Dataversity Webinar Review: Data Modeling and Data Governance

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A recent Dataversity webinar was titled “Data Model is Data Governance.” All the right words were there but they were in the wrong order. The presenters were Robert S. Seiner, KIK Consulting, and David Hay, Essential Strategies. While Robert tried to push the title, David more accurately addressed the question “Is data modeling data governance?” Both he and I agree that the simple answer is no yet they overlap in important ways.

Data modeling is understand the data, and Mr. Hay described it in the context of business by referring to the Zachman Framwork, with the management’s overview of corporate information fitting Zachman’s first row then things diving deeper into technology as you move down the rows.

What I found interesting in a webinar with that title is the following definition slide:Webinar - Data governance - definition slide

When those are your definitions, it doesn’t make sense to talk about them as being the same. David Hay regularly pointed out the same thing during the presentation and I thought his points were very useful to people considering the issue. The slide very clearly and correctly points out the different but overlapping nature of the relationship between modeling, stewardship and governance.

Towards the end of the presentation, another comparison came up that I’ve previously discussed. The topic was whether or not data modeling is an art or a science. One flaw was that David Hay seemed to be implying that the only art was in the presentation of the models to management – physical art. His belief seemed to be that the modeling was pure science. I disagree as understanding data means understanding meaning, different people often mean different things when using terms and see different things from the same information, so art is needed to mediate solutions.

My biggest annoyance with the discussion was the word that should have been discussed never made an appearance. For the same reasons I’ve argued that programmers aren’t software engineers, modeling is neither one nor the other: It is a craft. It is a blend of the two worlds of art and science. I don’t know why people today seem upset to admit to modeling and most software work as a craft, the word doesn’t denigrate the work but describes it very well.

The final point made that I thought was great was in response to a question about when to start on logical and physical models, whether that should happen before you’ve defined your business models or they need to wait. Robert Seiner accurately used the old cliché, starting the other models before you understand your business model, the thing that drives business, is to follow the failed logic of “Ready! Fire! Aim!”

Given the title, I must also say that governance was given short shrift, basically mentioned only in definition, but I didn’t really mind. While the webinar was misnamed, it was a great conversation about the relationship between data modeling and business. The conversation between the two was worth the listen.

Diyotta: Data integration for the enterprise

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I’m still catching up and reviewed a video of last month’s Diyotta presentation to the BBBT. The company is another young, founded in 2011, data integration company working to take advantage of current technologies to provide not just better data integration but also better change management of modern data infrastructures. In many ways, they’re similar to another company, WhereScape, which I discussed last year. Both are young and small, while the market is large and the need is great.

The presentation was given by Sanjay Vyas, CEO, and John Santaferraro, CMO. The introduction by Sanjay was one of the best from a small company founder that I’ve seen in a long time. He gave a brief overview of the company, its size, it’s global structure (with HQ in Charlotte, NC, and two offshore development centers). Then he went straight to what most small companies leave for last: He presented a case study.

My biggest B2B marketing point is that you need to let the market know you understand it. Far too many technical founders spend their time talking about the technology they built to solve a business problem, not the business problem that was addressed by technology. Mr. Vyas went to the heart of the matter. He showed the pain in a company, the solution and, most importantly, the benefits. That is what succeeds in business.

It also wasn’t an anonymous reference, it was Scotiabank, a leading Canadian bank with a global presence. When a company that large gives a named reference to a startup as small as is Diyotta, you know the firm is happy.

John Santaferraro then took over for a bit with mostly positive impact. While he began by claiming a young product was mature because it’s version 3.5, no four year old firm still working on angel investments has a fully mature product. From the case study and what was demo’d later, it’s a great product but it’s clear it’s still early and needs work. There’s no need to oversell.

The three main markets John said Diyotta aims at are:

  • Big data analytics.
  • Data warehouse modernization.
  • Hybrid data integration including cloud and on-premises (though John was another marketing speaker who didn’t want to use the “s” at the end).

