30 Days of Customer Success

I’ve been on the road for what feels like the last five weeks and have hardly been home at the weekend. So when I got back to London from California this weekend and walked through our local shopping area, I was somewhat surprised to see that Christmas had arrived.

The shops and streets are sporting festive decorations, and all the bars and restaurants are advertising their Christmas menus.  I’m a fan of the holidays – but not in November.

So in an attempt to defer Christmas (for now) and get through to mid December without having to see Santa Claus: The Movie seven times, I’ve decided a distraction is in order.

So please join me in celebrating my very own countdown to the holidays – 30 days of customer success!

‘Tis the Season for #30Customers

Each working day over the next six weeks I’m going to post a link on Twitter to a new customer story. These stories all inspire and show how enterprise business intelligence, agile visualization, and advanced predictive analytics are being used by our customers to drive collective insight.

At the end, I’ll summarize them all here in a blog.

Follow @jamesafisher and the hashtag #30Customers and not only will you not miss a single one, but by the time we’re done it will just about be time for the 12 days of Christmas!


Big Data Will Save the Planet!!!

Over the last few weeks, I’ve noticed a trend on Twitter where various folks are posting Tweets referring to articles and blogs about how “Big Data” will transform the travel industry, or Big Data will optimize marketing, or Big Data will revolutionize information security.  All are interesting posts, and all are pretty accurate, but even for me (a marketing guy) these types of headlines make my skin crawl.

Let there be no doubt that I’m all for getting away from talking about the technical aspects of managing Big Data or talking about ever-growing numbers of “V’s” to describe the challenge.  Instead, I much prefer to hear about the value and opportunity it presents. But can we please stay away from pithy headlines?

Big Data’s Value Doesn’t Need To Be Hyped

In my view, these headlines simply epitomize a lot that’s wrong with marketing hype around Big Data.  I know most Tweets are created to fit within 140 characters, but all I see when I read them are obvious statements that lack any depth because us marketing folks created them when we couldn’t think of anything better to say. It’s either that, or some internal marketing policy says that we always need to use an adjective to make it sound more impressive.

The simple fact is that Big Data’s value is real – so are the use cases and the resulting opportunities. Articulating that value has to be more beneficial than offering hype.

For the most part, when I talk to customers, they aren’t quite at the stage where they want to save the planet. Rather, they just want to find out how they can use the data and resources they have to perform better in their roles, across their teams, and ultimately their organizations.

Which article are you more likely to read?

a)     “Big Data to Transform Marketing”, or
b)     “5 Big Data Use Cases in Marketing”

Personally, I’d opt for the fact-based one, the one that sounds like it’s going to give me some real concrete examples, that’s going to give me some real ideas that I can put into practice.

Let’s keep it real!

Quick Wins? Big Wins? Or Can I Have My Cake and Eat It Too?

Over the last month I’ve clocked a lot of miles heading from the UK to the U.S., to South Africa for the AFSUG events and back to Europe for SAPinsider, and during that time I’ve spoken to a lot of customers.  Those conversations, as you can imagine, have been pretty varied, ranging from questions about the overall analytics market, trends around big data and mobility, to more detailed discussions about how they are building roadmaps to drive richer and more impactful enterprise wide analytics.

What was interesting is that in a good number of these conversations I was asked a similar question which went along the lines of, “But we also want to show some quick wins. What do you think? Does that make sense?”

Well here’s what I think…

In my humble opinion, the recognition that quick wins should be part of a longer journey is good.  The reason for this is simple – quick wins alone are unlikely to make the most of the opportunity that a broader enterprise-wide analytics strategy can deliver.  Yes, you gain value; yes, you can do some cool things quickly; and yes, you probably don’t have to bang on too many doors to make them happen. But I believe they can only get you so far. At some point a bigger, broader change may be required.

That said, I also don’t believe that the delivery of a broad, enterprise-wide analytics strategy is possible without incorporating quick wins as part of the delivery process.  The sort of value they can deliver in a short space of time is the perfect example of what could be possible. That’s great when it comes to gaining the sponsorship of you key stakeholders, but it’s also critical when it comes to proving to the skeptics across the business that an enterprise-wide approach is possible without huge disruption or the loss of the flexibility they crave.

The trick here, in my view, is to find a way to best combine the two, where you can have your cake and eat it. Where you get the sort of quick wins the business loves and easy to use self-service analytics that can be deployed to solve numerous challenges quickly but at the same time contribute to a broader strategy. Enter the value of enterprise self-service analytics.

And that’s where I closed the conversations. “Yes it makes sense, here’s why and by the way… have I told you about SAP Lumira? You can download it free now…”

“Big Data” Is OK, But Intuition Rules, OK?

I tend to be an early riser and this weekend was no exception.  In my usual quiet time in the morning, with coffee and Macbook in hand, I settle down in the kitchen, read the BBC News site, and generally see what’s going on in the world.

This weekend I stumbled upon a blog titled “Why Big Data Will Never Beat Business Intuition” and I have to say that quite simply, I agree.

I won’t recap the article for you, but needless to say, it makes a series of points to argue that we should take a little time to really think about how we use Big Data and it cautions us against blind interpretations without human intuition.

Let me add another example I’ve used in presentations over the last few weeks.  Analytics aren’t reserved for businesses or data scientists. We all use analytics everyday. My friend Donald MacCormick blogged on a great example last year when he used the BBC weather website as an example of an analytic, and again I agree.

But here’s the point – when I read the BBC weather website and it tells me the forecast for the day ahead, I don’t simply take it for granted. The first thing I’ll do is look out the window and ask myself if the weather looks like it’s supposed to. Quite often I’ll even open the door and really check how warm it is. That’s my human intuition telling me not to rely just on the data.

