Every day there’s another PR announcement about a Big Data platform, or an OP-ED about the failed promises of Big Data. For brands and marketers looking to find the true value of Big Data, it’s time to stop calling it Big Data.
The term and concept of Big Data is certainly not new. The explosion of information brought in by global networking brought about the need to quantify the growth rate in the volume of data, so the term Big Data was coined.
Today, massive volumes of user/public/enterprise-generated data and third party data are now widely available, along with the tools and platforms to organise, manage and analyse the collected data. It’s an age of organisation.
By constantly referring to Big Data in such terms, we’re often missing the true value it delivers, both to consumers and brands. A 2011 Strata Online Conference survey showed 33% of respondents had challenges in defining Big Data and how it can help their organisations.
I think we’ve come a long way since then and we now see that Big Data in an organisation is not just about collection and analysis, but a culture of learning. It characterises a new way of doing business—one that is driven by data-based decision making for products and services.
If not Big Data, what do we call it?
Think of it as it relates to marketing strategies and advertising campaigns and the consumer data available. We have transactional data and behavioural data. The transactional data is the purchase or conversion history for the consumer. Where transactional data can become dated (a customer may not have purchased from you in the last 6 months), behavioural data can be more relevant (customer visits to your website, their social media activity, etc.).
This is what we’re talking about when we say Big Data and it’s important not to lose sight of the practical value delivered by this data.
Large brands like Woolworths, Target and Walmart have data warehouses capable of storing trillions of bytes of customer data to represent every single purchase point recorded by their POS terminals worldwide. By applying machine learning to this data, these brands can detect patterns indicating the effectiveness of their decision-making to better manage their inventory, their supply chains, and of course, deliver a better customer experience.
Think about expanding customer intelligence.
Credit card companies, airlines and travel agencies have the ability to examine large data sets from a wide range of sources that include structured information such as historical purchase pata, CRM data, as well as newer media data such as social media, blogs, and videos. These behavioural data points provide insights into how consumers think and act when dealing with brands.
Deliver operational efficiencies and a better customer experience.
Analytics-infused CRM systems in call center industries can review multiple data sources in real time to suggest offers that a representative can present to a customer. Analytics integrated into daily business transactions can improve outcomes, speed up processes, detect potential fraud, reduce costs and drive productivity by presenting relevant next best offers and suggestions during the call.
Mobile means new data opportunities
A 2014 ComScore report shows that mobile is the leading digital platform, with 60% of consumer engagement with digital media taking place on a mobile device. The geo-locational aspect to the data also provides businesses with new processes of collection, analysis and contextualisation.
So How Can Marketers Capitalise On This Data
The promise of Big Data, which you now know to mean transactional and behavioural data, and particularly the promise of the modern collection and analysis platforms available, is real-time, contextual customer intelligence. These are the kinds of insights that transform the way businesses are run and tells us about customers, products, marketing and operations.
Companies that don’t know what drives their customers to their competition, that don’t know what people are looking at or liking about their products and how they’re interacting with it, these are the companies that will be left behind.
And this isn’t something that’s happening in 10 years, it’s happening right now. The analytical techniques, the technology, reporting tools available and the declining costs in running these processes means the information is becoming democratised.
Suggestions that it will never replace gut feel or true creativity are misguided and posit a binary that doesn’t need to exist. Human analysis complements the computation, it doesn’t compete.
We need to get human insights at machine scale. This can’t be achieved by instinct and creativity.
Challenges in Big Data
So with new opportunities comes new demands and challenges (which are opportunities in themselves).
The growing need for speed.
Meeting the need for speed and technical capabilities to support analytics require a pervasive adoption of a broader usage of analytics to generate an enterprise-wide ability to act with speed and precision. Speed-driven organizations must be able to manage the volume, variety and velocity of the data available.
Staffing the human component
Businesses are drowning in data and this has led to a new class of engineer, the “data scientist,” whose job is to perform the sophisticated mathematical gymnastics required to extract actionable information from a huge set of numbers. This involves crunching the numbers and visualising the results. This is a challenge we know all too well and have written about our data scientist solution before.
Big Data usually generates a lot of discussion about data privacy and ethics. We’ve said it many times before, privacy is dead. Technology killed it. However, there are still obligations by brands on how the collect and use customer data.
The issue of privacy and ethics is a conversation that takes place directly between brands and consumers. When deciding on the value you’re going to be offering as trade-off for the privacy violation, you must always see the value from a consumer’s perspective, not from the brand’s.
Legacy systems and contextualisation
In the same Strata Online Conference survey we mentioned earlier, 21% of respondents said that they were struggling to to integrate legacy services into Big Data technology. Almost five years later and we’re really not that much better off.
These customer data silos and the complexity of the vendor ecosystem introduces large inefficiencies for brands. I don’t think it helps when the traditional technology stacks acquire disparate technology platforms, tape them all together with quick-fix integrations and call it a unified marketing stack. These aren’t solutions. They’re band-aids and contribute to the lack of results marketing offices might be seeing in their Big Data platforms.
We’ve built the OptimaHub as a truly unified marketing stack where the technology that powers the collection, analysis and actioning of the insights, from a single customer view to media attribution, were built to work together. This allows the customer and marketing data to exist in a single system, meaning the data can be contextualised into a wider network.
Big data is about behaviour and transactions
The media, consumers and even marketers are doing themselves a disservice by using a generic term like Big Data. It removes the power of the real value and insight. What we’re talking about is behavioural and transactional data. And the entire process requires human learning and understanding.