Big Data — Deriving Value from the Data Deluge

The Future of Big Data

• A C-level Perspective: David Grant, CDO, Lloyds Bank
• An Academic Perspective: Richard Self, Professor of Analytics And Governance, Derby University
• A Digital Marketing Perspective: Joanne Sweeney-Burke, Director, Digital Training Institute

Introduction

A new era of mainstream adoption of big data and advanced analytics continues to open up for organizations across the globe. “This technology offers the promise of unprecedented insight into business operations and our customers, enabling us to not only improve operational efficiency, levels of service, revenue and business models, but also boosting customer-centricity.

Various sources of information are providing companies access to unprecedented amounts of data, whether they come from management systems, customer data, customer feedback, market data, or social media. However, a wealth of challenges remain. Many professionals are still to discover how to effectively convert, use and maximize the value of data collected into real business value. Some are struggling to know how to convince the Board that big data is worth the investment, let alone the attention. Others have been able to secure C-Suite buy-in but are now struggling to know how and where to invest, be it in skilled people, new technology and infrastructure or training programs. Then there’s the conundrum of how to actually measure the success of your projects…

As you will discover from this report and the results of a survey, the collective expedition into the world of big data is just beginning. Concrete case studies (successful or otherwise) are few and far between, and it is still to be confirmed whether there are best practices applicable to all scales of organizations and markets, or whether the path to big data realization must be different for everyone. Popular thinking sees big data not as a silver bullet, but as an enabler — and one that should be invested into with caution.

What is not under debate is just how captivating the big data “revolution” continues to be. From the Chief Data Officers now at the head of their field to the academics studying the ebbs and flows of this arena, and from the marketing gurus to the software designers, there is no element of modern business practice that has yet to notice the potential benefits of embracing the application. Whether the end game is revenue, engagement, research, or something entirely different, the common belief is that there are indeed opportunities lying in wait.

We hope these latest findings provide practical benefit for you and your business.

“Top-down means understanding what ticks the boxes of the senior executives…”

DAVID GRANT, CDO, LLOYDS BANK

David, how have you defined and established the role of CDO at Lloyds?

Data is really important. There is lots of commercial opportunity and lots of regulatory focus. The consultants were telling us that all our competitors have CDOs, so we should have one too — which, to be honest, is not the best way of making that decision. It was then very much a blank sheet of paper as to what the role and remit of the CDO is. For us, in the first couple of years it’s just about getting our arms around data and ensuring that we can manage it effectively. In other words, we’ve really begun to start thinking about data quality. How do we measure it? How do we import it? How do we fix it? How do we coordinate investment? How do we coordinate activity? How do we have a consistent data management framework, data governance, data policies, and so on?

It’s about managing data as an asset. We have very clearly said that it is still a business responsibility and not about running technology that hosts data, which is an IT responsibility. Right now, we’re around nine months into the process.

Given your personal experience over the years, what process changes are required to drive data-led transformation?

To my mind there are two key things. One is to actually link it to business requirements. I always stress the message that, at the end of the day, data is not there just because it’s data. It’s there to facilitate the business doing something, which means it has to drive value. This is critical because otherwise everyone just turns round and goes, well, why are we spending all this money on data? That’s the first part. The second part is very much a cultural change in driving the message within the organization that data is a Group asset and not an asset of an individual business area. It’s getting people to move away from simply delivering solutions to a particular problem, but in a way that is suboptimal for the Group, and actually trying to deliver some wider value. Ultimately, that is the cultural change needed in the organization.

So with that in mind, how can an organization successfully align people, process, culture and technology under its data plan, particularly as access to skills and resources can often be limited?

There isn’t an easy answer to it and the answer will be different in every organization. You just need to understand the culture and the dynamics, how the organization works, who the personalities are, and all these situational factors. In our case it’s a combination of what I call bottom-up and topdown. The bottom-up is we have lots of people in the organization who have spent their careers dealing with data and actually do recognize the issue. So the first step is mobilizing the bottom-up population. When I was appointed there were lots of people who were pleased because it showed that we’re now treating data seriously. It highlighted the fact that we’ve got some very willing proponents there.

Top-down means understanding what ticks the boxes of the senior executives and effectively selling the case for serious data analysis. Outwardly, many banks are very much focused on cost-efficiency. If, for example, you can show that by managing and coordinating data better, you can take cost out, what you have is a very, very powerful message to convince them to come on board.

