Tips About Data Science from SAP—Seriously?

When Business Intelligence software vendors that are to blame for keeping the information age advancing at a snail’s pace have the chutzpah to give advice about data science (the in-vogue term today for data sensemaking), I find it difficult to remain silent. The latest entry in the “Advice from the Clueless” category is an interview with Timo Elliott of SAP Business Objects, titled “What Is a Data Scientist? SAP’s Timo Elliott Says Leadership,” which appeared in Forbes on February 22, 2012. I’ll warn you now that my comments in this blog post are dripping with disdain. A less acerbic response would lack honesty.

Rather than writing a thorough review of Elliott’s comments, which isn’t warranted, I’ll just feature a few quotes from the interview, followed in each case by a short rejoinder.

Timo Elliott, Senior Director, Strategic Marketing, SAP Business Objects

To begin, let’s put Elliott’s comments into context by looking at his experience:

Elliott performed analytics for Royal Dutch Shell for about a year ending in 1988, when he joined BusinessObjects in Paris as the eighth employee. He has been with the company, now part of SAP, for more than 20 years.

“About a year” of analytics experience 24 years ago? Well in that case, let’s hang on his every word.

We have now entered an era in which technology is no longer the primary bottleneck to extracting meaningful business value from data. The primary bottleneck is actually human leadership, according to Timo Elliott, Senior Director, Strategic Marketing at SAP BusinessObjects. In other words, to expand the impact of data on your business, it is time to balance the focus on the technology of analyzing data with development of leadership in order to make sure that technology is put to good use.

SAP would love for us to believe that technology is no longer a bottleneck. Human leadership is indeed part of the problem, but savvy leaders would realize that technologies are still in fact a big part of the problem that we face. A good leader would advise the organization to stop relying on vendors like SAP when attempting to make sense of data.

Historically, what we now call “data science” has been somewhat limited to Web companies, which have had wide access to their activity logs and have been able to devise excellent products from them. But now, through the release of the latest crop of visualization and business intelligence (BI) products, that kind of capability now exists for all kinds of companies.

This is indeed beginning to happen, no thanks to SAP. Those who are using the term “data science” meaningfully and with integrity are doing so, in part, to distance themselves from the likes of SAP Business Objects, which has so far provided nothing useful but production reporting systems. When marketers like Elliott use terms like “data science,” they’re trying to give the impression that they’re on to something new and better without any real substance to support the claim. This is the same organization that introduced the embarrassingly impoverished Business Objects Explorer as “revolutionary.”

The reason many BI projects ultimately fail is too much focus on technology.

I couldn’t agree more. This is also the reason why so many BI products fail. Focusing exclusively on technology without understanding the needs and abilities of those who will use it—a common pitfall in software development—produces products that only a software engineer could love. This is especially true of products that support thinking, which must interface seamlessly with human perception and cognition. SAP Business Objects knows how to build production reporting systems, but not how to build tools for interacting meaningfully and efficiently with data.

“You have Scotty down in the engine room,” Elliott says. “He’s the guy who understands the technology perfectly, but he’s not the one leading the ship. You need the whole crew. You’ve got Spock, who’s an analytics person; you’ve got Bones, who’s the human relations person who does the emotional side; you’ve got Uhuru [sic] on communications. But the key person is Captain Kirk. Captain Kirk doesn’t know how the fusion generator works. He is a decider. His job is to lead people into whatever the situation is and make those tough decisions.

Much like former President George W. Bush, the “deciders” in many organizations are not in touch with the data. Relying on deciders in leadership (the Captain Kirks of the organization) will only work if they actually do have some idea of how that fusion generator propels the ship.

In some ways, a data scientist is equal parts Captain Kirk and Mary Leakey, the best-known member of the team that discovered and interpreted the early human skeleton “Lucy” in Egypt. The data scientist is part ship’s captain, part anthropologist. The data scientist is aware of the complexity of the systems at hand, but is less a deep technology expert than a comprehensive evaluator of the modalities of data used in an organization.

Okay, I’ll start by admitting that I don’t know what “comprehensive evaluator of the modalities of data” means. Probably nothing, giving the fact that marketers like Elliott don’t have to make sense as long as their words sound impressive. Unfortunately, data scientists—those who understand what’s going on based on evidence derived from data—are rarely given leadership positions. Knowing what’s really going on is seldom given priority in organizations.

