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DBTA Webinar: Cloud Data Warehousing Simplified

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A recent DBTA webinar was on how the data warehouse is still with us. It was by Sarah Maston, Developer Advocate, IBM Cloud Services. Simply put, it was a pitch for IBM and how their data warehousing solutions can help people more easily move to the cloud. Sarah was very knowledgeable, but she’s one of the smart folks I do suggest gets a class in presentation skills. IBM must have them and it would help her be even more powerful in her talks.

The core of the presentation was talking about how dashDB, IBM’s columnar, MPP database is perfect for data warehousing and how you can easily move information to it. Being at IBM, she had no hesitation talking about the big, visible name in Cloud: Amazon. Her claim is that IBM Cloudant is a much more powerful and agile tool for loading dashDB than is Amazon DynamoDB for Amazon Redshift. From my decades of high tech, I can believe it. IBM’s challenge is going to be whether or not they can communicate to the SMB market in ways they want to hear. That’s been a regular challenge for IBM.

One of the most interesting things Ms. Maston discussed was how to get information from systems into the data warehouse. A she said, in reference to IBM Bluemix, “meet the ODS.” I’ve previously said similar things and think it’s important to not forget the importance of the operational data store.

Data warehousing is not going away, it’s evolving. So too is the ODS. IBM is a company that often looks ahead very clearly but then sometimes misses the messaging. From the presentation, I see all the pieces are there, it’s early and they’ll grow, but it remains to be seen if they’ll learn how to address the market properly to get a major chunk of the business at which they’re aiming.

IBM, you BM, we all BM for … Spark!

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IBM at BBBT

A recent presentation by IBM at the BBBT was interesting. As usual, it was more interesting to me for the business information than the details. As unusual, they did a great job in a balanced presentation covering both. While many presentations lean too heavily in one direction or the other, this one covered both sides very well.

The main presenter was Harriet Fryman, VP of Marketing, IBM Analytics Platform. Adding information during the presentation were Steven Sit, Director of Product Management, Open Source Based Analytics Systems, and Steve Beier, Program Director, Spark Technology Center.

The focus of the talk was IBM’s commitment to Apache Spark. Before diving deep into the support, Ms. Fryman began by talking about business’ evolving data needs. Her key point is that “we all do data hording,” that modern technologies are allowing us to horde far more data than ever before, and that better ways are needed to get value out of the data.

She then proceeded to define three key aspects of the growth in analytics:

  • Applying analytics in more parts of business.
  • Understand the time value of data.
  • The growth of machine learning and cognitive systems.

The second two overlap, as the ability to analyze large volumes of data in near real-time means a need to have systems do more analysis. The following slide also added to IBM’s picture of the changing focus on higher level information and analytics.IBM slide - evolving approach to data

The presentation did go off on a tangent as some analysts overthought the differences in the different IBM groups for analytics and for Watson. Harriet showed great patience in saying they overlap, different people start with different things and internal organizational structures don’t impact IBM’s ability to leverage both.

The focus then turned back to Spark, which IBM sees as the unifying layer for data access. One key issue related to that is the Spark v Hadoop debate. Some people seem to think that Spark will replace Hadoop, but the IBM team expressed clear disagreement. Spark is access while Hadoop is one data structure. While Hadoop can allow for direct batch processing of large jobs, using Spark on top of Hadoop allows much more real time processing of the information that Hadoop appropriately contains.IBM slide - Spark markitechture

One thing on the slide that wasn’t mentioned but links up with messages from other firms, messages which I’ve supported, is that one key component, in the upper left hand corner of the slide, is Spark SQL. Early Hadoop players were talking about no-SQL, but people are continuing to accept that SQL isn’t going anywhere.

Well, most people. At least fifteen minutes after this slide was presented, an attending analyst asked about why IBM’s description of Spark seemed to be similar to the way they talk about SQL. All three IBM’ers quickly popped up with the clear fact that the same concepts drive both.

While the team continued to discuss Spark as a key business imitative, Claudia Imhoff asked a key question on the minds of anyone who noticed huge IBM going to open source: What’s in it for them? Harriet Fryman responded that IBM sees the future of Spark and to leverage it properly for its own business it needed to be part of the community, hence moving SystemML to open source. Spark may be open source, but the breadth and skills of IBM mean that value added applications can be layered on top of it to continue the revenue stream.

Much more detail was then stated and demonstrated about Spark, but I’ll leave that to the more technical analysts and vendor who can help you.

