Author Archives: David Teich

Splunk at BBBT: Messages Need to Evolve Too

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Our presenters last Friday at the BBBT were Brett Sheppard and Manish Jiandani from Splunk. The company was founded on understanding machine data and the presentation was full of that phrase and focus. However, machine data has a specific meaning and that’s not what Splunk does today. They speak about operational intelligence but the message needs to bubble up and take over.

Splunk has been public since 2012 and has over 1200 employees, something not many people realize. They were founded in 2004 to address the growing amount of machine data and the main goal the presenters showed is to “Make machine data accessible, usable and valuable to everyone.”

However, their presentation focused on Splunk’s ability to access IVR (Interactive Voice Recorder) and twitter transcripts and that’s not machine data. When questioned, they pointed out that they don’t do semantic analysis but focus on the timestamp and other machine generated data to understand operational flow. Still, while you might stretch and call that machine data, they did display doing some very simple analytics on the occurrence of keywords in text and that’s not it.

It’s clear that Splunk has successfully moved past pure machine data into a more robust operational intelligence solution. However, being techies from the Bay Area, it seems they still have their focus on the technology and its origins. They’re now pulling information from sources other than just machines, but are primarily analyzing the context of that information. As Suzanne Hoffman (@revenuemaven), another BBBT member analyst, pointed out during the presentation, they’re focused on the metadata associated with operational data and how to use that metadata to better understand operational processes.

Their demo was typical, nothing great but all the pieces there. The visualizations are simple and clear while they claim to be accessible to BI vendors for better analytics. However, note that they have a proprietary database and provide access through ODBC and an API. Mileage may vary.

There was also a confusing message in the claim that they’re not optimized for structured data. Machine data is structured. While it often doesn’t have clear field boundaries, there’s a clear structure and simple parsing lets you know what the fields and data are in the stream. What they really mean is it’s not optimal for RDBMS data. They suggest that you integrate Splunk and relational data downstream via a BI tool. That makes sense, but again they need to clarify and expose that information in a better way.

And then there’s the messaging nit. While talking about business as my main focus, technology presented with the incorrect words jars the educated audience. Splunk is not the first company nor will it, sadly, be the last, to have people who are confused about the difference between “premise” and “premises.” However, usually it’s only one person in a presentation. The slides and both presenters showed a corporate confusion that leads me to the premise that they’re not aware of how to properly present the difference between Cloud and on-premises solutions.

Hunk: On the Hadoop Bandwagon

Another messaging issue was the repeated mention of Hunk without an explanation. Only later in the presentation, they focused on it. Hunk’s their product to put the Splunk Enterprise technology on a Hadoop database. Let me be clear, it’s not just accessing Hadoop information for analysis but moving the storage from their proprietary system to Hadoop.

This is a smart move and helps address those customers who are heavily invested in Hadoop and, at least at the presentation level, they have a strong message about having the same functionality as in their core product, just residing on a different technology.

Note that this is not just helping the customer, it helps Splunk scale their own database in order to reach a wider range of customers. It’s a smart business move.

Security, Call Centers and Changing the Focus

The focus of their business message and a large group of customer slides is, no surprise, on network security and call center performance. The ability to look at the large amount of data and provide analysis of security anomalies means that Splunk is in the Gartner Magic Quadrant for SIEM (Security Information and Event Management).

In addition, IVR was mentioned earlier. That combined with other call center data allows Splunk to provide information that helps companies better understand and improve call center effectiveness. It’s a nice bridge from pure machine data to a more full featured data analysis.

That difference was shown by what I thought was the most enlightening customer slide, one about Tesco. For my primarily US readers, Tesco is a major grocery chain, with divisions focused on everything from the corner market to supermarkets. They are headquartered in England, are the major player in Europe and the second largest retailer by profit after Walmart.

As described, Tesco began using Splunk to analyze network and website performance, focused on the purely machine data concerns for performance. As they saw the benefit of the product to more areas, they expanded to customer revenue, online shopping cart data and other higher level business functions for analysis and improvement.

Summary

Splunk is a robust and growing company focused on providing operational intelligence. Unfortunately, their messaging is lagging their business. They still focus on machine data as the core message because that was their technical and business focus in the last decade. I have no doubts that they’ll keep growing, but a better clarification of their strategy, priorities and messages will help a wider market more quickly understand their benefits.