The final thing is that you do need a bit of a ‘stick’ approach. It’s vital here that we get approval to spend money, so what we’re tying into that investment approval process is that we’ll only sign things off from a data perspective if you follow the Group standards, you do it in a consistent way, and you actually add some value to the Group — rather than building a parochial solution.

Of course, one of the most important issues in this field today is leveraging Big Data. How can Big Data provide the most effective way for financial organizations to either engage with customers or increase revenue? What data strategies generally deliver the best ROI, and where are the common pitfalls?

We’ve developed some prototype benchmarks. We go into a whole variety of different dimensions, all the way from characterizing the number of errors, the data quality dimensions, the impacts on customers from data issues, and so on. They’re all quite new, so we’re experimenting with them as much in terms of behavior to whether we can get the data to populate properly. I actually come at it with a slightly skeptical view, which is to say that there has been a fantastic marketing hype to Big Data. It’s important to actually decompose what we mean by the term. To my mind, it is clearly about large volumes of information, about innovative analytic tools, and about being able to make use of data that is less structured — whether it’s social media, geolocation data, or similar. Here at Lloyds, we have done Big Data for many, many years — before it was ever a fashionable term. We have one of the largest customer databases in the UK, and what we call a customer database, many other people would call a very large, Big Data solution.

So, where does it add value? Clearly, a lot of the case studies revolve around customer insight and getting a better understanding of your customers. The very powerful cases that we started to explore have been around how this can drive business efficiency by taking the views of customer journeys, cross-channels and things like that. Then it becomes quite a powerful resource for the risk and regulatory space. There are lots of opportunities but it’s important to look across a number of dimensions. Yes, it’s Big Data, but it’s just as much Big Analytics. The new types of analytics we can undertake with emerging technology is probably going to drive the most value.

How can CDOs stay one step ahead of the data innovation curve?

By operationalizing it. You need to eventually do something with all that insight. There’s no point in having fantastic data and analytics if you can’t subsequently deliver benefit to your customers in terms of new products and services. This often takes us into the more challenging areas because it’s fine having a nice, new Big Data platform, but when you’ve still got your old legacy systems, it can perhaps take 12 months to make any system changes on.

Another way is in recognizing that you do need to allow people to experiment and you do need to take away the traditional, legacytype environment where it takes three or four months to do anything. You just need to let people in, let them experiment, and then kill things off quickly if they don’t work. Of course, we have seen that there are a lot of people who like to call themselves data scientists these days, so we need to be careful that the right people are making these decisions.

I don’t think any of us have perfect data, so actually understanding it, knowing its limitations and how it works remains critical when it comes to adapting for the future.

“Only 35 percent of big data projects will be successful on time, budget and quality.“

RICHARD SELF, PROFESSOR OF ANALYTICS AND GOVERNANCE, DERBY UNIVERSITY

According to our survey, the seniority of staff under which Big Data programmes are managed varies wildly from one organization to the next. Is there a growing need for dedicated data analysts? Is there a risk that the C-Suite can mishandle or misinterpret large-scale data?

It seems to very much depend on the size of the organization. What’s becoming quite clear is that a great debate is raging about whether there is still a role for a chief information officer or a fixed chief technology officer. Should we now have chief data officers instead? The real debate comes down, in effect, to technology versus data. The most enlightened companies seem to be beginning to realize that they’re going back to the old way — seeing the data as the value of the company, essentially the intellectual property. It goes back to what Nicholas Carr was writing about in 2003–2004 when he wrote Does IT Matter? It discussed the creation of sustainable value, whether it’s the technology itself or what we do with the technology? Carr was saying, very clearly, that the technology is just a utility now; everybody has access to it and we can’t get sustainable competitive advantage just through the use of technology because we’ve all got it.

When you start looking at the ‘data side’ of big data analytics, you have to break down all of the silos, you have to crosslink them, and you then have to start thinking about how you do that. We then get into the next round of debate, which is whether we need a central business intelligence or data analytics group. I’ve come across companies with both, but they are often working for the chief data officer or someone of that ilk, who will be charged with developing the mechanisms — be it through technology or practice — that allow the connection of all the internal operational and tactical data together with anything that’s of value from outside. That could mean social media, weather data, or various other interesting sets of data, which combined creates an ocean of information to be investigated. This can provide smart insights that certainly can translate to distinctive, competitive advantage.

So, is there a risk that the C-suite could mishandle large-scale data? Yes! It’s often the magic pixie dust, as far as they’re concerned. If you go back to around 2004, Professor Angell from the London School of Economics pointed out that one of the problems with IT is that the C-suite considers IT to be something you can sprinkle liberally over a problem and cause it to vanish. There is a problem there and it’s exactly why we need discussion on helping all levels of management to understand the capabilities, the limitations, the ethics and the governance of big data.

