One of our current projects it to design or re-design the user interface for the next generation of one of our client's products. While talking with the designers and engineers, you wonder why the people with the deepest understanding of the product aren't always the most qualified to design the interface.

The biggest challenge of designing the UI of a complex system is that as you become comfortable and knowledgeable of a design, the harder it is for you to accurately assess how simple and intuitive it is. How do you account for this? By constantly bring in fresh eyes at all aspects of the design process. The longer you've been toiling over a feature, the less likely that you yourself notice a glaring design flaw.

Lovely article in the New York Times business section on Sunday. Rite-Solutions, a software company, has built an internal marketplace to allow everyone in the company to invest in good ideas.

At Rite-Solutions, the architecture of participation is both businesslike and playful. Fifty-five stocks are listed on the company's internal market, which is called Mutual Fun. Each stock comes with a detailed description — called an expect-us, as opposed to a prospectus — and begins trading at a price of $10. Every employee gets $10,000 in "opinion money" to allocate among the offerings, and employees signal their enthusiasm by investing in a stock and, better yet, volunteering to work on the project. Volunteers share in the proceeds, in the form of real money, if the stock becomes a product or delivers savings.

This intrigues me both for its democracy and for the discipline of requiring people with ideas to write a clear, easy-to-read description of their project.

"We're the founders, but we're far from the smartest people here," Mr. Lavoie, the chief executive, said during an interview at Rite-Solutions' headquarters outside Newport, R.I. "At most companies, especially technology companies, the most brilliant insights tend to come from people other than senior management. So we created a marketplace to harvest collective genius."

Great ideas are equally possible coming from the bottom up as the top down. In particular, those "bottom" people have a lot of hands on experience with the way things actually work.

"There's nothing wrong with experience," said Mr. Marino, the company's president. "The problem is when experience gets in the way of innovation. As founders, the one thing we know is that we don't know all the answers."

I've worked at companies where a senior executive is vigorously pursuing an idea that most people know is doomed. No one person is willing to share the bad news, but what if he or she had to confront a slumping share price for their "expect-us"?

Funny thing happened to me last week on my way to an oil change - my car's engine was destroyed.

It all happened in a blink: I stopped by Jiffy Lube on my way to the bakery and swung up to the garage entrance, first in line. If you've ever had an oil change, you've probably experienced the old "preventative maintenance" up-sell: a technician pulls you out of the waiting room, gives you a grim, disappointed look, then explains the various parts of your car that are in severe need of service. In the past, I've been good at standing up to these automotive authority figures. I'd mumble "no thanks, maybe next time," not daring to look into the eyes which so clearly said: Don't you care about your own safety? This time, however, I broke down and gave the go ahead for an engine flush. I was assured that any sane car owner would have this procedure done every 15k miles; here I am at 80k without my first flush.

I knew something was wrong when I saw them pushing my car out of the garage half an hour later. I was assured it was no problem; they just needed to dry off my spark plugs. Two hours later I was calling for a ride.

All of which would have been a small inconvenience if I hadn't gotten a call the next morning letting me know they would need to replace my engine. Clearly something had gone terribly wrong with that engine flush.

I should say: I have little reason to gripe about Jiffy Lube. They are covering the engine replacement and a rental car. That said, there are a few lessons Jiffy Lube management might take from this situation:

• The edge cases matter. A while back we wrote (here and here) about analysis of anomalies and the opportunity for learning. One point that applies in this case: Collectively, outlier customers provide a service: they stress test the product and highlight unrealized strengths or weaknesses. In its desire to relentlessly upsell, Jiffy Lube has extended its service outside its comfort zone to a point of weakness.
• Data can make you smarter. I had an interesting conversation with the outside mechanic that is installing the replacement engine. He said I was lucky. My engine has a known problem with high levels of sludge build-up. He has seen other instances where an engine can be so full of sludge that an engine flush is incapable of breaking through the muck (like clogged arteries, I imagined) and the result is ruin. I get a refurbished engine with 80% new parts in place of an engine that was like the heart of an overweight cholesteral-holic. Maybe Jiffy Lube shouldn't be indiscriminantly upselling every customer. It wouldn't be difficult to build some filters into their system for high risk maintenance.
• Communicate with unhappy customers. Most companies would benefit from a simple alarm system for catching and responding to customers with particularly bad experiences. Something to appease them before they tell all their friends, family, and co-workers about their crappy experience (heck, they might even blog about it). All I ask as a customer is: a) recognize that you have created an inconvenience for me; b) convey that this isn't a status quo situation; and c) assure me that you will make me whole. Jiffy Lube wasn't effective in communicating any of these. They had an odd nonchalance that suggested this happens all the time, no single point of contact to speak to, and no apology for the inconvenience.

