(BYOD = Bring Your Own Device. BYOB = Bring Your Own Bottle.)

SAP Visual Intelligence 1.0 (internal project name “Hilo”) will generally available tomorrow*. It is the latest innovation in the SAP BusinessObjects Explorer solution family.

It is a desktop-based visualization and data manipulation solution that is designed to complement (not replace) the existing Explorer products. After downloading and installing the application (approximately 150 MB), users can connect to SAP HANA sources and start data discovery, analysis, and sharing.

Product Tour:

Interface:

Creating Charts:

The new product will be formally launched at SAPPHIRE NOW in Orlando this week, and more information is available on the site http://experienceexplorer.com. You can also try the solution for 30 days with your own data, using a virtual machine environment: Try it Now (SAP Community Network login required).

Later versions of the product will connect to other data sources, a further step towards business intelligence “data discovery.”

_______________________

* Originally said today. Date per FAQ here

Steve Lucas, EVP of Database and Technology, unveiled SAP’s Big Data Processing Framework at the keynote session of SAP Insider BI2012 in Orlando earlier this year.

I was honored to have the opportunity to be on stage with Steve for the first time, and we were ably assisted by Fred Samson.

In the recorded session below, we discuss what “big data” means, why you should care, and how SAP is making it easy to Ingest, Store, Process, and Present “extreme data” with a seamless set of technologies adapted to different needs, including SAP HANA, Sybase IQ, and open-source technologies like Hadoop.

There will be more details about the framework in SAPPHIRE NOW sessions in the US and Europe.

Jump straight to the content you’re interested in:

• 02:00 – What you can do with Big Data
• 32:50 – SAP Smart Meter Analytics with SAP HANA
• 43:30 – BI4.0 running with Amazon Cloud on top of Sybase IQ
• 52:00 – The Cloud
• 54:28 – Sharing reports with BI OnDemand
• 1:03:28 – Mobility
• 1:20:00 – Latest consumer application from SAP

You can download the presentation here:

• Big Data: Are You Ready? (Adobe PDF)
• Big Data: Are You Ready? (PowerPoint)

As usual, if you’re interested in SAP BusinessObjects Analytics, you should subscribe to http://www.blogs.sap.com/analytics.

Note: the following post is republished with permission from Technology Evaluation Center’s excellent blog. It is a part of a series of interviews by Jorge Garcia on trends in the analytic industry.

It is hard to imagine that organizations like SAP or former Business Objects (BO)—now a division of SAP—were once start-up companies. Nowadays on the business intelligence (BI) scene it’s almost impossible to avoid BusinessObjects. Like many other successful companies, SAP has grown into itself by offering a product that applied a radical approach to a business problem solution, and this is particularly true of its BusinessObjects stack of BI solutions. In this installment of my Thinking Radically interview series, I had the pleasure to speak with Timo Elliott, a prominent personality in the BI space.

Mr. Elliott was one of the first employees of the original French company Business Objects, which SAP acquired in 2007. He has held various positions, including Senior Product Director for SAP BO. With vast experience in the BI arena, Mr. Elliott is now part of SAP’s elite squad of “technology evangelists.”

Below, he comments on the past, present, and future of BI and SAP’s analytics products and technologies.

Hello, Mr. Elliott. Could you give us a brief overview of your career within the BI space and with SAP BusinessObjects?

TE: I’m a statistical economist by training, but when I left school, I wanted to see the world. I ended up working on an analytics project at Shell in New Zealand, with a makeshift system including a mainframe reporting tool, exports to Lotus 1-2-3, custom macros, and a pen plotter.

A few countries later, I joined Business Objects in 1991 as the eighth employee—prompted largely by the realization that the project that had taken me a month at Shell could be done in less than a day using SkipperSQL (as the BO product was then called).

I’ve had a variety of roles over the last 20 years, but one thing has been consistent: I’ve always been lucky enough to have a market-facing role, spending time understanding customers’ real-life information challenges. My role now is “technology evangelist”—I spend a lot of time at conferences and using social media, doing my part to explain new BI technology and how to achieve its full benefits.

