Much of the hype around big data focuses on volume, variety, and velocity, but do marketers have any clue on how to apply big data in our brands and campaigns?
One of the funniest statements describing the current state of Big Data goes:
“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”
While amusing, there’s a huge amount of truth in this statement, especially in the advertising/marketing industry. Personally, I’ve heard the term “Big Data” thrown around in almost every single one of my meetings within the last few months.
The biggest offenders are the media publishers. For example, one of the biggest events in the China digital ecosystem this year is Alibaba’s investment in Sina Weibo. Following the announcement of their partnership, “Big Data” was thrown around very liberally by top executives from both companies. The result: one additional Taobao banner on Sina Weibo.
Is this the manifestation of two large Chinese publishers’ Big Data project? A banner? You must be kidding me, for if they are truly doing big data, I sure as hell wouldn’t be shown women’s clothing ads.
The Big Data Fluff
So much of what we hear on Big Data today is plain fluff, yet there’s so much hype surrounding the phenomenon. With all this hype, we as marketers never stopped to ask the hard questions like:
1. Do marketers even need big data?
2. How do we make big data actionable for my brand?
3. How to quantify the value of big data?
4. What’s the marketing manifestation of big data?
The answers to these questions lie in the innate definition of big data that our industry has failed to acknowledge. Let’s examine this in detail.
The 5 V’s of Big Data
Big data can be summarized as the 5 V’s: Volume, Variety, Velocity, Viability, and Value. The first 3 V’s describes the innate properties of big data: the sheer size, different formats (both structured and unstructured data), and the speed that data is generated. The last 2 V’s were later added to make big data more “actionable”. Viability tells us to evaluate the data’s usefulness in driving insights and Value forces us to align data to solving high priority business questions.
So far, all the hype on big data is surrounding the first 3 V’s. Hence technical vendors came up with various IT solutions to process large amounts of data, including unstructured data like video and images. But in the end they are solutions to solve a technical problem. Marketers still have no clue how to apply big data within our brands and campaigns. Instead, the focus should be placed on making big data actionable for marketers via the latter 2 V’s: viability and value.
Data-Driven Marketing -> Big Data
Data viability and value are not entirely new concepts within the marketing industry. These two dimensions of big data are merely describing the very same principles of data-driven marketing, because identifying the business questions (viability) and actionable insights (value) are the very foundation in utilizing data for our marketing campaigns.
So I believe that big data is merely one form of manifestation in data-driven marketing. It all boils down to what kind of insights do marketers need for their brand? Once we define the business need, we can figure out the data sources. If the dataset to answer business questions indeed lie in many petabytes of unstructured data, then by all means let’s invest in the infrastructure to conduct a true big data project. But, if marketers only need simple unstructured datasets to provide actionable insights for their brand, then let’s not overcomplicate things by jumping onto the big data bandwagon. Judging by the current immature state of marketing analytics, I’d say most marketer’s big data needs fall in the latter category.
So do marketers need Big Data?
The answer is “it depends on the business questions you’re trying to answer”. Instead of following the hype, I urge marketers to practice the very basics of data-driven marketing before getting all riled up on Big Data.
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Currently digital director for Mindshare Beijing, Charlie has over five years of experience covering both IT consulting and ad agency industries. He was previously technical director at Tribal DDB Hong Kong, where he led the implementation of analytics and technical innovation across digital campaigns. His previous experiences include data analytics director at Bizcom Consulting in Beijing, and business intelligence analyst at Deloitte Consulting in the U.S. Charlie's project portfolio includes digital analytics, digital campaign production, marketing data integration, as well as large scale data warehouse implementations. He has worked on numerous digital accounts including: McDonald's, Manulife, Wrigley's, Smart Communications, Financial Times, and Friso.
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