For big data to be useful, marketers must first decide which data is relevant. Photograph: Alamy
Jamie Brighton, product marketing manager EMEA, digital marketing, Adobe
Decide which data is relevant: The biggest challenge that marketers face with big data is knowing which data is relevant. David Lloyd from TMW makes a great point in the intro — it should be about the right data and not just any data. Only once you’re looking at the right data points can you start to understand how to use that data to your advantage — by improving marketing campaign spend or onsite conversion rates for example.Value of big data: It also enables us to start using historic data to predict future interactions. One example from a media mix modelling view is the use of multi-channel big data to enable companies to predict what the impact of changing their traditional media spend will be on their digital business: ‘if we broadcast one less tv ad, what effect will that have on online sales?’
The human investment is necessary: My summary would be that your approach to the big data challenge should be: plan ahead, start small, choose a flexible tool or set of tools for working with the data, don’t be afraid to fail initially and use the results to build your case for bigger investment in the area. Finally, let the machines do the work where possible (algorithms, machine-learning etc) but don’t forget the human investment needed to really take advantage of your big data.
Mike Leverington, head of customer data, Guardian News & Media
Defining big data: I think it is time to drop the ‘big’ element of ‘big data’. It is just data.Ignore vanity metrics: Just because something is measurable it doesn’t mean that it is useful. Social media has led to explosion of vanity metrics for evaluating campaign performance (likes, tweets etc) rather than marketers focusing on ROI.
Using big data: My opinion is that you need to start with the commercial goal first. What is your business case basically. This will involve talking to a whole host of different stakeholders, consultants/advisor’s, tech partners. This needs to be done before you look at exactly how you are going to analyse the data. Building a database of the scale discussed here is an expensive investment and you need to make sure that it will pay back.
David Lloyd, data planning director, Tullo Marshall Warren
Don’t overuse the term big data: I’m optimistic about the potential for big data. However, equally sceptical about how the term is overused to mean pretty much anything to do with data of any size.Ensure your strategy is focused: I would say that the discipline of putting a data strategy together is the same as it ever was — it’s got be focussed on and an outcome beneficial to the organisation.
Because big data is immature in its use, I’d say, if you are starting out for the first time, then yes; pick a source of big data that fits this and test it. If you can’t find one, be aware that it could mean that smaller data sources are more useful to your particular outcome
Scott Logie, chair, Direct Marketing Association and strategic marketing director, St Ives Group
Is big data simply another buzzword? I’m a self-confessed big data sceptic. That’s not to say I don’t believe in big data. Obviously I understand that there is more data now than ever before. Actually, there was more data created in that last two years than existed up to that point, literally! So, yeah, there is more data than ever but fundamentally the challenge remains the same; to find the outcomes that make the biggest difference for our businesses and customers. Isn’t big data just another term like CRM used to stir us up and buy more technology?Benefits for small businesses: I also think that Big Data provides huge opportunities for small businesses, particularly locally. Local newspapers for example have started mining data to post relevant, local advertising online. I can see some real big wins for someone who can provide small chunks of local data to businesses to help them understand their very local consumer base.
Dr Duncan R Shaw, lecturer in information systems, Nottingham University Business School
Decide which data is relevant: ‘Knowing which data is relevant’ is a great point — there’s a catch-22 type situation when some solutions can be made by starting with the same old business objectives and working backwards into the analytics.But other times you need to play around with the data and you don’t know what insights it will produce. My Data Scientist colleagues call it the ‘discovery phase’.
Big data versus data mining: It’s easy to get bogged down in the IT side but it all comes down to ‘What can it do for my business that data mining couldn’t?’.
From my work with financial services, retailers and digital marketers, here’s a few answers to that question: sandboxing the ideas I get on the tube to find the one to take to my boss by lunch time; real-time mass personalisation (I love the potential of mobile and big data); sweating your data assets like never before and evidence-led decisions for all organisational levels when they need it.
You can download a Gartner report on big data and business here
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