Category Archives: Enterprise Software

Rocket Software at BBBT: A Tale of Two Products

1 Reply

Last Friday’s BBBT presentation by an ensemble cast from Rocket Software was interesting, in both good and bad meanings of that word. They have some very interesting products that address the business intelligence (BI) industry, but they also have some confusion.

Bob Potter, SVP and GM, Business Intelligence, opened the presentation by pointing out that Rocket has more than $300 million (USD) in annual revenue yet many tech folks have never heard of them. One reason for the combination is they’ve done a good job in balancing both build and buy decisions to provide niche software solutions in a variety of places and on a number of platforms. Another is a strong mainframe focus. The third is that they don’t seem to know how to market. Let’s focus on just the two products presented to demonstrate all of these.

Rocket Data Virtualization

Most of the presentation was focused on Rocket Data Virtualization (DV). There are two issues it addressed. The first is accessing data from multiple sources without the need to first build a data warehouse. DV is the foundation of what was first thought of as the federated or virtual data warehouse. It’s useful. Gregg Willhoit, Managing Director, Research & Development, gave a good overview of DV and then delved into the product.

Rocket Data Virtualization is a mainframe resident product to enhance data virtualization, running on IBM z. While this has the clear market limit of requiring a company large enough to have a mainframe, it’s important to consider this. There are still vast amounts of applications running on mainframes and it’s not just old line Cobol. Mainframes run Unix, Linux and other OS partitions to leverage multiple applications.

An important point was brought up when Gregg was asked about access to the product. He said that Rocket is working with other BI industry partners, folks who provide visualization, so that they can access the virtualized data.

However, if you want to know more about the product, good luck. As I’ll discuss in more detail later, if you go to their site you’ll find all marcom fluff. It’s good marcom fluff, but driving deeper requires downloads or contacting sales people. That doesn’t help a complex enterprise sale.

Rocket Discover

The presentation was turned over to Doug Anderson, Solutions Engineer, for a look at their unreleased product Rocket Discover. It’s close, in beta, but it’s not yet out.

As the name implies, Rocket Discover is their version of a visualization tool. It’s a very good, basic tool that will compete well in the market except for two key things. The first is that they claimed Rocket is aiming at “high level executives” and that’s not the market. This is a product for business analysts. Second, while it has the full set of features that modern analysts will want, it’s based on a look and feel that’s at least a decade old.

On the very positive side, they do have a messaging feature built in to help with collaboration. It needs to grow, but this is a brand new product and they have seen where the market is going and are addressing it.

Another positive sign is this isn’t a mainframe product. It runs on servers (unspecified) and they’re starting with both on-premises and cloud options. This is a product that clearly is aimed at a wider market than they historically have addressed.

While they have understood the basics of the technology, the question is whether or not they understand the market. One teaser that shows that they probably don’t was brought up by another analyst who pointed out that Doug and others were often referring to the product as just Discover. Oracle has had a Discover product for many years. While Rocket might not have seen it on the mainframe, there will be some marketing issues if the company doesn’t always refer to the product as Rocket Discover, and they might have problems anyway. Their legal and marketing teams need to investigate quickly – before release.

Enterprise IT v Enterprise Software: Understanding the Difference

The product presentation and a Q&A session that covered more issues with even more folks from Rocket taking part, show the problems Rocket will have. As pointed out, the main reasons that so many people have never heard of Rocket is it sells very technical solutions to enterprise IT. Those are direct sales to a very technical audience. However, enterprise software is more than enterprise IT.

Enterprise software such as ERP, CRM, SFA and, yes, BI, address business issues with technology. That means there will be a complex sales cycle involving people from different organizations, a cycle that’s longer and more involved than a pure sale to IT. I’m not sure that Rocket has yet internalized that knowledge. As mentioned above, their website is very fluffy, as if the thought is that you put something pretty (though I argue against the current fad of multiple bands requiring scrolling, it’s neither pretty nor easy to use) with mission and message only, then you quickly get your techies talking directly to their techies, is the way you sell. Perhaps when talking with techies only, but not in an enterprise sale.

That’s my biggest gripe about the software industry not understanding the need for product marketing. You must be able to build a bridge to both technical and business users with a mix of collateral and content that span the gap. I’m not seeing that with Rocket.

In addition, consider the two products and the market. DV is very useful and there are multiple companies trying to provide the capability. While Rocket’s knowledge of and access to mainframe data is a clear advantage, the fact the product only runs on mainframes is a very limiting competitive message. I understand they have tied their horses very closely to IBM, and it makes sense to have a z option, but to not provide multiple platforms or a way for non-mainframe customers to use their more general concepts and technologies will retard growth.

If their plan is to provide what they know first then spread to other platforms, it’s a good strategy; but that wasn’t discussed.

Both products, though, have the same marketing issue. Rocket needs to show that it understands it is changing from selling almost exclusively to enterprise IT and needs to create a more integrated product marketing message to help sell to the enterprise.