“What’s not data science is a business person with a business question going to an IT organization saying, ‘Give me this report,’ and the IT person coming back and saying, ‘Here’s your report,’” Elliott says. “That is not data science. Why? Because it’s not about the interface. That’s the business person basically trying to do their current job in the current way, using a little more data. That can be worthwhile, and I’m not saying IT organizations don’t provide value when they do that, but it’s not data science.

I agree that this scenario doesn’t reflect data science. But this scenario accurately represents the very model that SAP has fostered for years and continues to foster in its products today.

“Ultimately, the reason why a lot of the companies that have people called ‘data scientists’ are successful is not only because of the data scientists and their skills, but also because the people that run those companies are keenly aware how much of a difference data can make to their businesses.”

The suggestion that organizations with people called data scientists, rather than data analysts, business intelligence professionals, or decision support specialists, are more successful than others is pure nonsense. The term data scientist in most organizations is just the latest term that’s being used by people who do precisely the same work as those who use the other titles. Changing what you call these folks doesn’t magically improve their work.

Even as data-science technology is on the upswing—IT spending per head…may actually jump 60 percent in the coming years, according to Gartner—there is a growing realization among the most data-savvy companies that the culture is just as important as the technology.

And if Gartner says it, we know what that means, don’t we? After all, Gartner’s magic quadrant claims that SAP Business Objects is the second most visionary vendor in business intelligence, second only to IBM, neither of which have demonstrated any real vision in their products for years.

“Most of us are just really bad at analyzing information,” Elliot says.

Yep, this is absolutely true. Why? Because most people haven’t learned to think critically, haven’t learned basic analytical skills, and have grown less capable to the degree that they rely on dumb technologies such as SAP Business Objects to do their thinking for them. If SAP wants to provide leadership in data science or whatever you choose to call the work of data sensemaking, they themselves have some learnin’ to do. Until then, perhaps they should remain silent and concentrate on listening.

Take care,

20 Comments on “Tips About Data Science from SAP—Seriously?”


By Chris. March 8th, 2012 at 10:42 am

Great posting Stephen!

We’ve been a Business Objects customer since 2003, and have seen nearly zero innovation (in the areas of neither usability nor “data science”). They refuse to fix their broken products, and we’re working to replace them with a competitor such as OBIEE, MicroStrategy or Microsoft.

By Stephen Few. March 8th, 2012 at 10:50 am

Hi Chris,

Unfortunately, none of the competitors that you mentioned will extend your analytical reach significantly. Good data sensemaking products so far are primarily coming from software vendors that are spin-offs of academic research (e.g., Tableau and Spotfire) and from those that have traditionally focused on the needs of statisticians (e.g., SAS JMP).

By Jason. March 8th, 2012 at 4:51 pm

My favourite bit:

“What’s not data science is a business person with a business question going to an IT organization saying, ‘Give me this report,’ and the IT person coming back and saying, ‘Here’s your report,’”

For us (with SAP BO) the response is usually: “why? give us a business case”. Followed by, “the universe isn’t setup like that”.

Sigh.

By Timo Elliott. March 8th, 2012 at 11:05 pm

Here’s my own post on my skepticism/frustration with the “data scientist” term:

Data Analysts, Data Scientists, and the Rest of Us

Posted by Timo Elliot on Thursday, October 6, 2011

This post is part of a “blogorama” organized by smartdatacollective.

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 analytics 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 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…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.

Regards,
Timo

By Kelly Martin. March 9th, 2012 at 12:30 am

Oiy! The arrogance of big BI. It seems that with the adoption of products like Tableau (Qlikview, Spotfire) by the likes of us analysts in the business departments, IT and big BI are getting nervous. All we need from them now is to manage/govern the data and provide us with access to it. Now the Report Developers will be called ‘Data Scientists’ and they’ll still manage to produce unusable reports after 6 months of product development process with big BI software.

I’m not surprised that senior management are turning to ‘end user’ (a term used by our IT folks) products like Tableau within their departments. They are amazed at the speed at which they can get insight into their business problems without a business case from us plain old analysts.

Thank you Stephen for your unwavering voice of reason in this area. You must feel like you’re yelling at the bubble sometimes.

Kelly

By Alexandru. March 9th, 2012 at 6:16 am

Steve,

with all my respects to your postings and your books, your are not fair when you conclude: ‘“About a year” of analytics experience 24 years ago?’ T. Elliot worked in BI almost 25 years, so he should have more than one year of analytics experience.

