Is the dream of having endless data turning into a nightmare for marketers?

And to ask another question: Is there even any proof that Big Data improves or will improve brand management, advertising or business performance?

According to Cisco, the number of connected devices will grow by 500 percent until 2020 to reach 50 billion. Think about how much data will be accumulated. Imagine how much information about user behavior will be collected. But what are we going to do with it? 


Let’s not beat around the bush or talk the talk of many others. Instead let’s just face it: Big Data is nothing but a pile of useless junk if  you don’t know how to connect the dots. To do this you need to ask the right questions, or else the answers will remain unqualified and create nothing but confusion.


In short, there is much more to big data than only data collecting. One of the first barriers to making smart use of Big Data is collecting the right data, because the sources are just as important as the data itself. Before we even start to use data, we need to make sure that it makes sense and will be useful. Think about it as crude oil that is unusable until it has been refined. Or ore that has to be molten out of the rock and then forged into something new – something that will fulfil a specific purpose.

The challenge is not only to collect as much data as possible, which is how we might be inclined to view Big Data, but to apply curiosity, logic, intelligence, common sense, creativity and sometimes even use psychological values that will lead to real insights about real people and brands in a vast market that is becoming more fragmented than ever. Only when we face and master this challenge, will there be a new quality of intuition rising from this new and extensive knowledge. Only then we will have the insights we haven’t expected. Because we checked for it in places we knew would provide reliable data.


Which brings us to the next point: Big Data means Big Work. Work that cannot be done in a way it has always been done before. When Amazon started to build algorithms based on books, authors, price, frequency of purchase etc., it was acting much the same as the neighborhood bookstore that served its steady customers. But Amazon evolved the process further by comparing all its customers’ purchases to offer better recommendations and succeeded through service that was partly based on the smart usage of the collected data.

Does this mean that data driven decisions are the better decisions? Yes and No. There is no guarantee that these decisions will automatically lead to success.
Instead as the users of Big Data we need to stay curious and to apply insights on a trial and error basis; to find better results faster that enable us to learn, so we can optimize the communication (channels and content) accordingly and step by step get closer to the goal that has been set.


Data in itself is lifeless. What it needs is context, creativity, brainpower, appreciation and understanding. This means knowing how to interpret the data, make the right connections and draw the right conclusions. Only then can smart science and intelligent technology bring the ones and the zeroes to life.

As statistician Jianqing Fan of Princeton University and his colleagues explain: “[Big Data] creates issues of heterogeneity, experimental variations, and statistical biases, and requires us to develop more adaptive and robust procedures. To handle the challenges of Big Data, we need new statistical thinking and computational methods.” Even though Fan talks about scientific data, his guidance also applies to the world of economics, marketing, communication and brand management.


In January 2005 Germany has launched a toll billing system for commercial truck traffic on the German autobahn. The technology is GPS based and the trucks are equipped with so called “On Board Units” (OBU’s) that are used for monitoring, positioning and billing.  They also feature infrared interfaces that communicate with stationary control gantries along the autobahn.

The system collects the data about each truck and also accumulates data about the HGV traffic density. And this data is immediately available.  This enables a highly accurate forecast about the German production index.

Because the HGV traffic load coincides with the production activity, this data delivers a great early indicator of production as it is measured by the German Production Index. This in turn is a widely recognized leading indicator of the German Gross National Product.

And due to the instant availability of the data it has become possible to give an economical prognosis three months earlier than ever before.


