I’ve been skipping the last few TDWI webinars, not exactly knowing how to politely criticize some poor ones. However, I feel as I’m performing a disservice to those who read, so I’ll have to discuss today’s.

The title was “In-Memory Computing: Expanding the Platform Horizon Beyond the Database.” The pitch was that in-memory is so good for databases we should think about doing everything from ETL to everything else in the information chain there. One word critique: Oy.

In-memory analytics has been great for very fast processing. Having the data resident in memory is obviously a great way of providing rapid response for users of reports and analytic tools. However, it’s no panacea.

Simply put, two demands are limiting the cost effectiveness and even ability to do in-memory analytics: The amount of data and the number of users.

One of the repeated refrain of in-memory proponents is “memory is cheap!” Yes it is. However, massively parallel servers with the ability to efficiently link multiple cpus to large amounts of memory while providing coherence for multiple users aren’t. They quickly get very expensive with the costs of high end machines being much more on a pure memory amount level than commodity servers. There’s also an upper bound and with much of the larger data analytics today, multiple servers will be needed.

The other issue is the growth of self-service BI and mobile access to reports means that more memory is needed for non-database usage. A number of in-memory solution providers tell you that each user takes space in memory to satisfy individual needs. The more users the more space is taken from database availability.

The growth of server farms, being created in the Cloud now, is how the blend of in-memory versus space requirements will be addressed. “Fast enough” matters more than millisecond response time. With what we are constantly learning about both data manipulation and presentation, the strongest Cloud providers will win by keeping the most used information in-memory and sharing the rest among caches on multiple servers.

In-memory isn’t new and needs are much different than when it was. Listen to people talk about it and pay attention: If it’s the only thing discussed, they’re not being honest; if it’s a core part of the solution with its caveats addressed, the vendor, analyst or pundit is helping you.

In memory! Big Data! Data Analytics! The Death of BI!!!!

We in technology love the new idea, the new thing. Often that means we oversell the new. People love to talk about technology revolutions, but those are few and far between. What usually happens is evolution, a new take on an existing idea which can add new value to the same concept. The business intelligence space is no more immune to the revolution addiction that is any other area, but is it real?

Let’s begin to answer that by starting with the basic question: What is BI? All the varied definitions come down to the simple issue that BI is an attempt to better understand business through analyzing data. That’s not a precise definition but a general one. IT was doing business analysis before it was called that, it was either in individual systems or ad hoc through home grown software. Business intelligence became a term when MicroStrategy, Cognos, Business Objects and others began to create applications that could combine information across systems and provide a more consistent and consolidated view of that data.

Now we have a new generation of companies bringing more advance technologies to the fore to improve data access, analysis and presentation. Let’s take a look at a few of the new breed and understand if they’re evolution or revolution.

Some Examples

In-Memory Analysis

As one example, let’s discuss in-memory data analysis as headlined by QlikTech, Tableau and a few others. By loading all data to be analyzed into memory, performance is much faster. The lack of caching and other disk access means blazingly fast analytics. What does this mean?

The basic response is that more data can be massaged without appreciable performance degradation. We used to think nothing of getting weekly reports, now a five second delay from clicking a button to seeing a pie chart is considered excessive. However, is this a revolution? Chips are far faster than they were five years ago, not to mention twenty. However, while technologies have been layered to scale more junctions, transistors, etc, into smaller spaces, it’s the same theory and concept. The same is true of faster display of larger data volumes. It’s evolutionary.

Another advantage of the much faster performance is that you can do calculations that are much more complex, from simple pivot tables to being able to handle scatter charts with thousands of points is a change in complexity. However, it’s just another way of providing business information to business people in order to make decisions. It’s hard to find a way to describe that as revolutionary, yet some do.

Big Data

Has there been a generation of business computing that hasn’t thought there was big data? There’s a data corollary to Parkinson’s law which says that data expands to fill available disk space. The current view of big data is just the latest techniques that allow people to massage the latest volumes of data being created in our interconnected world.

Predictive Analytics

Another technical view of business is because software has historically been limited to describing what’s happened that business leaders weren’t using it for predictive analysis. Management has always been the combination of understanding how the business has performed and then predicting was would be needed for future market scenarios in order to plan appropriately. Yes, there are new technologies that are advancing the ability of software to provide more of the analytics to help with prediction, but it’s a sliding of the bar allowing software to do a bit more of what business managers had been doing through other means.