I agree with you that Mr. Elliot just makes a wrong statement when he writes data scientist is leadership. Than no data scientist will have the whole power in the business to take the decisions based on what he has found… you’re totally right. I find the title totally wrong, because you cannot reduce the skills of a data scientist role to soft skill like leadership.

It is true, T. Elliot is a marketeer, so he cannot renounce strong adjectives. This the illness of the whole branche of marketing. However, I read some of his posts and I found he can ‘unmask’ many myths inside BI-Business to the point. I appreciate his work, his posts, and I think I can do this honestly: I work in BI/DWH/OLAP since 1994.

Alexandru

By Johannes. March 9th, 2012 at 7:29 am

Great post, thank you! I complitely agree with you. I think tha BI has nothing to do with data science. Data science is a profession of statisticians and their tools are R, S-Plus or SAS. They try to find patterns behind the data and intepret what the data means. This sort of workflow is called statistical analysis.

By Stephen Few. March 9th, 2012 at 11:51 am

Alexandru,

Unfortunately, you cannot conclude that someone has “analytics experience” just because they work for a business intelligence software vendor. Very few of those who work for BI software companies have any actual experience doing the traditional work of business intelligence (i.e., technicians trained in the use of a particular software product to develop production reports), and a much smaller number have any experience doing analytics (i.e., the actual process of data sensemaking). Timo Elliott works in marketing at SAP Business Objects. If he has any actual analytics experience beyond his work at Dutch Royal Shell 24 years ago, this fact wasn’t revealed in the Forbes interview.

This is one of the reasons that attempts by most business intelligence vendors to develop analytics products miss the mark by such a wide margin. Even those who work in the professional services departments in companies like SAP—the folks who are sent out as experts to assist customers—are usually only expert in the use of the software, not in the actual skills of the trade.

Even more shocking, perhaps, is the fact that most “thought leaders” in the BI industry have no actual experience in analytics. In many cases they earned the BI industry’s respect doing work in the realm of data warehousing (the development of data stores that can be used for reporting and analysis) without ever doing the work of a data analyst. We should listen to them when they speak of things that they know, but not when they extend their reach beyond their expertise.

Whether or not Elliott is an expert in analytics, I cannot say, because I lack knowledge of his experience. Unlike you, I don’t follow his blog. It’s very possible that, as you say, he has useful insights to share. I merely countered specific statements that he made in the Forbes interview that are misleading and expressed disdain for the suggestion that SAP is a leader in the field of data science.

By Stephen Few. March 9th, 2012 at 12:12 pm

Johannes,

It doesn’t appear that we agree completely. The tools of data science, statistics, data analysis, or whatever you choose to call the process of data sensemaking, are not limited to R, S-Plus, and SAS, or even some of the other tools of statisticians (SPSS, Minitab, etc.). In fact, few of the traditional tools that statisticians use are good for exploratory data analysis (EDA), because they don’t support a seamless flow of analysis from one view to the next, but require programming to get to each new view of the data. I believe that John Tukey, the Princeton statistician who wrote Exploratory Data Analysis in 1977, were he alive today, would love the richer analytical experience of visual analysis tools that support rapid interaction with data.

Also, although I agree that the term “data scientist” probably applies best to statisticians, I’m not sure that we should limit it to them exclusively. While it is true that you cannot analyze quantitative data without some knowledge of statistics, you can do a great deal of data analysis with only a basic knowledge of statistics, especially when your efforts are augmented by the use of good visual analysis tools.

By Stephen Few. March 9th, 2012 at 12:20 pm

Timo,

Do you believe that SAP Business Objects has done a good job of supporting the actual work of data sensemaking? I believe that your company earned its reputation by developing a good production reporting platform. This is quite different, however, from helping people interact with data directly to explore, make sense of, and then communicate useful information to others in effective ways. This is the work of a data analyst (a.k.a., data scientist). What has SAP done so far to support this?

By Stephen Few. March 9th, 2012 at 2:13 pm

Jonannes,

Given what I’ve said about many “thought leaders” in the field, I should add that there are also many BI journalists who have no real experience in the field. In fact, many lack training in journalism as well. Readers tend to assume that articles in publications were written by people with relevant expertise, but this is often not true, especially in the BI industry. While there are a few journalists in the field with relevant expertise and journalistic integrity, for many of them this is just a job that they slipped into because they saw an opportunity. They’ll write anything—true or not—that will keep the revenue stream flowing, usually from BI vendors or from organizations that are in bed with the vendors. Much of what they right is little more than a rehash of information that was given to them by vendor marketing organizations. In other words, they are far from independent from the organizations that they are supposed to report about objectively.