Just found this (thanks instantcognition), which offers examples of poor charting design along with a discussion of how to make the charts better. Both the good and bad examples are repeatable in Excel.

This is a video created entirely using Excel bubble charts. It illustrates flows over the course of the year between two starting states and four ending states.

Click picture to view video

I want to stimulate discussion on creative charting methods using common tools, Excel or otherwise. If you have an example of a creative use of charting, let me know and we can all get a little better at illustrating information.

The business world is awash in piles of words mis-classified as PowerPoint presentations. Here are three slides from a webside development vendor's pitch:

Even at a distance, it's clear you wouldn't want to sit through this. Creating a compelling presentation is tough. There are, however, a few things that can make a big difference for your audience. Before you save your PowerPoint file as "final" and send to print, here's a checklist of things that you should always do, do more of, do less of, and never do.

Always...

• Tell a story. Great presentations tell a compelling and cohesive story. Stories have themes, characters, plotlines, and a message.
• Build a flow. Try reading your slide headlines. Do the slides connect? Do they tell a story? Don't be afraid to shuffle your slides to find a cleaner way for the concepts to fit together.
• Provide a roadmap. Its easy to lose an audience if you have a complex argument or storyline. Give them sign-posts that let them know where they are and where they are headed.
• Banish slides to an appendix. When I build a presentation, I will inevitably end up with slides that don't fit neatly into my story. Find a home for them in the appendix; it's the "green room" of your presentation where potentially useful material can hang out until it's needed.
• Simplify. Your story can be simpler. Your slides can have more whitespace. Be ruthless in cutting.

More...

• Pictures, including full page pictures. Cliff Atkinson of the beyond bullets blog is a big proponent of pictures in place of words and offers a good, though extreme, perspective.
• Changes of pace. Long, data intensive presentations easily become monotonous. Break these up with different kinds of slides, multimedia content, or audience interaction.
• Animation in slides. While animation can be a distraction, it does have its place. In particular, if you have a complex slide, consider building the layers of information. It forces your audience to focus on just one part at a time.
• Quick punchlines. It is always tempting to develop a presentation that gradually builds to an exciting conclusion. Save it for your great American mystery novel. Your audience is impatient to get to the point. Provide it to them up front.

Less...

• Words. People use extra words to cover up when they don't know precisely what they want to say. Enough said.
• Bullets. A list with more than five bullets is beyond most people's ability to process or remember. Long lists can be broken into subgroups.
• Bullet-points. For short, simple lists, remove the bullet points.
• Stock clipart: Microsoft has been generous enough to provide a world of clipart fun. Avoid that world as much as possible. Clipart is mostly useless filler.
• Animation between slides. Unlike in-slide animation which can focus your audience, animation between slides is simply a distraction.
• Capitalization. There are rules for when to capitalize. Capitalizing Almost Every Word in a Sentence is not One of Them.

Never...

• Use ScreenBeans. Check this out: I found the ScreenBean store. For just $59.95, you can get a whole package of digital ant people running around in business settings. Trust me, there are cheaper ways to ruin a presentation. On that note, I found this slide in a presentation entitled: "Effective Presentation: Using Microsoft PowerPoint." Ironic?

We've blogged recently about the rich data available from the census bureau and the ability to visualize it with low cost GIS tools like Google Earth. Our friend Ducky recently released her mash-up of Google maps and census data that shows the potential for dynamic and informative maps that are easy and free to use.

This data is available to everyone, but compare the data you find at the census bureau to some of the maps at Webfoot. The census bureau presents a bunch of numbers and Webfoot made them come alive. At Juice, we call this "democratizing data" and it shows the true potential of the next generation of web tools to empower your organization and put the data at the hands of the people that need it. Check out Absolutely Google Earth for a source of tools and resources to do the same.

Sometimes when you're starting a presentation, you need a little help getting over the hump in deciding how to present data and concepts. The following PowerPoint deck contains a collection of ideas and visuals that can help when you're brainstorming.

100 PowerPoint Ideas.ppt

I hope this is a valuable resource for presentation writers looking for inspiration, widgets, nice-looking charts and graphics, and ideas on structuring slides.

It's organized into four sections:

Widgets: Little graphics that help call out an idea or show where you are. Examples include: Harvey balls, bullets, brackets, arrows, and outline styles.

Charts: Different ways to use PowerPoint's built-in charting to create nice looking graphics. Examples of column charts, pie charts, commented pie chart, multi-column charts, waterfall chart, radar charts, and representing data with chart areas.

Graphics: This includes tables, value chains, linked chains, representing three dimensions of value, showing market forces, project structure, project timelines, segmentation schemes, ranges, positives and negatives, product roadmaps, flows, processes, Venn diagrams, filtering opportunities, and showing market space.