But there’s something new!

Those new technologies and techniques are all advancing the ability for business people to do their jobs faster and more accurately, but we can see how they are extensions of what’s happening. Nobody is saying “Well, I think I’ll dump my old dashboards and metrics and just use predictive analysis!” The successful new vendors are still providing everything described in “legacy BI systems.” It’s extension, not replacement – and that’s further proof of evolution in action.

Visio (ask your parents…) no longer exists, while Crystal Reports (again…), Cognos, Business Objects and others have been absorbed by larger companies to be used as a must-have in any business application. In the same way, none of the current generation of software companies is saying that the latest analytics techniques are sufficient. They all support and recreate the existing body of BI tools into their own dashboards and interfaces. The foundation of what BI means keeps expanding.

“Revolutionary”?

It’s really very simple, technology companies tend to be driven by technologists. Stunning, I know. Creating in-memory analytics was a great piece of technical work. It hadn’t been successfully done until this generation of applications. We knew were where getting more and more data accumulated from the internet and other sources, then some brilliant people thought about some algorithms and supporting technologies that let people analyze the data in much faster ways. People within the field understood that these technical breakthroughs were very innovative.

On the other side, there’s marketing. Everyone’s looking for the differentiator, the thing that makes your company stand out as different. Most marketing people don’t’ have the background to understand technology, so when they hear that their firm is the first, or one of the first, to think of a new way of doing things, they can’t really put it into context. New becomes unique becomes revolutionary. However, that’s dangerous.

Well, not always.

To understand that, we must refer to one of the giants in the field, Geoffrey Moore. In “Crossing the Chasm” and “Inside the Tornado,” Moore formally described something that many had felt, that there’s a major difference between early adopters and the mass market. There’s a chasm that a company must cross in order to change from addressing the needs of the former to the needs of the later.

The bleeding edge folks want to be revolutionary, that they’re visionaries. Telling them that they need to spend a lot of money and a lot of sleepless nights on buggy new software because it’s a step in the right direction doesn’t work quite as well as telling them it’s a revolutionary approach. As the founders like to think of themselves in the same place, it’s a natural fit. We’ve all joined the revolution!

There’s nothing wrong with that – if you are prepared for what it entails. There are people who are willing to get the latest software. Just make sure you understand that complexity and buggy young code doesn’t mean a revolutionary solution, just a new product.

Avoiding Revolution

Then there’s the mass market. To simplify Moore’s texts, business customers want to know what their neighbors are doing. IT management wants to know that their always understaffed, underfunded and overworked department isn’t going to be crushed under an unreasonable burden. Both sides want to know their investment brings a proper ROI in an appropriate time frame. They want results with as little pain as possible.

We’ve seen that the advances are in BI, not past BI. Yet so many analysts are crying the death of something that isn’t dying. How does that help the market or younger companies? It points people in the wrong way and slows adoption.

Not all companies have fallen for that. Take one of the current companies making the most headway with new technologies, Tableau Software. They clearly state, on their site, “Tableau is business intelligence software that allows anyone to connect to data in a few clicks, then visualize and create interactive, sharable dashboards with a few more.” They have not shied away from the label. They’re not trying to replace the label. They’re extending the footprint of BI to do more things in better ways. Or QlikTech’s self-description “QlikTech is the company behind QlikView, the leading Business Discovery platform that delivers user-driven business intelligence (BI).”

There are many companies out there who understand that businesses want to move forward as smoothly as possible and that evolution is a good thing. They’ll talk to you about how you can move forward to better understand your own business. They’ll show you how your IT staff can help your business customers while not becoming overwhelmed.

Know if you’re willing to be an early adopter or not, then question the vendors accordingly. As most of you are, purely by definition, in the mass market, move forward with the comfort that BI is not going anywhere and plenty of companies are merging the old and new in ways that will help you.

To paraphrase Mark Twain, the reports of BI’s death have been greatly exaggerated.

Years ago, I had a great boss. Very smart, good people manager, understood strategy and tactics. There was one flaw. One joke was that head never use a sentence if a paragraph would suffice. That showed most clearly is the voluminous number of slides that would be part of any presentation.

