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

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

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.

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!

In a previous post, I talked about the “illusory superiority” effect, and how it blinds people to the fact that, on average, it’s unlikely that they use data better than their competitors.

Guess what? It turns out that it applies to governance, risk, and compliance, too. Here are some figures from another Economist Intelligence Unit Survey, “Ascending the Maturity Curve, Effective Management of Enterprise Risk and Compliance”:

We can see that of those who haven’t experienced failures, only 1% believe that they are worse than average – and even among companies that have experienced failures, fully 87% believe that they are as least as good as their peers.  Unless the Economist has stumbled across a particularly great group of companies to study, it seems clear that most organizations are overestimating the quality of their GRC practices, and hence underestimating the real risks they are running…

There’s also data in the report that seems to indicate that the finance function is the mostly likely to be blindsided – as you can see in the chart below, they are far more likely to say that there was no significant risk or compliance failure in the past three years. Since this is not a group known for their exuberant optimism, it’s likely that they simply didn’t know about the risks run by the other teams…

In conclusion, if you’re in the finance function, and responsible for your GRC practices, it’s likely that you should be investing more than you are today. For more information, check out SAP’s GRC products, and Norman Mark’s blog on Governance, Risk Management, and Internal Audit.

Here’s a chart from a recent study by the Economist Intelligence Unit called “Big data: Harnessing a game-changing asset”, showing that only 9% of respondents believe that they use data worse than their competitors.

Let’s face it, they’re probably in denial. It’s often called “Illusory Superiority” or the “Lake Wobegon Effect” (a radio show where “all the children are above average”), and it’s a real force in the analytics market.

Because better data is often considered a “nice to have”, or “important but not urgent”, companies often only invest in better business intelligence when confronted with evidence that their competitors have the edge (this applies to BI vendors, too: long-clamored-for features mysteriously get prioritized the instant another vendor provides it).

This chart shows that companies are probably underestimating their competitors ability to use data – and therefore that they should probably be investing more in business intelligence…

This post is part of a “blogorama” organized by smartdatacollective.com– see “The Emerging Role of the Analyst” for links to other bloggers’ views of this theme.

Recently, I’ve been feeling like I’ve stepped through a looking glass to another similar-but-very-different world. I’m steeped in 20+ years in corporate data warehousing and business intelligence practice. Throughout that time, there have been big and small technology improvements, but nothing truly disruptive (although new analytic platforms are coming).

Meanwhile, a completely different thread of data analysis has emerged, with roots in open-source software, notably Hadoop, primarily designed for processing massive amounts of semi-structured web data. As the technology has advanced, it’s making more and more impact on “traditional” data warehousing.

The people using these new technologies have founded their own visions of what the role of an analyst looks like, or as they call it, a “data scientist”. DJ Patil and Jeff Hammerbacher coined the term a few years ago (there’s a nice graphical summary of the data scientist role by David Vellante), and DJ recently wrote an excellent piece defining the role.

He explains the skills a data scientist needs to be successful:

• Finding rich data sources.
• Working with large volumes of data despite hardware, software, and bandwidth constraints.
• Cleaning the data and making sure that data is consistent.
• Melding multiple datasets together.
• Visualizing that data.
• Building rich tooling that enables others to work with data effectively.

Reading the list, I couldn’t help but say to myself “people have been doing this since computers were invented! what’s the big deal!”, but ultimately I’m excited about the new technology possibilities and a new point of view, and  I’m looking forward to a synthesis of the best of the old and the new to get even more business value out of data.

But the booming interest in “data scientists” also worries me: the underlying premise is that (a) “advanced” analytics is what’s most important, and (b) analytics is the domain of “scientists”. The focus of the data scientists article is generally about elite teams working on advanced, strategic problems. A data science team is defined as:

“a group that includes people working in design, web development, engineering, product marketing, and operations” that “delve into existing data sources and meld them with external data sources to understand the competitive landscape, prioritize strategy and tactics, and provide clarity about hypotheses that may arise during strategic planning.”