There’s also the issue of how to balance the messages for the two products. For Rocket Data Virtualization to succeed, it really does need to work with the key BI vendors. Those companies will wonder about Rocket’s dedication to them while Rocket Discover exists. Providing a close relationship with those vendors will retard Rocket Discover’s growth. Pushing both products will be walking a tightrope and I haven’t seen any messaging that shows they know it.

Summary

Rocket is a company that is very strong on technology that helps enterprise IT. Both Rocket Data Virtualization and Rocket Discover have the basics in place for strong products. The piece missing is an understanding of how to message the wider enterprise market and even the mid- and small-size company markets.

Rocket Data Virtualization is the product that has the most immediate impact with the clear differentiation of very powerful access to mainframe data and the product I think should make the more rapid entrant into its space. The question is whether or not they can spread platform support past the mainframe faster than other companies will realize the importance of mainframe data. In the short term, however, they have a great message if they can figure out how to push it.

Rocket Discover is a very good start for a visualization tool, but primarily on the technology side. They need to figure out how to jump forward in GUI and into predictive and other analytics to be truly successful going forward, but the market is young and they have time.

The biggest issue is if Rocket will learn how to market and sell in broader enterprise and SMB sales, both to better address the multiple buyers in the sales cycle and to better communicate how both products interact in a complex market place.

Rocket is worth the look, they just need to learn how to provide the look to the full market.

TDWI Best Practices Report on Hadoop: A good report for IT, not executives

Leave a reply

The latest TDWI Best Practices Report is concerned with Hadoop. Philip Russom is the author and the article is worth a read. However, it has the usual issue I’ve seen with many TDWI reports, very strong on numbers but missing the real business point. In journalism, there’s an expression called burying the lede, hiding the most important part of a story down in the middle. Mr. Russom gets his analysis correct, bit I think the priorities or the focus needs work. It’s a great report to use as a source by IT, it’s not a report for executives.

Why am I cranky? The report starts with an Executive Summary. The problem is that it isn’t aimed at executives but is something that lets technical folks think they’re doing well. It doesn’t tell executives why they should care. What are the business benefits? What are the risks? Those things are missing.

First, let’s deal with the humorous marketing number. The report mentions the supposedly astounding figure that “Hadoop clusters in production are up 60% in two years.” That’s part of the executive summary. You have to slide down into the body to understand that only 16% of respondents said they have HDFS production. It’s easy for early adopters to grow a small percent to a slightly larger small percentage, it’s much tougher to get a larger slice of the pie.

Philip Russom accurately deals with why it will take a bit for Hadoop to grow larger, but it does it past the halfway point of the article. Two things: Security and SQL.

Executives are concerned that technology helps business. Security ensures that intellectual property remains within the firm. It also ensures that litigation is minimized by not having breaches that could be outside regulatory and contractual requirements. Mr. Russom accurately discusses the security risks with Hadoop, but that begins down on page 18 and doesn’t bubble up into the executive summary.

So too is the issue of SQL. After writing about the problems in staffing Hadoop, the author gives a brief but accurate mention of the need to link Hadoop into the rest of a business’ information infrastructure. It is happening, as a sidebar comment points out with “Hadoop is progressively integrated into complex multi-platform environments.” However, that progress needs to speed up for executives to see the analytics from Hadoop data integrated into the big picture the CxO suite demands.

The report gives IT a great picture of where Hadoop is right now. As expected from a technical organization, it weighs the need, influence and future of the mystical data scientist too highly, but the generalities are there to help mid-level management understand where Hadoop is today.

However, I’ve seen multiple generations of technology come in, and Hadoop is still at an early adopter phase where too many proponents are too technical to understand what executives need. It’s important to understand risks and rewards, not a technical snapshot; and the later is what the report is.

IT should read this report as valuable insight to what the market is doing. It’s, obviously, my personal bias, but the summary is just that, a summary. It’s not for executives. It’s something that each IT manager will use for its good resources to build their own messages to their executives.

JInfonet at the BBBT: OEM or Direct, a Decision is Necessary

Leave a reply

Let’s cut to the chase, this is another company with a very good product and no idea how to message. Unless they quickly figure out and communicate the right message, they’ll need to get ready for acquisition as an exit strategy.

Jinfonet is a company founded, it seems, to clone Crystal Reports in Java. Hence the awkward name. JReport, their product, is full featured and we’ll get to that, but the legacy name using report will leave them behind if that remains their focus.

The presentation was primarily by Dean Yao, Director of Marketing, with demo support brought by the able Leo Zhao, Senior Systems Consultant. However, the presentation indicated the message problem.

Reports? What Reports?

The name of the product is JReports, but at no time in the three hours did a report make an appearance. They showed two different analyst charts, Nucleus Research and EMA, of the business intelligence (BI) industry to show where they were placed. BI. Yet when asked about competition, Dean Yao repeatedly mentioned they didn’t compete against BI vendors but focused on reports.

Their own presentation begs to differ:

JReport solution areas

Notice that reports are a secondary feature of one focus.