There are many cautionary examples of how Big Data can be misinterpreted:
“In January 2014, researchers at Princeton University claimed that Facebook would lose almost 80 percent of its users within the next three years. They linked the social network’s rise to the spread of disease, and concluded that Facebook, like all epidemics, would eventually die off as people lost interest. The researchers’ claims were largely based on analysis of Google trends, which they used to determine how often people searched for the term “Facebook” on Google. Their analysis showed that searches for the social media site were on the decline, and noted that the long-since-forgotten MySpace and Bebo social networks followed a similar pattern before their own demise.”(Source “Silicon Angle”, Jan. 24, 2014)

In another example of data without context telling a misleading story, Google launched their Flu Trends website in 2008 with a map indicating the expansion of flu. During autumn and winter most areas turned deep red, reflecting strong expansion. Meanwhile the Centers for Disease Control (CDC) built up a flu-tracking system based on about 700,000 weekly reports collected from local doctors. The CDC’s data painted a less alarming picture. What explains the difference? Google used search data that also came from students and users, who simply searched for more information on flu. The CDC used recorded actual flu cases.


What these cases show us is that data is knowledge, but knowledge needs understanding. Google’s flu data, at first look, didn’t provide users a clear context for their data. Ultimately this might have led to an unjustified increase of vaccine sales to healthy people. The lesson for marketers is that Big Data isn’t just a continuously running source of information, but also potentially a source of errors that can lead to confusion. So even though we, more than ever before, have the opportunity to gain a wide-ranging grasp of markets, consumers, products, distribution, competitors and point-of-sale-locations, we also need communication strategy-based methods and tools to come to the right conclusions that will generate more value for our clients and their customers in the end.

Yes, we need to know as much as we can about consumer behavior. Yes, we are in the age of new possibilities. Yes, we can dive deeper into data than ever before. But in these opportunities lie many challenges. Sales, marketing and communication experts must find new roles and re-define the ways they structure their budgets, because in the end it is all about business.


Let’s talk business for a moment. Let’s say we have embedded a structure with content strategies and communication systems that not only gather big data from various channels, but also generate first party data and merge them perfectly. That alone is not an easy task, but it can be done.

At that point we have an information collection that represents a perfect blend of brand values, customer expectations and needs, intelligent insights, and sales triggers.


What happens next? We will build more insightful concepts and elaborate content strategies that in turn will generate more data. This leads to an ongoing communication loop that needs to be fed, nurtured and cultivated in the long run using relevant content.

Such a communication loop follows new rules of engagement and is not based on one campaign or one idea but on relevant content that enhances and drives a movement.

Sure, everybody has heard about content marketing driving brand values. But that has been done over years. There are some brands that have done this successfully, but for the most part this is a whole new game. In this new game, marketers can utilize content to generate first party data that in combination with market, sales and communication data provides unique knowledge about the consumer that defines and re-defines the course of the communication flow. That new flow looks like this:



#1 Gather – identify and collect the data needed

#2 Analyze – distill the information based on the KPI’s and communication objectives

#3 Understand – get the insights that define and are defined by the consumer

#4 Synthesize – combine all information to fuel the process based on the business requirements

#5 Create – build the relevant content for the target and interest groups

#6 Engage – involve the customer with the brand

#7 Define – define and re-define the information needed


Germany is a small country with leading industries and is one of the innovation drivers in the world. Also, Germany has developed a culture of sharing and co-operating, politically and economically, through associations and interest groups. Taking this into account it is quite conceivable that in the near future German companies and marketers form brand- and data-alliances, sharing their first party data to create a new kind of brand involvement that reaches across socio-demographic target groups into the core of the consumer groups, leading to joint communication, products and projects, where brand, advertising, and media channels become one experience.


Big data appears to be a great and wonderful world for communication experts. And it is. Still we should keep in mind that effective Big Data strategies start by identifying the business requirements. This is the groundwork, where we set the infrastructure, utilize the data sources and apply the analytics that together will be the pillars supporting the brand and hence the business itself.

We can fairly conclude that Big Data and smart brand management make it easier to optimize marketing ROI. But the true leap forward comes when we utilize the Big Data pool to reach a deeper understanding of what a brand can mean on a rational and emotional level. This will lead to brand- and business-solutions that are surprising, new, outstanding and creative. In short, the future of Big Data-based communication need not be a nightmare, but when applied thoughtfully can open new opportunities for the marketing industry.

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