Datameer at BBBT: The Chasm and the Niche

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Datameer showed up at the BBBT last Friday and it was interesting. The presenters were Stefan Groschupf, CEO, and Azita Martin, CMO. Stefan worked on Nutch project out of which Hadoop was born and he had a refreshingly non-open standard addicted viewpoint of the industry. He very clearly pointed out that Hadoop was big, slow and great for analyzing gathered information but not for speed. He also pointed out that RDBMS’s aren’t going anywhere. The most accurate and humorous thing I’ve heard for a while in a presentation is “hadumping,” Groschupf’s term for getting information into Hadoop.

On the UI front, they’ve picked and stuck with a very spreadsheet oriented view of the data, pointing out that’s what everyone knows so it’s easier to leverage the technology into companies and departments who have been using spreadsheets for years. Yet later he knocked SQL with Hadoop without realizing that’s the same thing. Even worse, he’d already pointed out just how expensive Hadoop programmers are to hire and seemed to miss that hire a few of them and then more affordable SQL workers, of whom there are many more than Hadoop experts, for the high level analytics might make sense.

Meanwhile, while the spreadsheets are familiar, modern BI is providing graphics. The interface they showed is very simplistic and needs work. While the individual graphics weren’t impressive, I did like that they provide a much more naturally looking dashboard paradigm that doesn’t lock in images and allows people to visualize context far more. That was showed in the demo and I didn’t get a good screen shot, but it’s a nice differentiator.

The slide that most impressed me, coming from a small company, was in Azita Martin’s section and presented the customer journey from Datameer’s perspective.

Datameer Customer Journey slide

Datameer – Customer Journey

It shows that management is thinking about the customer not just “how do I find somebody to fit the cool thing I’ve built?” They’re a small company thinking strategically. The question is: How strategically?

You were wondering when I’d mention the Chasm?

Stefan Groschupf mentioned Moore’s Chasm and that the industry is moving across it. However, given his admission of Hadoop’s message, I’m not sure that he has enough of a market for it to do the same.

Datameer is focused on analyzing large data sets to find relationships for things that have happened. You might get predictive information out of it. In fact, you hope to do so. However, this is not real-time analytics and isn’t meant to be. I think they have a good product for what they’re marketing. However, as big as that market is, it’s a niche. The growth in BI is moving towards meshing backwards looking, historical data with real-time information (regardless of the wide differences in the meaning of that word within difference industries and user parameters) and providing predictive analytics that can impact decisions that have immediate impact.

While BI is crossing the chasm, I think that analyzing large datasets will have a product life cycle and not a market life cycle. People will continue to want it but they’ll want it as part of a larger solution.

For instance, they briefly talked about enhancing operational analytics but much of what that market is doing, whether in hospitals, the transportation industry, oil fields or elsewhere is demanding faster analytics to find problems before they happen and take proactive actions. Slower analytics will help, but as a subset.

So what’s that mean for Datameer?

Summary

Datameer management has a great understanding of Hadoop’s strengths and weaknesses and they’ve done a good job of focusing on the strengths to create a company with a great short term future. Looking at the larger market, however, makes me think of two words: Acquisition bait.

Many founders think that success is only in terms of making it to IPO. Many others, thankfully, have a more open view to other exit strategies. Acquisition is nothing to be ashamed of and another great sign of success. If Datameer keeps focusing on their niche they’re going to build a strong customer base with a good technology that will fit somebody’s needs for enhancing an overall BI portfolio. There’s nothing wrong with that.

If you’re looking for a company to help you better understand large and diverse datasets, you should be talking with Datameer.

Datawatch at BBBT: Another contender and another question of message

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Yesterday’s presentation to the BBBT was by Datawatch personnel Ben Plummer, CMO, and Jon Pilkington, VP Products. As they readily admit, they’re a company with a long history about which most people in the industry have never heard. They were founded in the 1980s and went public in the 1990s. Their focus is data visualization, but much of their business has been reseller and OEM agreements with companies including SAP, IBM and Tibco.

The core of their past success was with basic presentation of flat file information through their Monarch product. It was only with the acquisition of and initial integration with Panopticon in 2013, providing access to far more unstructured data that they rebranded as data visualization and began to push strongly into the BI space.