What’s also good and bad is that Leo Zhao’s demonstrations showed a very richly featured product that does compete against the other vendors. The only major hole wasn’t in functionality, it’s that the rich set of visualizations weren’t as pretty as most of the competition. That is in part because they are self-funded with more limited resources and partly because they’re great techies who haven’t prioritized visualizations as they should.

OEM or Direct?

OEM, in JInfonet’s business model, doesn’t only mean the product embedded in third party applications. Mr. Yao discussed how JReport is also regularly embedded in departmental IT applications. That is different than when companies use JReport as a standalone product.

Dean talked about how 30% of their business in recent years was direct, with the rest being OEM. At the same time, he mentioned that last year was around 50/50. That’s not a problem. What is an issue is that they don’t know why it was. Did sales focus on direct? Was one major direct client a large revenue outlier which skewed the results? They don’t seem to know.

That matters because the OEM and direct models are very different. With OEM, you let the other company deal with business messages. All you’re doing is presenting to them a good technical story and cost point compared do simpler products, a tiny segment of competition or doing nothing and losing out to their competitors.

Enterprise sales, on the other hand, require a focus on the end user, the folks using the products and the business issues they have. That is what’s missing from the presentation, their web site and the few pieces of collateral I reviewed.

One thing should also be said about the OEM to departments model. The cloud is changing the build v buy balance for many departments for the applications in which JReport is embedded, so I’m not sure how much longer this model will be of significant revenue.

Mr. Yao said they don’t do enterprise sales, but just sell to SMB and enterprise departments, so that means they’re not really competing against other BI vendors. A lot of the analysts on the call quickly jumped on that, pointed out that even one of the largest companies openly talks about its strategy of land and expand. “Just land” is not a long term strategy.

What’s that mean?

Right now the enterprise market is very fragmented, so there’s a space for a small company, but that won’t last long. Crystal Reports had a long run based on the technologies of the day, but it no longer is independent. Today, things are changing far more rapidly. The cloud is allowing BI firms to address small to global companies with similar products and the major players (and most smaller ones) are focused on that full business market.

Given the current product, JInfonet can go one of two ways. They can decide to completely focus on OEM, keep a technical message and just sell enterprise as it happens.

The other option, one I openly prefer, is that they realize that they have a very good product that does compete in the direct model and they need to focus more messaging. They can still provide to OEM, but that’s easier – it’s a subset of the full featured message.

The solution, though, resides in the folks who weren’t in Boulder: The founders. The company has been self-funded since 1998 and the founders are used to their control. I’ve seen companies fail because owners were unwilling to see that times have changed. They mistakenly think that pivoting markets says they did something wrong in the past, so they’re hesitant. It doesn’t say that, but only that the people have enough confidence to adapt to a new market with the same energy and intellect with which they addressed the original market.

JInfonet has great potential, but it will require a strong rethink and clarification of who they are in order to convert that to kinetic. From what I’ve seen of the product and two people, I hope they succeed.

Tableau at the BBBT: Strengthening the Business in Business Intelligence

Leave a reply

Tableau was back at the BBBT last week. Last year’s presentation was a look ahead at v8.2. The latest visit was a look back at 2014 and a focus on v9.0. Francois Ajenstat, VP Product Management, was back again to lead us through product issues. The latest marketing presenter was Adriana Gil Miner, VP Corporate Communications.

Tableau Revenues

Ms. Gil Miner opened the morning with the look back at last year. The key point was thestrength of their growth. They are not only pleased with the year-over year growth, but thechart also shows last year’s revenue as a slice of revenue over Tableau’s lifetime. We’ll leave it simply as: They had a good year.

Another point in describing their size is that Adriana said they have 26,000 customer accounts. Some confusion with a later presentation number required clarification and this isn’t users, or even sites. We were told that the 26k is the number of paying company accounts. There were no numbers showing median account size or how far the outliers are on either extreme, but that’s a nice number for the BI space.

The final key point made by Adriana Gil Miner was localization. Modern companies almost all create products using unicode or other methods that allow for language localization, but Tableaus has made the strong push to provide localized software and data sets in multiple languages. My apologies for not listing them, there’s some weird glitch with my Adobe Reader that’s crashing only on their presentation while no other analyst is having the same problem, so I can’t provide a list. Please refer to your local Tableau rep for details.

Francois Ajenstat then took over. It was no surprise that his focus was on v9.0. He discussed it by focusing on nine points he views as key:

  • Access to more data sources
  • Answer more questions
  • Improve the user experience
  • Support analytics at scale
  • Performance as a differentiator
  • Support for mobile
  • Tableau Public redesign
  • Coming out next quarter

If you look at those, you might question why that many bullets? For instance, when it comes out is just a schedule issue and doesn’t rise to the level of the others. Tableau Public’s redesign just seems to be the obvious end of focusing on better user experience and performance.

However, a couple sound the same but should be differentiated. Analytics at scale and performance improvements overlap but aren’t identical. Francois showed both what they did to improve performance on clustered servers, helping both bigger data sources and more simultaneous users, and also demonstrating that they’ve done some great optimization in basic analytics for individuals.

