What I Found Interesting About Gartner BI Summit 2012 London

Timo Elliott

Gartner BI Summit 2012 london

As always, it was a huge pleasure to catch up with customers, colleagues, analysts, partners, and competitors at one of Europe’s largest BI conferences. It was a great show overall, and I came away even more optimistic about analytics for the coming year.

Here are Jason Rose and I having an off-the-cuff discussion of what we found interesting about the latest Gartner BI Summit 2012 in London this week:

And here are some longer thoughts about the conference compared to previous years:

The band played on: Gartner analysts Nigel Rayner and Andreas Bitterer performed the opening live show, loosely based on Tubeway Army’s “Are Friends Electric”, to tie in with the opening keynote analogy of “information as electricity” (I’m a fan of ‘80s electronica, but it might have been more fun to have something by AC/DC with Nigel as Angus Young… )


It’s no longer Business Intelligence! Gartner has bowed down to the market trend and dropped the unwieldy category name “Business Intelligence and Performance Management” in favor of the simpler umbrella term “Business Analytics”, following in the footsteps of other analysts (e.g. IDC) and vendors (SAS, SAP, etc.). There was also a shift from BI Competency Centers (BICCs) to Business Analytic Teams (BATs) – apparently because the word “center” (a) doesn’t really represent the reality of diversified real-world organizational structures and (b) has a negative connotation in the US (think of your experiences with shared service centers).

Was it just a simple name change? There was a half-hearted attempt in some sessions to associate Business Analytics with “more than BI”, emphasizing that business change must be the result, not just reports. This is of course absolutely true and essential – but this was always part of their previous category definition. It was clearly a recent change: all the conference content reflected the old naming.

Should we care about this change? No! Here’s an blog I wrote a while ago on business intelligence vs business analytics, with the conclusion: “everybody has an opinion, nobody knows, and you shouldn’t care”. In particular, if you need to continue to call it “business intelligence” to communicate with somebody who is comfortable with that term, then you should continue using it!


Analytics is hot! The conference was literally packed, with people uncomfortably stuffed into a slightly too-small venue. Every indicator pointed towards analytics having another banner year – it’s back to the #1 technology priority for CIOs, it’s estimated to be growing at 10%, faster than overall IT spending, and the number of users is set to rise to 50% by 2014. Next year’s Gartner BI Summit (BA Summit?) is going to be in Barcelona, and that decision is apparently at least in part because of the need for extra space.

Less technology, more business, more success. Last year’s opening keynote presentation was about the “four Vs”: volume, variety, velocity and validity, and talked a fair amount about technology. This year the emphasis had very clearly moved to business value – emphasizing the “why” rather than the “how” – analytics has to support business decision making and result in business innovation. I attended several excellent sessions on how to make analytics sessions more successful – for example, emphasizing that 20-30% of your time should be spent on “marketing” your analytic solution.

Tell stories, and make a difference. Out of the nominees for the Gartner BI Excellence awards, Medway Youth Trust shone out. They would have been favorites for the award anyway, because they are doing social work for the community, but there were three other other good reasons they won over the other nominees:

  1. While all the nominees had clearly done a great job of successfully implementing business intelligence in their organizations, Medway had the best case for both “technical innovation” (using text analytics to get value from unstructured information) and “business innovation” (the data really make a difference to their organization, by allowing them to focus their limited resources on the key business goal)
  2. As a small organization, they showed that you don’t need to have a big budget or a big team of people in order for analytics to make a difference.
  3. They told their story better. In particular, a Spanish insurance company did a slick presentation about the corporate benefits of their standardized BI efforts, and quoted some impressive figures, but there wasn’t really a concrete example of how they had really made a difference that the audience could connect with.

Overall, it reminded me how vital it is to have real stories to tell people when trying to sell the benefits of your project – ROI isn’t enough: it has to be about people, and (surprising) business change.


