Big Data or Big Hype?
While big data is still in its infancy, early adopters are starting to make progress, particularly in the social media space. Synthesizing enterprise data and bringing it all together remains a challenge that will require significant effort.
By Diane Liebenson
Insight Innovation (a GreenBook initiative) recently hosted a webinar, “Big Data or Big Hype.” Moderated by Lenny Murphy, panelists included:
- Greg Pharo, AT&T
- Michael Wolfe, Bottom Line Analytics
- David Johnson, Decooda
- David Weinberger Why5 Research
Here are my key takeaways:
- Big Data offers unprecedented opportunity as well as challenges
- Using big data to solve business issues is in its infancy, but corporate executives feel tremendous pressure to explore the uses of big data to win real business advantage.
- There are plenty of examples of Big Data initiatives thanks to early adopters
- Market Researchers appear to be watching from the sidelines
- Bridging the gap between the traditional marketing research business model and the Big Data paradigm requires a new set of skills
Defining Big Data
There’s a lot of confusion about the term “Big Data.” Big Data essentially refers to the fact that a lot more data exists today than even a few years ago and there’s a desire to somehow aggregate all of this data from multiple sources and synthesize it in a way that drives more informed business decisions.
The “4 Vs” of big data illustrate why we are truly in a big data revolution:
- The Volume of information. The sheer amount of data is massive.
- The Variety of data. Social media is only one piece of big data, albeit a big piece. Big Data also includes behavioral data, transactional information, videos, photos, GPS locator data, and even voice.
- The Velocity of data. The speed at which data can be accessed and analyzed is exploding, enabling analysis of streaming data.
- The Veracity of data. None of the above matters if the data can’t be successfully integrated together and synthesized, in real-time, in a way that brand marketers can use to make intelligent decisions. Matching data on what people say they will do (attitudinal data often captured in surveys) with what they actually do (behavioral or transactional data increasingly captured real-time through mobile commerce) is a game changer.
The Promise and the Challenge
The promise of big data is to tell a story that nobody could tell before, and to tell that story faster than ever. In essence, big data is about shifting from “What happened? or ‘What is currently happening’ to ‘What do the data mean’ and “What do you predict is likely to happen?’
The analysis of big data is currently in its infancy. There’s an explosion of data but insights haven’t kept pace with that explosion.
One challenge is to figure out what a conversation actually means, and then translate that into a metric that can be used to analyze customer experience and customer satisfaction. David Weinberg called this the “holy grail.”
An even bigger challenge is to figure out how to bring together and synthesize multiple data streams. Organizations have legacy systems – from historical tracking studies, syndicated sources, customer service data, online data from social media, data from mobile, etc. Pulling that data together across different data silos is a challenge whose solution is only just now being addressed.
The Future from a Technology Standpoint
David Johnson from Decooda said that he rarely talks to brand marketers about big data per se. Instead, he and his team discuss how they can help brands solve business issues faster and better by bringing together data and data sources into one centralized location and in a form that allows all stakeholders to access that data. Providing a 360-degree view of the customer experience, in real-time, is far more useful and insightful than relying on a single data source or using months-old data.
David also noted that most technology firms are math-based and look for patterns to analyze conversations. A more nuanced view of brand perceptions than a basic ‘thumbs up’ or thumbs down’ is critical and increasingly possible. By tapping experts with skills in cognitive science, computational math, and linguistics, it is possible to uncover valance beyond sentiment, i.e. emotions like disappointment, irritation, confusion, anger, or outrage. Understanding what people are most passionately talking about at a very granular level is notably more useful to a brand manager.
Moreover, finding a way to integrate this data with behavioral, transactional, and historical data would create the opportunity to do segmentation analyses that can be predictive of what will happen in the future.
The Business Imperative
While there is some discussion about using big data to build an Artificial Intelligence process, panelists indicated this is not a current reality and may not be for quite some time.
Corporate executives feel big data offers tremendous opportunity and, as a result, early adopters are experimenting with how to use it to best advantage. ROI is clearly an issue as brand manager’s focus on definable business issues such as using big data to improve a marketing or communication program, to assist in crisis management, or to solve some other specific business issue.
