Tag Archives: business intelligence

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.

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?

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.

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.”

Cloudera at the BBBT: The limits of Open Source as a business model

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Way back, in the dawn of time, there were ATT and BSD, with multiple flavors of each base type of Unix. A few years later, there were only Sun, IBM and HP. In a later era, there was this thing called Linux. Lots of folks took the core version, but then there were only Redhat and a few others.

What lessons can the Hadoop market learn from that? Mission critical software does not run on freeware. While open source lowers infrastructure costs and can, in some ways, speed feature enhancements, companies are willing to pay for knowledge, stability and support. Vendors able to wrap the core of open source up in services to provide the rest make money and speed the adoption of open-source based solutions. Mission critical applications run on services agreements.

It’s important to understand that distinction when discussing such interesting companies as Cloudera, whose team presented at last Friday’s BBBT session. The company recently received a well-publicized, enormous investment based on the promise that it can create a revenue stream for a database service based on Hadoop.

The team had a good presentation, with Alan Saldich, VP Marketing, pointing out that large, distributed processing databases are providing a change from “bringing data to compute” to “bringing compute to data.” He further defined the Enterprise Data Hub (EDH) as the data repository that is created in such an environment.

Plenty of others can blog in detail about what we heard about the technology, but I’ll give it only a high level glance. The Cloudera presenters were very open about their product being an early generation and they laid out a vision that seemed to be good. They understand their advantages are the benefits of Cloud and Hadoop (discussed a little more below) but that the Open Source community is lagging in areas such as access and control to data. It’s providing such key needs to IT that will help their adoption and provide a revenue stream, and their knowing that is a good sign.

I want to spend more time addressing the business and marketing models. Cloudera does seem to be struggling to figure out how to make money, hence the need more such a large investment from Intel. Additional proof is the internal confusion of Alan saying they don’t report revenues and then showing us only bookings, while Charles Zedlewski, VP Products, had a slide claiming they’re leading their market in revenue. Really? Then show us.

They do have one advantage, the Cloud model lends itself to a pricing model based on nodes and, as Charles pointed out, that’s a ““business model that’s inherently deflationary” for the customer.  Nodes get more powerful so the customers regularly get more bang for the buck.

On the other side, I don’t know that management understands that they’re just providing a new technology, not a new data philosophy. While some parts of the presentation made clear that Cloudera doesn’t replace other data repositories except for the operational data store, different parts implied it would subsume others without giving a clear picture of how.

A very good point was the partnerships they’re making with BI vendors to help speed integration and access of their solution into the BI ecosystem.

One other confusion that Cloudera, and the market as a whole, seems to be clearly differentiating that the benefits of Hadoop come from multiple technologies: Both the software that helps better manage unstructured data and simple hardware/OS combination that comes from massively parallel processing, whether the servers are in the Cloud or inside a corporate firewall. Much as what was said about Hadoop had to do with the second issue, and so the presenters rightfully got pushback from analysts who saw that RDBMS technologies can benefit from those same things and therefore minimizing that as a differentiator.

Charles did cover an important area of both market need and Cloudera vision: Operational analytics. The ability to quickly massage and understand massive amounts of operational information to better understand processes is something that will be enhanced by the vendor’s ability to manage large datasets. The fact that they understand the importance of those analytics is a good sign for corporate vision and planning.

Open source is important, but it’s often overblown by those new to the industry or within the Open Source community. Enterprise IT knows better, as it has proved in the past. Cloudera is a the right place at the right time, with a great early product, the understanding as to many of the issues that are needed in the short term. The questions are only about the ability to execute both on the messaging and programming sides. Will their products meet the long term needs of business critical applications and will they be able to explain clearly how they can do so? If they can answer correctly, the company will join the names mentioned at the start.