Validation of current trends. Analytic technology trends were covered in in-depth sessions:

  • Gartner called in-memory a “strategic imperative”, and advised that organizations should look at in-memory as “a quantum leap in their computing strategy” because “dramatically faster data access can profoundly change the nature of some applications”
  • Mobile business intelligence is clearly now a given in the market – for example, all the participants of the vendor panel agreed that mobile BI is not going to be a long-term differentiator (although the underlying mobile device management certainly still might be).
  • Cloud BI is still something for the future for most attendees – for many, only when the underlying operational systems are themselves running in the cloud.
  • Sessions on social networking analytics and operational analytics were no longer marginal, with full crowds.
  • The topic of “big data” – or “extreme data” as Gartner prefers to call it – is embedded in the new notion of a “logical data warehouse” that is poised to replace today’s more monolithic structures. One analyst mentioned that big Gartner customers were ripping up their current data warehousing plans and adapting them to the new technology possibilities. A session on big data by Roxanne Edjlali (formerly with Business Objects) was well-attended and well-received.

Big data not big enough? Overall, I don’t think Gartner had quite taken enough account of the appetite for more information about big data topics such as Hadoop, data science, etc. Every session with big data in the title was completely packed, and there didn’t seem to be many people from those communities at the show. I hope that Gartner’s conference team targets the big data constituency more aggressively next year — it would be a shame if people with the same underlying goals (turning information into business innovation) end up going to different conferences just because of some differences in the technologies they use (big data conferences are booming).


Fail in the right direction. Tim Harford, the Undercover Economist, was the guest keynote speaker. His presentation was very entertaining, but in general only tangentially related to analytics. The overall theme did resonate, however: that nobody has all the answers, and that it’s only through being humble about your knowledge that you have a change to succeed. The key is to “fail in the right direction”: make experiments, iterate, and learn from experience in order to move ever closer to better solutions.

Overall theme: going with the flow? This wasn’t really mentioned at the show, but if I had to pick one overall theme, it would be the move from batch-based BI to a greater appreciation of information flow, at every level of implementing systems and consuming information. New data warehouse technologies allow organizations to gather and structure information faster (and this is important: Bill Hostmann estimated that fully 70% of the requirements of a BI project change in the first year alone). Data discovery tools allow business people to iteratively structure and access new information in new ways. And businesses are realizing that analytics isn’t just something that you use to improve business processes: it can and should be part of the business processes themselves.

My presentation at the conference, “Business In the Moment, From Reactive to Proactive” was along the same lines – while there has been lots of technology change over the coming year, many organizations are still struggling to turn that new technology into business innovation opportunities. I talked about the big changes in the technology landscape and gave examples of organizations that had used these technologies to transform the way they did business, through removing business bottlenecks, rethinking business processes, or flipping business models to an “analytics first” approach.

You can download the slides in Microsoft Powerpoint or Adobe PDF format, and I’ll explain the main themes in a separate post (update: 2012: The Year Analytics Means Business). I look forward to Barcelona next year!


About Timo Elliott

Timo Elliott is an innovation evangelist and international conference speaker who has presented to business and IT audiences in over forty countries around the world. A 23-year veteran of SAP BusinessObjects, Elliott works closely with SAP development and innovation centers around the world on new technology directions. His popular Business Analytics blog at tracks innovation in analytics and social media, including topics such as big data, collaborative decision-making, and social analytics. Prior to Business Objects, Elliott was a computer consultant in Hong Kong and led analytics projects for Shell in New Zealand. He holds a first-class honors degree in Economics with Statistics from Bristol University, England.



Why 3D Printed Food Just Transformed Your Supply Chain

Hans Thalbauer

Numerous sectors are experimenting with 3D printing, which has the potential to disrupt many markets. One that’s already making progress is the food industry.

The U.S. Army hopes to use 3D printers to customize food for each soldier. NASA is exploring 3D printing of food in space. The technology could eventually even end hunger around the world.

What does that have to do with your supply chain? Quite a bit — because 3D printing does more than just revolutionize the production process. It also requires a complete realignment of the supply chain.

And the way 3D printing transforms the supply chain holds lessons for how organizations must reinvent themselves in the new era of the extended supply chain.

Supply chain spaghetti junction

The extended supply chain replaces the old linear chain with not just a network, but a network of networks. The need for this network of networks is being driven by four key factors: individualized products, the sharing economy, resource scarcity, and customer-centricity.

To understand these forces, imagine you operate a large restaurant chain, and you’re struggling to differentiate yourself against tough competition. You’ve decided you can stand out by delivering customized entrees. In fact, you’re going to leverage 3D printing to offer personalized pasta.