Over the years, we’ve made slow progress towards making everybody in the organization “responsible” for analysis, and it would be a shame if data scientists became the new high priests of knowledge. To get business value, number-crunching has to be combined with the knowledge spread through the company. I believe that it takes people to turn information into intelligence, and rather than focusing only on advanced analytics, we need to encourage all employees to be more data literate (see this example of what can go wrong) and encourage more shared analysis.

Luckily, it seems data scientists do indeed share these values:

“I’ve found that the strongest data-driven organizations all live by the motto “if you can’t measure it, you can’t fix it” (a motto I learned from one of the best operations people I’ve worked with). This mindset gives you a fantastic ability to deliver value to your company by:

• Instrumenting and collecting as much data as you can. Whether you’re doing business intelligence or building products, if you don’t collect the data, you can’t use it.
• Measuring in a proactive and timely way. Are your products, and strategies succeeding? If you don’t measure the results, how do you know?
• Getting many people to look at data. Any problems that may be present will become obvious more quickly — “with enough eyes all bugs are shallow.”
• Fostering increased curiosity about why the data has changed or is not changing. In a data-driven organization, everyone is thinking about the data.”

And:

“More sophisticated data-driven organizations thrive on the “democratization” of data. Data isn’t just the property of an analytics group or senior management. Everyone should have access to as much data as legally possible.”

These statements seem to be at odds with the whole notion of “data scientist” as an elite role, but maybe we’re “all data scientists now”?

Personally, I’m excited about the possibility of finding common ground, with new collaborative decision technologies such as SAP StreamWork that allow us not only to “get more people to look at data”, but share their different knowledge and points of view, align it with the key business concerns and learn from our past decision-making mistakes.

Here’s one real-life example of the limits in quantitative thinking observed in an average company, shared with me by Michael Thompson. He recently worked with Dan Rosenberg’s UX team on a SAP research project that isolated over 100 distinct, real-life uses of business analytics across dozens of roles and business functions.

A retail analyst was attempting to figure out the success of product promotions on the “sales” page of a web site. After gathering the data, he used Excel to create the table below. He compared the increase in each product sold with and without the promotions, and then tried to calculate the average percentage increase across the different products. The result was that “the promotions increased sales by 151% on average”.

This result was then used to create a rule-of-thumb of how much extra inventory the company should have on hand at the start of a promotion: “roughly double”. This lowball figure, it was felt, would generally provide enough stock for promotions, while reducing the chances that the company would be left with too much stock at the end of the promotion.

But there are a whole bunch of problems with this analysis. The most basic mathematical problem is that, while there are exceptions, calculating the average of percentages almost always gives an unwanted answer.

A better way to calculate the success of the promotions is to compare the average sales of all the products before and after a promotion (see table below). This shows that the promotions increased the average quantity of products sold by only 71% – considerably less than double.

* note that it would be it would be easier to compare the % increase in totals items sold, which would come out to the same result, but I’ve used the averages to make the comparison between the two methods as obvious as possible.

So if the company did use the proposed rule of thumb they might have a nasty shock. Instead of an expected “lowball” inventory that would be used up by the promotions, they should instead expect to have an extra 29% of the original items left over at the end.

And beyond the numbers, there’s a lot of unanswered questions about the analysis that call into question its usefulness:

• Is units sold the best measure? Wouldn’t it be better to work with revenue or profit?
• There’s huge variability between the different categories of product. Is the notion of an average even meaningful? Maybe categories of products could be created for more meaningful analysis?
• Do these percentages stay reliable over successive promotions, or are they also very variable?
• Is it in fact better to have too little stock than too much? By how much?
• Might external variables or seasonal variations have lead to the observed results, rather than the promotion? (e.g. maybe it was the 4th of July, and charcoal sales would have soared anyway)

In general, people are tempted to throw up their hands at this point and say “well, we have to make some sort of prediction for inventory levels – isn’t this better than nothing?” And the answer is yes (as long as you get the basic math right!), but somebody in the company should be aware of how limited the analysis currently is, and be constantly trying to improve it. Unfortunately, as in this case, real-world business people often rely on basic, faulty analysis, and lack the curiosity (or incentive?) to push it any further.

Given human nature, it’s probable that this example is not an isolated case. If you really want to improve your company’s return on investment in analytics, you may want to consider investing in more training rather than yet another technology solution.