Your data is meant for action.
Do you have your child’s drawing on the wall of your cube, office or maybe at home on the fridge? Can you remember visualizing the world that simply? When was the last time you looked at anything quite that way? What if you did?
Well, we did just that. And, our effort resulted in a video to share with people about what we do here at Juice. We hope you like it.
(P.S. Thank your kid for the artwork covering that stain on your wall — and for the great analogy.)
Juice’s own Ken Hilburn brings it home at the Strata conference. He sits down with Mac Slocum of O’Reilly for a few data softballs. Here’s a second-by-second account.
[0:03] Q: What are the most common visualization mistakes that people are making?
[0:03] A: The bottom line is usability. Stay focused on your purpose, making decisions, taking action. Stay simple, if you have to explain you’re failing.
[0:15] Brett Favre endorses Wranglers, but Ken wears Data Wanglers [not shown].
[0:59] Dropping names, and twisting the knife on usability.
[1:25]? What better time to confront a little gap in your knowledge than when you’re being filmed? It’s Antoine de Saint Exupéry.
[1:48] Q: Do we need different tools to create simple visualizations?
[2:05] Plentiful shout outs to friends in the industry. Even Business Objects gets a friendly mention, is Ken getting soft?
[2:53] A difficult point to cover in a short time. We speak of data journalism and telling stories with data, but there are really no great tools that allow this in an everyday business environment. There is a lot of attention, not much progress.
[3:15] Q: What makes a great dashboard?
[3:25] A: Zach and Ken just delivered a 3 hour tutorial on the subject earlier in the week. Can Ken cover it in 30 seconds? Attention, context, and data drilling are keys but there’s precious little time to do more than mention big concepts.
[4:20] Q: Are dashboards too complicated?
[4:22] A major softball to close the session. Cheshire cat grin from Ken.
[4:54] “Getting your brain around it.” What Ken isn’t saying is that we know today’s businessfolk aren’t just looking at one dashboard, they have to look at many. They have to integrate info from lots of disparate systems into a picture of how their business is doing. If your info is harder, more complex, presents a bigger cognitive load, or is slower to load, then it’s going to be less valuable.
If you haven’t had the pleasure of getting to know Ken, stay tuned. We’ll be doing more Viva Visualization events in ’11 with more detailed prescriptions for great dashboards and there’s no better way to spend your morning than eating a nice warm bagel and listening to Ken.
Enough complaining about the broken bits of Business Intelligence; it’s time to highlight the things that are good and right in the industry. Like most industries, the renewal and innovation occurs at the fringe, beyond the comfort zone of established vendors.
I’ve created five categories and a catch-all to capture the solutions and companies (not so much technologies) that are leading the next generation of Business Intelligence. The categories are:
Naturally I’ve focused on areas of Juice expertise and focus — not coincidentally, the places where we feel BI has neglected end-users. According to a study by the Business Application Research Center, BI end-user adoption sits at a lowly 8%.
I’m happy to take your suggestions (and update the post) for things I’ve missed in these categories or for entirely new categories.
Tools that make it easy for analysts to pull data from multiple sources, analyze, visualize and share it.
Winner: Tableau, the reigning king of visual analytics tools, has added more web-based functionality to allow for online sharing and collaboration.
Runner-up: Good Data has arrived on the market with a web-first platform designed to democratize analytics. I had a chance to get a demo from the management team and was impressed with the ease of use and high-quality data presentation.
“A frequently updated analytical display that is clear and concise” (via a recent post)…and not likely to draw the rage of Stephen Few.
Winner: BonaVista Systems wants to make Excel a “first choice dashboard tool.” From the humble position of sparkline plug-in vendor, BonaVista has taken a leadership role in encouraging more effective dashboard design.
Runner-up (tie): Two BI companies, Qlikview and Microstrategy, seem to be following BonaVista’s lead. Unfortunately, they may only be dipping in a toe as I found just a couple examples that break from the traditional over-glossy, gauge-riddled dashboard interface.
Companies that serve a narrow slice of the BI world extremely well. The desire to be all things to all people has been an Achilles Heel of the BI industry. The general purpose BI platforms often prove too broad and too generic to serve the unique problems of specific industries or functional areas.
Winner: Wall Street on Demand is a brilliant, below-the-radar provider of information solutions to the financial sector. Their sparse, articulate marketing text and few screenshots hint at a company that knows exactly what they do and deliver high-quality BI solutions. I wish I knew more.
Runner-up (multiple): The following are just a few companies that have focused on an industry or functional segment to deliver targeted BI solutions:
(I know there is a difference.)