Slide Structures: Ways of framing the content of a slide, including personas, multi-charts, section breaks, and calling out important concepts.

We're cutting through it this week with an extra helping of Microsoft Office help. Coming soon: PowerPoint brain food, Excel charting de-gunkulator, and Excel charting extreme edition.

But first, check out fightthebull.com, an add-in for Word and PowerPoint that takes syntax checking in a more useful direction. This adds a toolbar to your apps that determines how readable your writing is and how prone you are to biz-talk.  Because, wihle you cn sltil raed tihs, you probably can't decipher this:

One of Unicorp's corporate objectives is to develop strategic relationships with key customers and be recognized for our ability to deliver services of superior value. This competitive advantage will be achieved through continued focus on our core competencies, management attention to the development of operations and process management excellence in all parts of our business, the identification and application of best processes, and continued attention to direct and indirect cost management. The focus on core competencies will promote the concentration of knowledge in select areas consistent with the tenets of Unicorp's strategic plan, Unicorp 2000. Management's attention to operations and process management excellence in all business areas will be achieved through the continued expansion of our management and technical staff, as well as through consistent application of corporate quality programs such as benchmarking and continuous improvement, leading to the establishment of Unicorp's superior business processes in each core competency. Finally, continued attention to direct and indirect cost management will enable Unicorp to offer customers a superior, value-added package of high-quality service at a competitive price.

You can get a lot faster with Excel if you learn to use keyboard shortcuts instead of the mouse. Here's a fun Excel-based game that we use at JuiceCo to teach one basic keyboarding skill. I guarantee this will make you better and faster with Excel.

Click picture to view video. The audio seems to be infested by aliens and drops out midway through. Many apologies.

Excel Keyboarding Game.xls

If you're viewing this in a feedreader, click though to see a screencast of the game.

I confess to a lack of ambition. When Tom Davenport's article on "Competing on Analytics" came out in the Harvard Business Review in January, Zach and I critiqued Tom's list of 10 things that are "what it takes to be an analytics competitor" because it offered a good example of condensed misperceptions about what analytics can and should do.

But we didn't address the heart of the matter; the biggest way analytics will change over the next decade and the reason we're in business.

Niel Raden does. He offers a critique of "Competing on Analytics" that is focused and deep. I invite you to read it as well as his other publications. This is good thinking by a very experienced analytics consultant.

There are two schools of thought when it comes to the value of BI in general. One is that it is best used by “quantitative” types and other analytical business people, who can spot trends and analyze patterns to assist in the big decisions and set and direct strategy. The other position is that BI is at its best when helping a broad range of people and processes at an operational level, marginally improving performance, repeatedly and often. The former is the commonly held view of management consultants and, previously, BI practitioners a decade ago. The latter position gained currency in the last few years and is now widely seen as borne out in practice. Using BI to form a new strategy for a global financial services firm makes for good marketing collateral, but when it comes to ROI, lots of small improvements are the way to go.

Why is centralizing analytics bad and decentralizing analytics good? Why shouldn't your organization have a single centralized "brain" that directs the far-flung body to intelligent, purposeful action?

Centralized control of data and analytical expertise may not seem very controversial, but what Davenport is implying is not only centralized control, but also centralized design. This is another naïve assumption, because many organizations are not only decentralized—they’re dysfunctional. Separate units within organizations often need autonomy because they are just so different from the rest of the organization. In addition, as an organization becomes more “agile,” which is a definite trend, decision-making, even for the big decisions, will become more decentralized. Imagine how difficult it will be to buy or sell pieces of a company if the “brain,” the centralized analytical capability, stays with the parent and there is no local expertise?

In our experience, there are hundreds of decisions that need to be made each day, even in a medium-sized organization. In most cases, those decisions are being made in a vacuum—on faith, trust, gut-feel, and partial information. Statistical significance tests aren't needed to improve these decisions, just basic, easy-to-use visibility into business processes.

Statistical analytics can be helpful, but perspective and experience are even more useful to find insights in data. Democratizing data in your organization, making it easier to put more eyes, more experience, more brains against your data is the challenge of the next ten years in analytics. The Internet abounds with examples of what people can do when they can get their hands on data.

Incidentally, if you do want to hear Tom Davenport's side of the argument, you can catch a webcast on Thursday, March 16th at 1:00pm EST. Here's a link.

A friend of mine with years of analytics and management experience at large companies wrote recently. He put his finger squarely on a real issue with enterprise data warehouses--they're built to solve today's problems (not tomorrow's) and they're designed for the needs of analysts that are more statisticians than experts about the business.

