Why Big Data needs board level sponsorship

Econsultancy just published a really useful free report: Big Data Trends Briefing: Key takeaways from Digital Cream London 2013.They were kind enough to include a few quotes from me but the reason I like it even more than that is the huge number of examples, case studies and other resources that it highlights. It’s a really useful gateway to all things Big Data: the main issues, opportunities, barriers and potential solutions.

Big data analytics is inherently ‘multilevel’ – it’s not just about very big, company-wide issues; or about very personalised and immediate consumer issues. It’s about both of these and every level in between.

Making it work at every level of the organisation, and in real-time, is now possible. One size fits all is no longer mandatory and as a result much greater efficiencies can be gained. This is an example of ‘good complexity’ – using an data analytics strategy that personalises customer experiences and staff insight and information requirements at every level.

But Big Data’s scale means that it crosses organisational silos, you can see this in the fragmentation issues that have been thrown up already. The upshot is that the sponsorship for Big data projects needs to be much higher up, i.e. a board-level sponsor for board-level issues.

Which means that you need to talk about board-level benefits when you argue the business case, e.g. a commercial director of a big ecommerce site recently told me that he was really interested in increasing Stock Turn (i.e. greater overall sales volume with the same working capital).

That got me thinking about analysing sales data to highlight products with characteristics that impact Stock Turn (sales value, velocity), then checking for the specific customers who buy them or would buy them and then influencing those customers using direct marketing like texts and email. Marketers in different digital areas do this stuff all the time but I haven’t seen it linked to how to influence specific Board members.

Big Data is now moving towards being more about analytics and Organisational Change, i.e. there’s no point in deciding what to do next if you can’t then do it.

This is what we are researching right now:

  • the need for arguing business cases, persuading and visualising Big Data analytics in terms of different staff roles, current needs and choices of options;
  • using analytics to shift the balance a bit from centralised to decentralised decision-making whilst still maintaining control; and
  • the other things on this blog.

We’re looking for research partners so if you want to have your Big data itch scratched and get the benefits of a few of the opportunities that I blog about then let me know: duncan.shaw@nottingham.ac.uk.

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What retailers want – and how to get it (3/3)

New Analytic Strategies: lots of new toys plus Big Data – what to do now?

The key objectives of Retail remain the same. Measures like recency, frequency and value are what defines the profitability of the different retail ‘entities’ such as SKUs, lines, ranges, single stores, regions, chains, brands, campaigns, seasons, segments, channel and even individual customers.

But the increasing availability of Big Data information resources makes it hard to figure out what to analyse for and how to go about doing so. The many new shopping technologies and new supply chain technologies mean that there are too many new data sources and associated methods of using them to make it a simple choice.

The business outcomes are the same. But how can you connect these outcomes to the appropriate analytics approach, and the analytics approach to the vast choice of data that could be used?

This problem is related to part of the fragmentation problem below. When I work with retailers on customer data projects a really common barrier is fragmentation of data sources and multiple versions of the truth. I think that’s what’s behind some of the issues in Econsultancy’s report on what is holding back social marketing.

But I don’t get it. OK, there are lots of sources of data, internal and bought-in, but they are mostly known and its not a new problem so there are lots of IT integration platforms.

So what’s the problem? I think its because some firms do not really understand what the actually use of the data is – i.e. its not well specified. Then the integration (in all senses of the word) problem becomes a moving target. Which makes the problem complex.

You cannot take data at face value. Before you can use it you need to know its context – its history, provenance and the circumstances of how it was generated. You need to have permission. But permission depends increasingly on what you want to use it for and there are so many different things that a single piece of data can be used for. Right now and in the future.

The challenge is not a technical issue. It’s a matter of knowing what data you actually need and have, what analytical approaches are available to you and what strategic questions you can ask. The pattern of data, analytical processes and strategic questions can be configured in so many different ways that it makes it hard to choose. It also makes it hard to know you have made a good enough choice.

Its like the ‘chicken and the egg’ question – which came first? If you have this data what analytical process can you feed it into and what will this tell you? Or, starting at the other end, if you have these strategic questions then what analytical process will give you an answer and what data will that require?

I think the problem is about getting to grips with choosing New Analytic Strategies. A lot of confusion is caused by too many new analytical approaches that can now be used to deliver the usual business objectives. It can be unclear which approach to use and what data to feed into it.

Solution: as with all complexity problems its always best to start with the outcomes that you want and then work backwards. Working backwards damps down unmanageably large numbers of options. Here, it can systematically map out the flows of cash, transactions, customer decisions, queries, attention and awareness in a sort of ‘reverse funnel’.

Don’t start with what data do you have – you don’t know what you want it for yet and you can always manufacture more or buy it in. And certainly do not start in the middle with sexy new analytical fashions that are just begging to be used.

Use a ‘reverse funnel’ to investigate what your analytical processes needs to end up looking like. You can then compare what different analytics methods and software are able to deliver – and what data they need to do so.

Think of your analytical strategy as an array of Business Intelligence telescopes that tell you different things about your customers, your business and your environment. Here’s how Google+ and Facebook are starting to configure their very different arrays of Business Intelligence telescopes on their members.

What retailers want – and how to get it (1/3)

What retailers want – and how to get it (2/3)

What retailers want – and how to get it (1/3).

Retail is being disrupted by lots of new technologies right now. The social media that networks shoppers together in lots of different ways. The mobile devices that people use anywhere and everywhere as well as the apps that run on these devices. And the Big Data that shoppers and supply chains continuously generate.

But the technology is not the problem. The really difficult thing to figure out is how people will use these new technologies. Remember Web 2.0? It only got a name when the different ways that people used it started to settle down a little bit into common patterns.