Back in the early days of BO, what do you think it did that was so radically differently to be a leader in the BI space?

TE: Clearly, we had the right product at the right time: organizations had valuable information locked away in their databases, and you had to have technical knowledge to get it out (although Oracle at the time positioned SQL as an “English-like language” for power users). We pioneered the notion of a “semantic layer” that let business people access information using standard business terms and which automatically generated the complex SQL required to get the data from the database.

BO also benefited from great management from its founders, Bernard Liautaud and Denis Payre. They were determined to create a “Silicon Valley start-up” on the outskirts of Paris that included rapid expansion and a global vision. There were a few stumbles along the way, but in the end, it became the second European software start-up (after SAP!) to reach a billion dollars (USD) in revenue.

What are the main differences in the way organizations do BI now compared with how they did it in the past?

TE: Clearly, the technology has changed a lot over the years, but the business requirements remain remarkably similar: cutting costs, finding new opportunities, beating the competition, getting closer to customers… The biggest change is perhaps that BI is now clearly mainstream—only a tiny fraction of organizations don’t have some sort of BI in place, even if it’s only using spreadsheets.

What are the most common complaints from companies about what a BI solution should do but doesn’t?

TE: The dream of most organizations is to have a BI solution that just works—people believe strongly in the benefits of BI, but wish that it were easier to put in place robust solutions.

A new wave of technology, including in-memory techniques, holds the promise of making some of the processes much simpler. But many of the most intractable problems stem from business processes themselves (organizations with 20 different definitions of “customer” etc.), and are consequently much harder to fix. There’s also the problem of expectations: as soon as you provide better BI systems, business people can (and should) move on to new and even more difficult questions. I don’t believe people will ever be completely happy with their information systems (and if they were, it might be a sign that they needed more imagination!).

SAP is working hard to position HANA with its customers and in the market. Regarding its relationship with SAP’s BI stack of products, how can SAP HANA be a real game changer for SAP BO customers and SAP customers in general?

TE: SAP HANA combines a series of technologies (in-memory, column stores, in-database calculations, etc.) to create a truly innovative alternative to traditional BI infrastructures. Early customers are now starting to reap the benefits in the form of radically faster access to large quantities of data. This is allowing them to make better decisions, earlier, and more often, and fix potential problems in real time, rather than analyzing what failed in the past.

How does HANA modify the traditional BI cycle, and what is the impact on the business?

TE: I think the biggest benefit of HANA isn’t actually its speed—after all, every new generation of databases has been faster than previous versions. What’s different about HANA is the way it “collapses the layers” between data and analysis, and radically simplifies the implementation of new BI projects. When business people come up with a new analytic need, IT should no longer have to say, “Come back in six months when we’ve managed to get the data into the data warehouse.”

An analogy for how BI has changed would be the move from film to digital photography. In the old days, you’d have to buy film, load it into your camera, take pictures, send the film off to experts to get it processed, and after three days you’d get your pictures back—only to realize that they weren’t quite what you wanted. A lot of enterprise BI today works along exactly the same lines—as a business person, I have to rely on experts to get the solutions in place, it’s slow, and what I get back is often not quite what I need.

HANA is like the digital camera—it’s faster, but the real benefit is getting rid of the redundant layers in the process. Today, I can take a picture without an expert’s help, and if it’s no good, I can quickly take another while I still have the subject to hand: we’ve all become better photographers because of digital cameras, and better BI will be the result of in-memory technologies like HANA.

How about cloud computing? What is your view on SAP’s cloud service strategy, especially for BI and in light of SAP’s recent acquisition of SuccessFactors?