Many experts we have heard from recently have been underscoring the idea it is the people over the technology that matters. How can organizations know if you’ve got the right people involved if you’re not relying on the C-suite? And should they be sourcing those people?

If we look at reports from last year on future projections, they were suggesting that by 2020, in the UK, something like 56,000 jobs will require highly data specialists and scientists. By the same time, 500,000–550,000 of today’s existing employees are going to need to be upskilled to understand what can and can’t be done so they can intelligently guide the analysts and scientists into creating the right models or approaches. Smart decisions need to be made to add value to the organization and avoid the vulnerabilities and risks that can emerge if you go about it in the wrong way, as happened with the recent Target data breach.

In that sense, there does seem to at least be a swing in the direction of affirming that the role of the CDO is becoming necessary. There are certainly more this year than last and there seems to be an understanding that responsibility and accountability are critical. However, according to our survey, there remains an issue of convincing not just the upper tier to invest monetarily, but also getting the entire organization on board and moving in the same direction…

I think that reflects a couple of things. One is the fact that big data analytics is still riding the hype curve that Gartner are famous for, and although there are quite a lot of statistics being poured out by the industry in general that illustrate big data analytics will improve your bottom line, these are looked on very skeptically by many, many organizations. This Is because we have seen these claims time and time again about different types of technologies. So there’s a healthy skepticism. There’s also a recognition as well that while we hear many examples of success, we don’t hear many of failure — or, as Standish Group call them, ‘challenged projects’. The evidence seems to be that these big data analytics projects are no different at all from any other IT related project, which means 35 percent will be successful on time, budget and quality. There’ll be something like 35–40 percent that are challenged, or in other words, late, over budget, and delivering only a small proportion of the expected benefits. Then there’s the usual 30–35 percent, which are outright failures and never deliver. So we’re hearing about a 30–35 percent percent success rate. I think the only failure we’ve head much about is the one with Target, which, at a technical level, was a stunning success. They could identify women as being incredibly early into their pregnancy. That’s great as a technical capability but it ended up being catastrophic in terms of public image. It’s very much a problem of being one of the latest fads, but is also something that, in principle, can deliver very, very strong results in terms of sustainable, competitive advantage. That does mean that those who are successful will potentially do very well, but it doesn’t necessarily mean that everybody has to do it because people’s intuitions, particularly in small organizations, are often pretty good already. That’s why they’re successful at starting up a new business.

The allure is perhaps understandable, but there is also that fear of knowing where to start, from a technological perspective. It could mean overhauling everything that’s been in place to begin with, or trying to integrate legacy systems into the process. How do you even know where to start?

You really need to start with asking yourself a whole range of questions about the governance of the project. By governance, I’m talking about the broadest level — whether you are going to do the right action in the right way at the right time.

Most in this field are familiar with the four Vs of big data: volume, variety, velocity, and veracity. My students have taken that a little bit further — up to 13 or 14 terms beginning with V that are all really important considerations for big data analysis. Things like ‘value’. What’s the value in it for me and for the various stakeholders, for the customers, the suppliers, the partners, and so on. Then what sort of value does that entail? Is it always monetary or is it better service? By prompting creativity, it’s not so difficult for them to work out whether they should do it, and then, once they’ve identified a pilot project, they can start thinking about the roles and skills needed for their techies, and about upskilling their own internal management.

Where will they source the right people? Well, that’s going to be really fun because the 56,000 skilled big data analytics and data science graduates is a real problem. Universities can’t tool up fast enough. There aren’t enough kids coming out of school who actually recognize the wondrous nature of analytics and data science to get us to the point we would need by 2020.

Looking at 2020 and beyond, no doubt there will continue to be an adoption of Big Data, regardless of whether there is enough of a workforce to take it to its limit…but how do you see this actually impacting the markets? Do you have an idea as to what the landscape will look like?

We’re already seeing various experiments in the use of things like social media, monitoring live discussions. Some of these experiments are going overboard in trying to get more than is probably realistic, given that the machine understanding of language is not particularly good. Computers struggle to understand irony, for instance. It’s similar to the problems we, as humans, sometimes have in reading meaning in emails if they’re not carefully crafted.