Winner: Pentaho offers an open-source end-to-end BI suite that is a competitive alternative to the big-guys. Of course, the implementation it isn’t necessarily cheap or easy.
Runner-up: If anything should scare the BI industry, it is the possibility of a Google Analytics model extended into more general data analysis and visualization tools. Google Fusion Tables may just be the tip of the iceberg.
Bringing leading-edge visualization techniques out of academia and into the business world.
Winner: Many Eyes continues to impress with high-quality visualizations. They are easy to create and clean in design and usability. Impress your boss with a slick visualization in your next presentation.
Runner-up (tie): Openviz / Advanced Visual Systems and Panopticon appear to be the two BI vendors battling it out for leadership in advanced visualization solutions. Unlike Many Eyes, these guys lack Tufte-esque sophistication in infoviz design. That said, there is a big difference between creating a one-off New York Times-quality visualization and delivering a toolset that is re-usable in many different situations.
Free charts with good default design. InetSoft’s Style Chart and Google Charts offer free, embeddable charts.
Jargon-free BI marketing. With few exceptions, BI web sites are densely populated with those awful stock-photography people sitting around conference tables (or worse, the ethnically-diverse V-formation marching at you) and meaningless business jargon and techno-babble. I really appreciate Blink Logic’s web site with its straight talk and clean, readable design.
Beyond the desktop. RoamBI has a great-looking iPhone application that is designed to “transform your data into insightful, interactive visualizations delivered to the iPhone.” It makes the Oracle and Qlikview iPhone apps look old-school.
Over the past few years we’ve made the point that today’s BI vendors stop short of joining data to decision makers at the point of decision and action. We like to call this problem the “last mile”. As it turns out, Gartner does, too.
According to a recent article, Gartner analyst Kurt Schlegel states in the report “Overcoming the Gap Between Business Intelligence and Decision Support” that most companies still aren’t able to link BI to “the last mile” of making decisions that actually help their businesses.
Gartner joins a short list of other prominent voices (Tableau, SAS) in the BI community that have already come on board with Juice on this concept. We’re very glad to see others addressing the gap of making information really and truly useful for decision makers.
While we’re at it, that’s not the only theme that has seeped into the Gartner perspective: Gartner’s global BI manager Ian Bertram says the fundamental problem with BI isn’t about technology, it has to do with making BI work better for people. In other words, “BI isn’t a technical problem, it’s a social one”
So Gartner Folks, if you’re out there and following our blog, we’re excited to see you coming along side with us. And as long as you’re listening, here’s a few other ideas we’d love to see you consider as well:
This post is the 2nd part in a 2 part series. Last time we talked about how organizations use Tribal Elders and Static Reports to find answers to questions that they already know. Today we’ll talk about the other three phases of Data Analytics Maturation.
Once the organization realizes that they need answers to questions that they don’t yet know, they start to extract all sorts of permutations on all of the data that they have and distribute those reports to the “need to knows” on a regularly scheduled basis. In most cases, an analytics team is set up to manage the requests from the business for more or different information. Sometimes the reports are modified, but many times new reports are created because the users already know how to use the old reports. The analytics team works hard to maintain the information flow to the individual requests with the intent to provide all the information that would ever be needed by the consumers.
The down side is that this typically manifests itself in the form of the dreaded 124 page monthly report. So, the reporting “Oracle of Delphi” shows up in the inbox. For a little while there’s some excitement along the lines of “I never knew we could get all this information.” However, soon folks realize that interpreting the data for “questions you don’t know” turns out to be pretty difficult and once they figure out where are the answers to the questions they know, they just look at those few rows of the report and leave the rest for analysis “later” (which probably means it ends up in the recycle bin… if we’re lucky).
Phase 4 begins when a few folks who get the 124 page data dump realize “if I could just filter the data down a little I could much better understand the answers to this specific question”. So the organization provides the ability for end users create ad-hoc reports. Now the user has the ability to construct their own custom reports to answer the specific and unique questions they have about their data.
Sadly enough, however, most people who need to know the answers get stuck in any of a few traps down in the weeds. The first trap is that they may be sure they know what questions to ask, but even in spite of their confidence, they’re really asking the wrong ones. Secondly, most people in this situation are more business oriented and less technical (presumably the more technical ones have already figured out how to query the data directly). In all but a few cases, the tool that is provided requires too much technical expertise for most business people to overcome in order to be really productive. Thirdly, even if they can actually get to the data that really does help them to be more productive, they lack the analytical expertise to interpret the data and turn it into usable information. The end result of these three hurdles is that the users end up either in analysis paralysis, or just plain giving up.