He comes to mind because I attended Tableau Software’s presentation to the Boulder BI Brain Trust (BBBT). 93 slides. Yes, 93. Even if folks were not familiar with BI, I think the presentation would have been a bit, shall we say, extensive. Just like that boss, the presenters had a lot of very interesting to say, but finding it was often hidden in too much explanation. So I don’t make the same mistake while describing the presentation, here are my key takeaways:

• Balanced on-premises and Cloud strategy.
• Strong forethought about changing relationship between IT & business users.

Everyone even vaguely involved in BI should already be familiar with the basics of Tableau. It is one of the leaders in the latest generation of BI solution providers. They are known for their visualization skills yet haven’t scrimped at the technology needed to manage large data sets. They had a tag team of four presenters for the presentation. The first presenter was Suzanne Hoffman, Sr. Director of Analyst Relations, who gave the overview of company growth since last year’s BBBT presentation.

Francois Ajenstat, Sr. Director, Product Management, then followed with, as would be expected from product management, a review of products including a discussion of v8.1. In a review of a number of things, the one item that caught my attention was the extension of their Cloud packages. It was clearly stated that the product wasn’t redesigned for the Cloud, just moved outside the firewall to a hosted environment, but that’s a fine first step considering the rest of the work they’ve done. What’s critical is they’ve thought through the Cloud business model and how that is different than what’s needed for purchased software.

The one issue I have, from an analyst perspective, was the very brief discussion of the supposed success and early adoption of their 8.1 offering. While describing the number of companies who have rapidly adopted it, there was no breakdown. How many were new customers versus old? Were they larger or smaller companies? While it is to be expected that newer companies and departments, as well as smaller companies, would be the first to adopt, and while that’s not necessarily an issue, clarity would have helped.

In a look ahead about v8.2, which we were told is not under NDA was a look at what seems to be an extremely professional Mac port. It’s not just moving what was on MS Windows to the Mac, as many companies do. It looks like a product designed for the Mac that will make very happy any user of that platform. While Francois did a good job explaining why they wanted to port to a platform that has 10% of the business market, I think Tableau should be more explicit on who that 10% is and why that matters. Marketing. Yes, I know, I’m biased towards marketing, but think about the fact that marketing was the home of the Mac during the lean years. Why? Because the Mac has always been better for visualization, so marketing continued to use the platform for creating ads, graphics, charts and more. Providing a great Mac port enhances the ability of marketing to serve good analytics and presentations to the rest of the firm.

The presentation was then handed to Robert Kosara, a research scientist, to talk about visual storytelling. Here, again, there were too many examples and a blurred message. While trying to describe how the concept was different than the visualization theories that have happened from Tufte onwards, the difference wasn’t made very clear. There seemed to be two main takeaways. First, they’re only describing the fact that modern BI allows isolation of and drill down into segments of the visualization, things that aren’t possible in old-line static print medium. That seems intuitively obvious rather than a giant leap forward. The second part of it isn’t with theory but with some of Tableau’s plans (again, we were told that this level isn’t under NDA) to provide technology that merges visualization with presentation layout, where the application will be able to take the equivalent of slides of the visualization to increase the presentability of analysis. That’s still early in work but could be very interesting as it matures.

The final presenter was Ellie Fields, VP of Product Marketing. Maybe I’m biased, as my background is in product marketing, but I wish this section had not been last. The most important thing is her presentation was what I think best brings out why Tableau is a leader. Her section focused on the changing environment for BI implementations. Everyone in the industry acknowledges the power of the BI front-end is giving business users more and more ability to be involved in creating and managing their own analytics. We know that IT is going to have to change from a more controlling focus to one of service. Ellie Fields has thought this through and clearly defined how the change doesn’t obviate the need for IT but only changes the relationship between the two sides. The most important slide in the deck is the one below, where a high level description of that partnership is presented.

The past growth of Tableau Software has clear reasons. It was a good technology introduced to the market at the right time. What they’ve done so far is a sign of management’s ability to understand an early market. Continued growth is across Moore’s Chasm and a longer term strategy is critical. In my (probably not so humble) opinion, while technology is critical there are far too many technology companies that failed with great products. It’s the vision shown in both Francois Ajenstat’s discussion of managing both on-premises and Cloud markets combined with Ellie Field’s understanding of how to position the company to help businesses through a major change in how IT and business users will relate that show the strength of the company moving forward.

I just wish we heard that information bubbled and  more visible rather than lost in a bunch of other words that weren’t really needed.