Tag Archives: tdwi

TDWI Webinar Review: IoT’s Impact on Data Warehousing: Defining IoT in Terms of Its Data Requirements

Two TDWI webinars in one week? Both sponsored by SAP? Today’s was on IoT impacting data warehousing, and I was curious about how an organization that began focused on data warehousing would cover this. It ended up being a very basic introduction to IoT for data warehousing. That’s not bad. In fact. it’s good. While I often want deeper dives than presenters give, there’s certainly a place for helping people focused on one arena, in this case it’s data warehousing, get an idea of how another area, IoT, could begin to impact their world.

The problem I had was how Philip Russom, Senior Research Director for Data Management, TDWI, did that. I felt he missed out on covering some key points. The best part is that, unlike Tuesday’s machine learning webinar, SAP’s Rob Waywell, Director Hana Project Management, did a better job of bringing in case studies and discussing things more focused on the TDWI audience.

Quick soap box: Too many companies don’t understand product marketing so they under utilize their product marketers (full disclosure: I was one). I strongly feel that companies leveraging product marketing rather than product management in presentations will be more able to address business concerns rather than being focused on the products. Now, back to our regular programming…

One of the most interesting takeaways from the webinar was a poll on what level of involvement the audience has with IoT. Fifty percent of the responders said they’re not collecting IoT data and have no plans to do so. Enterprise data warehouses (EDW) are focused on high level, aggregated data. While the EDW community has been moving to blend more real time data, it tends to be other departments who are early into the IoT world. I’m not surprised by the results, nor am I worried. The expansion of IoT will bring it in to overlap EDW’s soon enough, and I’d suggest that that half of the audience is aware things will be changing and they have the foresight to begin to pay attention to it.

IoT Basics for EDW Professionals

Mr. Russom’s basic presentation was good, and folks who have only heard about IoT would do well to listen to it. However, they should be aware of a few issues.

Philip said that “the tendency is to push analytics out to the devices.” Not wholly true, and the reason is critical. A massive amount of data is being generated by what are called “edge devices.” Those are the cars, refrigerators, manufacturing robots and other devices that stream information to the core servers. IoT data is expected to far exceed the web and social media data often referred to as big data. That means that an efficient use of the internet means that edge analytics are needed to aggregate some information to minimize traffic flow.

Take, for instance, product data. As Rob Waywell mentioned, many devices create lots of standard data for which there is no problems. The system really only cares about exceptions. Therefore, an edge device might use analytics to aggregate statistics about the standard occurrences while immediately passing exceptions on to be handled in real-time.

There is also the information needed for routing. Servers in the core systems need to understand the data and its importance. The EDW is part of a full data infrastructure. the ODS (or data lake as folks are now calling it) can be the direct target of most data, while exceptions could be immediately routed to other systems. Whether it’s the EDW, ODS, or other system, most of the analysis will continue in core systems, but edge analytics are needed.

SAP Case Studies

Rob Waywell, as mentioned above, had the most important point of the presentation when he mentioned that IoT traffic is primarily about the exceptions. He had a couple of quick case studies to talk about that, and his first was great because it both showed IoT and it wasn’t about cars – the most used example. The problem is that he didn’t tie it well into the message of EDWS.

The case was about industrial worker safety in the area of gas detection and response. He showed the different types of devices that could be involved, mentioned the multiple types of alert, and described different response paths.

He then mentioned, with what I felt wasn’t enough emphasis (refer to my soap box paragraph above), the real power that a company such as SAP brings to the dance that many tinier companies can’t. In an almost throwaway comment, Mr. Waywell mentioned that SAP Hana, after managing the hazardous materials release instance, can then communicate to other SAP systems to create the official regulatory reports.

Think about that. While it doesn’t directly impact the EDW, that’s a core part of integrated business systems. That is a perfect example of how the world of IoT is going to do more than manage the basics of devices but also be used to handle the full process for with MIS is designed.

Classifications of IoT

I’ll finish up with a focus that came up in a question during Q&A. Philip Russom had mentioned an initial classification of IoT between industrial and consumer applications. That misses a whole lot of areas, including supply chain, logistics, R&D feedback, service monitoring and more. To lump all of that into “manufacturing” is to do them a disservice. The manufacturing term should be limited to the actual manufacturing process.