To solve the barriers presented by having a lot of data available only to technical users, maturing organizations provide solutions targeted at specific business areas that make exploration accessible to those who can impact business performance (in other words, everyone involved in the workflow). These solutions are not about the technology or even the data, but rather about providing information that translates easily in to getting stuff done.
The results are provided in a fashion that makes access to the right information easy by guiding the user through a process to help them answer the known questions, discover new questions to ask, and explore answers to these questions. It’s sort of like the guide you might hire on a photo safari. The experienced guide will make sure you find the animals that you came to see in the first place, but will also point out really interesting things along the way that you had never thought of. And you might even discover something amazing and exciting that you didn’t even know existed. Good information tools are just like an experienced safari guide.
The sad part about “experienced guide” information tools is that there are so few that exist. The good news is that we see more and more information workers and decision makers “seeing the light” when it comes to understanding their need for these sorts of tools. And, we believe that as more and more organizations mature and experience the challenges of the first 4 Phases of Analytics Maturation that more and more will see the benefits of Phase 5, and implement solutions that help us all be more effective and efficient users of information.
Recently, while meeting with one of our clients, they mentioned their desire to provide their customer’s business team with the ability to run ad-hoc reports. This notion spurred me to think about whether or not I thought this was a plan for success. Would having this additional analytics ability help the non-analyst be more effective at getting their job done?
Over the next few days, we’ll be exploring the different stages of maturity that information workers go through as they try to become more effective and efficient at consuming and acting on information. By our reckoning, we figure there are 5 Phases in the maturation cycle:
Phase 1: Tribal Elders
Phase 2: Static Reports
Phase 3: Bigger Static Reports
Phase 4: Ad-hoc reports
Phase 5: Experienced Guide
As we go through the different stages, we’ll discuss the breadth (how wide is coverage of all available information), depth (how deep is the understanding about covered information), reach (how easy is the access to the covered information), the typical user of the analytics method, and the signs that the organization is outgrowing each phase in the model. So, without further ado, let’s get started.
The earliest stage of analytics maturity is one in which the organization relies entirely on the expertise of one or two individuals who use their business savvy to provide analytics. These folks, we’ll call them Tribal Elders, have been around the company for a long time and have “seen it all.” Just like the those “elders” in the movies, they’re wizened leaders who can mash all the data in their head and join it with their experiences to make good decisions. I guess you would say that technically, there are no formal analytics that are performed during this stage. However, everyday, the expert is using their training in the school of hard knocks to observe, analyze, act and advise on what they know to be the best for the business.
An organization outgrows this phase when the business becomes complex either through growth or through changing environment (such as variance in market conditions, or the expert leaving the business). All of a sudden, the leaders find themselves in a situation where they can’t scale the decision making quickly enough to continue to drive the business. The huge asset of the expert’s experience has turned into a liability that acts like an anchor on the organization’s maneuverability.
An organization has reached the second phase when they have realized that they have outgrown their ability to rely wholly on what they can get out of the Tribal Elders to run the company. So they start to write down all the questions they normally ask. They use this list to start to build reports that that can provide answers to those questions that they know. Once completed, the organization now has the ability to enable a broad audience to answer the questions that have been asked on a regular basis.
The limitation of this approach is that the Tribal Elders are still needed to answer the questions that fall out side of the standard “what I know to ask” category. The beginning of the end of this phase happens when an event that was unforeseen occurs that dramatically and negatively impacts performance. The logical question arises “why didn’t we see this coming?” followed by the answer “we didn’t have that data.” The organization then begins the transition to Phase 3.
Next time we’ll discuss the remaining three phases of maturation.
(Update: Here’s Part 2 of the 5 Phases of Data Analytics Maturation – Enjoy!)
In case you missed it, Information Week recently released a report that listed, among other things, the “top 10 roadblocks to BI success” (skip to page 48 for the list). So, does anyone want to guess what IW found the top barrier to be?
As it turns out, it wasn’t “data throughput”, or “access to data sources/more data”, or even “more features”. The number one barrier to BI success, according to IW, is (get this) “Complexity of BI tools and Interfaces”. That’s right, it’s not technology, but usability that keeps people from getting value from BI solutions. People actually want software that’s easier to use, not harder.
In our eyes, this is just another example that the BI industry isn’t being constrained by the technology. We don’t need more tools, or even more features. The problem isn’t going to be solved by technology. What we need are solutions that, for people who depend on information, make it easier to see, understand, and use the information that really matters. What we need are solutions that are designed for a purpose, that transform data into easy to understand information, and that are beautifully usable.
Oh. Wait a minute. That’s what we do.