The demo was very standard. Everyone wants to show their design interface and how easy it is to build dashboards. Their demonstration was in the middle of the pack. The issue I had was the messaging. It’s no surprise that everyone claiming to be a visualization company needs to show visualization, but if you’re not one of the very flashy companies, your message about building your visualization should be different.

Datawatch’s strengths seem to be two-fold:

  • Access a very wide variety of data sources.
  • Access in motion data.
  • Full service from data access to presentation.

While Ben’s presentation talked about the importance of the Internet of Things and that real-time data is transactional, Jon’s presentation didn’t support those points. Datawatch is another company working to integrate structured and non-structured data and they seem to have a good focus on real-time, those need to be messages throughout their marketing, and that means in the demo.

Back from that tangent to the mainline. The third point is a major key. Major ETL and data warehouse vendors aren’t going away, but for basic BI, it adds costs and time to have to look at both and ETL and a data visualization tool which may not work together as the demoware indicates (A surprise, I know…). The companies who can get the full stream data supply chain from source to visualization can much more quickly and affordably add value for the business managers wanted better BI. I know it’s a fine line in messaging that and still working with vendors who overlap somewhat, but that’s why Coopetition was coined.

They seem to have a good vision but they haven’t worked to create a consistent and differentiated message. That could be because of resources and hopefully that will change. In February of this year Datawatch issued a common stock offering that netted them more cash. Hopefully some of that will be spent to focus on created strong and consistent marketing. That also includes such simple things as changing press releases to be visible from the PR link as html, not just pdfs.

Summary

I know you’re getting tired of hearing the following refrain, but here it is again. The issue is that I’ve heard this message before. The market is getting crowded with companies trying to support modern BI that’s a blend of structured and unstructured data. Technologists love to tweak products and think that minor, or even major technical issues that aren’t visibly relevant to the market should sell the product all by themselves. Just throw some key market points on top of them and claim you have no competitors because your technology is so cool.

BI and big data are cool right now and there are a large number of firms attempting to fill a need. Datawatch seems to have the foundations for a good, integrated platform from heterogeneous data access to visual presentation of actionable information. That message needs to quickly become stronger and clearer. This is a race. Being in shape isn’t enough, you have to have the right strategy and tactics to win the race. Datawatch has a chance, will they stumble or end up on the podium?

Cisco Composite at BBBT: Supporting the Internet of Things

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Last Friday’s BBBT presentation was by Cisco Composite. Composite Software was a company in the data virtualization space until it was bought by Cisco last year. The initial problem is what is it now? My internet search bubbles up two pages:

  • The old company’s site which looks like it hasn’t been updated since last year. There’s no copyright year at the bottom and the news page includes articles only through 2013.
  • The Cisco page which has nothing of use on it.

The presenters assumed folks at the BBBT knew who they were because of a previous presentation, but the group’s grown a lot in the last year. The people should have started with a basic overview of product and company. Bob Eve gave an introduction, but it was very brief and made too many assumptions. Composite was in the business of providing access to disparate data sources in a way that allowed business applications to leverage that data into information and then insight. Think of them as ETL without the L. So why did Cisco buy them?

The confusion was slowly cleared up over the course of the presentation and Q&A. The data visualization tool is being incorporated into Cisco to help create a full solution offering for the Internet of Things (IOT). Mike Flannagan, General Manager, Data and Analytics Business Group, showed a good slide that I think is too crowded to really show in the blog format, but provided three different layers for Cisco’s strategy for delivering enterprise solutions. From bottom to top, they are: Ready to build, ready to integrate and ready to consume. From network management components at the bottom to solution sets such as Connected City at the high level, it’s a well thought out approach for a major organization to provide flexibility and control across markets.

Mr. Flannagan’s key point is that that IOT means the amount of data flowing through networks has massively increased and will continue to do so, and that edge devices, the network and the applications they support all have to adapt to meet the changing environment. While that might be obvious, there’s an old saying that common sense isn’t. It’s good to see the Cisco is looking at the full range from edge to application, and not just concentrating on the network.

Jim Green, former CEO of Composite and now CTO, Data and Analytics Business Group, Cisco, gave an NDA presentation. I can’t wait until some of it is announced because a key part is very interesting, so I’ll talk about it at some point – just not today.