One of the best parts was the honesty, now that they’re close enough to releasing v9.0, in admitting that early versions ran slowly. They showed quotes from beta testers talking about major performance improvements. In addition, Tableau Public is a great source of testing real-word analytics. Mr. Ajenstat pointed out that they took the 100 most accessed visualizations in Tableau Public and analyzed performand differences, seeing a 4x increase in performance on average. While it’s always important to generate internal tests to stress potential use, focusing on how business really use the tool is even more important in ensuring performance is seen as good in day-to-day usage by knowledge workers, not only in heavy loads by analysts doing discovery.

LOD Expressions

The one thing that really caught my eye about v9.0 is the incorporation of Level of Detail (LOD) expressions. BI firms have been adding drill-down analytics for a decade. Seeing a specific level of detail and then dropping down to a lower level is critical. However, that’s not enough.

What’s needed is to be able to visually compare the lower level details with overall numbers. For instance, a sales VP regularly wants to know not just how an individual sales person is doing, but also how that compares to the region and national numbers. Only within context can you gain insight.

Among the other things LODs help is the ability to bin aggregates. Again we can turn to sales to think about retail sales across categories while also comparing those to total sales or in a trend analysis.

While many companies are working to add more complex analysis, it’s clear that Tableau hasn’t only looked at how a very technical person can create an LOD. They’ve worked on an interface, that from the demo, has a simple and clean interface that business end users can user. Admittedly, that’s what demos are supposed to do, but I’ve seen some try and fail miserably. This seems to be a good attempt to understand business intelligence with an emphasis of the first word.

Summary

Some of the very new startups make the mistake of thinking even the first generation BI companies are too old to innovate. Those companies aren’t and are still a threat. However, Tableau is not even in the first generation and is still more nimble yet. They have their eyes on the ball and are moving forward. Even more importantly, while still focusing on their technology, as do many startups, they seem to have become mature enough to start shifting focus from the IT and business analysts to the information consumers.

Understanding what the business knowledge workers throughout the business hierarchy need, in data and performance, is what will drive the next growth spurt. Tableau seems to have them in target.

TDWI Webinar: Embedded Analytics

Leave a reply

The latest TDWI webinar was on embedded analytics. The speakers were Fern Halper, the director of TDWI research for advanced analytics, and Mark Gamble from OpenText. For those of you who hadn’t heard, Actuate was acquired by OpenText and is being rebranded but, according to Mark, will remain an independent division for now.

Ms. Halper’s main point is that embedded has a lot of different meanings for different audiences and that she wants to create a clear framework for understanding the terminology within the analytics space. She’s clear that what’s meant isn’t just into the mass market idea of wearable software, but that analytics can be embedded in specific applications, broader systems and, yes, devices such as mobile and wearable items.

Early in the presentation she presented a two axis image comparing structured and unstructured data combined with human and machine generated data. While I think the coloring should rotate, to emphasize that the difference between machine versus human generated information is a bigger issue than structured v unstructured, it’s a nice way of understanding some of the data streams.

TDWI Embedded Analytics - Data Sources

That, however, was a definitional slide and discussion. The real mean of Fern Halper’s presentation was the framework she described to help understand the steps of embedding analytics.

TDWI Embedded Analytics - Framework

Operationalized analytics are those that are involved in the full process of decision making. For instance, a call center employee might be talking to a prospect whose finances are flagged as a question mark. That prospect must be sent to another person to process the decision based on analytics.

Integrated analytics are those that allow the call center operator to see the analysis and immediately make decisions based upon guidelines.

Automated analytics are those that provide the operator with a decision tree response based on analytics done behind the scenes.

The only issue I take with the framework is it doesn’t necessarily mean true real time. The example discussed shows that the integrated approach can be real time for what humans think of as real time within our own interactions. Meanwhile, real-time might not be a necessary component to some automated decisions. Real-time is a separate issue and I think Fern’s framework would be better served by eliminating that item.

Fern Halper followed the framework with the usual and interesting TDWI survey numbers. This time, the questions were focused on the adoption of analytics tied to the framework. The numbers showed the unsurprising fact that analytics adoption is still in its infancy. One of the great parts of TDWI’s numbers is they show the reality which contradicts the industry’s hype.

One set of numbers I’d like to see wasn’t included. The responses were only IT responses in general, who has started using what analytics. I would have loved to see one slide that clearly showed only the sub-segment of companies who are already using analytics tools and where those companies are within Ms. Halper’s framework. Are are the bleeding edge folks doing at moving through the framework to automated solutions?

OpenText

The rest of the program was a fast presentation by Mark Gamble, pointing to OpenText’s (Actuate’s) main benefit claim of enterprise scalability and the other factors. One of the phrases I liked was his reference stating they “adhered to a low code methodology.” It’s nice to hear folks admitting that as much as we want to eliminate coding, some of that is still required. Honesty isn’t a negative in marketing and I liked that turn of phrase.