Now this technological tsunami has reached retail – online and off-line.

What retailers want – E-commerce strategists, CRM Directors, Heads of Marketing, CIOs, Analytics and Insight staff, User Experience teams and Social Media managers  – can be boiled down to solving three key business problems:

  • Dealing with Fragmentation – multiple data sources, retail channels, mobile devices, business process silos, brand partners and other variables make it very hard to even choose appropriate strategies, never mind action them.
  • Creating an Indispensable Customer Experience – bribes are not the true drivers of loyalty. But usefulness, ease and enjoyment are. How then can we be indispensible?
  • Designing New Analytic Strategies – retailers know the business outcomes that they want. But the vast range of new multi-channel shopping technologies, and their associated new analytical technologies, makes it difficult to say which analytical approach will generate the strategic plan to deliver them. What’s possible, not just what to do and how to do it?

Fragmentation

Nobody – customers as well as supply chain partners and competitors – has settled on their ‘usual’ ways of using disruptive new technologies like mobile devices, social communications, multi-channel synergies and Big Data analytics.

New hand held devices and web services launch all the time, things keep changing. Everyone is still figuring out how to use them – there is no norm, no ‘usual’. People are still getting used to the features of the devices, apps, sites and new information resources. They are still figuring out what works best for them and what new features they can offer their customers.

This has led to a stage of growing fragmentation – multiple data sources, retail channels, mobile devices, business process silos and brand partners are all variables. They present many different strategic options and opportunities for shoppers, as well as for retailers, and they are so new that there are no obvious or accepted patterns of use yet.

So shoppers, retailers and brand partners are confronted with too many different strategies to comfortably choose between. They also get the slightly disconcerting feeling that they are missing out on some even better ones.

Shoppers have way more sources of advice and new ways to shop than they have needs to satisfy. Retailers have to deal with shoppers who keep changing their customer journey and with brand partners who now interact directly with shoppers at scale, using social media. Also, brand partners now have to deal directly with shoppers’ obscure queries and frank reviews – and be watching out for dissatisfied rants as well as random image-building opportunities.

So in reality, retail is not fragmenting. It’s just that the links that connect shoppers to retailers and the rest of the supply chains are fragmenting. Complexity comes from the number of ways things connect up not from the number of things.

The destination is the same but there are lots of new ways to get there.

Solution: use the vast expertise that comes from your position in the supply chain to give customers and brand partners the advice that’s right for them, i.e. personalised content marketing. Not just what products suit them the best but how these solutions fit into their lives, and the tools and information they need to make the buying decisions themselves. Your position in the supply chain potentially gives you the total knowledge of all your staff, brand partners and past customers.

And also, if its going to fit into your customers’ lives then they need to tell you about their lives…so you get to know them even better.

What retailers want – and how to get it (2/3)

What retailers want – and how to get it (3/3)

Big Data – the need for integration.

Sandy Pentland of MIT points out some of the key issues about Reinventing Society in the Wake of Big Data here. Data ownership is important because it concerns the main driver, as well as one of the main barriers, to developing Big Data services.

The main driver for developing Big Data services is value creation – not just commercial value for firms and the value that customers get from great services – but also the possibility of sharing some of the value that personal data generates with the person that generated it. It’s the main driver because people commonly do things because they get something in return that they value.

One of the main barriers to developing Big Data services is fragmentation and the fragmentation of ownership is critical. Big Data is the personal diary of everyone and everything. This personal diary is sometimes called the ‘data exhaust’ . It is co-created by customers consuming a service and the service provider as it produces the service. I.e. as all the service provider’s staff and machines produce the service.

But who owns this flow of bi-products, this data exhaust? Is it the service provider? Is it the service provider’s partners in its supply chain, the telecommunication company, the content providers, the payment services partner, the security and authentication partner or others? Or is it the customer?

What makes matters more complicated is that data is always copied rather than consumed. So potentially all stakeholders could use the information – for different purposes. And maybe here lies the solution.

If the value of data depends on its use and it is infinitely copyable then we should be asking who owns the right to use it for specific purposes rather than just asking who owns it? Maybe the problem of ownership fragmentation is a licensing problem. Framing data ownership as a licensing problem may partly help to solve the problem of controlling the use of customers’ personal data as well all the problem of sharing out the value that it can create.

Another Big Data fragmentation problem that Sandy Pentland mentions is that Big Data rarely has one source. It’s nearly always generated on different parts of the firm and partly bought-in from partners – and even in a single firm there are database and business process silos.

Database fragmentation makes it hard to integrate the vast volumes of data even when you are aware of what data is available. It’s another main barrier to developing Big Data services because data services are emergent – they only exist as a whole and they can be combined in many different ways.  So its difficult to judge which services will be a success.

It is much easier to start with a good description of customers’ service-needs and then work backwards. But that’s difficult for the firms in the supply chain with tonnes of data and little direct customer knowledge.

That’s why the supermarkets are taking over the world – direct customer contact gives them deep customer knowledge, which helps them to build-up current and new business areas. Then the new business areas give them new insights into different aspects of their customers’ lives – which enable them to build up further new business areas. Look at how Tesco and Sainsbury’s went from groceries to clothes, consumer electronics, then financial services and more services.

If your firm has tonnes of data that you are sure can be leveraged to create new value, but it’s fragmented and you do not know how to use it or when more data  needs to be bought in, then I’d suggest using a customer journey analysis approach, like PIGs. Fragmented product and service components always come together at the point of consumption. And anyway, if you want to assess service quality, value is ‘in the eye of the beholder’ – the consumer.