While the other two are important, I think it’s the middle one that’s the sweet spot. They focus on metadata to abstract business knowledge of sources and targets. While many IT organizations are experimenting with Hadoop and big data, getting a better understanding and improved control over the entire EDW and data infrastructure as big data is added and new mainline techniques arrive is where a lot more immediate pain exists.

Another marketing miss that could have incorporated that key point was when Mr. Santaferrero said that the old ETL methods no longer work because “having a server in the middle of it … doesn’t exist anymore.” The very next slide was as follows.

Diyotta markitechture slide

Diyotta still seems to have a server in the middle, managing the communications between sources and targets through metadata abstraction. The little “A’s” in the data extremities are agents Diyotta uses to preprocess requests locally to optimize what can be optimizes natively, but they’re still managed by a central system.

The message would be more powerful by explaining that the central server is mediating between sources and targets, using metadata, machine learning and other modern tools, to appropriately allocate processing at source, in the engine or in the target in the most optimal way.

While there’s power in the agents, that technology has been used in other aspects of software with mixed results. One concern is that it means a high need for very close partnerships with the systems in which the agents reside. While nobody attending the live presentation asked about that, it’s a risk. The reason Sanjay and John kept talking about Netezza, Oracle and Teradata is because those are the firms whose products Diyotta has created agents. Yes, open systems such as Hadoop and Spark are also covered, but agents do limit a small company’s ability to address a variety of enterprises. The company is still small, so as long as they focus on firms with similar setups to Scotiabank, they have time to grow, to add more agents and widen their access to sources; but it’s something that should be watched.

On the pricing front, they use pricing purely based on the hub. There’s no per user or per connector pricing. As someone who worked for companies that used pricing that involved connectors, I say bravo! As Mr. Vyas pointed out, their advantage is how they manage sources and targets, not which ones you want them to access. While connecting is necessary, it’s not the value add. The pricing simplifies things and can save money compared with many more complex pricing schemes that charge for parts.

The final business point concerns compliance. An analyst in the room (Sorry, I didn’t catch the name) asked about Sarbanes-Oxley. The answer was that they don’t yet directly address compliance but their metadata will make it easier. For a company that focuses on metadata and whose main reference site is a major financial institution, it would serve their business to add something to explicitly address compliance.

Summary

Diyotta is a young company addressing how enterprises can leverage big data as target and source alongside the existing infrastructure through better metadata management and data access. They are young and have many of the plusses and minuses that involves. They have some great technology but it’s early and they’re still trying to figure out how to address what market.

The one major advantage they have, given what I’ve seen in only a two hour presentation, is Sanjay Vyas. Don’t judge a startup on where they are now or where you think they need to be. Judge them on whether or not management seems capable of getting from point A to point B. Listening to Mr. Vyas, I heard a founder who understands both business and technology and will drive them in the direction they need to go.

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.

SAS: Out of the Statisticians Pocket and into the Business Briefcase

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I just saw an amusing presentation by SAS. Amusing because you rarely get two presenters who are both as good at presenting and as knowledgeable about their products. We heard from Mike Frost, Senior Product Manager for Data Management, and Wayne Thompson, Chief Data Scientist. They enjoy what they do and it was contagious.

It was also interesting from the perspective of time. Too many younger folks think if a firm has been around for more than five years, it’s a dinosaur. That’s usually a mistake, but the view lives on. SAS was founded almost 40 years ago, in 1976, and has always focused on analytics. They have been historically aimed at a market that is made up of serious mathematicians doing heavy statistical work. They’re very good at what they do.

The business analysis sector has been focused on less technical, higher level business number crunching and data visualization. In the last decade, computing power has meant firms can dig deeper and can start to provide analysis SAS has been doing for decades. The question is whether or not SAS can rise to the challenge. It’s still early, but the answer seems to be a qualified but strong “yes!”