Kevin Ott, General Manager, Data Virtualization Business Unit, focused on the massive amounts of data sources that have to be comingled in an intelligent fashion. He has one of the best graphics I’ve seen to show that, displaying just some of the no-SQL technologies in the market today.

Cisco no-SQL market graphic

Some no-SQL options…

However, then he brought up something that made my marketing mind shudder. Kevin rightfully pointed out that there are lots of clouds, not just the Cloud that’s marketing. Individual sets of servers inside and outside corporate firewalls. He posited that some term in needed to refer to the entire set of networked things and came up with Intercloud. What?!?!? We have had this thing called the internet for quite a while now. Back when we old client server folks were drawing clouds to represent the internet while designing systems, we knew that the cloud referred to the internet. There’s no need for a new term. I know many in marketing love to invent words, but there’s just no need in this case.

Summary

The network is a key component in today’s information infrastructure. The explosion of Cloud applications combined with the supernova of edge device growth in the Internet of Things means that a networking company must adapt if it is to help the industry and itself. While the key component of Cisco’s plan to do so is not something I can yet discuss, and while the presentation was a bit disjointed and some concepts still need clarification, I can state that it seems as if Cisco knows what it’s doing in this arena.

Their own problem seems to be they don’t yet have their message together and coherent. Hopefully that will be fixed in the near future, and I’ll watch it carefully. The right mindset and solution are necessary, but not sufficient. They need to communicate better.

Cisco Composite is on the right path to helping Cisco build a strong and holistic set of offerings to help us manage the information explosion across the internet, from edge devices to the BI applications for the business knowledge workers.

NuoDB at the BBBT: Another One Bringing SQL to the Cloud

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Today’s presentation in front of the BBBT was by NuoDB’s CTO, Seth Proctor. NuoDB is a small company with big investments. What makes them so interesting? It’s the same thing as in many of the other platform presenters at the BBBT. How do we get real databases in the Cloud?

Hadoop is an interesting experiment and has clearly brought value to the understanding of massive amounts of unstructured data. The main value, though, remains that it’s cheap. The lack of SQL means it’s ok for point solutions that don’t stress its performance limitations. Bringing enterprise database support to the cloud is something else.

The main limitation is that Hadoop and other unstructured databases aren’t able to handle transactional systems while those still remain the major driver in operating businesses.

NuoDB has redesigned the database from the ground up to be able to run distributed across the internet. They’ve created a peer-to-peer structure of processes, with separate processes to manage the database and SQL front end transaction issues.

Seth pointed out that they ““Have done nothing new, just things we know put together in a new way.” He also pointed out they have patents. My gripe about patents for software is an issue for another day, but that dichotomous pairing points to one reason (Apple’s patent on a rounded rectangle is another example of the broken patent system, but off the soap box and onwards…).

It’s clear that old line RDMS systems were designed on major, on-premise servers. The need for a distributed system is clear and NuoDB is on the forefront of creating that. One intriguing potential strength, one about which there wasn’t time to discuss in the presentation, is a statement about the object-oriented structure needed for truly distributed applications.

Mr. Proctor stated that the database schema is in object definitions, not hard coded into the database. He added that provides more flexibility on the fly. What it also could mean is that the schema isn’t restricted to purely RDBMS schemas and that future versions of their database could support columnar and even unstructured database support. For now, however, the basic ability to change even a standard row-based relational database on the fly without major impacts on performance or existing applications is a strong benefit.

As the company is young and focused on the distributed aspects of performance, it was also admitted that their system isn’t one for big data, even structures. They’re not ready for terabytes, not to mention petabytes of data.

The Business

That’s the techie side, but what about business?

The company is focused on providing support for distributed operational systems. As such, Seth made clear they haven’t looked at implementations supporting both operational and analytical systems. That means BI is not a focus and so the product might not be the right system for providing high level business insight.

In addition, while I asked about markets I mainly got an answer about Web sites. They seem to think the major market isn’t Global 1000 businesses looking for link distributed operational systems but that Web commerce sites are their sweet spot. One example referred to a few times was in transactional systems for businesses selling across a country or around the world. If that’s the focus, it’s one that needs to be made more explicit on their web site, which really doesn’t discuss markets in the least.