Santander provides a rather lovely example. They use social media not to find out problems and react, but to instead receive early warning of potential issues, which can then be investigated and fixed before they go very wrong. That relies on the understanding that people who tweet and Facebook about something going wrong will usually communicate it to ten other people, whereas, on average, people who have had a good experience will possibly tell as many as three. So there’s a huge imbalance in social media anyway when it comes to good versus bad experiences. Santander were therefore, in my judgement, extremely wise to use social media as a source of customer intelligence.

Within social media, there are many other applications. For example, people are using it to design or redesign new products because people tweet about products they’d like to have. Aside to social media is the extremely fast rise of the connected world and IoT. That’s going to generate staggering amounts of data, streaming at huge velocities. It will impact industries like the automotive industry, where we’ve heard that the likes of GM, Ford and BMW are all busy developing mechanisms on top-end cars to notify back to base how the vehicle is being driven. It’s effectively what the aerospace industry has been doing since the mid-80s, generally so that mechanics on the ground can be notified of problems. This raises some interesting questions in terms of data governance and personal identifiable information because, of course, a car is attached to an owner by name, hence data protection requirements are required. All this leads to questions over who owns the data. Is it the driver? Is it the owner of the car? Or the manufacturer? Or, for that matter, the insurance company, who is collecting the data? As we move into the world of autonomous, self-driving cars, over the next five to ten years, will present bigger issues about liability and so on. We’ve seen that already with things like cognitive computing, advisory systems like IBM Watson and the Watson Oncology — IBM are sensibly telling people who are going to use it that you, the human, have to make the decision. The worry however is

that, as with many other forms of technology, the early adopters may understand the issues but the junior staff coming through the training system will simply see the seniors using it and accept the recommendations almost all of the time without understanding how the senior person is connecting or comparing the advice with their own field of expertise.

Ultimately — and I’ve seen this happen several times within different industries and different areas of technology — the juniors don’t go on to build their own internal model; they instead become totally reliant on the technology, with no overriding knowledge of how things work. It doesn’t matter whether it’s in the field of oncology or insurance policy, this problem poses a great danger as we move into the future.

“Video is going to be a game-changer in terms of the data that it provides.”

JOANNE SWEENEY-BURKE, DIRECTOR, DIGITAL TRAINING INSTITUTE

Joanne, from a social perspective, how can big data be leveraged to provide real insight into customer behaviour?

When we look at the social web, it tracks customer behaviour by its very nature. So every time we take an action — follow somebody on Twitter; like something on Facebook — we’re telling the social web quite a lot about ourselves. We’re telling them who we like and what interests we have; we’re sharing our opinions; we’re having conversations; we’re sharing who we’re connected with and the nature of that relationship. Very quickly, by one or two actions on the social web, we’re providing a huge volume of data.

The other thing to say is that, social networking sites are built on data. That’s the reason why Facebook is now trying to compete with Google for the search market, because they have aggregated far more data about their users than Google has about [Facebook’s] users. So, big data, in terms of social web, is absolutely phenomenal, and it’s a new phenomenon that I think most companies trading on the web haven’t even grasped. The other thing to note is that we’re not seeing Software as a service (SaaS) becoming one of the big industries because they have realized that data sells.

Being about to use analytical tools to run your business and to drive sales marketing is absolutely invaluable now. For me, I use multiple software as a service from accounting packages to analytical tools, to marketing and CRM — that’s certainly the future. A lot of these sites and companies are creating APIs to interact with other pieces of software. Our own campaign at the moment is looking at the benefits that social media can provide to law enforcement. Within that, I can target people who are interested in law enforcement in social media. If they’re searching the social web for these key words, I can tell the internet to place my ad in front of them — not only when they visit my site, but when they visit other sites.

Do you, from your position, see any trends in terms of the sectors that are grasping this idea quicker than the others, or engaging with it in a particularly effectively way?

Just from my own observations, certainly the tech and digital marketing world are doing it well, simply because this is their industry. I would say to a lesser extent you have other types of industries doing it well because it’s central to what they need. Recruitment agencies are all about people; they’re all about data; they’re all about profiling individuals. They’re using big data to help them find the right people and produce an executive search. That’s certainly one industry that has embraced it.

Retail has embraced it too, but I would say there’s a huge divide. Some companies are doing it well and still experimenting, but I don’t think anybody has ‘cracked the nut’ or has a perfected plan. The big players in professional services, like accountancy and legal, have not made it there yet but they’re certainly testing things and putting plans in place when it comes to using data and using SaaS.

I can’t see any others that are racing ahead. The tech and digital marketing people are almost acting as the testers and the trailblazers. Everyone else is following behind and really trying to find a place for themselves.