TE: It’s clear that cloud computing is the future, and it’s also clear that on-premise installations are going to be with us for a long, long time. SAP’s strategy of “orchestration”—helping organizations make the best use of the combination of technologies (increasingly including mobile)—sounds like the right approach to me. From an analytics point of view, BO was, and continues to be, a pioneer in on-demand business intelligence with our OnDemand platform, which now offers HANA-based BI in the cloud.

From your point of view, what is the difference between BI and business analytics, and the difference in the way they are applied by organizations?

TE: The “nomenclature wars,” as I call them, drive me nuts. People often conflate two things: the technology that is being talked about (this changes over time, requiring new terminology) and the underlying business needs that are being addressed (this doesn’t really change).

I read a lot of rubbish about how BI is “backward looking” and analytics is “forward looking.” I can assure you that BI has always been about actionable information. At the end of the day, what counts is using data to improve the way you do business. Call it whatever you like, but vendors in particular shouldn’t try to belittle what we’ve been doing for decades just because they have some new technology they want to sell.

What is your view on the general maturity level of businesses in regard to BI applications? Are there key areas for improvement organizations still have to address? Which ones?

TE: I present regularly on topics such as “why BI projects fail and what to do about it” and “how to implement BI competency centers.” Overall, there’s higher maturity in BI, but it’s widely scattered, with each new generation relearning most of the same lessons.

The key area of improvement that is required is to always remember that BI is about people and the business, not about the software architecture. The technology is, of course, often a challenge, but when BI projects fail, it’s almost inevitably a problem with organization, culture, expectation setting, and business alignment. If you run a BI project, you should be spending more time on these things than the underlying IT infrastructure.

What is your position regarding the adoption of big-data technologies (especially Hadoop) and SAP’s strategy for adopting these technologies?

TE: MapReduce, Hadoop, and related technologies have proven their worth in enterprise contexts surprisingly quickly, and every vendor in the BI space is busy providing tighter integration. The latest version of SAP’s Sybase IQ database has tight links with Hadoop, and you’ll see a lot more coming out this year, both in terms of integration and best practice (i.e., determining where it makes the most sense to use these new technologies).

What’s your vision for BI, your expectations for its future?

TE: As I sat down to gather my thoughts about BI in 2012, I quickly came up with the same long laundry list of BI topics as everybody else: in-memory, mobile, predictive, social, collaborative decision-making, data discovery, real time, etc.

All of these things are clearly important, and we’re going to continue to see great improvements this year. But I think that the real next big thing in BI is what I’m seeing when I talk to customers: they’re using these new opportunities to not only improve analytics, but also fundamentally rethink some of their key business processes.

Instead of analytics being something that is used to monitor and eventually improve a business process, I’m seeing analytics become a more fundamental part of the business process itself. One example is a large Telco company that has transformed the way it attracts customers. Instead of laboriously creating a range of rate plans, promoting them, and analyzing the results, it now uses analytics to automatically create hundreds of more complex, personalized rate plans. The plans are then thrown out into the market, monitored in real time, and those that aren’t successful are quickly culled. It’s a way of doing business that would have been inconceivable in the past, and will be a lot more common in the future.

What is your favorite wine?

TE: When at Business Objects we used our own technology to run things, we put a French spin on the phrase “eating our own dog food” and claimed that we were “drinking our own Champagne.” So that’s my answer!

I’ll be presenting at Decoded Fashion next week at the Lincoln Center in New York:

“The first Fashion and Technology Forum Series connecting the world’s best startups and most noteworthy technologies to the Fashion, Beauty and Retail industries to accelerate innovation and increase the bottom line.”

It promises to be fascinating, with a wide range of sessions on different aspects of using technology in the fashion business, including leading-edge topics such as “Secrets from Social Curation Pros” and “Consumer-Powered Design”.

The speaker list is a who’s who of hot technology startups, and I’m especially interested in hearing the keynote from David Karp, CEO/Founder of Tumblr and Mashable’s Associate Editor Lauren Indvik, and hearing from Amy Cole of very-much-in-the-news Instagram (I’m a huge fan of both photo platforms).

