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 electonica, 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 decisions 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. 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.

I’ll confess upfront that I’ve always been slightly dubious about the assertion that “sustainability is about making money”. After all, if it’s about making money, it’s something that companies should be doing anyway – it’s only when there’s a tradeoff between sustainability and money that there’s room for interesting debate about to what extent it should be one of the goals of the organization.

However, as Diversey’s CEO Curt Johnson points out:

“CO₂ is waste, so if you minimize CO₂, you minimize waste and you maximize efficiency and increase profits”

It turns out that putting in the effort to calculate the carbon footprint of the entire supply chain, from raw products to final consumption, is a great opportunity to identify savings that might previously have been missed by more step-by-step, siloed efficiency efforts.

Walkers Crisps is a great example of this. The company sells potato chips, or “the UK’s favorite crisps” as they are known locally, and is part of Frito-Lay, which is in turn a division of PepsiCo. In 2007, Walkers became the first company in the world to display a carbon reduction logo on their product, and discovered considerable savings in the process. From The Economist:

“The process of calculating the carbon footprint for Walkers crisps revealed an unexpected opportunity to save energy. It turned out that because Walkers was buying its potatoes by gross weight, farmers were keeping their potatoes in humidified sheds to increase the water content. Walkers then had to fry the sliced potatoes for longer to drive out the extra moisture. By switching to buying potatoes by dry weight, Walkers could reduce frying time by 10% and farmers could avoid the cost of humidification. Both measures saved money and energy and reduced the carbon footprint of the final product.”

The report doesn’t mention what products and procedures were used to do the carbon footprint analysis, but “Business Objects is the key database for Walkers” for reporting, according to the PepsiCo UK web site. You can read more about the carbon footprint of a packet of cheese & onion crisps on their web site.

Viewed in this way, calculating the carbon footprint of your products is “just” another form of efficiency analytics. If you’re interested in doing this for your organization, you can read more about the business case for sustainability, SAP products that can help you calculate your carbon footprint and view SAP’s own Sustainability Report.

Why did it take so long to discover the source of the deadly outbreak of E.coli poisoning in Germany? Since the start of the outbreak, 35 people have died, over 100 will need kidney transplants or life-long dialysis, more than 3,000 people are ill, and falsely-accused Spanish cucumber producers have lost millions of Euros.

Researchers initially relied on the most obvious source of data: the tests carried out on suspect food. Unfortunately, the thousands of tests on farms throughout the country came up negative, even in the organic farm in Lower Saxony that now seems to be at the cause of the problem. They also carried out two surveys. The first showed that people who consumed raw tomatoes, cucumbers, and lettuce were more often sick than the others, and another case study showed a strong association between the consumption of foods from the salad bar and E. coli infection – but this wasn’t enough to narrow down the origin of the outbreak.

It was only when a detailed cohort study was carried out, including one group of nineteen Swedes that had all eaten in the same restaurant, that the data showed that those who had eaten the sprouts were nine times as likely to have gotten sick.

According to Daryll E. Ray & Harwood D. Schaffer:

“To ascertain the consumption of raw fruit and vegetables by patients and controls more objectively and less dependently on memory, RKI used the following approach in the ‘recipe-based restaurant cohort study:’ Five groups (travel groups, clubs, etc.) that comprised a total of 112 participants and included 19 individuals who acquired EHEC infection were questioned regarding the foods they consumed after eating in a common restaurant. Additionally, the menus ordered by the participants were identified by means of order lists and meal receipts. The restaurant kitchen was questioned in detail regarding the preparation and the type and quantity of ingredients in each menu ordered by any of the study participants. Furthermore, available photographs taken by travel group members were analyzed to confirm which food items, including toppings, were seen on the plates. The data thus gathered was analyzed in a cohort approach that permits the retrospective estimation of the relative risk of infection for the restaurant customers. Results of this analysis showed that customers who ate sprouts had an 8.6-fold increased risk [of] illness compared to those who did not. This study also revealed that 100% of those who contracted the illness had eaten sprouts.”

The decision process was made all the more difficult because it appears some records were held on paper rather being completely computerized, and health authorities were sometimes communicating with hospitals and each other via letters in the post rather than electronically, and were having trouble tracking who had become ill.