Both for good and bad, SAS is the largest privately held software company, still driven by founder James Goodnight. That means a good thing in that technically focused folks plow 23% of their revenue into R&D. However, it also leaves a question mark. I’ve worked for other firms long run by founders, one a 25+ year old firm still run by brothers. The best way to refer to the risk is that of a famous public failure: Xerox and the PC. For those who might not understand what I’m saying, read “Fumbling the Future” by Smith and Alexander. The risk comes down to the people in charge knowing the company needs to change but being emotionally wedded to what’s worked for so long.

The presentation to the BBBT shows that, while it’s still early in the change, SAS seems to be mostly avoiding that risk. They’re moving towards a clean, easier to use UI and taking their first steps towards collaboration. More work needs to be done on both fronts, but Mike and Wayne were very open and honest about their understanding the need and SAS continuing to move forward.

One of the key points by Mike Frost is one I’ve also discussed. While they disagree with me and think the data scientist does exist, the SAS message is clear that he doesn’t work in a void. The statistician, the business analyst and business management must all work in concert to match technical solutions to real business information needs.

LASR, VAE and a cast of thousands

The focus of the presentation was on SAS LASR, their in-memory analytics server. While it leverages Hadoop, it doesn’t use MapReduce because that involves disk access during processing, losing the speed advantage of in-memory applications.SAS LARS archictecture slide

As Mike Frost pointed out, “It doesn’t do any good to run the right model too late.”

One point that still shows the need to think more about business, is that TCO was mentioned in passing. No slide or strong message supported the message. They’re still a bit too focused on technology, not what sells the business decision makers on business intelligence (BI).

Another issue was the large number of ancillary products in the suite, including Visual Data Explorer, Data Loader and others. The team mentioned that SAS is slowly moving through the products to give them the same interface, but I also hope they’re looking at integrating as much as possible so the users don’t have the annoyance of constantly moving between products.

One nice part of the demo was an example of discussing what SAS has termed “poorly structured data” as opposed to “unstructured data” that’s the rage in Hadoop. I prefer “loosely structured data.” Mike and Wayne showed the ability to parse the incoming file and have machine intelligence make an initial pass at suggesting fields. While this isn’t new, I worked at a company in 2000 that was doing that, it’s a key part of quickly integrating such data into the business environment. The company I reference had another founder who became involved in other things and it died. While I’m surprised it took firms so long to latch onto and use the technology, it doesn’t surprise me that SAS is one of the first to openly push this.

Another advantage brought by an older, global firm, related to the parsing is that it works in multiple languages, including right-to-left languages such as Hebrew and Arabic. Most startups focus on their own national language and it can be a while before the applications are truly global. SAS already knows the importance and supports the need.

Great, But Not Yet End-To-End

The only big marketing mistake I heard was towards the end. While Frost and Thompson are rightfully proud of their products, Wayne Thompson crowed that “We’re not XXX,” a reference to a major BI player, “We’re end-to-end.” However, they’d showed only minimal visualization choices and their collaboration, admittedly isn’t there.

Even worse for the message, only a few minutes later, based on a question, one of the presenters shows how you export predicted values so that visualization tools with more power can help display the information to business management.

I have yet to see a real end-to-end tool and there’s no reason for SAS to push this iteration as more than it is. It’s great, but it’s not yet a complete solution.

Summary

SAS is making a strong push into the front end of analytics and business intelligence. They are busy wrapping tools around their statistical engines that will allow them to move much more strongly out of academics and the very technical depths of life sciences, manufacturing, defense and other industries to challenge in the realm of BI.

They’re headed in the right direction, but the risk mentioned at the start remains. Will they keep focused on this growing market and the changes it requires, or will that large R&D expenditure focus on the existing strengths and make the BI transition too slow? I’m seeing all the right signs, they just need to stay on track.

 

TDWI Webinar Review: David Loshin & Liaison on Data Integration

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The most recent TDWI webinar had a guest analyst, David Loshin of Knowledge Integrity. The presentation was sponsored by Liaison and that company’s speaker was Manish Gupta. Given that Liaison is a cloud provider of data integration, it’s no surprise that was the topic.