Panel: Crystal Approach into Consumer Behavior

I’ll be moderating a panel session entitled Crystal Ball Approach into Consumer Behavior, discussing the use of analytics and business intelligence technology in the industry:

“ In the new social era, consumers no longer abide by set fashion rules and define their own style and trends. Real-time fashion analytics is a powerful tool to predict consumers’ real desires today and tomorrow.”

I have three panelists with deep experience of analytics in the fashion industry:

Lilly Berelovich, President, Fashion Snoops: Lilly Berelovich is the Co-Founder of Fashion Snoops, one of the first online trend services ever launched to become a renowned global research and advisory company. Lilly leads brands like Disney, Hang Ten, Sears, Wal-mart, and Warner Bros in the areas of licensing, branding, merchandising, and design. [Video profile here: First Comes Fashion]

Geoff Watts, Co-Founder, EditD. Geoff’s background in big data began way before the term “big data” was even coined. Because we process more data at EDITD than anyone else in the fashion business, his experience is essential. Geoff invents amazing tools from new technology with a great team of people, and he’s on a mission to make EDITD into the definitive real-time resource for the industry. [Here’s a great example of the company’s work: http://editd.com/reports/2011-a-year-in-fashion/]

Rohan Deuskar, CEO/Co-Founder, Stylitics. Rohan Deuskar is the CEO and Co-founder of Stylitics, a consumer insights company that gives consumers an intelligent and engaging platform to manage their clothing choices and share them with brands and friends. Rohan attended The Wharton School where he gained his MBA, and in the process also started Stylitics. Prior to Wharton, Rohan was Director of Innovation for Vibes Media. [Check out the Campus edition of the Stylitics site to find out the “Most Stylish Campus in America”]

How Analytics Can Help the Fashion World

From my initial discussions with the panelists, it’s clear that the fashion industry is going through the same type of technology revolution as other business sectors:

1. There has been massive changes in the amount of data available and what can be done with it – and many organizations are still aware of the new possibilities
2. The industry is slowly shifting from a batch-based, date-oriented supply chain to something more flexible, iterative, and data-driven, taking account of the new real-time opportunities
3. The consumer is becoming an integral part of the business processes, not just an end-consumer of it, helping shape industry directions and product designs
4. The biggest barriers to change and business value are cultural and organizational rather than technological

Looking down the list of sessions at the event, analytics is at the heart of almost all the fashion industry developments, including:

Retail optimization. Better analytics can directly help profitability. Better analytics can help optimize every aspect of the fashion business, including the supply chain, customer segmentation, spotting hot items, avoiding stockouts, monitoring profitability, etc. Companies like Burberry have reduced costs by over $150 million by driving down stock inventory levels thanks to data coming from their SAP systems.  And analytics is essential to answer more strategic questions such as “what’s the right tradeoff between profitability, customer loyalty, and brand value?” and “how can we optimize the depth and timing of markdowns in order boost sales?”  There have been recent changes to underlying analytics technology, including in-memory computing systems like SAP HANA that mean that companies can do this with large amounts of very detailed data in near-real time. SAP has a range of specific fashion industry solutions available.

Fashion trends. Peering into the future of upcoming fabrics and colors is a huge part of the industry dynamic. New “big data” techniques mean that massive amounts of structured (numbers) and unstructured (text, pictures, video…) data can now be gathered and combined to get an aggregate view of the key trends.

Social fashion. It’s now possible to analyze all the public information now available about fashion on the internet and using it to track brand trends and sentiment. And fashion-oriented social communities are emerging that let like-minded people share information about their fashion interests. This becomes a real-time source of analytics data for consumers and brands alike, and social network analysis can reveal who are the key influencers.

Customization and iteration. Rather than the traditional cycle (design collection/sell collection), it’s now possible to create more “analytics first” fashion.  Companies can move to a more iterative, analytics-based. customized approach, where clothes are made in smaller batches based on the particular desires of an individual or community. Retailers can then iterate designs by monitoring sales in real time, making constant changes and tests to improve sales and profitability.

