Customer data: Why small is the new big - and what it means for marketing

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I love the promise of Big Data. However, somewhere between the vision and making that vision a practical business reality, there's been a fairly big disconnect.

That’s why I believe in thinking small when it comes to Big Data: in other words, focusing less on the size of any dataset and more on exploiting data in practical ways to generate real value for the business.

Why are we seeing a growing discontent with Big Data, and a shift in emphasis from ‘bigger is better’ tools designed (for the PhD in statistics) to ‘simpler is better’?

I don’t think I am alone here – as a growing number of organisations are starting to refocus their efforts around using “everyday” data to solve problems, and using analytical solutions to help that don't need a data scientist to drive them.

Indeed, anyone underwriting Big Data work needs to be reminded that while computers love data, people like answers. Want to get more ROI from your Big Data initiative? Provide useful insights that have the broadest possible appeal: focus less on data gathering and more on connecting users to the most relevant pieces of information, suggestions, or conclusions.

Why we need the ‘Fourth V’

If you adopt this sort of outlook, you quickly shift the debate from the size of your data store to the reach and insight it offers.

Overall, this kind of ‘Small Data’ philosophy is very much about creating consumer-style, more responsive information and social apps that turn insight into action. Compared to traditional Big Data, which is all about the ‘3 Vs’ of variety, velocity and volume (machines and processing power), Small Data is about value - the fourth, often neglected ‘V’- and thinking about the end-users, context, and individual data requirements.  Big Data tends therefore to be about amassing more data, while Small Data is about harnessing the data that's already available to us, discerning its meaning, and delivering insights and answers to the largest set of business users.

Yet, whether you begin with (and sign up to) Big or Small Data, it's critical to focus on specific business issues, frontline workers, and daily tasks to demonstrate value and seek a return. After all, to be successful in harnessing the potential of information at all levels of the organisation – Big or Small, we need to start with proper business objectives.

Beyond the research lab, the purpose of Big Data seems to be its potential to revolutionise the way businesses interact with customers, change how customers access and consume (and even wear, in the case of devices like Google Glass or Nike FuelBand) useful data, and in so doing redefine what’s involved in the relationship between buyers and sellers.

In other words, we need to get to the buying public and understand what they're asking, and streamline their route to reaching the best answer.

And I think marketing needs to lead the way.

The role of proactive data-driven marketers

Specifically, marketers need to be more proactive in defining and driving Big and Small Data initiatives and start framing the discussion to get everyone fully focused on what offers the best value for the most modest investment.

Plus, marketing teams looking for analytical solutions need to demand personalised analytics and campaign tools that are tailored to business roles, rather than designed around the more technical users, as well as asking for integrated platforms that make it easy to access, test, apply, and share intelligence, in a manner that is integrated with their daily tasks and workflow.

Even more so, as long as the IT department are driving the technical side of your Big Data project, marketing needs to stand up and be counted and represent the voice of the (non-technical) user. Especially when ‘users’ includes external customers.

The good news is that we've been here before. Recall when everyone was raging on about artificial intelligence (AI) and knowledge management (KM)? I was in the middle of that storm as a researcher and data scientist in the early 1990s. Like Big Data, AI or KM was going to change the world – with Fortune 100 firms loading up on AI talent and even hiring ‘chief knowledge officers’ to lead the way.

It was a vision that got lots of press excitement, helped earn consultants lots of money, but it made little or no difference to the bottom lines of those that bought into the hype cycle. Why? Without specific applications for everyday tasks, these were technologies looking for uses, not the other way round – as it should be. Sound familiar?

It’s just a fact that all technology is cyclical. A large number of us think Big Data is a brand new idea. It’s not. The core developments that shape today's Big (and Small) Data initiatives go back 20 years or more. However, today we have more processing power, ubiquitous connectivity and more varied data sources – we also have increasingly savvy customers who demand Google-style answers wherever and whenever they need them.

This is why our data and reporting efforts should be less about the size of our data store – and more about finding the right data to get closer to customers, create compelling propositions and experiences for them, provide the right advice, offer, or answer to get them on their way.

That’s what we are in business for, after all.

Allen Bonde is VP of product marketing & innovation at ActuateFollow Actuate on Twitter at @Actuate or visit www.actuate.com/bigdata. Follow Allen Bonde on Twitter at @abonde or email him at [email protected]

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