David Loshin gave a good overview of the basics of data integration as he talked about the growth of data volumes and the time required to manage that flow. He described three main areas to focus upon to get a handle on modern integration issues:

  • Data Curation
  • Data Orchestration
  • Data Monitoring

Data curation is the organization and management of data. While David accurately described the necessity of organizing information for presentation, the one thing in curation that wasn’t touched upon was archiving. The ability to present a history of information and make it available for later needs. That’s something the rush to manage data streams is forgetting. Both are important and the later isn’t replacing the former.

The most important part of the orchestration Mr. Loshin described was in aligning information for business requirements. How do you ensure the disparate data sources are gathered appropriately to gain actionable insight? That was also addressed in Q&A, when a question asked why there was a need to bother merging the two distinct domains of data integration and data management. David quickly pointed out that there was no way not to handle both as they weren’t really separate domains. Managing data streams, he pointed out, was the great example of how the two concepts must overlap.

Data monitoring has to do with both data in motion, as in identifying real-time exceptions that need handling, and data for compliance, information that’s often more static for regulatory reporting.

The presentation then switched to Manish Gupta, who proceeded to give the standard vendor introduction. It’s necessary, but I felt his was a little too high level for a broader TDWI audience. It’s a good introduction to Liaison, but following Mr. Loshin there should have been more detail on how Liaison addresses the points brought up in the first half of the presentation – Just as in a sales presentation, a team would lead with Mr. Gupta’s information, then the salesperson would discuss the products in more detail.

Both presenters had good things to say, but they didn’t mesh enough, in my view, and you can find out far more talking to each individually or reading their available materials.

Why things have been quiet here

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Before I catch up on some interesting presentations, I’m going to go off topic to discuss why things have been so quiet. In a word: Sasquan! The 73rd World Science Fiction Convention (Worldcon) was held here, in beautiful Spokane, WA, USA, Earth. Wednesday through Sunday saw multiple presentations, speeches, panels, autograph sessions and other wonderful events. As a local, I volunteered from setup last Monday to move-out today. I’m tired but overjoyed.

The art show was wonderful, the vendor booth and exhibits packed, and many of the panels were standing room only. We set a record for the most attended Worldcon and had far more first time Worldcon attendees than even the most optimistic planners expected.

Along with move in and move out, I volunteered at the information desk, to keep the autograph lines moving and in many other areas. I’m exhausted but happy.

My favorite big author in the autograph sessions: Vonda McIntyre. She had a long line and stayed past her time to finish signing for all the folks who waited.

I’m not much of an autograph person myself, but as long as I was handling the lines, Joe Haldeman signed my first addition paperback of Forever War, which I bought in a used bookstore the year it came out and which has followed me around. He and his wife were very gracious and it was nice to meet them.

The Hugo Awards had a lot of controversy this year, with a very conservative group of people putting forward a slate they hoped would stop progress. What it ended up doing is causing the largest number of no awards ever in a year. However, the ceremony will more importantly leave the great image of Robert Silverberg telling the story on the 1968 event in Berkeley and then leading everyone in the Hari Krishna chant. That hilariously relieved some of the tension.

The worst note had nothing to do with the conference. Eastern Washington is on fire. Three firefighters have died (as of this writing and hopefully total this season) from the many fires in a very dry summer. There’s been a haze of ash most of the time, but Thursday was terrible, with many folks needing surgical masks to go outside. I hope we get rain soon and my best wishes to the brave fire fighters and sympathy to the families of those who died.

Now it’s time to get back to the business blogging, but that was my week.

 

Webinar review: TDWI on Streaming Data in Real Time, in Memory

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The Internet of Things (IOT) is something more and more people are considering. Wednesday’s TDWI webinar topic was “Stream Processing: Streaming Data in Real Time, in Memory,” and the event was sponsored by both SAP and Intel. Nobody from Intel took part in the presentation. Given my other recent post about too many cooks, that’s probably a good thing, but there was never a clear reason expressed for Intel’s sponsorship.