It’s also an entry into the larger financial markets space. It and medical have always been two key verticals for new database technologies due to the volumes of information. That also means they need to prioritize the admitted lack of large database support or they’ll hit walls above the SMB market.

The one business thing the bothers me is their pricing model. It’s based on the number of hosts. As the product is based on processes, there’s no set number of processes per host. In addition, they mentioned shared hosting, places such as AWS, where hosts may be shared by multiple of NuoDB’s customers or where load balancing might take your processes and have them on one host one day and multiple hosts the next.

Host base pricing seems to be a remnant of on-premises database systems that Cloud vendors claim to be leaving. In a distributed, internet based setup, who cares how big the host is, where the host is, or anything else about the host? The work the customer cares about is done by the processes, the objects containing the knowledge and expertise of NuoDB, not the servers owned by the hosting firm. I would expect that Cloud companies would move from processors to process.

Summary

NuoDB is a company focused on reinventing the SQL database for the Cloud. They have significant investment from the VC and business markets. However, it would be foolish to think that Oracle, IBM and other existing mainstream RDBMS vendors aren’t working on the same thing. What NuoDB described to the BBBT used most of the right words from the technology front and they’re ramping up their development based on the investments, but it’s too early to say if they understand their own products and markets enough to build a presence for the long term.

They have what looks like very interesting technology but, as I keep repeating in review after review, we know that’s not enough.

MapR and Skytree Webinar Shows the Need for Product Marketing

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Yesterday I listened to a webinar by MapR and Skytree supposedly on Hadoop and machine learning. As someone with a background in artificial intelligence (from way back in the 1980s…), I was interested in the topic title. However, to be very sadly blunt, I came away not having learned anything.

The Presentation

There seemed to be two major problems:

1)    They didn’t know who was their target audience.

2)    They don’t know presentations.

I couldn’t tell if they were focused on a technical or business audience.  After all, they didn’t start the presentation by explaining either predictive analytics or machine learning. The MapR guy who opened mentioned he was going to describe machine learning and then only showed a table showing how machine learning can be used in multiple use cases. The table did didn’t differ between historical and predictive analytics nor did it emphasize predictive analysis. However, he talked about MapR as if everyone should understand what it is and how it works, so that might have been aimed at a technical audience but without technical details.

After a bit more blather, he turned the presentation over to the Skytree presenter who opened with the statement that the company exists to “translate big data into actionable intelligence.” That is some differentiator…

While he also failed to give a good explanation for machine learning and how it differs from any other type of analytics, he at least had one good slide about his company’s focus.

Skytree market slide

Skytree for High-Value Problems

How they’re going to do that and how they’re different than their competitors? Well, as the old statement from textbooks says, that’s an exercise left to the student.

The Need for Product Marketing

The entire webinar failed and I, being the humble sort I am, know why. It was not vaguely technical enough for a technical audience nor specific enough for a business audience. They never clearly differentiated their products, sticking to very generic messages. That’s often a clear symptom of something missing from companies: Product marketing.

What they had was one technical guy from MapR and one supposedly marcom guy from Skytree, neither of whom understood how to position a solution in clear terms. The key job of product marketing is inherent in the two halves of the name, understand product and create accurate messages for the market. Unfortunately, smaller companies have founders working closely with development and sales, using marketing just to create basic collateral and presentations.

There doesn’t seem to be someone who knows the market the companies were really trying to hit or how to explain their differentiators to that market. That’s why the presentation spent lots of time on platitudes and almost none of a focused message to communicate differentiators.

There may be a good solution hidden in the partnership, but this presentation did nothing to show it.

Teradata Aster at the BBBT. Is a technology message sufficient?

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Last Friday’s visitors to the BBBT were from Teradata Aster. As you’ve noticed, I tend to focus on the business aspects of BI. Because of that, this blog entry will be a bit shorter than usual.

That’s because the Teradata Aster folks reminded me strongly of my old days before I moved to the dark side: They were very technical. The presenters were Chris Twogood, VP, Product and Services Marketing, and Dan Graham, Technical Marketing.

Chris began with a short presentation about Aster. As far as it got into marketing was pointing to the real problem concerning the proliferation of analytic tools and that, as with all platform products, Aster is an attempt to find a way to address a way to better integrate a heterogeneous marketplace.