Rob Staywell then went a different direction. He seemed to imply the purpose of IoT was solely to handle event-driven, real-time, actions. Coming from a product manager for Hana, that’s either an understandable mistake or he didn’t clearly present his view.

There is a difference between IoT data to be operationalized and that to be analyzed. He might have just been focusing on the operational aspects, those that need to create immediate actions, without minimizing the analytical portion, but it wasn’t clear.


This was a webinar that is good for those in the data warehousing and core MIS functions who want to get a quick introduction to what IoT is and what might be coming down the pike that could impact their work. For anyone who already has a good idea of what’s coming and wants more specifics, this isn’t needed.

TDWI Webinar Review: Putting Machine Learning to Work in Your Enterprise

It’s been a while since I watched a webinar, but since business intelligence (BI) and (AI) are overlapping areas of interest, I watched Tuesday’s TDWI webinar on Machine Learning (ML). As the definition of machine learning expands out of the pure AI because of BI’s advanced analytics, it’s interesting to see where people are going with the subject.

The host was Fern Halper, VP Research, at TDWI. The guests were:

  • Mike Gualtieri, VP, Forrester,
  • Askhok Swaminathan, Senior Director, Product Management, SAP,
  • Chandran Saravana, Senior Director, Advanced Analytics, SAP.

Ms. Halper began with a short presentation including her definition of ML as “Enabling computers to learn patterns with minimal human intervention.” It’s a bit different than the last time I reviewed one of her webinars, but that’s ok because my definition is also evolving. I’ve decided to use my own definition, “Technology that can investigate data in an environment of uncertainty and make decisions or provide predictions that inform the actions of the machine or people in that environment.” Note that I differ from my past, purist, view, of the machine learning and adjusting algorithms. I’ve done so because we have to adapt to the market. As BI analytics have advanced to provide great insight in data discovery, predictive analytics and more, many areas of BI and the purist area of learning have overlapped. Learning patterns can happen through pure statistical analysis and through self-adaptive algorithms in AI based machines.

The most interesting part of Fern Halper’s segment was a great chart showing the results of a survey asking about the importance of different business drivers behind ML initiatives. What makes the chart interesting, as you can see, is that it splits results between those groups investigating ML and those who are actively using it.

What her research shows is that while the highest segments for the active categories are customer related, once companies have seen the benefits of ML, the advantages of it for almost all the other areas jump significantly over views held during the investigation phase.

A panel discussion then proceeded, with Ms. Halper asking what sounded like pre-discussed questions (especially considering the included and relevant slides) to the three panelists. The statements by the two SAP folks weren’t bad, they were just very standard and lacked any strong differentiators. SAP is clearly building an architecture to leverage ML using their environment, but there weren’t case studies and I felt the integration between the SAP pieces didn’t bubble up to the business level.

The reason to listen to this segment is Mr. Gualtieri. He was very clear and focused on his message. While I quibble with some of the things he said about data scientists, that soap box isn’t for here. He gave a nice overview of the evolving state of ML for enterprise. The most important part of that might have been missed by folks, so I’ll bring it up here.

Yes, TensorFlow, R, Python and other tools provide frameworks for machine learning implementations, but they’re still at a very technical level. They aren’t for business analysts and management. He mentioned that the next generation of tools are starting to arrive, one that, just like the advent of BI, will allow people with less technical experience to more quickly use models in and gain insights from machine learning.

That’s how new technology grows, and I’d like to see TDWI focus on some of the new tools.


This was a good webinar, worth the time for those of you who are interested in a basic discussion of where machine learning is within the enterprise landscape.

What Makes Business Intelligence “Enterprise”?

I have an article in the Spring TDWI Journal. It has now been six months and the organization has been kind enough to provide me with a copy of my article to use on my site: TDWI_BIJV21N1_Teich.

If you like my article, and I know you will, check out the full journal.


TDWI & Teradata: An overview of data-centric security

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.