One final note put here so it didn’t distract from the main message or clutter the summary. Harriet, please. You’re a great expert and a top marketing person. However, when you say “premise” instead of “premises,” as you did multiple times, it distracts greatly from making a clear marking message about the cloud.

Summary

IBM sees the future of data access to be Apache Spark. Its analytics group is making strides to open not only align with open source, but to be an involved player to help the evolution of Spark’s data access. To ignore IBM’s combined strength in understanding enterprise business, software and services is to not understand that it is a major player in some of the key big data changes happening today. The IBM Spark initiative isn’t a marketing ploy, it’s real. The presentation showed a combination of clear business thought and strategy alongside strong technical implementation.

Rocket Software at BBBT: A Tale of Two Products

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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.

IBM and the Cloud? Don’t write it off

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At today’s investor meeting, IBM execs announced a target of $40 billion in revenue for cloud, analytics, mobile, social and security software by 2018. I’ve expect to see folks talk about dinosaurs not being able to turn fast enough and predicting failure to meet that goal. I don’t know if they can do it, but to make such ardent predictions you’d have to ignore history.

Mid-sized Unix servers came along and folks talked about IBM going away.

IBM blew a chance to own PC industry and the same predictions followed them.

Linux? Freeware was going to destroy the mainframe. Oops, Linux partitions run on mainframes.

Now we know the large growth of the cloud. Much of it has been on commodity boxes. However, as data gets larger, analytics more powerful and networks become more robust, there’s clearly space for a company with such a strong history in hardware, services and adapting to changes.

After all, too many people still think of IBM as a hardware company. While it’s too early for the 2014 report, you can check the 2013 Annual Report and check page 7. Look at what a tiny percentage of the bar is hardware. Software and services are fairly even in splitting the vast majority of the revenue stream.

It’s a strong goal and will take a lot of pushing. How many politely phrased “re-orgs” will happen to lay off staff? Who knows? Will they succeed? No clue. All I expect is that they’ll continue to grow and nobody should count them out.

Webinar: IBM, Actuate and Cirro describe faster analytics

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Today a webinar was hosted by Database Trend and Applications. While there are important things to talk about, I’ll start with the amusing point of the inverse relationship between company size and presenter title found in every webinar, but wonderfully on display here. The three presenters were:

  • Mark Theissen, CEO, Cirro
  • Peter Hoopes, VP/GM, BIRT Analytics Division, Actuate
  • Amit Patel, Program Director, Data Warehouse Solutions Marketing, IBM

The topic was “Accelerating your Analytics for Faster Insights.” That is a lot to cover in less than an hour, made more brief by a tag team of three people from different companies. I must say I was pleasantly surprised with how well they integrated their messages.

Mark Theissen was up first. There were a lot of fancy names for what Cirro does, but think ETL as it’s much easier. Mark’s point is that no single repository can handle all enterprise data even if that made sense. Cirro’s goal is to provide on-demand distributed analytics, using federation to link multiple data sources in order to help businesses analyze more complete information. It’s a strong point people have forgotten in the last few years during the typical “the latest craze will solve everything” focus on Hadoop and minimizing the role of getting to multiple sources.

Peter Hoopes then followed to talk about doing the analytics. One phrase he used should be discussed in more detail: “speed wins.” So many people are focused on the admittedly important area of immediate retail feedback on the web and with mobile devices. There, yes, speed can win. However, not always. Sometimes though helps too. That’s one reason why complex analysis for high level business strategy and planning is different that putting an ad on a phone as you walk by a store. There are clear reasons for speed, even in analytics, but it should not be the only focus in a BI decision.

IBM’s Amit Patel then came on to discuss the meat of the matter: DB2 Blu. This is IBM’s foray into in-memory, columnar databases. It’s a critical ad to the product line. There are advantages to in-memory that have created a need for all major players to have an offering, and IBM does the “me too!” well; but how does IBM differentiate itself?

As someone who understands the need for integration of transaction and analytic systems and agrees both need to co-exist, I was intrigued by what Amit had to say. Transactions going into normal DB2 environment while being shadowed into columnar BLU environment to speed analytics. Think about it: Transactions can still be managed with the row-oriented technologies best suited for them while the information is, in parallel, moved to the analytics database that happens to be in memory. It seems to be a good way to begin to blend the technologies and let each do what works best.

For a slightly techhie comment, I did like what Mr. Patel was saying about IBM’s management of memory and CPU. After all, while IBM is one of the largest software vendors in the world, too many folks forget their hardware background. One quick mention in a sentence about “hardware vendors such as Intel and IBM…” was a great touch to add a message that can help IBM differentiate its knowledge of MPP from that of pure software companies. As a marketing guy, I smiled big time at the smooth way that was brought up.