In the other direction, he mentioned there were over fourteen million downloads of BIRT and that the company “believes” they have over three million users. I’m not interested in belief but they don’t seem to have a clear figure on adoption.

The main problem I had was the demo. Mark showed experimental work positing to show live acquisition of basic automotive information such as speed and RPM displayed on a computer, phone and watch. It was not only not a business case but one that seemed to go back to the misunderstanding about the meaning of embedded which was addressed by Fern. Yes, it was embedded on two devices, but the demo didn’t show how it might be embedded in business applications. It stuck with the flashy concept of wearables.

OpenText might have something good with their analytics portability, but I don’t think the demo presents it to a business audience. Yes, techies will understand the underpinnings that make it cool, but the business folks writing checks need to see something that justifies the expenditure and I don’t think that’s shown.

Summary

Fern Halper did another good job of putting the adoption of analytics into perspective. This time, with a framework for better understanding embedded analytics.

Mark Gamble did a passable job of presenting OpenText’s solution but I feel he must do a better job of figuring out a business message.

TDWI’s data shows the early state of adoption that exists in the market. Fern Halper’s framework will help companies better understand how to move into the arena, but only if the companies providing those solutions can better present how they’ll help solve business issues.

MapR at BBBT: Supporting Hadoop and still learning

Leave a reply

I’ve probably used this in other columns, but that’s life. MapR’s presentation to the BBBT reminded me of Yogi Berra’s statement that it feels like déjà vu all over again. Wait, if I think I’ve done this before, am I stuck in a déjà vu loop?

The presentation was a tag team effort of Steve Wooledge, VP Product Marketing, and Tomer Shiran, VP Product Management.

The Products and Their Aim

The first part of the déjà vu was good. People love to talk about freeware, but mission critical solution won’t be trusted on such. Even before Linux, before Unix, software came out and it took companies to package it with service and support to provide constancy and trust for widespread IT adoption. MapR is a key company doing that with Apache Hadoop, the primary open source technology for big data applications.

They’ve done the job well, putting together a strong company that, quite reasonably, has attracted some great investors and customers. Of course, because Hadoop is still in its infancy, even a leading company such as MapR only mentions 700 customer, companies paying for licenses; but that’s a statement about big data’s still fairly limited impact in operational systems not a knock on MapR.

Their vision statement is simple: “Empowering the As-it-happens business by speeding up the data-to-action cycle.” Note the key: Hadoop is batch oriented and all the players realize that real-time analysis matters for some key sales and marketing applications. Companies are now focusing on how fast they can get information out of the databases, not what it takes to get data in. A smart move but only half the equation.

One key part of the move to package open source into something trusted was pointed out by Steve Wooledge. When the company polled customers about why they chose MapR, the largest response was availability, the up time of the system. Better performance wasn’t far behind, but it’s clear that the company understands that availability is a critical business issue and they seem to be addressing it well.

Where the déjà vu hits in a not-so-positive way is the regular refrain of technologists still not quite getting business – even when they try. This isn’t a technology problem but an innovator’s problem. When you get so wrapped up in the cool things you’re doing, you think that you need to lead with the cool things, not necessarily what the market wants.

One example was when they were describing the complexity of the MapR packaging. Almost all the focus was on the cool buzzwords of open source. Almost lost in the mix was the mention that their software supports NFS. It was developed more than 30 years ago and helps find files on networks. That MapR helps link both the latest and the still voluminous data in existing file systems is a key point, something that can help businesses understand that Hadoop can be integrated into existing systems and infrastructure. However, it’s not cool so the information is buried.

The final thing I’ll mention about the existing products is that MapR has built a nice three product suite, providing open source, mid-tier and full enterprise versions. That’s the perfect way to address the open source conundrum and move folks along the customer curve.

Apache Drill: Has it Bitten Off Too Much?

Sorry, couldn’t help the drill bit reference. Tomer Shiran took the later part of the presentation to show off Apache’s latest data toy, Apache Drill, intended to bridge the two worlds of data. The problem I saw was one not limited to Tomer, MapR or even Apache, but to all folks with with what they think of as new technology: Over hype and an addiction to revolutionary rather than evolutionary words and messages. There were far too many phrases that denigrated IT and existing technology and implied Drill would replace things that weren’t needed. When questioned, Tomer admitted that it’s a compliment; but the unthinking words of many folks in the industry set out a pattern inimical to rapid adoption into the Global 1000’s critical information paths.

Backing up that was a reply given to one questioner: ““CIO of one of the largest tech companies said they can’t keep doing things the same way.” Tech companies tend to be bleeding edge by nature, they do not represent the fuller business world. More importantly, the idea that a CIO saying she needs to change doesn’t mean the CIO is planning on throwing out existing tools that work. It means she wants to expand and extend in a way to leverage all technology to provide better decision making capabilities to the rest of the CxO suite.

Another area of his talk finally brought forward, through a very robust discussion, of one terminology issue that many are having. Big data folks like to talk about “no schema” but that’s not really true. Even when they modify the statement to be “schema on read” it’s missing the point.

