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.”

 

 

 

 

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

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)

I recently saw a presentation mentioning that there “are more mobile phones than toothbrushes in the world”, and according to some research by the 60 second marketer, it seems that in all likelihood, this is absolutely true:

My next thought was “why not combine the two?!”. Here’s my quick mockup of an iPhone case of the future:

But as usual, truth is stranger than fiction. Just when you think you’ve seen every use of analytics possible, along comes a real “iPhone toothbrush”. The Beam Toothbrush is a bluetooth-enabled toothbrush and associated iphone application:

It provides detailed analytics, recording how long each person brushes their teeth:

And like any good performance management tool, it uses gamification techniques to encourage the right behavior…

It seems as if analytics is being embedded in every device possible, such as the new Nike Hyperdunk+ basketball shoes and associated application:

Anybody else have any favorite examples of how analytics are taking over the world of consumer products?

I will be the host of the next #SAPChat, an interactive panel discussion carried out entirely over Twitter. It’s scheduled for 9am PST / 18:00 CET on Wednesday February 29th, and the subject will be “Business Intelligence Best Practice”. We will be discussing how best to implement business intelligence projects successfully, with an emphasis on people-focused hints and tips rather than technical discussions. And with only 140 characters available, the advice is guaranteed to be succinct!

You can participate in the discussion by asking your question over twitter, by including the hashtag #SAPChat, or just tune in on the day to watch the information flow. You can use your favorite Twitter tool, or join the action at http://tweetchat.com/room/SAPChat. The goal is to make the discussion as interactive as possible – don’t hesitate to answer questions from other people, or expand on the points made.

If you’d like an idea of what the chats look like, take a look at the transcripts from previous sessions such as SAP’s CIO Oliver Bussmann on the Consumerization of IT and a deluge of questions around SAP HANA.

For the session, I will be accompanied by two world experts on the topic of successfully deploying business intelligence projects:

Alys Woodward

Alys Woodward is program manger for European Business Analytics, Enterprise Collaboration and Social Solutions at IDC. Alys has been in the business analytics space since 1995, and an analyst since 2003. I’ve shared many stages with her over the years, most recently in Russia, and I have been consistently impressed with how much I’ve learned in each presentation. You can find out more about IDC’s research on making BI more pervasive with this research sponsored by SAP:

Improving Organizational Performance Through Pervasive Business Intelligence:

An increasing number of organizations are making BI and analytics functionality more broadly available to all decision makers inside and outside the organization. Internally, more pervasively available BI solutions lead to greater accountability by all employees and greater consistency in performance management. Externally, relationships with supplier and partners can be strengthened through effective sharing of key performance indicators (KPIs). However, having pervasive BI means more than having the appropriate BI tools distributed to all stakeholders. In pursuit of pervasive BI, organizations should focus on the five key factors that can be directly influenced to increase diffusion of BI. They are:

• Degree of training on the data, tools, and analytic techniques
• Design quality of the BI solution
• Prominence of data governance
• Nonexecutive involvement in promoting the design and use of BI solutions
• Prominence of a performance management methodology

You should follow Alys on Twitter: http://twitter.com/alyswoodward

Cindi Howson

Cindi Howson is the founder of BI Scorecard, a resource for in-depth BI product reviews, based on exclusive hands-on testing. I have known Cindi ever since she was the manager responsible for rolling out business intelligence at one of Business Objects’ largest customers, many years ago. Cindi carries out a key industry survey each year which gives insights into how successful organizations are with their business intelligence efforts. Cindi is a TDWI (The Data Warehousing Institute) faculty member and a contributing expert to Information Week. She has been now been advising clients on BI tool strategies and selections for more than 15 years, and wrote my favorite BI book:

Successful Business Intelligence: Secrets to Making BI a Killer App.

Business intelligence has the power to change people’s way of working, to enable businesses to compete more effectively and efficiently, and to help non-profits stretch their dollars further. The book draws on exclusive survey data and real-world case studies of BI success stories to identify proven BI best practices you can put to use in your organization, including:

• Gaining executive support and aligning your BI strategy with business goals
• Organizing BI teams and experts for success
• Choosing the best BI tools to meet user and business needs
• Improving data quality so decision-makers trust the BI solution
• Finding the relevance of BI to all employees, including front-line workers
• Using agile development processes to deliver BI capabilities and improvements at the speed of business
• Measuring success in multiple ways

You should follow Cindi on Twitter: http://twitter.com/BIScorecard

As you’re thinking of questions to ask, take a look at some of the great blog posts on the SAP Analytics blog and the Decision Factor blog.