Fern Halper began with overview of how TDWI is seeing data streaming progress. She briefly described streaming as dealing with data while still in motion, as opposed to data in warehouses and other static structures. Ms. Halper then proceeded to discuss the overlap between event processing, complex event processing and stream mining. The issue I had is that she should have spent a bit more time discussing those three terms, as they’re a bit fuzzy to many. Most importantly, what’s the difference between the first two?

The primary difference is that complex event processing is when data comes from multiple sources. Some of the same things are necessary as ETL. That’s why the in-memory message was important in the presentation. You have to quickly identify, select and merge data from multiple streams and in-memory is the way to most efficiently accomplish that.

Ms. Halper presented the survey results about the growth of streaming sources. As expected, it shows strong growth should continue. I was a bit amused that it asked about three categories: real-time event streams, IOT and machine data. While might make sense to ask the different terms, as people are using multiple words, they’re really the same thing. The IoT is about connecting things, which interprets as machines. In addition, the main complex events discussed were medical and oil industry monitoring, with data coming from machines.

Jaan Leemet, Sr. VP, Technology, at Tangoe then took over. Tangoe is an SAP customer providing software and services to improve their IT expense management. Part of that is the ability to track and control network usage of computers, phones and other devices, link that usage to carrier billing and provide better cost control.

A key component of their needs isn’t just that they need stream processing, but that they need stream processing that also works with other less dynamic data to provide a full solution. That’s why they picked SAP’s Even Stream Processor – not only for the independent functionality but because it also fits in with their SAP ecosystem.

One other decision factor is important to point out, given the message Hadoop and other no-SQL folks like to give. SAP’s solution works in a SQL-like language. SQL is what IT and business analysts know, the smart bet for rapid adoption is to understand that and do what SAP did. Understand the customer and sales becomes easier. That shouldn’t be a shock, but technologists are often too enamored of themselves to notice.

Neil McGovern, Sr. Director, Marketing, at SAP gave the expected pitch. It was smart of them to have Jaan Leemet go first and it would have been better if Mr. McGovern’s presentation was even shorter so there would have been more time for questions.

Because of the three presenters, there wasn’t time for many questions. One of the few question for the panel asked if there was such a thing as too much data. Neil McGovern and Jaan Leemet spent time talking about the technology of handling lots of streaming data, but only in generalities.

Fern Halper turned it around and talked about the business concept of too much data. What data needs to be seen at what timeframe? What’s real-time? Those have different answers depending on the business need. Even with the large volume of real-time data that can be streamed and accesses, we’re talking about clustered servers, often from a cloud partner, and there’s no need to spend more money on infrastructure than necessary.

I would have liked to have heard a far more in-depth discussion about how to look at a business and decide which information truly requires streaming analysis and which doesn’t. For instance, think about a manufacturing floor. You want to quickly analyze any data that might indicate failures that would shut down the process, but the volumes of information that allow analysis of potential process improvements don’t need to be analyzed in the stream. That can be done through analysis of a resultant data store. Yet all the information can be coming across the same IoT feed because it’s a complex process. Firms need to understand their information priority and not waste time and money analyzing information in a stream for no purpose other than you can.

Email Etiquette 101: Wait for an intro if you don’t want to be reported as a spammer

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Last week, I received an email from some unknown company asking me to go to their site and enter my SSN. I’ve never heard of the company. I responded by letting my security software know the email was spam.

Today I received an email from a client. It seems the firm is changing their accounting, payroll, finance firm. While the client sent contact information for their suppliers to the new company, they didn’t think to tell us about it and the new company didn’t think to check before emailing. The email I just received apologized and asked everyone to please go out to the new firm’s site. I clearly wasn’t the only one who properly responded to an unknown company asking for personal information.

People need to more clearly consider their actions in the internet age. All contact with people outside your organization need to be considered in the same way as marketing — messages that set an image for your company.

Semantics and big data: Thought leadership done right

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Dataversity hosted a webinar by Matt Allen, Product Marketing Manager at MarkLogic. Mr. Allen’s purpose was to explain to the audience the basic challenges involved in big data which can be addressed by semantic analysis. He did a good job. Too many people attempting the same spend too much time on their own product. Matt didn’t do so. Sure, when he did he had some of the same issues that many in our industry have, of over selling change; but the corporate references were minimal and the first half of the presentation was almost all basic theory and practice.

