Tag Archives: teradata

TDWI & Teradata: An overview of data-centric security

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Yesterday’s TDWI webinar was focused on data-centric security. The tag team was Fern Halper, Research Director for Advanced Analytics, TDWI, and Jay Irwin, Director of InfoSec, Teradata. It’s always nice when the two halves of a sponsored presentation fit well. For that reason and for the content, this was a nice presentation.

Everyone in the industry knows that data breeches happen, and we all talk about the issue. I’ve seen a few articles and lists about the number of successful attacks, but Fern Halper pointed us to a nice graphic from Information is Beautiful. She also pointed to another study that showed that “In 2013, 33% of respondents said their company had a data breach. In 2014 the percentage has increased to 43%.” It’s always a race between black hats and white hats, so it’s important to minimize not only your chance of getting hacked, but also to minimize the importance and usefulness of data gained from successful hacks.

Ms. Halper than discussed four types of data security:

  • Perimeter security: monitoring network access for intrusion detection.
  • Authorization and Access: Password and role based data protections.
  • Encryption: Using cryptography to encode data.
  • Logging and monitoring: Analyzing access patterns.

Each part is necessary but insufficient. Authorization is only as strong as people’s passwords. If it’s easy to steal the encryption key, encryption doesn’t matter. A robust security system leverages all the types.

One important note: Later in her presentation and throughout Jay Irwin’s section, encryption didn’t exist alone but alongside tokenization. The later is a different security technology, where characters, words, numbers and fields are replaced with other symbols, or tokens, that still look as if they’re real and can still be used in analysis. Mr. Irwin pointed out he prefers “data protection” as a rubric that covers all the techniques of data level security.

Along with that clarification, Jay Irwin also described the multiple layers as “Defense in Depth,” a concentric ring of security to ensure there’s no single point of failure. Jay also provided my favorite slide of the presentation. While it’s too wordy, it’s a pretty clear view of Teradata’s top-down approach.Teradata data security top-down pyramid

An organization must start with understanding the rules and regulations that drive data security. Only then can you identify the data assets that need special attention in order to protect them from hackers.

Jay has a lot more to say in a lot more detail, and I won’t cover it all. While I blog about webinars so you don’t have to watch, this one’s an exception. If you want to get a good, broad view of core data security issues, take some time and listen to the webinar.

Teradata Aster: NLP for Business Intelligence

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Teradata’s recent presentation at the BBBT was very interesting. The focus, no surprise, was on Teradata Aster, but Chris Twogood, VP Products and Services Marketing, and John Thuma, Director of Aster Strategy and Analytics, took a very different approach than was taken a year earlier.

Chris Twogood started the talk with the usual business overview. Specific time was spent on four recent product announcements. The most interesting announcement was about their support for Presto, a SQL-on-Hadoop project. They are the first company to provide commercial support for the open source technology. As Chris pointed out, he counted “13 different SQL-on-Hadoop variants.” Because of the importance of SQL access and the perceived power of Presto, Teradata has committed to strengthening its presence with that offering. SQL is still the language for data access and integrating Hadoop into the rest of the information ecosystem is a necessary move for any company serving any business information market. This helps Teradata present a leadership image.

Discussion then turned to the evolution of data volumes and analytics capabilities. Mr. Twogood has a great vision of that history, but the graphic needs serious work. I won’t copy it because the slide was far too busy. The main point, however, was the link between data volumes and sources with the added capabilities to look at business in a more holistic way. It’s something many people are discussing but he seems to have a much better handle on it than most others who talk to the point, he just needs to fine tune the presentation.

Customers and On-Site Search

As most people have seen, the much of the new data coming in under the big data rubric is customer data from sources such as the web, call logs and more. Being able to create a more unified view of the customer matters. Chris Twogood wrapped up his presentation by referring to a McKinsey & Co. survey that pointed out, among other things, that studying customer journeys can increase predictive accuracy of customer satisfaction and churn by 30-40%. Though it also points out that 56% of customer interactions are through multi-channel means, one of the key areas of focus today is the journey through a web site.

With that lead-in, John Thuma took over to talk about Aster and how it can help with on-site search. He began by stating that 25-30% of web site visitors using search leave the site if the wanted result isn’t in first three items returned, while 75% abandon if the result isn’t on first page. Therefore it’s important to have searches that understand not only the terms that the prospective customer enters but possible meanings and alternatives. John picked a very simple and clear example, depending on the part of the country, somebody might search on crock pot, slow cooker or pressure cooker but all should return the same result.

While Mr. Thuma’s presentation talked about machine learning in general, and did cover some of the other issues, the main focus of that example is Natural Language Processing (NLP). We need to understand more than the syntax of the sentence, but also improve our ability to comprehend semantic meaning. The demonstration showed some wonderful capabilities of Aster in the area of NLP to improve search capabilities.

One feature is what Teradata is calling “apps,” a term that confuses them with mobile apps, a problematic marketing decision. They are full blown applications that include powerful capabilities, applications customization and very nice analytics. Most importantly, John clearly points out that Aster is complex and that professional services are almost always required to take full advantage of the Aster capabilities. I think that “app” does a disservice to the capabilities of both Aster and Teradata.

One side bar about technical folks not really understanding business came from one analyst attending the presentation who suggested that ““In some ways it would be nice to teach the searchers what words are better than others.” No, that’s not customer service. It’s up to the company to understand which words searchers mean and to use NLP to come up with a real result.

A final nit was that the term “self-service” was used while also talking about the requirement for both professional services from Teradata and a need for a mythical data scientist. You can’t, as they claimed, used Aster to avoid the standard delays from IT for new reports when the application process is very complex. Yes, afterwards you can use some of the apps like you would a visualization tool which allows the business user to do basic investigation on her own, but that’s a very limited view of self-service.

I’m sure that Teradata Aster will evolve more towards self-service as it advances, but right now it’s a powerful tool that does a very interesting job while still requiring heavy IT involvement. That doesn’t make it bad, it just means that the technology still needs to evolve.

Summary

I studied NLP almost 30 years ago, when working with expert systems. Both hardware and software have moved forward, thankfully, a great distance since those days. The ability to leverage NLP to more quickly and accurately to understand the market, improve customer acquisition and retention ROI and better run business is a wonderful thing.

The presentation was powerful and clear, Teradata Aster provides some great benefits. It is still early in its lifecycle and, if the company continues on the current course, will only get better. They have only a few customers for the on-site optimization use, none referenceable in the demo, but there is a clear ROI message building. Mid- to large-size enterprises looking to optimize their customer understand, whether for on-site search or other modern business intelligence uses, should talk to Teradata and see if Aster fits their needs.

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.