Mobile, sensors, and monitoring. Location-based promotions can now be created using mobile applications. Research and pilot programs involving sensors, RFID chips, and related technologies are evolving. Large-scale retailers like Walmart are looking to optimize inventory tracking by embedding RFID chips in jeans and underwear. Vendors of expensive items (shoes, handbags, etc.) are increasingly interested in using embedded chips to thwart counterfeiters. This has interesting implications for privacy (imagine a customer buying shoes in a store, then setting off a profiling algorithm at the entrance each time they visit…), and as the RSA web site points out, the key aspect of this is creating the database…

Session Outline

During the session itself, we’ll be discussing the following topics:

• The changes in the industry and what is now possible with new analytic technologies and solution
• How companies overwhelmed with the choices can make sensible decisions about how to get started
• Barriers to moving forward effectively with analytics
• Some of the more exciting future trends

I hope you can join us, but if not, you can get a glimpse of the sessions by following the @decodedfashion hashtag.

Fun Fashion Analytics Bonus

I couldn’t resist mentioning this wonderful piece of fashion analytics from the Economist, illustrating “size inflation” in the UK (the US trends are very similar). Sorry to break it to you, but if you’ve been wearing the same size clothing for the last ten years, you’re actually 4+ inches thicker around the waist. And no, men don’t get off any better:

“Studies in America and Britain have found that some brands of men’s trousers labelled “waist 36 inches”, say, are in fact up to five inches bigger.”

 

 

 

 

I’ve had the huge pleasure of presenting at a series of Innovation by SAP events across the Nordic region. Please find below my two presentations from the sessions in Oslo, Copenhagen, Helsinki, and Stockholm (note: only incremental changes from the presentations in the Baltic region in a previous post)

The keynote presentation: Business In The Moment: From Reactive to Proactive

2012 will be a year of business transformation. New technologies such as in-memory computing, mobility, and analytics have shaken up existing technology paradigms and opened up dramatic new business opportunities. Business expectations have risen, fuelled by the fast-paced innovation in the consumer world, raising the bar for Finance and IT organizations to provide better systems to support business activities. This session will examine the big trends facing the analytics industry, and how real-life organizations are “changing the game”, using new technologies to:

• Remove information bottlenecks and dramatically speed up business operations.
• Make better decisions in real-time and fix potential problems before they affect business activity.
• Increase competitive advantage through new, analytics-based business models

Download “Business In the Moment: From Reactive to Proactive” in Adobe PDF format

Download “Business In the Moment: From Reactive to Proactive” in Microsoft PowerPoint format

My IT track session presentation: Turn “Big Data” into Business Value with Real-Time with BI

Best-practice data warehousing has been relatively stable for a decade, but is now being rapidly disrupted by a combination of new technologies, including in-memory, column data stores, predictive analytics, and new “NoSQL” technologies such as Hadoop. User interfaces and expectations have fundamentally changed, with new opportunities for mobile access, data discovery, social data, collaborative decision-making, and new visualization methods. This session looks in detail at how organizations can combine and harness the power of these new technologies, illustrated with real-life examples of how companies are implementing the new solutions.

Download “Turn Big Data into Business Value With Real-Time BI” in Adobe PDF format

Download “Turn Big Data into Business Value With Real-Time BI” in Microsoft PowerPoint format

An excellent article in Health Data Management by Greg Gillespie gives some wonderful examples of the power of analytics to improve health outcomes, looking at data from some of the 2,000+ clinical trials that Cleveland Clinic is currently running.

I strongly encourage you to read the original article (also available in pdf format), but here are summaries of the three use cases highlighted: hand-washing analytics, central-line analytics, and blood-transfusion analytics.