I wanted to provide some comments on the enterprise wide data warehouse and the challenges it presents at large corporations. Jim Novo certainly seems to support the roll up approach (I'm on his mailing list) but I agree with Juice that it is too slow, too costly, and results in restricted analytics the way most large companies build them. Most of the large data warehouses I've worked with only include data variables that are key to managing a business TODAY as the warehouses are too big and costly to store data variables with a low usage frequency. They also attempt to cleanse the data by classifying. This makes life easier for an analyst with statistical experience but a limited knowledge of the business. However you're losing information. Problem: You do not know what will be important in the future. Distributed databases at a line of business or product level tend to store more raw data to manage and analyze the business. Sure, the amount of space used would be the same if you simply put the data into the warehouse but that is not the way decisions are made. Decision makers look at the frequency of use of the data variables (TODAY) and the cost to include them then decide which get deleted or excluded. Also, the analysts who are disconnected to the business lines do not understand the raw data.

Let me give you a real world example. Our data warehouse classifies claims into a limited number of claim reason categories. This might be considered a poor design but please see above about analysts that are disconnected from the business lines. When a new type of claim is developing, the person classifying the claims (claims rep) does not have a category to select so they just select what works best to fit into the pre-defined categories. Information is lost due to the restrictions of the allowed categories within the data warehouse. If the notations from the claims system would have been stored (an unforeseen variable) in the warehouse and text mining analytics were being done, a specific word would have been found associated with claims at an alarming rate. Identification of this word would have allowed for early recognition of the issue. It cost us a lot of money in claims due to the missing data but no one thought to include the notes due to the size and costs. Well, we have them now.

Zach mentioned our penchant for telling a story about customers using little data-dense pictures. Happily, this style of data visualization won "Best in Show" in DM Review's 2005 data visualization competition. Follow this jump for an excellent example of a data dense visualization created using Tableau.

To a stranger, my two dogs look alike. To me, they couldn't be more different. They came from different dog shelters and are more than two years apart (that's 14+ human years). Here they are: Maddie has her chin resting on Ally.

Ally is twitchy, a mama's girl, frightened of loud noises, and getting creaky. Maddie is confident, independent, curious about the loud noise, and energetic. Ally loves other dogs and distrusts new people. Maddie adores all people and is suspicious around certain dogs. Their features and personalities couldn't be more different. I've had some time to get to know them.

When we meet a stranger on a walk (particularly one who isn't a dog owner), we often get: "They must be related." Our denials don't seem to phase these people as they point to the obvious evidence: "...but they are exactly the same size and color."

Superficial judgements are natural - a first level of defense to categorizing and manage a complex world. However, it's unhealthy to not try to dig deeper. For some businesses, superficial characteristics are as far as the analysis goes when segmenting or profiling customers. A better approach is to look at customer behaviors which provides a much more accurate reflection of interests and needs. Jim Novo, marketing consultant, agrees:

Customer behavior is a much stronger predictor of your future relationship with a customer than demographic information ever will be

Simple customer characteristics can be easy to come by; age, income, zip code are probably part of your basic customer database. In contrast, behavioral segmentation is a more initimating analytical challenge. Here's the approach we've used successfully at Juice:

1. Create individual pictures of customers that visually show their behaviors over time. The trick is to create a "visual language" that represents actions and is intuitive
2. With a dash of Excel, SAS, and python code, we generate thousands of these pictures of individual customers
3. We visually scan for common patterns of behaviors and the associated success/failure points (e.g. repurchase, upsell, churn, etc.)
4. Finally, we work backwards from our new understanding of behaviors to segment customers based on statistical measures of behaviors.

This approach differs from traditional data mining-based approaches that drill down from the top looking for patterns. We start at a very granular level and looks for patterns (using the power of the human visual system). It may sound a little crazy, but we've found that it can be both insightful and highly predictive.

We love Google Earth because it puts the power to explore data in the hands of average folks. We've been exploring uses for census data and wanted to share some of this data with the world.

What you're seeing here is a map of the counties in the United States colorized by median age. Lighter colors are older.

Census data is also available at the block group level which is much, much more detailed.

Without further ado, what follows are three sets of links for each state which allow you to explore population density, median age, and male/female ratio in each state at two levels of detail. Google Earth is required. We did have some ftp issues when uploading these files, so if you have any problems, let me know and I'll re-upload the file.

Population Density

Lighter is higher population density (white is 800+ people per square mile), Dark is lower population density (black is 2 or fewer people per square mile)

Median Age

Lighter is older median age (white is 46.0 years median age), Dark is younger median age (black is 29.0 years median age)

Male/Female Ratio

Lighter means more men than women (white is 55% men), Dark means more women than men (black is 45% men)

If you want to know more about Google Earth, check out our Absolutely Google Earth a collection of tools and resources to get you started.

We're working on a project to make this and other simple mapping applications more widely available. If you're a python guru who is interested in building great mapping applications like Chicago Crime give me a jingle.