By ssp. March 11th, 2012 at 4:56 pm

While I do not consider/annoint myself a “thought leader” or a “BI journalist” at the risk of being shot down, I have implemented a variety of BI/DSS/Data solutions using BOBJ,COGNOS,MSFT,OBIEE with a variety of platforms and feel comfortable to offer the following.

1. The blog is a bit acerbic and tries to have a personal edge to make a point. Of course, that is Mr. Few’s prerogative
2. BOBJ has its own flaws and so do many other products but is an excellent and robust tool with strength in ad-hoc analysis
3. The discussion on whether tools such as Tableu or BOBJ are the better ones is a red herring. If visual analysis is the starting point of analysis, Tableu and such are useful but if the visual analysis is an end product and end user data analysis is the goal, then BOBJ or OBIEE have the edge by miles
4. I agree with Mr.Few on the aspect of someone being called a data scientist does not have an effect but I also see the marketing point of view that by rebranding the position, there could be renewed emphasis on the push to make the enterprise more knowledge and data decision oriented

By Stephen Few. March 11th, 2012 at 6:34 pm

SSP,

You state that Business Objects (BOBJ) “is an excellent and robust tool with strength in ad hoc analysis.” Please substantiate your claim with evidence. The only tool that SAP Business Objects has that it positions for ad hoc analysis is Business Object Explorer, which is an embarrassingly bad product. Do you have a different product in mind or do you actually believe that Business Objects Explorer supports ad hoc analysis well?

You speak of visual analysis as “an end product” and of “end user data analysis” as the goal. In what sense is visual analysis an end product? Visual data analysis is a particular way of performing data analysis. Whether speaking of data analysis in general or visual data analysis in particular, the process involves data exploration and sense-making, and the goal is understanding. To say that one is an end product and the other is the goal is illogical. Are you well acquainted with all three of these products: Tableau, Business Objects, and OBIEE? If not, you’re not in a position to compare them. If you are, then please substantiate your claim that Business Objects and OBIEE “have the edge by miles.” Please describe the analytical edge of these products compared to Tableau.

By Victor Blaer. March 12th, 2012 at 4:17 am

SSP/Stephen -
I´m new to the BI world so I´m lucky enough to start from scratch with no biases. I´ve been spoiled by apple UI´s, Facebook and best practices in Web design. As such I´ve taken “Usability” for granted. My firm uses BO. My first day on the job a BO user for 5 years gave me a BO tour. Jesus Christ I thought. In BO you first need to have an IT guru build something called a “universe” (which will not talk to any other non SAP program, including the Babel fish of Visualization software, Tableau), then in some weird/cluttered UI I built my query and got a result. The result was a table of numbers, for some strange reason, spread across multiple pages that I had to click through. And if I wanted to manipulate the table, I had to drag and drop with a Sniper´s precision onto a frustratingly small space in the table.
I still hand´t begun analyzing the data. Then I transformed the BO results from the table into “graphs from the 80´s”. I was annoyed with the pathetic excuse of a graph (3d angled)staring me in the face. I finally just exported everything into Excel, only to find it in excel 2003 format, every cell besides the table was in “text” format. So I built my own excel graphs, added some buttons and interactivity(1 day of work). And said I would never do that again.
Then I did some research and found Tableau. I downloaded it, took less than 5 minutes to install, watched a getting started video on their site (you know, bc I´m spoiled by youtube tutorials) and 30 minutes later, I had an interactive dashboard that actually looked like something coded by a reasonably sane person. In 1 hour I had exceed the deliverables of expert BO users. In the end, we set up a data challenge, what could each group do in 1 hour. Me, a Tableau user for 1 week, vs 2 BO users with 5 years experience. After 40 minutes I had; several dashboards complete with dynamic filtering (i.e. “show me top (x) customers within this region”) beautiful scatterplots showing outliers and a nice cappuccino because I still had 20 minutes. We bought a license and I never have to open “BO” again.

By ssp. March 12th, 2012 at 12:13 pm

I have zero bias for or against any tool out there - just objective observations. Each of the tools you described has a place in business. It all depends on the culture of the business and the kind of “analyst” you are catering your solution to. I get your point and frustrations (believe me– I want to kick the big BI tools myself) but IMHO it is a bit biased and not a well rounded point of view.