From those experiments, has anything risen to the top of the pile as being proven principles that we need to start applying across the board?

What I’m seeing at the moment is that content marketing is a huge thing — and it’s actually changing. User-generated content and engagement is the future. You need to have other people talking about you, as opposed to you talking about yourself.

Also, video is transforming the social web. It creates three layers of data. The first is the keywords behind the video. The second is the video itself. The third is people sharing it. Now, with 360-degree video, we not only have the main player that is shooting the video, we have every single individual featured in that video and indirectly becoming part of the story. They’re predicting that by 2018, 80 percent of the content on the web viewed on mobile will be video, and you can see that Facebook are transforming the way they operate to reflect that — moving from text-only status updates, to photo-sharing, and now video.

Broadcasting, as a tool, is becoming important. We’re deepening the conversation layers. To have a social media conversation today could involve Periscoping and livestreaming. Blab is coming into the market now, which is all about hosting a live broadcast with several people and allowing people on a Twitter Feed to jump in. What that’s doing is bringing trust to an extra level. I might be an influencer who you’ve heard about from someone or you’ve come across my blog — but if we suddenly have a weekly TV show using the platform Blab, we’re now deepening conversations between influencer and audience. It’s both live and recorded (for redistribution). People are chatting on the fly in front of their keyboards, and it’s there on the social web forever. These are very loose conversations, providing real customer insight at a deeper level. Video is going to be a gamechanger in terms of the data that it provides.

So you’ve got your massive stack of data ready to sift through and apply commercially. How do you know where to start, given that there is so much information flooding in? Is it all relevant?

No, the internet is a noisy place. It’s really hard to disseminate the relevant from the irrelevant; the influence from the chaff; the potential buyer from the time (and resource) waster.

I was at the Web Summit in Dublin and much of the conversation over the three days was about predictive analytics. We’re now no longer looking at last week’s traffic in making marketing decisions. We’re now pulling all the data of what our customers have done over a period of time and using it to predict what they’re going to do next. We’re ‘future selling’ to them.

SaaS is a huge business because you’ve got lots of companies trying to decipher the relevant from the irrelevant. For example, Channel Mechanics is a company headquartered at the Technology Transfer Centre at NUI Galway with offices in the US and the UK. They’ve built a sales and project management system to speed up getting promotions and sales programmes to market. That’s what it’s all about. We need the software to do all the automation and analysis for us because it’s just no longer possible for a human being to do it. So I think SaaS, predictive analytics, and automation of human tasks is probably what we need.

Does that make the human redundant?

Oh, no. We’ll always need humans. When we lived through the industrial revolution, we thought that the motor car would replace animals and humans, but it’s just a natural evolution. We’re becoming more intelligent and we now have access to this technology. We actually don’t have enough people producing the type of engineering software needed to deal with big data. In my book on law enforcement and social media, I talk about the future of law enforcement communication departments, and the fact that they should not be made up of police officers. They should be made up of software engineers; data analytical people; people skilled in content creation and curation. We’re behind in terms of up-skilling people to deal with this new phenomenon. People will definitely always have a role, but we need to push more people into the IT sciences, engineering and technology. That’s where the skills gaps are.

In my own company, 2016 will focus on systems, processes and software, in order to try and solve my business problems. I’ve done the rest — the community, the brand, the products — but, now it’s about driving forward on the customer base and driving sales, and that will require both analytical people and software.

So what’s the ultimate vision for the Digital Training Institute?

The aim is to scale online. While we’re a very small company we’re having global conversations. We’ve got unique content and intellectual property that’s relevant to lots of sectors and we don’t just sell in Ireland alone anymore. We are building the online business to sell globally online. My goals are to re-purpose eight years of high level content into products (courses, webinars, coaching programmes, eBooks) and build a global online community, to develop my speaking and coaching business and to create new strategic partners. I’m also doing more specialised social media research and work on digital citizenship, law enforcement, online reputation. I want to connect with strategic partners because we live in a collaborative economy and together we are stronger.

Key Takeaway Points

• Customer-centricity is the main driver for big data programs
• Most big data programs are at the early stages of development.
• The success of most big data programs is being judged on improvements made to end-to-end customer / client journeys
• Recruitment of new people with data-specific skills is the leading investment priority.
• There is an increasing focus towards internal training of big data culture for existing staff.
• Dealing with fragmented and legacy data systems is seen as the core challenge.
• Confidence is high for the potential benefits of big data.