They seem to be confusing fixed layout, relational records with the theory of schemas. XML is a schema for data exchange. It’s very flexible and can be self-defined, but it’s a schema. As it came from SGML, it’s not even the first iteration of flexible schemas. The example Mr. Tomer gave was just like an XML schema. Both data source and data recipient have to know some basic information such as field names in order to make sense of data, so there’s a schema.

Flexible schemas not only aren’t new, they don’t obviate the need for flexible schemas. They’re just another technique for managing the wide variety of data that business wishes to turn into information. As long as big data folks misusing a term and acting as if they have something revolutionary, the longer they’ll retard their needed incursion into IT and business information.

Summary

Hadoop and big data aren’t going anywhere except forward. The question is at what speed. There are some great things happening in both the Apache open source world and MapR’s licensed support for that world, but the lack of understanding of existing IT and business is retarding adoption of the new and exciting technologies.

When statements such as “But the sales guy won’t do X” are used by folks who have never been in and don’t understand sales, they’re missing the market. Today’s sales person is looking for faster and more accurate information, and is using many tools people would have said the same thing about only ten years earlier. In the meantime, sales management and the CxO suite who provide guidance for the sales force are even more interested in big picture information coming from massaging large data sources.

The folks in the new arenas such as Hadoop need to realize that they are complementary to existing technologies and that can help both IT and business. When pointing that out, I was asked by one of the presenters if that meant he should do two case studies, one with Hadoop, flexible schema and one with old line uses, I gave a clear no. It should be one with new and one that shows new and existing data sources combining to give management a more holistic picture than previously possible.

Evolution is good. MapR can help. They need to do the tough part of technology and more their view from what they think is cool to what the market thinks is needed.

Teleran at BBBT: Great technology, again with the message…

Leave a reply

The BBBT started off 2015 with yet another company with a great technology and far too simplistic strategy and message. It’s the old problem: Lots of folks come to the BBBT because their small companies are starting to get traction and they want wider exposure, but the management doesn’t really understand Moore’s Chasm so are still pitching to their early adopters rather than the larger market.

Friday’s presenter was Kevin Courtney, VP Business Solutions, Teleran. Back in the day, I evaluated a small technology company for acquisition of their technology and inclusion into Mercury Interactive’s testing suite. It was an SQL inspector that let our products see the transactions going between clients and servers to help improve performance testing.

Teleran has the same basics but has come much further in recent years. The company starts with the same technology but has layered great analytics on top in order to help companies understand database usage in order to optimize application and network performance. They’ve broken down the issues into three key areas of business concern:

  • Performance and value: How are queries being performed in order to minimize dead data transfer and increase the value of existing computing infrastructure.
  • Risk and Compliance: Understanding who is doing what with data in order to minimize risk and prove regulatory and contract compliance.
  • Modernization, migration.re-platforming: Understanding existing loads, transactions and queries in order to better prepare for upgrading to new technologies – both hardware and software.

In support of these capabilities, Kevin mentioned that they have 8 software patents. While my understanding of patent, trademark and copyright laws leads me to understand that software patents shouldn’t be legal, they are and the patents do show innovation in the field. Hopefully.

Mr. Courtney also did a good job giving stories that supported each of these areas. I’ll quickly describe my two favorite (I know, three bullets, but that’s life).

One example showed that value is more than just a dollar value. He described a financial trading house using Teleran to analyze the different technology and data usage patterns between their top and bottom performing agents, then used that analysis to provide training to the bottom tranche (yes, I did have to use that word while discussing finance) in order to improve their performance.

The second example combined performance and modernization. He described a company where there were seven unsanctioned data marts pulling full data sets from operational systems. That had a severely negative impact on performance throughout their infrastructure. The understanding of those systems allowed for planning to consolidate, upgrade and modernize their business intelligence infrastructure.

So what’s the issue?

They have a great product suite, but what about strategy? The discussion, with additional information from Chris Doolittle, VP Marketing, via phone, is that they have a system that isn’t cheap and they readily admit they have trouble proving their own value.

Take a look at the Teleran site. Download some case studies. What you see is lots and lots of discussion about the technology. However, even when they do discuss some of the great stories they told us, the business value is still buried in the text. They’re still selling to IT and not providing IT the clear information needed to convince the business users to write the checks.

What’s needed is the typical chasm move of turning things upside down. They need to overhaul their message. They need to boldly lead with the business value and discuss how it’s provided only after describing that value.

What’s also needed is something that will be even harder: Changing the product in synchronization with the message. The demonstration showed a product that has little thought put into the user interface. At one point, Kevin said, after going through four different tables, “if you take x, y and z, then you can see that…” Well, that needs to be clear in a business intelligence driven interface rather than having the pieces scattered around requiring additional information or thought to figure it out. It’s overcrowded, very tabular and dashboards aren’t really dashboards. They need to contract with or hire some UI experts to rethink their interface.