Data Transparency and Hand-Washing Compliance

Cleveland Clinic uses SAP BusinessObjects Dashboards (Xcelsius) to display information, change behavior, and avoid infections:

Cleveland Clinic has developed a program where staff from compliance anonymously watch workers in different departments and record whether they do in fact follow hand hygiene guidelines. Their findings are uploaded into Cleveland Clinic’s enterprise analytics system and are accessible via a dashboard tab.

Four years ago, the system was showing a 40 percent compliance rate with hand hygiene guidelines. Now the compliance rate is staying well over 90 percent, staving off a significant number of hospital-acquired infections and other complications arising from hygiene issues.

“That’s the critical value of data transparency—you can show people what they’re really doing as opposed to what they think they’re doing, and we can show it on a department, unit-by-unit or individual practitioner level,” says Steve Davis M.D. “I’ve found that when you put that kind of information in front of physicians, their competitive streak really comes out. No one likes to get a ‘C’ on their report card, and if you don’t have data everyone assumes they’re getting an ‘A.’ When they find out they’re not, then they get moving.”

[By coincidence, a post in the Decision Factor blog also takes up the theme of hand-washing this week, arguing that data cleansing is the single most important means of avoiding bad decisions. ]

Reducing Infections While Saving Money

By carefully collecting and analyzing data, Cleveland Clinic has been able to reduce infection rates, spend less on equipment, and avoid costs of up to $30,000 per affected patient:

The Centers for Disease Control and Prevention estimate that nearly 250,000 of the bloodstream infections occur annually from procedural issues associated with inserting and maintaining central lines—tubes inserted near the heart or a large blood vessel that are used to give fluids, antibiotics, medical treatments such as chemotherapy, and liquid food.

Overhauling the health system’s approach to central-line infections had a significant financial return in addition to the clinical benefits.

Before clinical and business analytics were applied, each individual unit was responsible for ordering their own lines, which meant that more than 30 different lines (and more than 90 different PICC lines, another type of tube) were being used across Cleveland Clinic, which was not only financially inefficient but also clinically dangerous.

By streamlining the purchasing to one vendor, the equipment and maintenance costs dropped significantly. And standardizing the clinical processes resulted in major cost avoidance—it’s estimated by the Health Research & Educational trust that central-line infections add upwards of $30,000 in treatment costs per afflicted patient.

Best-Practice Blood Transfusions

A blood transfusion dashboard helps identify physicians that haven’t kept up with the latest information in health best practice, improve the supply of blood, and reduces costs:

Andrew Proctor, administrative director of medical operations for Cleveland Clinic has developed a blood utilization dashboard that enables department heads and others to drill down to a physician level how much blood is being used for transfusions.

Standard industry practice used call for ordering transfusions if a patient’s hemoglobin count was below 10 after surgery or due to critical illness, But about a decade ago, says Davis, medical research showed convincingly that blood transfusions given at those hemoglobin values, and even significantly lower, in nearly all cases did more harm than good, providing few benefits and increasing the risks of nosocomial infections.

“Blood transfusions is another area where physician behavior has changed slower than the evidence, and our data is helping drive that behavioral change by enabling us to determine where blood utilization still goes against best practices, and addressing the issue on a unit or individual physician basis,” Davis says.

The result has been a significant reduction in blood utilization, which equates to a significant reductions in costs associated with maintaining the blood supply, and an improvement in patient outcomes.

Health Data + Analysis Saves Lives

I believe we’ve only scratched the surface of what is possible using analytics. New developments in big data mobile, cloud, analytic, and collaborative technology are combining to create new ways of improving health care.

Examples include the new SAP Collaborative E-Care Management application that connects patients, care providers and their families through medical monitoring software and mobile devices to better manage their health with individualized treatment plans:

And the pioneering work being done in conjunction with Charité, Europe’s largest teaching hospital, to enable mobile access to health data anytime, anywhere, including the SAP HANA-based Oncolyzer cancer-research application.

For more information, visit the Healthcare area of SAP.com

I’m in the Baltics this week (Estonia, Lithuania, Latvia), presenting a business innovation keynote and a big data track session as part of the SAP Innovation Forum.