Summary

The three presenters did a good job in pointing out that the heterogeneous nature of enterprise data isn’t going away, rather it’s expanding. Each company, in its own way, put forward how it helps address that complexity. Still, it takes three companies.

As the BI market continues to mature, the companies who manage to combine the enterprise information supply chain components most smoothly will succeed. Right now, there’s a message being presented by three players. Other competitors also partner for ETL, data storage and analytics. It sounds interesting, but the market’s still young. Look for more robust messages from single vendors to evolve.

TDWI and IBM on Predictive Analytics: A Tale of Two Focii

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Usually I’m more impressed with the TDWI half of a sponsored webinar than by the corporate presentation. Today, that wasn’t the case. The subject was supposed to be about predictive analytics, but the usually clear and focused Fern Halper, TDWI Research Director for Advanced Analytics, wasn’t at her best.

Let’s start with her definition of predictive analytics: “A statistical or data mining solution consisting of algorithms and techniques that can be used on both structured and unstructured data to determine outcomes.” Data mining uses statistical analysis so I’m not quite sure why that needs to be mentioned. However, the bigger problem is at the other end of the definition. Predictive analysis can’t determine outcomes but it can suggest likely outcomes. The word “determine” is much to forceful to honestly describe prediction.

Ms. Halper’s presentation also, disappointingly compared to her usual focus, was primarily off topic. It dealt with the basics of current business intelligence. There was useful information, such as her referring to Dave Stodder’s numbers showing that only 31% of surveyed folks say their businesses have BI accessible to more than half their employees. The industry is growing, but slowly.

Then, when first turning to predictive analytics, Fern showed results of a survey question about who would be building predictive analytics. As she also mentioned it was a survey of people already doing it, there’s no surprise that business analysts and statisticians, the people doing it now, were the folks they felt would continue to do it. However, as the BI vendors including better analytics and other UI tools, it’s clear that predictive analytics will slowly move into the hands of the business knowledge worker just as other types of reporting have.

The key point of interest in her section of the presentation was the same I’ve been hearing from more and more vendors in recent months: The final admission that, yes, there are two different categories of folks using BI. There are the technical folks creating the links to sources, complex algorithms and reports and such, and there are the consumers, the business people who might build simple reports and tweak others but whose primary goal is to be able to make better business decisions.

This is where we turn to David Clement, Product Marketing Manager, BI & Predictive Analytics, IBM, the second presenter.

One of the first things out of the gate was that IBM doesn’t talk about predictive analytics but about forward looking business intelligence. While the first thought might be that we really don’t need yet another term, another way to build a new acronym, the phrase has some interesting meaning. It’s no surprise that a new industry where most companies are run by techies focused on technology, the analytics are the focus. However, why do analytics? This isn’t new. Companies don’t look at historic data for purely nostalgic reasons. Managers have always tried to make predictions based on history in order to better future performance. IBM’s turn of phrase puts the emphasis on forward looking, not how that forward look is aided.

The middle of his presentation was the typical dog and pony show with canned videos to show SPSS and IBM Cognos working together to provide forecasting. As with most demos, I didn’t really care.

What was interesting was the case study they discussed, apparel designer Elie Tahari. It’s a case study that should be studied by any retail company looking at predictive analytics as a 30% reduction of logistics costs is an eye catcher. What wasn’t clear is if that amount was from a starting point of zero BI or just adding predictive analytics on top of existing information.

What is clear is that IBM, a dinosaur in the eyes of most people in Silicon Valley and Boston, understands that businesses want BI and predictive analytics not because it’s cool or complex or anything else they often discuss – it’s to solve real business problems. That’s the message and IBM gets it. Folks tend to forget just how many years dinosaurs roamed the earth. While the younger BI companies are moving faster in technology, getting the ears of business people and building a solution that’s useful to them matters.

Summary

Fern Halper did a nice review of the basics about BI, but I think the TDWI view of predictive analytics is too much industry group think. It’s still aligned with technology as the focus, not the needs of business. IBM is pushing a message that matters to business, showing that it’s the business results that drive technology.

Businesses have been doing predictive analysis for a long time, as long as there’s been business. The advent of predictive analytics is just a continuance of the march of software to increase access to business information and improve the ability for business management to make timely and accurate decisions in the market place. The sooner the BI industry realize this and start focusing less on just how cool data scientists are and more on how cool it is for business to improve performance, the faster adoption of the technology will pick up.