They do OEM Qlik, but there are two problems with that. It looks like they’re using a very old version and aren’t taking advantage of Qlik’s modern BI toolsets. Also, the window with the information has a completely Qlik title. It should read Teleran’s product powered by Qlik in order to keep context.

Summary

Teleran is another company with a great technology that needs to change in order to cross the chasm. Their advantage is that their space, performance analysis, is far less crowded than the database or BI end points. If they can clarify their products and messages, they can carve out a very nice chunk of the market.

Denodo at BBBT: Data Virtualization, an Important Niche

2 Replies

Data virtualization. What is it? A few companies have picked up the term and run with it, including last week’s BBBT presenter Denodo. The presentation team was Suresh Chandrasekaran, Sr. VP, North America, Paul Moxon, Sr. Director, Product Management & Solution Architecture, and Pablo Alvarez, Sales Engineer. Still, what I’ve not seen is a clear definition of the phrase. The Denodo team did a good job describing their successes and some features that help that, but they do avoiding a clear definition.

Data Virtualization

The companies doing data virtualization are working to create a virtual data structure where the logical definitions link back to disparate live systems instead of overlaying a single aggregated database of information. It’s the concept of a federated data warehouse from the 1990s, extended past the warehouse and now more functional because of technology improvements.

Data virtualization (and note that, sadly, I don’t create an acronym because DV is also data visualization and who needs the confusion. So more typing…) is sometimes thought of as a way to avoid data warehouses by people who hear about it at a high level, but as the Denodo team repeatedly pointed out, that’s not the case. Virtualization can simplify and speed some types of analysis, but the need for aggregated data stores isn’t going away.

The biggest problem with virtualization for everything is operational systems not being able to handle the performance hits of lots of queries. A second is that operational systems don’t typically track historical information needed for business analysis. Another is that very static data in multiple systems that’s accessed frequently can create an unnecessary load on today’s busier and busier networks. Consolidating information can simplify and speed access. Another is that change management becomes a major issue, with changes to one small system potentially causing changes to many systems and reports. There are others, but they in no way undermine the value that is virtualization.

As Pablo Alvarez discussed, virtualization and a warehouse can work well together to help companies blend data of different latencies, with virtualization bringing in dynamic data to mesh with historic and dimensional information to provide the big picture.

Denodo

Denodo seems to have a very good product for virtualization. However, as I keep pointing out when listening to the smaller companies, they haven’t yet meshed their high level ideas about virtualization and their products into a clear message. The supposed marketechture slide presented by Suresh Chandrasekaran was very technical, not strategic. Where he really made a point was in discussing what makes a Denodo pitch successful.

Mr. Chandrasekaran states that pure business intelligence (BI) sales are a weak pitch for data virtualization and that a broader data need is where the value is seen by IT. That makes absolute sense as the blend between BI and real-time is just starting and BI tends to look at longer latency data. It’s the firms that are accessing a lot of disparate systems for all types of productivity and business analysis past the focus on BI who want to get to those disparate systems as easily as possible. That’s Denodo’s sweet spot.

While their high level message isn’t yet clarified or meshed with markets and products, their product marketing seems to be right on track. They’ve created a very nicely scaled product

Denodo Express is free version of their platform. Paul Moxon stated that it’s fully functional, but it can’t be clustered, has a limitation of result set size and can’t access certain data sources. However, it’s a great way for prospects to look at the functionality of the product and to build a proof-of-concept. The other great idea is that Denodo gives Express users a fixed time pricing offer for enterprise licensing. While not providing numbers, Suresh stated that the offer was working well as an incentive for the freeware to not be shelfware, for prospects to test and move down the sales funnel. To be blunt, I think that’s a great model.

One area they know is a weakness is in services, both professional services and support. That’s always an issue with a rapidly growing company and it’s good to see Denodo acknowledge that and talk about how they’re working to mitigate issues. The team said there are plans to expand their capital base next year, and I’d expect a chunk of that investment to go towards this area.

The final thing I’ll note specifically about Denodo’s presentation is their customer slides. That section had success stories presented by the customers, their own views. That was a strong way to show customer buy in but a weak way to show clear value. Each slide was very different, many were overly complex and most didn’t clearly show the value they achieved. It’s nice, but customer stories need to be better formalized.

Data Virtualization as a Market

As pointed out above, in the description of virtualization, it’s a very valuable tool. The market question is simple: Is that enough? There have been plenty of tools that eventually became part of a larger market or a feature in a larger product offering. What about data virtualization?

As the Denodo team seems to admit, data virtualization isn’t a market that can stand on its own. It must integrate with other data access, storage and provisioning systems to provide a whole to companies looking to better understand and manage their businesses. When there’s a new point solution, a tool, partnerships always work well early in the market. Denodo is doing a good job with partners to provide a robust solution to companies; but at some point bigger players don’t want to partner but to provide a complete solution.