As with others who have presented to the BBBT, Chris Twogood also pointed out the R and other open source solutions aren’t any more sufficient for a full BI solution managing big data and analytics that are pure RDBMS solutions, so that a platform has to work with the old and the new.

The presentation was then handed over to Dan Graham, that rare combination of a very technical person who can speak clearly to a mixed level audience. His first point was a continuation of Chris’, speaking to the need integrate SQL and Map Reduce technologies. In support of that, he showed a SQL statement he said could be managed by business analysts, not the magical data scientist. There will have to be some training for business analysts, but that’s always the case in a fast moving industry such as ours.

Most of the rest of the presentation was about his love of graphing. BI is focused on providing more visual reporting of highly complex information, so it wasn’t anything new. Still, what he showed Teradata focusing upon is good and his enthusiasm made it an enjoyable presentation even if it was more technical than I prefer. It also didn’t hurt that the examples were primarily focused on marketing issues.

The one about which I will take issue is the wall he tried to set between graph databases and the graph routines Aster is leveraging. He claimed they’re not really competing with graph databases which was, Dan posited, because they are somehow different.

I pointed out that whether graphs are created in a database, in routines layered on top of SQL or in Java, or were part of a BI vendor’s client tools only mattered in a performance standpoint, that they were all providing graphical representations to the business customer. That means they all compete in the same market. Technical distinctions do not make for business market distinction other than as technical components of cost and performance that impact the organization. There wasn’t a clear response that showed they were thinking at a higher level than technological differences.

Summary

Teradata has a long and storied history with large data. They are a respected company. The question is whether or not they’re going to adapt to the new environments facing companies with the explosion of data that’s primarily non-structured and having a marketing focus. Will they be able to either compete or partner with newer companies in the space.

Teradata is a company who has long focused on large data, high performance database solutions. They seem to clearly be on the right path with their technology and the implications are that they are in their strategic and marketing focus. They built their name focused on large databases for the few companies that really needed their solutions. Technology came first and marketing was almost totally technically focused on the people who understood the issue.

The proliferation of customer service and Web data mean that the BI market is addressing a much wider audience for solutions managing large amounts of data. I trust that Teradata will build good technology, but will they realize that marketing has to become more prominent to address a much larger and less technical audience? Only time will tell.

TDWI Webinar Review: Business- Driven Analytics. Where’s business?

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Today’s TDWI webinar was an overview of their latest best practices report. The intriguing thing was the numbers show that BI & Analytics still aren’t business driven. As Dave Stodder, Director of Research for Business Intelligence, pointed out, there are two key items contradicting that. First, more than half of companies have BI in less than 30% of the organization, pointing out that a large number of businesses aren’t prioritizing BI. Second, most of the responses to questions about BI show that it’s still something controlled and pushed by IT.

One point Dave mentioned was still the overwhelming presence of spreadsheets. They aren’t going away soon. A few vendors who have presented at the BBBT have also pointed out their focus at integrating spreadsheets rather than ignoring all the data that resides in them or demanding everything be collected in a data repository. The sooner more vendors realize they need to work with the existing business infrastructure rather than fight against it, the better off the industry will be.

Another interesting point was the influence of the CMO. I regularly read about analysts and others talking about how the “CMO has a bigger IT budget than the CIO!” The numbers from the TDWI survey don’t bear that out. One slide, a set of tables representing different CxO level positions’ involvement in different areas of the IT buying process show the CMO up near the CIO for identifying the need, but far behind in every other category – categories that include “allocate budget” and “approve budget.” In tech firms, and especially in Silicon Valley, people look around at other firms involved in the internet and forget they’re a small subset of the overall market.

Another intriguing point was brought out in the survey. Of companies with Centers of Excellence or similar groups to expand business intelligence, the list of titles involved in those groups shows an almost complete dearth of business users. It seems that IT still thinks of BI as a cool toy they can provide to users, not something that business users need to be involved in to ensure the right things are being offered. Only 15% show line of business management involved while a pathetic 4% show marketing’s involvement.

