Juice Analytics

Presentation guru Andrew Abela recently published his first book Advanced Presentations by Design. Abela shares his 10-step technique for developing influential business presentations. Before reading this book, I thought I had a pretty good idea how to make a compelling presentation; it turns out I mostly knew how to throw together a bunch of non-boring slides. There are a few key themes that summarize the book for me:

1. Focus on your audience.

“Your presentation should be all about serving your audience. You need to show them that you see everything from their perspective — their problem, in their terms, their motivation and issues. This also means that you have to be bound by their constraints. There is no point in raising an important problem and proposing new investments to solve it if your audience just does not have any money to spend this year.” (p55)

2. Solve a problem.

“Focus your entire presentation deliberately and undividedly on solving an important problem of theirs (the audience)” (p6)

“Your objectives should be about how your audience will change as a result of your presentation: how they will think and act differently after they leave the room.” (p5)

3. Tell a story.

“An effective way to reframe your evidence and involve your audience is to present your information in the form of a story…Stories are a coherent whole, where one thing flows to the next, so we tend to remember the whole thing.” (p65)

“By presenting your information in the form of a story, by setting up a tension and resolving it, and repeating as necessary, you can create this physical desire in your audience for your message.” (p77)

If you make presentations for a living or just as a hobby, I can wholeheartedly recommend this book. Abela does an impressive job of teaching his process and keeping it interesting. My one point of concern is that I felt he didn’t offer much help with the critical transformation from story outline (he recommends you shouldn’t open up PowerPoint until you are most of the way through the process) to presentation slides.

I also enjoyed this book because it connects to, and expands upon, the messages we emphasize in our design of Information Experiences for reporting, dashboards, and analytical tools. (Even the introduction gives us a nod: “I’ve become convinced of how crucial the last mile of communication is to driving organizational impact.”) Here is a short checklist of considerations articulated by Abela that bridge any communication of complex information:

• When presenting data, pay particular particular attention to what is new or different.
• Drive action. Or in Abela’s words: “What does it allow them to start doing, stop doing, or continue doing that would be difficult or impossible without this information.” (p47)
• Respecting the challenges faced by users. Understand what problems and levers the audience has available to them.
• Consider your audience “type”. How does the audience best absorb information?
• Consider the presentation environment. In what context will the audience be engaging with the information?
• Use different types of data (e.g. statistical, anecdotal). Sometimes specific data points can help focus attention better than an aggregate metric.
• Identify problems, then give people the tools to address the problem. This parallels Abela’s storytelling technique of creating and resolving tension.
• Users before technology. Usability before features. Abela notes: “Presentation and advice and tools have been developed for the benefit of the presenter, not the audience.” (p5)

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.