Tag Archives: fern halper

TDWI Webinar: Innovations and Evolutions in BI, Analytics, and Data Warehousing

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TDWI held a webinar to announce their latest major report. While there are always a lot of intriguing numbers in the reports, it’s also important to remember the TDWI audience is self-selecting. People interested in the latest information lean towards the leading edge so their numbers should be taken as higher than would be in the general IT market place. Still, the numbers as they change over time are valuable and the views of the analysts are often worth hearing.

As the webinar was pushing a major report, the full tag team was in attendance: David Stodder, TDWI Director for BI, Fern Halper, TDWI Director for Analytics, and Philip Russom, TDWI Director for Data Management.

David Stodder presented his section first, and one important point he made had nothing to do with numbers. He briefly discussed one quote and user story and it was from a government employee. Companies using Hadoop to better understand internet business and relationships tend to get almost all the press, but David pointed out the importance of data and analytics in helping governments better address the needs of their citizens.

A very intriguing set of numbers David provided was on how many responders were on current versions of software versus older versions. While you can see that some areas are more quickly adopting the SaaS model, that’s not the key the he pointed out. Only 27% of respondents said they’re on the current version of their data security software. A later slide shows that security is one reason for hesitation in the move to mobile, but Mr. Stodder rightly points out that underlying all the information channels is the basis of data security. It’s not a question of if you’ll get hacked but when, so data security should be kept updated.

The presentation was then turned over to Fern Halper. I look a bit askance at the claim that the Internet of Things (IoT) is a “trend.” Her data shows only 18% taking advantage of it today and 40% might be using in within three years. We’ve been talking about IoT for a while, and it’s clearly being slowly integrated into business, I wouldn’t say it’s as fashionable as the word trend would imply.

On the more useful side is the table she showed that’s simply titles “Analytics hits mainstream.” It not only shows that massive adoption of the last decade’s focus on dashboards and BI tools, but around 30% of respondents are using many of the newer tools and techniques and the next three years indicate a doubling in usage.

Philip Russom gave the final segment of the presentation. His first slide on the adoption of newer technologies for data warehousing showed something that many have finally admitted in the last year or no: No-SQL is an excuse made by people who don’t understand how business technology works. While the numbers show 28% of respondents using Hadoop, it also shows 22% using SQL on Hadoop. The number over the next three years are even more interesting: 36% say they’ll be using Hadoop and 38% will be using SQL on Hadoop. That means existing No-SQL folks will be moving to SQL.

The presentation ended with the team of analysts presenting their list of ten priorities for those people interested in emerging technologies. To me, the first isn’t the first among equals, it is set far above all the rest: Adopt them for their business benefits. All the other nine items are how IT addresses the challenges of new technologies, but those things are useless unless you understand how technologies will support business. Without that, you can’t provide an ROI and you can’t get business stakeholders to support you for long. That’s strategy, all the other points are just tactics.

As usual, get the report and browse it.

TDWI Webinar Review: Fast Decision Making with Analytics

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This is more of a marketing flavored post as the recent presentation seemed to miss its own point. The title implied it was about fast decision making, but Fern Halper, TDWI Research Director for Advanced Analytics, gave a rather generic presentation about the importance of operationalizing analytics.

Fern gave a nice presentation about operationalizing analytics, but it was not significantly different than her last few. In addition, some of the survey issues discussed were clearly not well thought out. For instance, Ms. Halper listed the expected growth of predictive analytics and web/mobile analytics as if they belonged in the same discussion. The fact that web and mobile are methods of display doesn’t overlap with whether they are used to display descriptive or prescriptive analytics. The growth of those display methods also don’t move away from the use of dashboards in CRM and ERP applications, as was implied, since those applications will migrate views to the new display methods.

The best thing mentioned by both Fern Halper and the SAP presenters was the fact that there were multiple references to that need for multiple data sources. Seeing the continued refocusing of many firms on wide data rather than big data is a good thing for the industry. Big data is more of a technical issue while wide data more directly addresses complex business environments.

Now I’m hoping for more people to begin to refer to loosely structured data rather than unstructured data. Linguists, I’m sure, are constantly amused at hearing languages referred to as unstructured.

The case study was by Raj Rathee, Director, Product Management, SAP. It was an interesting project at Lufthansa, where real-time analytics were used to track flight paths and suggest alternative routes based on weather and other issues. The business key is that costs were displayed for alternate routes, helping the decision makers integrate cost and other issues as situations occur. However, that was really the only discussion of fast decision making with analytics.

The final marketing note is that the Q&A was canned but the answers didn’t always sync up. For instance, the moderator asked one question of Fern, she had a good answer, but there was no slide in the pack about her response, just the canned SAP slide referenced by Ashish Sahu, Director, Product Marketing, SAP, after Ms. Halper spoke.

I think the problem was that the presenters didn’t focus down on a tight enough message and tried to dump too much information into the presentation. The message got lost.

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.

TDWI Webinar and Best Practices Report: Real-Time Data, BI and Analytics

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TDWI held a webinar this morning to promote their new Best Practices Report on real-time data, BI and analytics. It’s worth a glance.

The report and presentation were team efforts by Philip Russom, David Stodder, and Fern Halper. The report, as usual, was centered around a survey and was a survey of IT people rather than business users. The report relates, “The majority of survey respondents are IT professionals (63%), whereas the others are consultants (20%) and business sponsors or users (17%).” Not much room there for the opinions of the people who need to use BI. Still, for understanding the IT perspective, it’s interesting.

The most valuable pointer in the presentation was given by Dave Stodder, who pointed out what too many folks ignore: Much of the want for real-time data is bounded by the inability of the major operational systems, such as ERP and CRM, to move from batch to real-time support. While BI firms can prepare for that, it’s the other vendors providing and the users adopting systems that allow real-time extraction in an effective manner that is the big bottleneck to adoption.

One issue that the TDWI folks and many others in our industry have is a misconception around the phrase “operational systems.” Enterprise software folks have grown up thinking of operations as synonymous with business operations. That’s not the case. All three of the analysts made that error even while discussing the fact that the internet of things means more devices are becoming data sources.

Those people who provide manufacturing software understand that and have for years. There’s much that can be leveraged from that sector but I don’t hear much mentioned in our arena. Fern Halper did mention IT operations as an area already using basic analytics, but I think the message could be stronger. Network management companies have decades of experience in real time monitoring and analysis of performance issues and that could be leveraged.

Build, buy or borrow are options for software as well as other industries, but I only see people considering building. We should be looking more to other software sectors for inspiration and partnerships.

There was also a strange bifurcation that Dave Stodder and Fern Halper seem to be making, by splitting BI and analytics. Analytics are just one facet of BI. I don’t see a split being necessary.

At the end of the presentation, they reviewed their top ten priorities (page 43 of the report). Most are very standard but I’ll point to the second, “Don’t expect the new stuff to replace the old stuff.” It’s relevant to the discussion vendors seem to think that revolutionary trumps evolutionary. It doesn’t. Each step in new forms of BI, such as predictive analytics, extends the ability to help business users make better decisions. It’s layered on top of the rest of the analysis to build a more complete picture, it doesn’t replace it.

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

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

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

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

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

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

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

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

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

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

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

Summary

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

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