TDWI Webinar — Engaging the Business, again from the technologist’s perspective

This week’s TDWI hosted webinar was about engaging business and, once again, it came from the standpoint of technologists rather than from business. There were some very good things said. However, until our industry stops thinking of business knowledge workers as children to be tutored and begins to think about them as people whose knowledge is the core of what we must encapsulate, we’ll continue to miss the mark and adoption of solutions will remain slow.

The main presenter was David Loshin, President of Knowledge Integrity. He began the presentation with a slide that describes his view of the definition of “data driven,” including three main points:

  • Focus on turning data into actionable knowledge that can lead to increased corporate value.
  • Aware of variance that can cause inconsistent interpretation.
  • Coordination among data consumers to enforce standards for utilization.

We should all clearly understand that the first item is not new and was not created by the business intelligence (BI) industry. Business has always been data driven. What we’re able to do now is access far more data than ever before so that we can provide a more robust view of the corporation.

Inconsistent Data v Inconsistent Utilization

The second bullet is a core point. Mr. Loshin used a couple of example such as sales territory and other areas where definitions are fuzzy. One clear difference to me is one I directly experienced 25 years ago, and more directly addresses the visualization side of the BI conundrum. I was working for a major systems integrator (SI) and my client was, well, let’s just say it was a large, fruit based computing company.

A different SI had created an inventory system for the client’s manufacturing facility but the system was a failure though all the right data was in the system. The problem was that the reports were great for the accounting department, not for inventory and manufacturing. We interviewed the inventory team and then rewrote reports to address and present the information from their standpoint.

Too often, technologists get lost in the detailed data definitions and matching fields across data sources. That is critical, but it loses the big picture. Even when data is matched, different business people use data differently.

Which brings us to David Loshin’s third point. No, we don’t need to enforce exact standards for utilization. We need to ensure that the data each consumer refers to is consistent, but we must do a better job in understanding that different departments can utilize the exact same data in a variety of ways.

Business Drivers and Data Governance

David did get to the key issue a bit later, on a slide titled Operationalizing Business Policies. He points out that it’s critical to ensure that “Information policies model the data requirements for business policy.” This is key and should be bubbled up higher in the mindset of our industry. While I hear it mentioned often, it seems to be honored more in the breach.

Time was spent discussing the importance of understanding different users and their varying utilization of data. As I mentioned in the introduction, the solution to the new complexities then veers from addressing business needs to ignoring history. In a previous blog post, I discussed how many in the industry seem to be ignoring the lessons to be learned from the advent of the PC. Mr. Loshin seems to be doing that when he talks about empowering the business users to set their own usability rules. He splits IT and business in the following way:

  • Business data consumers are accountable for the rules asserting usability for their views of the data.
  • IT becomes responsible for managing the infrastructure that empowers the business user.

The issue I have with that argument is a phrase that didn’t appear in this webinar until Linda Briggs, the moderator, mentioned it in a poll question right before Q&A: Data Governance. Corporations are increasingly liable for how they control and manage information. It does not make sense to allow each user to define their own data needs in a void. Rather than allow for massively expanded and relatively uncontrolled access to data and then later have to contract access, as corporations had to regain a handle on what was being done on scattered desktop computers, BI vendors should be positioning data governance from the start.

Whether it’s by executive fiat, a cross-functional team, or some other method, companies need to clarify data governance rules. Often, IT is the best intermediary between groups, actively participating in data governance definition as an impartial observer and facilitator. It is then the job of IT to ensure that it provides as open access as possible to business workers given their needs and the necessity of following governance rules.

There was one question, during Q&A, on the importance of data governance. I thought David Loshin again understated its importance while Harald Smith, Director of Product Management at Trillium, the webinar sponsor, had the comment that “everyone is responsible for data governance.” That is my only mention of the sponsor, as I felt his portion of the presentation was a recitation of sound bites, talking points and buzz words that didn’t provide any value to the hour.


David Loshin has a clear view of engaging the business and gets a number of key things correct. However, that view is one of a technologist looking over a self-imagined bridge separating technology and business. There’s not a bridge separating IT and business. They overlap in many critical areas and both must learn from and work well with each other.

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

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

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.

TDWI Webinar Review: David Loshin & Liaison on Data Integration

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.

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

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.