An overly-manual and confused decision-making process hindered the discovery of the “sproutbreak” that lead to so much suffering and hardship. But at least some good has come out of it: a better process, and a more detailed questionnaires will now be a standard part of outbreak investigation in the future.

All organizations need to be able to document and improve their decision-making processes, even if (thankfully) it’s rarely a case of life or death. Technologies like SAP Streamwork are starting to allow companies to apply standard templates to frequently-occurring decisions, and collaborate to come to the best conclusions, using an on-demand platform.

Could you improve decision-making in your organization by optimizing decision collaboration?

Other Links

• http://scienceblogs.com/aetiology/2011/06/german_officials_declare_e_col.php
• http://scienceblogs.com/thepumphandle/2011/06/culprits_in_germanys_foodborne.php
• http://www.rki.de/EN/Home/homepage__node.html

What’s the difference between Business Analytics and Business Intelligence? The correct answer is: everybody has an opinion, but nobody knows, and you shouldn’t care.

Having worked in the industry over twenty years, I can confidently say that everybody has a different notion of what ANY particular term associated with analytics means.

For example, when SAP says “business analytics” instead of “business intelligence”, it’s intended to indicate that business analytics is an umbrella term including data warehousing, business intelligence, enterprise information management, enterprise performance management, analytic applications, and governance, risk, and compliance.

But other vendors (such as SAS) use “business analytics” to indicate some level of vertical/horizontal domain knowledge tied with statistical or predictive analytics.

At the end of the day, there are two things worth differentiating:

1. The first is the business aspect of BI — the need to get the most value out of information. This need hasn’t really changed in over fifty years (although the increasing complexity of the world economy means it’s ever harder to deliver). And the majority of real issues that stop us from getting value out of information (information culture, politics, lack of analytic competence, etc.) haven’t changed in decades either.
2. The second is the IT aspect of BI — what technology is used to help provide the business need. This obviously does change over time — sometimes radically.

The problems in nomenclature typically arise because “business intelligence” is commonly used to refer both of these, according to the context, thus confusing the heck out of everyone.

In particular, as the IT infrastructure inevitably changes over time, analysts and vendors (especially new entrants) become uncomfortable with what increasingly strikes them as a “dated” term, and want to change it for a newer term that they think will differentiate their coverage/products (when I joined the industry, it was called “decision support systems” – which I still think is a better term in many ways).

When people introduce a new term, they inevitably (and deliberately, cynically?) dismiss the old one as “just technology driven” and “backward looking”, while the new term is “business oriented” and “actionable”.

This is complete rubbish, and I encourage you to boo loudly whenever you hear a pundit say it.

The very first use of what we now mostly call business intelligence was in 1951, as far as I can tell, with the advent of the first commercial computer ever, dubbed LEO for Lyons Electronic Office, powered by over 6,000 vacuum tubes. And it was already about “meeting business needs through actionable information”, in this case deciding the number of cakes and sandwiches to make for the next day, based on the previous demand in J. Lyons Co. tea shops in the UK.

And It most emphatically was not “only IT” or “only looking in the rear-view mirror” as some people pompously try to dismiss “old-style BI”.

At the end of the date, nobody important cares what this stuff is called. If you’re in charge of a project, what matters is working out the best way to leverage the information opportunity in your organization, and putting in place appropriate technology to meet that business need — and you can call that process whatever you like: it won’t make any difference…

If you have strong opinions on the topic, you may want to join in on this thread on the the brand new, business-oriented forum on the SAP Community Network.  In the meantime, here’s why I changed the name of this blog from “BI questions” to “Business Analytics” a few months ago:

Google Trends on “business intelligence” – slow decline (note this is relative to overall search volume, not absolute)

Google Trends on “business analytics” – rising sharply

Here’s a great example of a BI competency center (BICC) explaining and promoting BI to the rest of the organization. In this case, it’s SAP’s own BICC that created the video, destined for SAP’s 5,000+ employees worldwide. It was uploaded by Matthias Wild of SAP’s Global IT group (NOT marketing): BI4ALL in Plain English (but with a slight German accent ).

I love the use of low-tech graphics and great editing — and I’m sure this will increase interest in using BI internally within SAP…

You can hear more from Matthias and the goals of his team in this post on the SAP community network: The Concept of Business Intelligence for ALL (part 1 of 3)