 2012] The article states, “Everything we share, everywhere we go, everything we say and everyone we follow or connect with, generates valuable information that can be used to improve consumer experiences and ultimately improve products and services.” The article continues:

“This ‘big’ data that will help businesses evolve and adapt in a new era of connected consumerism. More importantly, the study and understanding of relevant big data will shift organizations from simply reacting to trends to predicting the next disruption and adapting ahead of competition — thus, marking the shift from rigid to adaptive business models. From business to education to government and everything in between, without studying how the undercurrent of behavior is evolving, organizations cannot effectively adapt to new trends and opportunities. Change though, cannot be undertaken simply because of pervasive data.”

Like other pundits, analysts at the Social Media Observatory emphasize that the goal of gathering and analyzing data is to gain actionable insights — with emphasis on action. They assert, “Without interpretation, insight and the ability to put knowledge to work, any investment in technology and resources is premature.” The article continues:

“The reality is … that how organizations connected with customers yesterday is not how customers will be served tomorrow. Meaning, the entire infrastructure in how we market, sell, help, and create now requires companies to not only study data and behavior but also change how it thinks about customers. This is a bona fide renaissance and to lead a new era of customer engagement requires knowledge acumen. I refer to the confluence of data and interpretation as the human algorithm—the ability to humanize technology and data to put a face, personality, and voice to the need and chance for change. Data tells a story, it just needs help finding its rhythm and rhyme. … Much of what we see today is important, but it’s measuring activity not translating behavior into creativity or strategy.”

According to the article, “The human algorithm is part understanding and part communication. The ability to communicate and apply insights internally and externally is the key to unlocking opportunities to earn relevance.” It concludes:

“Beyond research, beyond intelligence, the human algorithm is a function of extracting insights with intention, humanizing trends ad possibilities and working with strategists to improve and innovate everything from processes to products to overall experiences. The idea of the human algorithm is to serve as the human counterpart to the abundance of new social intelligence and listening platforms hitting the market every day. Someone has to be on the other side of data to interpret it beyond routine. Someone has to redefine the typical buckets where data is poured. And someone has to redefine the value of data to save important findings from a slow and eventual death by three-ring binders rich with direction and meaning. … Even though sophisticated tools can help track data points that can lead to these insights, it still takes a human touch to surface them and in turn advocate findings within the organization. It’s the difference between insights, actionable insights, and executed insights. … Those who don’t plug in and invest in technology’s human counterparts are in turn making an investment toward potential irrelevance. But remember, data is just the beginning. Data must always tell a story and that takes a human touch to extract data, surface trends, and translate them into actionable insights across the entire organization.”

Clearly, the Big Data Era is inextricably connected to technology. We need to remember, however, that big data also is inextricably connected to people.

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