By nickjohnson - July 31st, 2013

Big data. The term that launched a thousand white papers.

 

And when you look at some of the statistics floating around, one shouldn’t be surprised that consultancies, agencies and though leaders are clustering around the term like never before.

 

  • 85% of Fortune1000 companies have big data initiatives planned or in progress

  • 83% say they’ll consider making greater use of real-time data in 2013

  • 63% say use of info (including big data) and analytics is creating competitive advantage. 73% say leveraging data has increased value.

  • 84% say using data helps make better business decisions

  • 65% say company leadership sees data management/analysis as source of value, not a drain on resource

 

This article is based in large part on Econsultancy’s new white paper on big data, which collates findings from their recent ‘Digital Cream’ event.

 

Big Data - What does it actually refer to?

One thing the report highlights is the fact that one can understand ‘big data’ to mean a variety of different things.

 

According to an SAP survey quoted by the author, there are four key definitions

 

  1. 28% say the most important definition is the massive growth in transaction data

  2. 24% say it concerns the new technology developed to address the challenges of the volume, variety, and velocity challenges inherent in big data management

  3. 19% say we’re talking about the requirements on a company to store and archive data for regulatory compliance

  4. 18% say big data is about the rise in new data sources, like social, mobile and machine-generated

 

What are the key challenges facing brands when it comes to big data?

The central challenge raised at the ‘Digital Cream’ event was one of “How to translate data into value”.

 

It seems that brands are no longer worried about the challenges in collecting and synthesising data, and have moved on to the next step in the road to leveraging this new phenomenon.

 

By ‘value’, the attendees primarily meant “identifying customer needs and catering to them”. Practically, this can involve dissecting customer journeys in more detail, so you can see ROI on a more granular level, and then make resourcing decisions that are more targeted and effective.

 

The key difficulty in doing this is finding the right data to analyse, and then making sense of it.

 

A secondary challenge is around speed - how to adapt quickly to what you learn and make changes. Reacting faster gives you an edge on the competition, according to the brands in attendance.

 

Thus it follows that large brands need processes in place to both identify these core consumer needs, and to react to them at speed. Yet only 41% do.

 

What’s the Marketer’s Big Data To Do List?

Big data, as we are routinely informed, will have a seismic impact on pretty much every area of a business’ operations. Happily, the marketing department is not responsible for all these impacts.

 

The Econsultancy report suggests that a Marketer should aim to succeed with big data in three main areas:

 

  1. Get deeper customer understanding: Aim for a system where one can apply data insights on the level of individual customer. Andrew Campbell puts this in more verbose terms: “The biggest challenges relate to understanding the human, social, psychological and anthropological factors influencing consumer behaviour and locking these down into a solid theoretical and strategic framework”

  2. Identify new consumer groups: Take advantage of better tracking to identify new consumer groups that one can market to more effectively than pre-existing groups

  3. Do better and more useful marketing analysis: Quantifying marketing better - tracking success.

 

Big Data must drive better collaboration

Another challenge highlighted by the brands in attendance at Digital Cream concerned deliloisation - ie connecting data from different sources. As you’ll know, data on the customer journey is currently typically siloed in a variety of different repositories. To leverage this data more effectively, one must be able to merge this data together and spot trends/similarities.

 

There is a prosaic problem associated with this - in that data is collected by different teams who are not used to sharing this frequently.

 

Cross-functional teams are an obvious solution, but they haven’t happened yet. Because establishing an entirely new cross-functional team with responsibilities spanning several business units is rather tougher than it appears on paper.

 

There’s an ownership question too. Who is it within the organisation who should drive this necessary integration of different silos?

 

Obviously, the more senior this person, the more likely to succeed. But you also need someone with the right skillset - and that’s a big challenge for companies at the moment - “a lack of staff to gain insights from data and the resource to fund and support such staff is a key frustration for those involved in managing analytics”.

Saying that, it looks like that’s changing - 48% of companies surveyed by Econsultancy are planning on upping spend on these analysts in next 12 months.

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