The last major point I’ll discuss is an interesting but flawed question/answer table. The question was on how the business-side leadership is doing during different aspects of a BI project. The numbers aren’t good. However, as we’ve just discussed, business isn’t included as much as they should be. There are two things that make me consider:

  • What would the pair of charts look like if the chart was split to look at how IT and business respondents each look at the question?
  • Is it an issue of IT not involving business or business not getting involved when opportunities are presented?

Summary

TDWI’s overview of the current state of business-driven BI & analytics seems to show that there’s a clear demand from the business community but there doesn’t seem to be the business involvement need to finish the widespread expansion of BI into most enterprises.

What I’d like to see TDWI focus on next is the barriers to that spread, the things that both IT and business see as inhibitors to expanding the role of modern BI tools in the business manager’s and CxO suite’s daily decision making.

It’s a good report, but only as a descriptive analysis of current state. It doesn’t provide enough information to help with prescriptive action.

TDWI & Actuate Webinar: Predictive Analytics for the Business Analyst

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Today’s TDWI webinar was a good one. Fern Halper is one of the few analyst who managed to speak to the points that are relevant to the sponsor, so that the full presentation works. Today was no different. Today’s sponsor presenter was Allen Bonde, VP, Product Marketing and Innovation, Actuate, and they made a good team.

The presentation was a good overview of the basics of predictive analytics. It started with an overly complex but accurate description by Fern as predictive analytics being “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 is an overused term that still manages to be too limiting, it doesn’t belong there. Neither does the description of different types of data as some predictive analytics, such as in operations, don’t necessarily need unstructured data. I’d just say it’s the analysis required to increase the ability to better understand the likelihood of near term outcomes.

What I really liked about both presenters is that they tended to use customer facing examples from sales, marketing and customer support to discuss predictive analytics. Too often, the focus is on operations, either at a detailed process level or a very high business review at the CxO level. The fact that there are many more applications that impact a larger body of business users is good for the market to see.

One thing that I think Ms. Halper didn’t quite think through was how decision trees, her favorite tool (at least as per this presentation) for predictive analytics. While she did briefly mention that there’s overlap between predictive analytics and other types of analysis, I’m not in agreement that trees fit the description – especially as her main example was customer support. In that arena, and others such as financial approvals, decision trees have long been used for call scripts and process flow. They aren’t used to make predictions but to help the service folks make decisions regardless of the outcome at each step. I’d like to hear more about how she thinks they tie into the other predictive tools she mentioned.

Another key point Fran made was how the new technology means that the tools are available closer to the actual business knowledge worker, with applications becoming useable by business analysts, not just statisticians. The numbers from TDWI Best Practices Report Predictive Analytics for Business Advantage, 2014 were interesting.

TDWI Harper predictive analytics tool use

TDWI Best Practices Report Predictive Analytics for Business Advantage, 2014

It was humorous, but no surprise, that the first question in Q&A was from somebody who probably defines him or herself as a “data scientist.” There was umbrage at mere business people being able to work with predictive analytics packages. Halper tried to allay the fears that the money spent on an MS might become useless by pointing out that detailed math is needed to create the processes and some understanding of the analytics is still needed to intelligently use the results of the analysis.

Still, I expect the numbers for analysts and “other business users” to grow in the near future while the statistician is more properly used to build the algorithms and think of new tools that can then be provided to the knowledge worker through modern tools.

Allen Bonde’s section of the presentation, unlike others who have been too technical or salesy, was too high level and didn’t differentiate enough from Fran Halper’s. While we want to see companies positioning themselves as thought leaders push concepts, they are the sponsor and need to tie the thoughts back to their business.

Let’s start with Actuate’s tagline: The BIRT company. What’s BIRT? A bleeding edge audience will know, but TDWI has a wide audience of knowledge in its audience, from open source to large enterprise, still almost exclusively proprietary software shops. He needed to give just a couple of sentences but he didn’t.

The Business Intelligence Reporting Tools (BIRT) project is an attempt to create an open source BI interface. It was started by Actuate who turned it over to a foundation to drive open development. As usual, for most of us, refer to the appropriate Wikipedia article for a brief overview.

His main point was, as he put it, “Fast is the new big.” Though the concept isn’t new and is rather just a return from the focus on misnamed Big Data to how to take whatever data we have, regardless of size, and shorten the time to analyze and provide that information to decision makers. Most of the rest of what he said also wasn’t new to people who have been in the BI industry for a while, but it was at a good level to reinforce Halper’s point to an audience who is just getting familiar with the state of the market.