And that in a nutshell is why I think Tim Leberecht’s blog makes sense. And that’s why, when it comes to Big Data, I believe two things are critical – the discussion on the use case and application of the data, and the education of the people using it.

Of course, flexible, self-service, and real-time analytics are then needed to allow people to use their intuition in a natural way, as opposed to a machine-driven way, but it’s that intuition and education that really makes their application work.

This blog was first posted on the SAP Analytics Blog

Its June…Time for Tennis

Its the end of June and here in south west London where I live that means only one thing…Wimbledon.  So along with unseasonal weather that means that British tennis fans are typically now reaching the highest peak of their inflated expectations regarding the prospect of a British winner of this historic major championship.

Now what normally happens is that in about 10 days time they then enter the trough of disillusionment as all the British players are knocked out, the champagne is warm and the strawberries are running out.

However my own interest in Tennis was revived when I had the opportunity to co-present with former professional tennis player, broadcaster and SAP Ambassador Justin Gimlestob at Sapphire Now in Orlando.

Justin explained how analytics are now playing a huge part in the game, in his analysis of it and the fan experience.  Justin made me see a whole new tactical side to the game that frankly had been lost on me due to the hype and fixation around a British winner of Wimbledon. That appeals to my analytical nature and the result is that this year I’m watching and enjoying WImbledon once again. Although the sight of expectant British tennis fans draped in Union Jacks still turns me off a little…


You can see our presentation on the Sapphire Now website.  If you don’t want to listen to me then start the video at about 12minutes 30 seconds which is when Justin joins me.


A Question of Analytical Education Verses Analytical Simplification

At the Gartner BI and Analytics Summit in Barcelona this week, I found myself participating in an interesting debate on whether the growing army of business users, in this new age of pervasive business intelligence and analytics, should receive education and training in analytics or whether the tools should just be easier to use.

Gartner directly posed the question during a panel discussion – should we improve user skills rather than simplify the tools? The premise was that if analytics were easier to use (with features like guided discovery and intuitive, easy-to-use interfaces), users would need to know absolutely nothing before they apply  statistical packages, visualization templates, and predictive algorithms to uncover the gems of insight buried in the data. This  feels a little like we’re suggesting that even though we’re living in a world with a burgeoning surfeit of data, as long as users have the right analytics tools, the data will simply tell its own story.

Me –  I’m not so sure. I’ve always found that if I just wade into a plethora of  information without any prior hypotheses of the type of relationships and trends that I might find, it’s always difficult to separate out the useful data from the misleading or confusing data that’s simply noise. I guess I need to go through a process similar to classical market research: first, develop testable hypotheses through qualitative focus groups and face-to-face interviews to surface the issues, then collect and analyse quantitative data to measure which ones are true and important enough to act on.  The parallel in business is having a dialogue with colleagues and customers before diving into the data.

Is the Traditional Scientific Method Obsolete?

Chris Anderson challenged this accepted approach in an article he wrote for Wired back in 2008 called ‘The End of Theory: The Data Deluge Makes the Scientific Method Obsolete’. He suggested that in the petabyte world of big data, the traditional approach of hypothesize – model –  test is obsolete and “the new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world.”  Many people have criticized such optimism, using planetary weather as an example to point out that too much ‘noise’ in the data about planetary weather made it  unlikely that trends in global warming would have been uncovered without researchers having a prior hypothesis.

Seems to me that Anderson’s bold statement also denies us – the human – any role in analytics. While I don’t doubt that some data-driven predictions can succeed, I’m convinced that a questioning mind, a better knowledge of the math, and an appreciation of the common misconceptions that people typically make, will always result in better assessments and reduce some of the inherent risk that results from poor business decisions.

Analytical Education Still Relevant and Increasingly Vital

So the point I was trying to make, albeit in 140 characters on Twitter, is that although we do need to make tools easier to use,  we (vendors, organizations and, increasingly, our educational systems) also have a responsibility to ensure that anyone using analytic tools is provided with a general grounding in simple statistics and research methods.  These users can then develop a questioning approach to using data and, by default, analytics in order to support the business decisions they make in their working lives. This approach could entail:

  • Questioning the validity of the data they’re working with. For example, we know that cancer patients can be misdiagnosed.  Some who are told they have the disease don’t (i.e. a false positive), while others who are told they don’t have the disease in fact do, (i.e. a false negative). You can bet we encounter the same issue in business all the time and never even think about it!
  • Teaching about probability and significance so we appreciate that findings and predictions are generally range based rather than a single data point – and how this impacts results such as elections or sales wins which have binary (win all or lose all) outcomes.
  • Helping us to become ‘diligent skeptics’ when it comes to data. A colleague tells a great story about an experience in insurance when a business analyst from the special lines division merged some previously siloed data sets and found considerable crossover between customers taking out one or more of their policies (cover for pets, music instruments, extended warranties) and was adamant this was evidence of “significant customer loyalty”. The reality was the insurer was the market leader in special lines, all of which were sold under different brand names through different channels, and the crossover was inevitable rather than evidence that indicated the likely success of a concerted campaign of cross selling.
  • Showing students how best to present and visualize data so it’s easy to understand and can be quickly digested.

If we don’t focus on analytical education and training, we risk entering an age of pervasive analytics that could rapidly become dysfunctional as people act on insights that are beautifully presented but entirely misplaced.  Analytics, forecasting, and predictions will always have some degree of error. Everything we can do to minimize that will improve the decisions we make and benefit our businesses.

Even the black belts get it wrong occasionally. Nate Silver, the statistician who became famous for predicting the voting outcome of every state in the 2008 and 2013 US Presidential elections through the use of sophisticated statistical analysis, fouled up on predicting the result of the Superbowl last weekend. If experts can get it wrong, where does that leave the rest of us?