Yesterday’s sessions in Tallinn were fascinating, including:

• Industry thought leader Peep Aavikso, on why smart companies need smart people
• Janeck Gustavson of Eesti Energy talking about their use of SAP Business Planning and Consolidation (BPC)
• A very passionate talk from Olga Lustsik of Swedbank Estonia explaining the power of activity based costing, and how they have implemented SAP Profit and Cost Management (PCM)
• An interactive whiteboarding session with SAP’s Santosh Takoor explaining how to align corporate strategy with systems of execution for best in class operations in finance (with SAP Enterprise Performance Management and lots of compelling customer examples)
• SAP’s Dr. Jens Baumman on rapid deployment solutions for SAP ERP
• SAP’s Eero Koskinen on the power of SAP HANA
• And finally a GREAT demonstration of the latest SAP solutions and prototypes by the SAP Demo team

My keynote presentation was based on some of the material from a previous post: 2012, The Year Analytics Means Business

Here are the slides:

• Business In The Moment: From Reactive to Proactive (pdf)
• Business in The Moment: From Reactive to Proactive (ppt)

And for my track session:

• Turn Big Data Into Business Value With Real-Time BI (pdf)
• Turn Big Data Into Business Value With Real-Time BI (ppt)

Today’s sessions are in Vilnius, Lithuania, including a fascinating presentation from Sarunas Chomentauskas, Co-founder of Metasite Business Solutions, on how they have used open-source Big Data technologies for their customers including customer churn, and machine learning techniques including how to explain the difference between a dog and a cat to a computer…

I’ll update this post over the course of the coming days. You can follow the tour on twitter using the #SAPInnovation hashtag.

Dawn this morning over Vilnius, Lithuania

Steve Lucas and Timo Elliott onstage at SAP BI 2012, Las Vegas. Image courtesy of Matthias Wild

At this week’s SAP BI2012 conference, I had the honor of co-presenting the keynote, “How Big Data Shapes Business Results”  with Steve Lucas, SAP EVP Business Analytics, Database & Technology, and with demo support from Fred Samson.

The big theme of the last year has been big data. There was a lot of innovation in many areas, but big data has had  a huge impact on both how organizations plan their overall technology strategy as well as affecting other specific strategies such as analytics, cloud, mobile, social, and collaboration.

Steve kicked off by addressing the confusion (and cynicism) about the definition of “big data” — noting that people had supplied at least twenty different definitions in response to his question on Twitter. The popularity of the term has been driven by the rise of new open-source technology technology such as Hadoop, but it is now typically used to refer to what Gartner calls “extreme data”.

Extreme data is on the high end of one or more of the ‘3Vs’: Volume, Velocity, and Variety (and some note that there’s a fourth V, validity, that must be taken account of: data quality remains the #1 struggle for organizations trying to implement successful analytic projects).

To address all of these effectively, any “big data solution” has to encompass a wide range of different technologies. SAP is proposing a new “Big Data Processing Framework” that includes integration to new tools such as Hadoop, but also addresses the need for the other ‘V’s for a global approach to ingesting, storing, processing, and presenting data from both structured and less-structured sources. Many more details about this framework will be available in the coming months.

The keynote session went on to talk about how big data is related to other technologies, such as real time, mobile, cloud, virtualization, and social, with highlight demonstrations of some of the latest SAP technology. These included: in-memory smart meter analytics, SAP BusinessObjects 4.0 running on Sybase IQ, BI On-Demand powered by HANA, SAP NetWeaver Landscape Virtualization Management, a mobile Hospital Oncology analytics application, and two new mobile consumer applications from SAP: Recalls Plus, and an upcoming project called iLike

Dave Rathburn put together a nice overview of the session, and Matthias Wild wrote up a post on the SDN site.

A reply of the session is now available, and here’s a copy of the presentation available for download:

• How Big Data Shapes Business Results (pdf)
• How Big Data Shapes Business Results (ppt)