Summary

Fran Halper and Allen Bonde gave a good, high level overview of some of the key points about predictive analytics. I had nits with some issue and think that a little more meat might help, but I suggest going to TDWI to view the webinar if you are someone who wants to know the basic issues needed to start using predictive analytics as part of a robust BI solution to help business make better decisions.

@fhalper, @abonde, @tdwi, @actuate

EXASOL at the BBBT: Big Data, fast database. Didn’t I just hear this?

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Friday’s EXASOL presentation to the BBBT brought a strong feeling of déjà vu. I’ve already blogged about the Tuesday Actian presentation and, to be honest, there were technical differences but I came to the same conclusion about the business model. But first, a thanks to Microsoft for the autocorrect feature. Otherwise typing EXASOL in all caps each time would have been bothersome.

The EXASOL presenters were Aaron Auld (@AaronAuldDE), CEO, and Kevin Cox (@KJCox), Director Sales and Marketing.

I mentioned technical differences. First, and foremost, they didn’t start with hardware but with an initial algorithm for massively parallel processing (MPP). They figured it was a great way to speed up database performance and stuck with columnar oriented relational technology. That’s allowed them to work on multi-terabyte systems with fast performance.

They have published some great TPC-H benchmark numbers, often being two orders of magnitude better than the competitors. While admitting that TPC stats are questionable since they’ve been defined by the big vendors to benefit their performance, often don’t reflect real life queries and often don’t use typical hardware, the numbers were still impressive. In addition, it was a smart business move as a small company blowing away the big vendors’ benchmarks helps elevate visibility and get them into doors.

However, let’s look back at Actian. They also talked about TPC, but they used the TPC-DS benchmark. How do you compare? Well, you can’t.

One other TPC factoid is, just like their competitor, there’s no clear information on true multi-user performance in today’s mobile age. No large numbers of connected clients was mentioned.

So results are great, but how do they fight the Hadoop bandwagon? They understand that open source is cheaper from a license standpoint, but also point out their performance saves in direct comparison when you total all costs for an implementation. People forget that while hardware prices have dropped, servers aren’t free.

Unfortunately, from a business model, it looks like they’re making the typical startup mistake of focusing on their product rather than business needs. They understand that ROI matters, but it seems to be too far down the list in their corporate messaging.

Another major advantage they have in common with the previous presenters is the sticking with SQL involves an easier build of ecosystem to include the existing vendors from ETL through visualization. However, they seem to be a bit further behind the curve in building those partnerships. While they have a strong strategic understanding of that, they need to bubble it up the priority list.

Exasol platform offering

One critical business success they have is their inclusion in the Dell Founders Club 50. That means advice and cooperation from Dell to help improve their performance and expand their presence. For a small company to have access not only to Dell at the technical level but also to bring customers to Dell Solution Centers for demonstrations is a great thing.

While they have been focused on MPP and large customers, the industry move to the Cloud also means they are looking at smaller licensing including a potential one-node free trial.

However, as mentioned in the lead, they seem to have the same business model issue as their competitors: They’re focused on the bleeding edge market who think the main message is performance. While they know there are other aspects to the buying decision, they went back, again and again, to performance. They have the whole picture in mind, but they’re not yet thinking of the mass market.

Organizations such as TDWI, Gartner and Forrester have all reported the high percentage of organizations that are considering big data and how to get a handle on the vast volume of information coming from heterogeneous sources. There’s clearly demand building up behind the dam. The problem seems to be they’re trying, as major IT organizations always do, to understand how best to integrate new technologies and capabilities with as little pain as possible. Meanwhile, the vendors seem to still be focused on the early adopters with their messaging. That leaves dollars on the table and slows adoption of new technology.

Summary

EXASOL seems to have a strongly performing and highly scalable database technology to work with large data sets. Yet, like many companies in the business intelligence space it comes back to audience. Are they still aiming at early adopters or will they focus on the mass market?

Have BI and big data advanced to the point where people need to think about the chasm and how to better address business needs not just technical issues. I think so, and I hope they adjust their business focus.

The company seems to have great potential, but will they turn that into reality? As the great Yogi Berra said, “It’s like deja vu all over again.”