1. Do you have a different product in mind or do you actually believe that Business Objects Explorer supports ad hoc analysis well

I actually believe that Business Objects Web intelligence (or Cognos or OBIEE BI answers for that matter) is a robust ad-hoc data analysis tool, if you know what you need to do with it. Additionally, the development of a universe is not a bad monster as you are making it out to be. It abstracts out the issues of joins in the tables to the end users and protects them from making contextual errors. Not every data analyst who is an expert at visual analysis is an expert at underlying data and not all systems in the real world have perfect stars or cubes with data definiions. Consider this case, a tableu report with the sample data sources of the coffee chain and the superstore. A join (innocuously built by an analyst to analyze data of discount,inventory and margins) between the access db and the excel sheet of 2 data sets based on market and Region and a cross tab of market, year and Market size in columns and product, measure names in rows will yield WRONG results by summing discounts for the market regardless of the product name. An additional join is needed to ensure that the discounts for the right category are shown. The abstration of logical from physical will ensure that the data set served up for analysis is properly vetted

2. In what sense is visual analysis an end product?
Different people analyze data from different perspectives. A business analyst who is well versed with the functions of the business may not need spiffy graphs as a starting point but could prefer to construct analytic questions to ask of the data and can write them in the Web intelligence world. The end product of this analysis could be the spiffy “visual analysis” data set that can then be handed off to the more visual oriented analysts or used in presentation. A spatial analyst on the other hand may start from a visual representation and drill down to the attribute or location analysis from that point.

3. Please describe the analytical edge of these products compared to Tableau

I confess I do not have much experience with tableu server but have worked with tableu desktop,OBIEE and BOBJ. As far as me substantiating my argument, I think it should be the responsibility of the “BI journalist” to post results of their comparison and test environment especially if they are propogating a particular tool at the expense of another. Not the other way around. I am aware of excellent performance by tableu on some large analytical data sets but have personally run into issues on normalized transactional databases

Tableu is a great piece of software that can complement but is not a replacement for these systems on an enterprise scale. It is simply not ready or proven yet. I am sure they may supplant the older BI platforms or get bought by some of them in the future. These tools can be used for personal BI to generate cool looking graphics and data analysis from blending excel data with access but not on enterprise data warehouses where data governance is key or on heavily normalized transactional systems

By Stephen Few. March 12th, 2012 at 2:49 pm

SSP,

Despite our efforts to remain objective, we all have biases in that we see the world through the narrow lens of our own experience. Some of these biases can only be overcome by broadening our experience. I appreciate the time that you’ve taken to post your comments and explain your perspective. I can easily understand your perspective, because it is quite similar to my perspective during the first 18 years that I worked doing what we now call business intelligence. My perspective changed, however, about 12 years ago when I was exposed for the first time to data visualization. I’m not talking about those “spiffy charts” that people might create as the “end product” of data analysis. I’m talking about the use of visual representations of data to explore, make sense of, and then communicate data in ways that cannot be done as well otherwise. Data visualization is required for data exploration and analysis because of the way that our brains work. Without visual representations, we cannot see patterns in the data, nor can we compare entire series of values to one another, which are both core aspects of data exploration and analysis.

By calling visual analysis an “end product,” you’ve clued me into the fact that you and I aren’t talking about the same thing. I’m not talking about “spiffy graphs” that analysts can choose to use or ignore based on their personal preferences. Visual analysis (a.k.a., information visualization, which is the exploration and analysis subset of data visualization) is defined as “the use of computer-supported interactive visual representations of abstract data to amplify cognition.” (Card, Mackinlay, and Shneiderman, “Readings in Information Visualization: Using Vision to Think,” 1999) This is not something that you do after data analysis, it is a way of doing data exploration and analysis; often the only way.