Semantics and Complexity

On a related tangent, one of the books I’m reading now is Stanley McChrystal’s “Team of Teams.” In it, he and his co-authors point to a distinction between complicated and complex. A manufacturing process can be complicated, it can have lots of steps but have a clearly delineated flow. Complex problems have many-to-many relations which aren’t set and can be very difficult to understand.

That ties clearly into the message put forward by MarkLogic. The massive amount of unstructured data is complex, with text rather than fields and which need ways of understanding potential meaning. The problems in free text are such things as:

  • Different words can define the same thing.
  • The same word can mean different things in different contexts.
  • Depending on the person, different aspects of information are needed for the same object.

One great example that can contain all for issues was given when Matt talked about the development process. At different steps in the process, from discovery, to different development stages to product launch, there’s a lot of complexity in meanings of terms not only in development organizations but between them and all the groups in the organization with whom they have to work.

Mr. Allen then moved from discussing that complexity to talking about semantic engines. MarkLogic’s NoSQL engine has a clear market focus on semantic logic, but during this section he did well to minimize the corporate pitch and only talked about triples.

No, not baseball. Triples are a syntactical tool to link subject (person), predicate (operates), object (machine). By building those relationship, objects can be linked in a less formal and more dynamic manner. MarkLogic’s data organization is based on triples. Matt showed examples of JSON, Turtle and XML representations of triples, very neatly sliding his company’s abilities into the theory presentation – a great example of how to mention your company while giving a thought leadership presentation without being heavy handed.

Semantics, Databases and the Company

The final part of the presentation was about the database structure needed to handle semantic analytics. This is where he overlapped the theory with a stronger corporate pitch.

Without referring to a source, Mr. Allen stated that relation databases (RDBMS’) can only handle 20% of today’s data. While it’s clear that a lot of the new information is better handled in Hadoop and less structured data sources, it’s a question of performance. I’d prefer to see a focus on that.

Another error often made by folks adopting new technologies was the statement that “Relational databases aren’t solving a lot of today’s problems. That’s why people are moving to other technologies.” No, they’re extending today’s technologies with less structured databases. The RDBMS isn’t going away, as it does have its purpose. The all or nothing message creates a barrier to enterprise adoption.

The final issue is the absolutist view of companies that think they have no competitor. Mark Allen mentioned that MarkLogic is the only enterprise database using triples. That might be literally true. I’m not sure, but so what? First, triples aren’t a new concept and object oriented databases have been managing triples for decades to do semantic analysis. Second, I recently blogged about Teradata Aster and that company’s semantic analytics. While they might not use the exact same technology, they’re certainly a competitor.

Summary

Mark Allen did a very good job exposing people to why semantic analysis matters for business and then covered some of the key concepts in the arena. For folks interested in the basics to understand how the concept can help them, watch the replay or talk with folks at MarkLogic.

The only hole in the presentation is that though the high level position setting was done well, the end where MarkLogic was discussed in detail had some of the same problems I’ve seen in other smaller, still technology driven companies.

If Mr. Allen simplifies the corporate message, the necessary addition at the end of the presentation will flow better. However, that doesn’t take away from the fact that the high level overview of semantic analysis was done very well, discussing not only the concepts but also a number of real world examples from different industries to bring those concepts alive for the audience. Well done.

Marketing lesson: How to cram too many vendors into too short a timeframe

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I’ll start by being very clear: This is a slam on bad marketing. Do not take this column as a statement that the products have problems, as we didn’t see the products.

Database Trends and Application magazine/website held a webinar. The first clue there was something wrong is that an hour long seminar had three sponsors. In a roundtable forum, that could work, and the email mentioned it was a roundtable, but it wasn’t. Three companies, three sequential presentations. No roundtable.