Finally, if you’re interested in this session, you’ll be interested in this presentation I did late last year called “Why BI Products Fail and What To Do About It” (also available as slides: Why BI Projects Fail — PowerPoint Version)

The real trend this year is not the technology. It’s about helping business people make better decisions, and actually change the way companies do business. Analytics has always been about transforming business, but the recent huge changes in analytic technology have created interesting new opportunities for business innovation.

Most organizations are now starting to understand the technical opportunities, but many struggle to apply those new opportunities to their business processes. This blog post attempts to explain what’s going on in the analytics market and give concrete examples of how other companies have implemented the new technologies in “game-changing” ways (sorry kittens).

Wrenching Change and A Foggy Outlook

The chart below illustrates the wrenching effects of recent financial problems on the world gross domestic product: companies today have to be ready to react to unprecedentedly fast changes to their economic environment.

And the economic environment is fraught with extreme uncertainty. This year, the people who run the world will change, and so will many of the policies of the countries they manage. Financial markets have still not completely stabilized, notably with the future of the Euro still not assured.

Companies have reacted to this uncertainty by slashing costs and accumulating cash, and now need to start investing that cash into future development. Since interest rates are low and the business outlook is still uncertain, many of them are using the money for new technology that can help them prepare for the future.

In particular, companies want better visibility about what’s going on in their market, and increased organizational agility in order to be able to deal with change fast. It’s like driving in the fog without a map – in order to survive, you should invest in better visibility, brakes, and steering to be able to spot and avoid fast-moving objects looming out of the fog.

Analytics provides these capabilities: business intelligence to peer into the road ahead, risk-management to provide fast alerts to new obstacles, and flexible financial planning systems to help swerve around them.

Analytics: Hotter Than Ever

Companies are investing heavily in analytics:

• Analytics is the #1 top technology priority for both CIOs and CFOs, according to Gartner
• Nucleus Research recently released a report showing that organizations get $10.66 of value for every $1 invested in analytics
• IDC has increased growth forecasts faced with stronger-than-expected figures for recent years
• IDC analyst Dan Vesset: “After three decades, the business analytics market is finally reaching the mainstream” and “There are few growth inhibitors in the foreseeable future”
• At the recent BI Gartner Summit in London, Gartner’s Dan Sommer announced an early estimate of 10%+ growth in analytics during 2011, outpacing general IT growth.

Fast-Moving Technology

Analytics technology has been changing fast. On the back end, new technologies have come together to provide what Gartner calls “extreme data performance”. These include in-memory, column data stores, in-database calculations, massively parallel architectures, complex event processing, Big Data / NoSQL / Hadoop, and cloud architectures.

The combination of these technologies provides a opportunity to access massive amounts of a greater variety of data, faster, and more flexibly. The key opportunity is that these new platforms “collapse the stack” so that organizations can implement and update analytic projects much faster than ever before.

And on the front end, various technologies are coming together to provide unprecedented levels of context-based “actionable insights”, including self-service data discovery, advanced visualization including maps, mobile analytics, predictive analytics, collaborative decision-support. They help provide more action-oriented interfaces optimized for the context of the users, both inside and outside the organization.

These technology advances are clearly important, and we’re going to continue to see great improvements this year. The new opportunities have reached a tipping point similar to the rise of digital cameras vs. analog photography – and you don’t want to leave it too late to make the change, like Kodak, which recently filed for bankruptcy protection!

However, the real opportunity is using these new possibilities not only to improve analytics but fundamentally rethink key business processes.

High Resolution Management

University researchers have pointed out that today’s management techniques are based on the limitations of information scarcity:

“How many times has someone in your company uttered, “We don’t have that level of accuracy in the information, so we have to make aggregated estimates”? Under the current paradigm, it is sometimes impossible to drill down and understand what is happening at a highly detailed level.”

They coined the term “High Resolution Management” to describe what becomes possible with the new technology opportunities:

“We contend that these technologies will change drastically how management makes decisions. Why? Because with access to the finest granularities of information, management will be able to move freely from macro to micro levels and will be able to measure, plan and act accordingly. With increased resolution come more options to drill down, eliminate inefficiencies and cut costs.”