When I first began working in the realm of business intelligence (what we called “decision support” in the early 1980s), I used a product called Focus, which was a programming tool for analyzing data, building reports, and building analytical applications. It was a predecessor of the BI products that are familiar today. It was similar to many of the statistical programming languages that are still used today, but it couldn’t do graphics. With the advent of the PC, then GUI interfaces, and eventually the Web, tools such as Business Objects Web Intelligence developed as the natural evolution of earlier tools like Focus. During my last corporate gig, I managed the implementation and roll-out of Business Objects (not my choice) and the development of the first data warehouse for the fifth largest software company in the world at the time. What Web Intelligence did at that time and still does today does not qualify as exploratory data analysis. When I worked for Brio Software 15 years ago (Brio was later acquired by Hyperion, which was in turn acquired by Oracle), we were doing in the client-server environment what Web Intelligence does today with little difference beyond the fact that it’s now done via the Web. The functionality is almost exactly the same. Navigating through a series of tabular views of data is extremely limited. Tables work great for looking up facts and for viewing values precisely, but not for data sensemaking.

What I’m suggesting is that your experience is limited by an old paradigm that we’ve now surpassed. Even the vendors that you refer to — SAP Business Objects and Oracle — are recognizing the limitation of this old paradigm and are trying to catch up with products like Tableau and Spotfire. They’re struggling, however, because their vision is limited by the old paradigm in ways that they don’t even realize. There are very bright and committed people in these organizations that are working to break from the past, but it’s hard to turn a big ship around.

I have written many articles and one book — “Now You See It” — that describe visual analysis and demonstrate how it is superior to the old BI paradigm, which progresses slowly in fits in starts from one tabular view to the next, when what we need is a seamless flow of questions and answers using visual representations of data enriched by rapid analytical interaction. I invite you to read my book “Now You See It” to learn more about information visualization. If you do so and don’t come to believe that tools such as Web Intelligence are hobbled, I’ll gladly refund your money. Hold me to it — I’m quite serious. You obviously care about your work and are open to better ways of doing it. I invite you to consider something very different from the BI that you know.

By ssp. March 13th, 2012 at 7:28 am

Thanks Stephen. I am obviously not questioning your experience or analysis. I will read your book. I am familiar with data visualization though and agree with you that BOBJ and such do NOT provide good visualization tools. I have presented papers in location intelligence (which is an hyper visual BI with GIS) and am absolutely convinced that visual exploration is a key feature. But to brand it as the ONLY BI and everyting else as “OLD” BI is something I do not agree (But you made me look old though ;-). Like I said, there is a place for everything. Whether it is visual analysis or not, they are just means to reach a decision point. All I am saying is that there is a spiral process of analysis where questions could be asked from a visual perspective or questions can lead to a visual data set that could then be analyzed further. Alternately, as you suggested, you can start by visually decomposing the dataset to get to an intelligent decision.

It has been an interesting discussion to have and I look forward to reading your book.

By Stephen Few. March 13th, 2012 at 1:14 pm

SSP,

Thanks for the discussion. I hope you find “Now You See It” useful.

As a final comment, I must point out that I did not say that visual BI is the “only BI”, as you seemed to suggest. What I said is that BI is hobbled (limited) without visualization. Data sensemaking in support of better decisions (that is, BI) can be done without visualization, but only in limited ways. Many of the stories that live in your data will never be found and understood without visualization. This is not an opinion or a preference, but a fact that is rooted in the human brain. With geospatial analysis, which you engage in, you cannot look at a table of numbers and imagine geo-spatial patterns; you must see the data displayed visually on a map. The same is no less true for the other relationships that exist in quantitative information, such as distributions and correlations. Numbers expressed as text cannot make visible to our brains what a visualization can bring to light quite easily. In other words, although BI can be done without visualization, it cannot be done well, completely, and efficiently without it.

By Brad. March 15th, 2012 at 8:16 am

As a BusinessObjects user for the last 7 years I can tell you they have had no innovation. They have done nothing major with their flagship production (BusinessObjects later renamed to Desktop Intelligence) but decommission it. They replaced it with Web INtelligence which is a lite version that is very buggy. Why? Because it was easier for them to support.

Forget that you are replacing it with an inferior product that can’t do what you could for the last 10 years. Forget that it has more bugs than many Beta products, they are pushing you to it because they are decommissioning the alternative. They desperately want the business world to get stuck on their products rather than make products people want to use.

By Dan Murray. March 24th, 2012 at 6:47 am

In addition to my general agreement I would add that I advise my clients to ignore the established big players until they demonstrate some competence in the field. I also ask them to consider the “single version” of the truth dictum…which has never and will never exist in a single data repository. Modern tools exist today that allow information consumers to query and visualize multiple sources intelligently. I don’t see the big BI providers racing to create tools that allow their customers to easily analyze sources external to their system.

It has been small and nimble innovators that are filling the void.