It was titled “The Future of Big Data: Hybrid Architectures and Best-of-Breed”. The presenters were Reiner Kappenberger, Global Product Manager, HP Security Voltage, Emma McGrattan, SVP Engineering, Actian, and Ron Huizenga, ER/Studio Product Manager, Embarcadero. They are three interesting companies, but how would the presentations fit together?

They didn’t.

Each presenter had a few minutes to slam through a pitch, which they did with varying speeds and content. There was nothing tying them into a unified vision or strategy. That they all mentioned big data wasn’t enough and neither was the time allotted to hear significant value from any of them.

I’ll burn through each as the stand-alone presentations they were.

HP Security Voltage

Reiner Kappenberger talked about his company’s acquisition by HP earlier this year and the major renaming from Voltage Security to HP Security Voltage (yes, “major” was used tongue-in-cheek). Humor aside, this is an important acquisition for HP to fill out its portfolio.

Data security is a critical issue. Mr. Kappenberger gave a quick overview of the many levels of security needed, from disk encryption up to authentication management. The main feature focus on Reiner’s allotted time is partial tokenization, being able to encrypt parts of a full data field. For instance, disguising the first five digits of a US Social Security number while leaving the last four visible. While he also mentioned tying into Hadoop to track and encrypt data across clusters, time didn’t permit any details. For those using Hadoop for critical data, you need to find out more.

The case studies presented included a car company’s use of both live, Internet of Things feeds and recall tracking but, again, there just wasn’t enough time.

Actian

The next vendor was Actian, an analytics and business intelligence (BI) player based on Hadoop. Emma McGrattan felt rushed by the time limit and her presentation showed that. It would have been better to slow down and cover a little less. Or, well, more.

For all the verbage it was almost all fluff. “Disruption” was in the first couple of sentences. “The best,” “the fastest,” “the most,” and similar unsubstantiated phrases flowed like water. She showed an Actian built graph with product maturity and Hadoop strength on the two axis and, as if by magic, the only company in the upper right was Actian.

Unlike the presentations before and after hers, Ms. McGrattan’s was a pure sales pitch and did nothing to set a context. My understanding, from other places, is that Actian has a good product that people interested in Hadoop should evaluate, but seeing this presentation was too little said in too little time with too many words.

In Q&A, Emma McGrattan also made what I think is a mistake, one that I’ve heard many BI companies get away from in the last few years. An attendee asked about biggest concern when transitioning from EDW to Hadoop. The real response should be that Hadoop doesn’t replace the EDW. Hadoop extends the information architecture, it can even be used to put an EDW on open source, but EDWs and big data analytics typically have two different purposes. EDWs are for clean, trusted data that’s not as volatile, while big data is typically transaction oriented information that needs to be cleaned, analyzed and aggregated before it’s useful in and EDW. They are two tools in the BI toolbox. Unfortunately, Ms. McGrattan accepted the premise.

Embarcadero

Mr. Huizenga, from Embarcadero, referred to evidence that the amount of data captured in business is doubling every 1.2 years and how the number of related jobs is also exploding. However, where most big data and Hadoop vendors would then talk about their technologies manipulating and analyzing the data, he started with a bigger issue: How do you begin to understand and model the information? After all, schema-on-write still means you need to understand the information enough to create schemas.

That led to a very smooth shift to a discussion about the concept of modeling to Embarcadero. They’ve added native support for Hive and MongoDB, they can detect embedded objects in those schemas and they can visually translate the Hadoop information into forms that enterprise IT folks are used to seeing, can understand and can add to their overall architecture models.

Big data doesn’t exist in a void, to be successful it must be integrated fully into the enterprise information architecture. For those folks already using ERwin and those who understand the need to document modeling, they are a tool that should be investigated for the world of Hadoop.

Summary

Three good companies were crammed into a tiny time slot with differing success. The title of the seminar suggested a tie that was stronger than was there. The makings existed for three good webinars, and I wish DBTA had done that. The three firms and the host could have communicated to create an overall message that integrated the three solutions, but they didn’t.

If you didn’t see the presentation, don’t bother. Whichever company interests you check it out. All three are interesting though it might have been hard to tell from this webinar.