Lets take a look at three different types of High Resolution Management opportunity, letting companies:

• Remove bottlenecks
• Rethink business
• Flip business models

Remove Bottlenecks

Better technology always means business opportunity, but the new analytic platforms are rapidly eliminating some of the key bottlenecks that have prevented organizations from getting value from their data:

Faster, more flexible data access. Companies like Red Bull have been able to speed up and simplify their data warehousing environments. Using the HANA in-memory database, the company can now load detailed data twenty-five times faster into their data warehouse, and they were able to eliminate several levels of data staging, increasing the flexibility of the solution.

Data volumes and complexity. Companies like Colgate-Palmolive, Provimi, and Danone have long had access to vast amounts of detailed data about their production facilities and sales channels – but the quantity of data meant that they were unable to run full analytics in a reasonable time frame. That has now changed. For example, according to Colgate-Palmolive CIO Tom Greene:

“We will be able to run analytics at a local level on specific brands and locations, and at the lowest level of detail in real time”

And Danone can now measure the carbon emissions of 35,000 different products, with new systems that:

“collect, measure, and analyze data across the entire product life-cycle, from sourcing through production, transport, retail, distribution, consumption, and end of cycle”

New forms of data: ‘unstructured’ data such as text has long been difficult to effectively analyze and incorporate into mainstream corporate analytics. The new systems make it much easier for companies like Medtronic to access and analyze the large amount of complaints and feedback data they receive about their products, combine it with other data sources, and provide it to business users with dynamic interfaces:

New interfaces and users. Companies like Altron have been able to get the data to their users where they needed it. As Debra-Lynn Marais, Group Information Manager explains:

“The days of our users and execs being in the office have gone. They work from home or on the road. We had to develop a solution that gets information out to where our people are. Everything we do is mobile first. In addition, it’s less cumbersome and cheaper to buy and use a tablet than any other form.”

Rethink Business

Many companies are going beyond “just” improving their existing analytic capabilities, using analytics in new ways to change the way they do business. Instead of analytics being something that is used to monitor and eventually improve a business process, analytics is becoming a more fundamental part of the business process itself.

Proactive Analytics. Instead of using analytics only to assess previous performance, companies are using the new capabilities to get data fast enough to make a real difference. For example, online grocer Fresh Direct, instead of just understanding what problems happened yesterday, can now understand what problems will happen in the next few hours, so they can actually fix them before a customer is impacted:

“FreshDirect has an operations center that manages its fleet of delivery trucks. In a large metropolitan area like New York, traffic doesn’t always flow predictably. A traditional approach to BI would be to print a report showing the level of on-time deliveries (OTDs) the day before and then ask the transportation department what went wrong for the orders that were delivered late. FreshDirect uses analytics in a more impactful way.”

“The company monitors the delivery rate of every truck and enters that data into the BI system on an ongoing basis. Every hour, it uses the previous hour’s data to predict how many deliveries will be on-time in the next hour. If the predicted OTD rate is below FreshDirect’s target, the company sends out an auxiliary truck or trucks to help make deliveries. The company holds 10 trucks in reserve for just this purpose.”

Integrated Risk Assessments. Among other products, publisher Houghton Mifflin Harcourt produces educational books. Schools pass orders in June or July after the end of the school year, and then expect delivery for the start of the next school year in September. Getting books printed during the summer is expensive, as many publishers compete for the limited supply of printers available.

To avoid these extra costs, Houghton Mifflin Harcourt uses using sophisticated, risk-based forecasting. The company prints books in January or February, when printing is much cheaper. In order to minimize of excess inventory, it has carefully analyzed all the causes of previous forecasts, and now takes account of all the different things that influence book obsolescence.

Before, the buying team just ordered based on the volume forecast from sales. Now they have much greater context for their decisions. For example, if there’s a vote coming up on schools funding that may result in the canceling of a math adoption program for the year, they can decide to hold back on those purchases until the outlook is clearer. The fast, more accurate forecasting mechanism has saved them tens of millions of dollars, and they have more of the products their customers want.

New Customer Services. International grocery chain Casino is rolling out a new mobile shopping application for its customers. It provides data from its enterprise systems directly to its customers, resulting in increased shopping convenience and increased customer loyalty.

German healthcare provider AOK (“the good health organization”) is committed to helping its members avoid illnesses in the first place. It is planning to introduce a new, market-differentiating service: personalized healthcare advice for each customer, with tools that:

“Conduct real-time analyses of the tremendous amounts of medical data we receive, recognize potential health risks, assemble various preventive care programs and respond to those risks appropriately and ahead of time.”