That means data virtualization companies are going to need to spread into other areas or be acquired. Suresh Chandrasekaran thinks that data virtualization is now at the tipping point of acceptance. In my book, given how fast the software industry, in general, and data infrastructure markets, in particular, grow and evolve, that leaves a few years of very focused growth before the serious acquisitions happen – though I wouldn’t be surprised if it starts sooner. That means companies need to be looking both at near term details and long term changes to the industry.

When I asked about long term strategy, I got the typical startup answer: They’re focused on internal growth rather than acquisition (either direction). That’s a good external message because folks who want a leading edge company want it clear that they’re using a leading edge company, but I hope the internal conversations at the CxO level aren’t avoiding acquisition. That’s not a failure, just a different version of success.

Summary

Denodo is a strong technical company focused on data virtualization in the short run. They have a very nicely scaled model from Denodo Express to their full product. They seem to understand their sweet spot within IT organizations. Given that, any large organization looking to get better access to disparate sources of data should talk with Denodo as part of their evaluation process.

My only questions are in marketing messages and whether or not Denodo be able to change from a technical sales to a higher level, clearer vision that will help them cross the chasm. If not, I don’t think their product is going anywhere, someone will acquire them. Regardless, Denodo seems to be a strong choice to look at to address data access and integration issues.

Data virtualization is an important niche, the questions remain as to how large is the niche and how long it will remain independent.

Data Governance and Self-Service Business Intelligence: History Repeating?

1 Reply

Self-Service BI is a big buzz phrase these days even though many definitions exist. However, one thing is clear: It’s driving another challenge in the area of data governance. While people are starting to talk about this, it’s important to leverage what we’ve learned from the past. Too many technology industry folks are so enamored by the latest piece of software or hardware that they convince themselves their solutions are so new they are revolutionary, “have no competitors” or otherwise rationalize context. However, the smart people won’t do that.

A Quick History Lesson: The PC

In 1982, I was an operator at one of Tymshare’s big iron floors. It was a Sunday and I was reading my paper sitting at the console of an IBM 370/3033, their top of the line business computer. An article in the business section was an article titled something like “IBM announces their 370 on a chip.” I looked up at my behemoth, looked back at the article and new things would change.

Along came the PC. Corporate divisions and departments frustrated at not getting enough resources from the always understaffed, under financed and overburdened IT staff jumped on the craze. Out with the IBM Selectric and in with the IBM AT and its successors and clones.

However, by the end of the decade and early in the 1990s, corporate executives realized they had a problem. While it was great that each office was becoming more productive, the results weren’t as helpful. It’s a lot harder to roll-up divisional sales data when each territory has a slightly different definition of their territories, lead and funnels. It’s hard to make manufacturing budget forecasts when inventory is stored in different formats and might use different aging criteria. It’s hard to show a government agency you’re in regulatory compliance when the data in in multiple and non-integrated systems.

Data governance had been lost. The next twenty years saw the growth of client server software such as that by Oracle and SAP, working to link all offices to the same data structures and metadata while still working to leave enough independence. That balance between centralized IT control and decentralized freedom of action is still being worked out but is necessary.

While the phrase “single version of truth” is often mistakenly applied to mean a data warehouse and a “single source of truth,” that’s not what it means. A single version of the truth means shared data and metadata that ensures that all parties looking at the same data come up with the same information – if not the same conclusions from that information.

Now: Self-Service BI

Look at the history of the BI market. There have always been reports. With the advent of the PC, we had the de facto standard of Crystal Reports for a generation. Then, as the growth of packaged ERP, CRM, SFA and other systems came along, so did companies such as Cognos and Business Objects to focus on more complex analysis. However, they were still bound by the client/server model that was tied primarily to mid-tier Unix servers and Microsoft/Apple PCs.

What’s changed now are the evolution of the internet into the Cloud and phones into smartphones and tablets. Where divisions and departments were once leashed to big iron and CICS screens, divisions who have been more recently tied to desktops are feeling their oats and interested in quickly developing their own applications that allow their knowledge workers to access information while not seated in the office.

Self-Service BI (And, no, I’m not going to make an acronym as many have. Don’t we have enough?) is the PC of this decade. It’s letting organizations get information to people without waiting for IT, who’s still underfunded, understaffed and overburdened, distribute information widely. Alas, that wide distribution comes without controls and without audit trails. Data governance is again being challenged.

I’ve listened to a number of presentations by vendors to the BBBT, and there is hope. Gone are the days when all BI companies talked about was in helping business people avoid using IT. There’s more talk about metadata, more interest in security and access control, and a better ability to provide audit trails. There’s an understanding that it’s great to allow every knowledge worker to look at the data and understand those pieces of information arising that address their needs while still ensuring that the base data is consistent and metadata is shared.

Summary

We can learn from history. The PC was a great experiment in watching the pendulum swing from almost complete IT control to almost no IT control then back to a more reasonable middle. The BI community shows signs of learning from history and making a much faster switch to the middle ground. That’s a great thing.

Technologists working to help businesses improve performance through data, BI and analytics need to remember the great quote from Daniel Patrick Moynihan, “Everyone is entitled to his own opinion, but not his own facts.”