Measuring ROI has been difficult because there are multiple touch points in marketing – a buyer can listen to an ad on the radio, go online, visit a store, and then buy. Michael Wolfe is leading the industry by creating the first ROI metric for social media marketing specifically, and for other types of activities as well.
Examples of Big Data Initiatives and The Companies That are Leading the Pack
Panelists identified several examples of how big data are being used:
- Researchers can translate social media conversations into basic metrics such as the number of likes, the number of visitors, Klout scores (measures of influence), and descriptive information regarding who is on a social media site.
- A client might ask: “How do I acquire more customers?” Big data tools can take a client’s customer satisfaction data, including conversations via social media and mobile, and link them to survey attitudinal data. A data set with new metrics can be developed from these data, offering a new, more holistic understanding of how to acquire more customers.
- At AT&T, strategy has traditionally been a top-down exercise using aggregated data. Increasingly, however, there is value in looking at granular, transactional or account level data. Big data has created the opportunity to take these kinds of data and leverage that information in ways that traditionally weren’t possible for strategic insights.
- AT&T has used big data to link together data that previously couldn’t be combined. For example, attitudinal and satisfaction data for questions like “How do you like your carrier?” and “Are we doing a good job on dropped calls?” have been linked to actual experiential data on how well the network actually performed in terms of dropped calls. Big data’s ability to link these data resulted in a more accurate, holistic view.
- Coca Cola’s very successful Super Bowl campaign this year integrated data across multiple channels. The campaign is discussed in an article in Ad Age.
- One famous example that emerged in the Q&A session is the (perhaps apocryphal) story of Walmart putting beer and diapers next to each other on store shelves after discovering that dads shopping for nappies would also buy beer because they had less time to visit the local pub.
Panelists noted that while some larger companies are doing interesting work, there’s tremendous fragmentation and nobody has a compete strategy. In fact, David Johnson noted that his firm ends up in a co-creation mode with many of their clients because they need help figuring out how new data analytic tools could help them. As David said, “this is a journey that has only just begun and it’s very much in its infancy.”
Notwithstanding the above, panelists noted that Walmart, Starbucks, and Fedex are doing interesting work and that Coca Cola and Starbucks have a great attitude toward analytics.
The travel industry, particularly the hotel chains and online travel sites, were also singled out for using big data to predict who their best customers are and what types of messaging they should have. They are using micro-segmentation to target specific messaging to different segments.
Difference between Traditional MR Business Model and the Big Data Paradigm
Greg noted that AT&T typically reaches out to specialists who have experience with big data. As a result, they rarely use their traditional market research clients for this kind of work.
Market researchers run the risk of being marginalized given that the use of market research’s ‘bread and butter’ research techniques – surveys and focus groups – are likely to decline in the face of big data’s ability to capture and synthesize real-time conversations and combine it with data on actual behavior.
One significant challenge that market researchers face is that the skill set needed to deal with the big data paradigm and predictive analytics is broader than that of the traditional market researcher. Perhaps this is one reason that marketing researchers have been slow to embrace this area.
Four distinct professions were identified as needing to come together to create the data scientist of the future:
- Traditional analytics
- Data governance
- Traditional consumer-facing research
- Consultancy/business optimization
Those individuals who possess the broad skills of the Data Scientist often have an extensive background in math, physics, and psychology, and they additionally have the ability to derive meaning from data and tell a story. Because it is extremely difficult to find people with all of these skill, AT&T has created teams that combine individuals with deep data governance experience with those who have consumer research experience.
Greg Pharo from AT&T mentioned that while his department works closely with the IT department to think through issues related to data management/architecture, he emphasized the importance of having a data governance team within the market research organization itself.
The research tools available as a result of big data are changing, making predictive analytics possible. This has created a significant and pressing need for Data Scientists, who can unlock the meaning behind big data and communicate that information effectively for business gain. While market researchers are not the ‘go to’ source for insights using this new data paradigm, they nonetheless potentially have the skills to play a role in articulating actionable insights that can be derived from the hose of big data.
While big data is still in its infancy, early adopters are starting to make progress, particularly in the social media space where they are experimenting with taking conversations and creating meaningful metrics to help brand managers target their messaging. Synthesizing enterprise data and bringing it all together remains a challenge that will require significant effort.