As an added bonus, they also believe that this tailored prevention program will result in significant cost reductions by preventing expensive unneeded treatments.

BC Hydro is saving $70 million dollars a year through the installation of new smart electricity meters, using SAP systems, and offering new services to commercial customers based on the new data possibilities. Companies like Centrica are planning to use SAP’s Smart Data Analytics, giving them deep understanding into consumer consumption.

Flip Business Models

The really interesting opportunity for businesses is where companies have managed to use analytics to fundamentally flip the way their businesses work: instead of analytics being part of a process, it “becomes the business model”.

Tim Ferriss, author of the 4-hour workweek, is an interesting example of this. He didn’t do what most authors do: write a book, and then figure out how to publicize it. He used an analytics-first approach: he bought Google Ads, with mockups of book covers, with a variety of titles of books that he might be interested in writing – and then wrote the book that got the most clickthroughs! This is one step beyond using analytics such as focus-groups, which are typically there to validate existing products. The next generation of products and services are being created “on the fly” based on an analysis-first approach.

The clothing brand Zara shook up fashion retailing with “analytics first” – instead of having a designer creating clothes and then trying to sell them six months later, they realized new manufacturing techniques meant they could create clothes “in the moment”. They could observe what people were wearing in the street, quickly make small batches of variations on that theme, and get them into the stores. If they sold well they made more, if they didn’t sell they discounted quickly. Instead of a season-oriented, “batch” business, they switched to a flow-oriented business, using new technology capabilities.

The new analytic platforms mean that this analytics-first approach is available to many more businesses than in the past. For example, T-Mobile is in the process of transforming the way they attract customers. Instead of laboriously creating a range of rate plans, promoting them, and analyzing the results, they now use analytics to automatically create hundreds of more complex, personalized rate plans. They then throw them out into the market, monitor in real time, and quickly cull any that aren’t successful. It’s a way of doing business that would have been inconceivable in the past, and a lot more common in the future.

Conclusion

2012 is the year to rethink your analytic technology to take account of new opportunities:

• On the back end, for extreme data performance
• On the front end, for actionable insights

And it’s time to rethink your business:

• Remove today’s bottlenecks to successful analytics caused by data volumes, data variety, or data access
• Rethink business processes by embedding real-time decisions
• Create new products and services that could only exist because of today’s analytic power

Organizations are using this technology to change the way they do business. If you run an analytics project, you are in the forefront of these changes – it’s your job to help explain to the rest of the business how these technologies should be changing their existing processes. Good luck!

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If you’re interested in slides that go along with this article, please see this post about the recent Gartner BI Summit in London that includes a download of my presentation at the conference.

I presented at my first conference of the year last week, the Heliview Business Intelligence and Data Warehousing 2012 conference in ‘s Hertogenbosch, Netherlands.

The first keynote, by Erasmus scholar Dr. Roeland Dietvorst, was about “Performance Management in the Brain”, and a subject close to my heart: the (severe) limitations we evolved apes have when trying to make rational decisions. He illustrated his point with examples of great research that show that:

• Including “irrelevant” product choices can make big changes to preferences;
• Our emotional state has a big influence on what choices we make;
• Men shown pictures of attractive women tend to make worse financial decisions;
• etc

He also reviewed some of his own research, scanning the brains of sales people to see if there was a correlation between brain activity, competence in understanding other people’s state of mind, and selling skills. He finished by underlining that we have “two brains,” and that business intelligence can help us move decision making to our more rational, less emotional side.

I did the other morning keynote on “Real time enterprise from theory to practice”:

“The amount of information we face is growing by the day. Both volume and type of information. IT organizations not only incorporate customer or product data, but new sources such as location data and data streams from social media play a significant role. In addition, we want information available in the context in which we operate, and preferably independent of the site or device. To make this possible innovative solutions are needed that can work with large volumes of data. In-memory computing may by some be considered as not yet available but the reality is different. There are several examples where this is being successfully implemented and numerous organizations are achieving demonstrable benefit. This presentation shows that the real-time revolution has already started and how it’s being practiced today.”

The presentation reviews the state of the BI market and technology, and ends with some examples of companies using information to change the way they do business. As usual, here’s a copy of the slides, in Adobe PDF and Powerpoint PPTX format.

I’ll be in London next week presenting at the Gartner BI Summit 2012 — hope to see you there!

Update: here’s a nice write-up from the Dutch team about the event.