I recently came across a white paper on the “five styles of BI” and thought that would be an interesting read. As it turns out, more interesting than I expected. In this paper, the vendor (in order to protect the innocent, we’ll just call them MacroTactics) made a statement regarding the performance capacity of this particular vendor’s solution: 72,000 reports per hour. Let’s see, 72,000 reports per hour… that would be 576,000 reports in an 8 hour day… and 149,760,000 reports per year. Wow. Who’s reading that stuff?
Now, I fully buy in to the fact that applications that deal with lots and lots of data need to be hugely scalable, but what I don’t buy is how this is in any fashion a measure that anyone can use to figure out if a particular BI solution is right for them. I can just imagine the requirements spec for that solution: “15.1.182.f – Solution must be capable of creating 70,000 reports per hour. Alternately, solution will be able to generate 140,000,000 reports per year.” 140 million reports! Incredible. (Now, what did I do with my mini-me?)
Seriously, here’s the thing. More reports is rarely the answer. We already have plenty of data and plenty of reports. What buyers and users really want is fewer reports and more information that helps them get their jobs done better and faster.
We’d encourage business intelligence vendors to think of themselves more as data storytellers than data factories churning out generic report widgets…even if they can do it at incredibly high speeds. From this perspective, you wouldn’t want to hear Steven Spielberg bragging about his ability to pump out a dozen movies a year or J.K. Rowling trumpeting her ability to write 1000 pages a year (hmm, wait a sec).
Here’s a little predictive analytics:
About a year ago, I took a swipe at the “$80 million supercomputer to analyze NYC student achievement.” It smelled more like a super sales job than a super useful analytical tool.
At the time I had said:
Teachers are underpaid, hardly appreciated, and overworked. I can only wonder what the half-life is of a system that asks teachers to log on to get information delivered by the “chief accountability officer.”
Well, it appears that things haven’t gone that smoothly with the supercomputer. Today, I received a link from Leonie Haimson, a NYC education advocate, to a story entitled SCHOOLS COMPUTER AN $80M ‘DISASTER’.
Not only has the supercomputer struggled to gain much traction with users (“The school system’s new $80 million computer super system to track student performance has been a super debacle, teachers and principals say”), it has coincided with severe budget cuts.
We see these data warehousing problems all the time with our clients, and the NYC supercomputer displays all the hallmarks:
It does offer one thing that I haven’t seen before: a Chief Accountability Officer.
There is little ‘r’ reporting and there is big ‘R’ Reporting, and the difference is vast:
reporting is the stuff that comes straight out of your reporting solution. It gets created by choosing a few parameters and typically shows up as a table of data with an accompanying chart.
Reporting is something altogether different. It is concise set of graphics and data that tell a focused story. It is crafted to focus on the key information and exclude everything else. It may come in the form of a single dashboard or a 20=page deck, but it is always audience-friendly. It is informed by context and provides explantation. Reporting is not about the numbers, it’s about what the numbers tell you.
By analogy, what if we didn’t make a distinction between a raw fish pulled out the sea and a prepared fish dinner? When the waiter slapped a still-squirming sea bass on my plate, I probably wouldn’t take much consolation in getting a deboning knife and a hot plate.
In the wild, the two species of reporting are often confused. To help you identify one from the other, I’ve put together a couple of examples with tell-tale signs:
The difference comes down to a gut-feeling: Was this document created to address the questions of a specific audience with a specific problem?
This may be a distinction that is implicitly well-known. My concern is more about explicit acknowledgement of the gap between them. And in the process:
1. Avoiding passing off reporting as Reporting. In particular, vendors who offer reporting tools think they are delivering the ability to communicate performance, when in fact they are mostly providing the raw materials.
2. Recognizing the level of effort required to transform reporting into Reporting. Analysts spend a huge amount of time filling this gap; it is one of the wasteful backwaters of modern enterprises.
This has been a common theme in my recent client discussions. People are sick of slogging through their reporting tools to build useful information for management. Ultimately, developing great Reporting requires an understanding of problems, the audience, and thoughtful design. But that doesn’t mean it should be so painful to construct. We are working on a solution to help, but in the meantime here are a few general things we do:
We're in the process of preparing just that. Email us if you'd like to be included in the early access video at info [at] juiceanalytics.com ...
Missed both of these. Will they be available to review after the fact? ...
Amitp, That's a great point. It makes it clear that visualization is a toolkit that has to be properly applied by a data storytelling practitioner. Which raises the question: What other tools should a ...
This is werid there is not 100 ideas on here ..... LAME!! ...
Listening to Hans Rosling talk vs using his gapminder tools to explore the data is a great example showing how important the storytelling is compared to the visualization alone. ...
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