With 3D printing technology, you can make one-off pasta dishes on the fly. You can give customers a choice of ingredients (gluten-free!), flavors (salted caramel!), and shapes (Leaning Towers of Pisa!). You can offer the personalized pasta in your restaurants, in supermarkets, and on your ecommerce website.

You may think this initiative simply requires you to transform production. But that’s just the beginning. You also need to re-architect research and development, demand signals, asset management, logistics, partner management, and more.

First, you need to develop the matrix of ingredients, flavors, and shapes you’ll offer. As part of that effort, you’ll have to consider health and safety regulations.

Then, you need to shift some of your manufacturing directly into your kitchens. That will also affect packaging requirements. Logistics will change as well, because instead of full truckloads, you’ll be delivering more frequently, with more variety, and in smaller quantities.

Next, you need to perfect demand signals to anticipate which pasta variations in which quantities will come through which channels. You need to manage supply signals source more kinds of raw materials in closer to real time.

Last, the source of your signals will change. Some will continue to come from point of sale. But others, such as supplies replenishment and asset maintenance, can come direct from your 3D printers.

Four key ingredients of the extended supply chain

As with our pasta scenario, the drivers of the extended supply chain require transformation across business models and business processes. First, growing demand for individualized products calls for the same shifts in R&D, asset management, logistics, and more that 3D printed pasta requires.

Second, as with the personalized entrees, the sharing economy integrates a network of partners, from suppliers to equipment makers to outsourced manufacturing, all electronically and transparently interconnected, in real time and all the time.

Third, resource scarcity involves pressures not just on raw materials but also on full-time and contingent labor, with the necessary skills and flexibility to support new business models and processes.

And finally, for personalized pasta sellers and for your own business, it all comes down to customer-centricity. To compete in today’s business environment and to meet current and future customer expectations, all your operations must increasingly revolve around rapidly comprehending and responding to customer demand.

Want to learn more? Check out my recent video on digitalizing the extended supply chain.


Hans Thalbauer

About Hans Thalbauer

Hans Thalbauer is the Senior Vice President, Extended Supply Chain, at SAP. He is responsible for the strategic direction and the Go-To-Market of solutions for Supply Chain, Logistics, Engineering/R&D, Manufacturing, Asset Management and Sustainability at SAP.

How to Build Customer Loyalty Through Digital Emotional Affinity

Volker Hildebrand, Lori Mitchell-Keller, Christopher Koch, and Polly Traylor



When the Amazon site was launched in 1995, followed by the heady years of dot-com mania, marketers believed they had reached nirvana with the speed, convenience, selection, and plain cool factor of online stores.

Digital technology revolutionized the way companies interacted with customers by making the research and purchasing processes more convenient. Yet research shows that companies have a long way to go in effectively using digital technologies to engage with their customers.
Just 49% of consumers say their experiences using Web sites on desktop and laptop computers are excellent, while a mere 18% of consumers say the same for shopping with mobile Web sites or apps.

The reason behind the failings of all this great technology is that loyalty is driven by positive emotions not just efficiencies. Ninety percent of purchasing decisions are made subconsciously, according to Caroline Winnett and Andrew Pohlmann of The Nielsen Company. Meanwhile, neuroscientist Antonio Damasio has found that for patients with brain damage affecting their abilities to feel emotions, making any decision at all is difficult.

By developing a concerted strategy to foster positive emotions in digital, companies can reduce churn, lower customer acquisition costs, and grow revenues per customer. “It’s getting harder and harder to put your message in front of customers effectively and efficiently,” says Tim Peter, founder of Tim Peter & Associates, an e-commerce, Internet marketing, and business strategy consultancy. “If you can get them once, you will more likely reengage them. My clients see emotional engagement as a differentiator.”

How does a company move from the robotic, unfeeling interface of technology to an experience where the customer can sense the people and brand behind it all? There’s no single method here; improving emotional affinity in digital requires a culture that’s hyper about monitoring and pleasing customers. It also begs for a hybrid approach of merging human experiences with digital, investing in omnichannel integration, and developing more creative approaches to online branding.

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Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.


About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP