Magnitude/Kalido Webinar Review: Automated and Agile, New Principles for Data Warehousing

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I watched a webinar yesterday. It was sponsored by Magnitude, the company that is the result of combining Kalido and Noetix. The speakers were Ralph Hughes, a data warehousing consultant operating as Ceregenics, and John Evans of Magnitude.

Ralph Hughes’ portion of the presentation was very interesting in a great way. Rather than talking about the generalities of enterprise data warehouses (EDW) and Agile, he was the rare presenter who discussed things clearly and in enough detail for coherent thought. It was refreshing to hear more than the usually tap dance.

Webinar - Magnitude - Ceregenics slide

Ralph’s slide on the advantages of agile development for EDW’s is simple and clear. The point is that you don’t know everything when you first acquire requirements and then design a system. In the waterfall approach, much of coding, testing and usage is wasted time as you find out you need to do extra work for new requirements that pop up. Agile allows business users to quickly see concepts and rethink their approaches, saving both time to some productivity and overall time and effort of development.

After talking about agile for a bit, he pointed out that it does save some time but still leaves lots of basic work to do. He then shifted to discuss Kalido as a way to automate some of the EDW development tasks in order to save even more time. He used more of his presentation to describe how he’s used the tool at clients to speed up creation of data warehouses.

One thing he did better in voice than on slides was to point out that automation in EDW doesn’t mean replacing IT staff. Rather, appropriately used, it allows developers to move past the repetitive tasks and focus on working with the business users to ensure that key data is encapsulated into the EDW so business intelligence can be done. Another key area he said automation can’t do well is to manage derived tables. That still requires developers to extract information, create the processes for creating the tables, then moving the tables back into the EDW to, again, enhance BI.

Notice that while Mr. Hughes spoke to the specifics of creating EDWs, he always presented them in context of getting information out. Many technical folks spend too much time focused on what it takes to build the EDW, not why it’s being build. His reminders were again key.

John Evans’ presentation was brief, as I always like to see from the vendors, rounding out what his guest speaker said. He had three main points.

First, the three main issues facing IT in the market are: Time to value, time to respond to change and total cost of ownership. No surprise, he discussed how Magnitude can address those.

Second, within his architecture slide, he focused on metadata and what he said was strong support for master data and metadata management. Given the brief time allotted, it was allusion to the strengths, but the fact that he spoke to it was a good statement of the company’s interests.

Third, he discussed the typical customer stories and how much time the products saved.

Summary

The webinar was very good exposure to concepts for an audience thinking about how to move forward in data warehousing, whether to build EDWs or maintain them. How agile development and an automation tool can help IT better focus on business issues and more quickly provide business benefit was a story told well.

Revolution Analytics at BBBT: Vision and products for R need to mesh

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Revolution Analytics presented to the BBBT last Friday. The company is focused on R with a stated corporate vision of “R: The De-facto standard for enterprise predictive analytics .” Bill Jacobs, VP, Product Marketing, did most of the talking while Steve Belcher, Sales Engineer, gave a presentation.

For those of you unfamiliar with R as anything other than a letter smack between Q and S, R is an open source programming language for statistics and analytics. The Wikipedia article on R points out it’s a combination of Scheme and S. As someone who programmed in Scheme many years ago, the code fragments I saw didn’t look like it but I did smile at the evolution. At the same time, the first thing I said when I saw Revolution’s interactive development environment (IDE) was that it reminded me of EMACS, only slightly more advanced in thirty years. The same wiki page referenced earlier also said that R is a GNU project, so now I know why.

Bill Jacobs was yet another vendor presenter who has mentioned his company realized that the growth of the Internet of Things (IOT) means a data explosion that leaves what is currently misnamed as big data in the dust as far as data volumes. He says Revolution wants to ensure that companies are able to effectively analyze IOT and other information and that his company’s R is the way to do so.

Revolution Analytics is following in the footsteps of many companies which have commercialized freeware over the years, including Sun with Unix and Red Hat with Linux. Open source software has some advantages, but corporate IT and business users require services including support, maintenance, training and more. Companies which can address those needs can build a strong business and Revolution is trying to do so with R.

GUI As Indicative Of Other Issues

I mentioned the GUI earlier. It is very simple and still aimed at very technical users, people doing heavy programming and who understand detailed statistics. I asked why and was told that they felt that was their audience. However, Bill had earlier talked about analytics moving forward from the data priests to business analysts and end users. That’s a dichotomy. The expressed movement is a reason for their vision and mission, but their product doesn’t seem to support that mission.

Even worse was the response when I pointed out that I’d worked on the Apple Macintosh before and after MPW was released and had worked at Gupta when it was the first 4GL language on the Windows platform. I received as long winded answer as to why going to a better and easier to use GUI wasn’t in the plans. Then Mr. Jacobs mentioned something to the effect of “You mentioned companies earlier and they don’t exist anymore.” Well, let’s forget for a minute that Gupta began a market, others such as Powersoft did well too for years, and then Microsoft came out with its Visual products to control the market but that there were many good years for other firms and the products are still there. Let’s focus on wondering when Apple ceased to exist.

It’s one thing to talk about a bigger market message in the higher points of a business presentation. It’s another, very different, thing to ensure that your vision runs through the entire company and product offering.

Along with the Vision mentioned above, Revolution Analytics presents a corporate mission to “Drive enterprise adoption of R by providing enhanced R products tailored to meet enterprise challenges.” Enterprise adoption will be hindered until the products reflect an ability to work for more than specialist programmers but can address a wider enterprise audience.

Part of the problem seems to be shown in the graphic below.

Revolution Analytics tech view of today

Revolution deserves credit for accurately representing the current BI space in snapshot. The problem is that it is a snapshot of today and there wasn’t an indication that the company understands how rapidly things change. Five to ten years ago, the middle column was the left column. Even today there’s a very technical need for the people who link the data to those products in order to begin analysis. In the same way, much of what is in the right column was in the middle. In only a few years, the left column will be in the middle and the middle will be on the right.

Software evolves rapidly, far more rapidly that physical manufacturing industries. Again, in order to address their enterprise mission, Revolution Analytics’ management is going to have to address what’s needed to move towards the right columns that mean an enterprise adoption.

Enterprise Scalability: A Good Start

One thing they’ve done very well is to build out the product suite to attract different sized businesses, individual departments and others with a scaled product suite to attract a wider audience.

Revolution Analytics product suite

Revolution Analytics product suite

They seem to have done a good job of providing a layered approach from free use of open source to enterprise weight support. Any interested person should talk with them about the full details.

Summary

R is a very useful analytical tool and Revolution Analytics is working hard to provide business with the ability to use R in ways that help leverage the technology. They’re working hard to support groups who want pure free open source and others who want true enterprise support in the way other open source companies have succeeded in previous decades.

Their tool does seem powerful, but it is still clearly and admittedly targeted at the very technical user, the data priests.

Revolution Analytics seems to have a start to a good corporate mission and I think they know where they want to end up. The problems is that they haven’t yet created a strategy that will get them to meet their vision and mission.

If you are interested in using R to perform complex analysis, you need to talk to Revolution Analytics. They are strong in the present. Just be aware that you will have to help nudge them into the future.

The Market Positioning Document: Consistency Helps the Message

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I’m regularly commenting on how companies are communicating their benefits. One thing I see seems often to be a scatter-shot approach to marketing. Some ideas are semi-formalized and then thrown out to whatever channels are handy. That usually doesn’t work.

You may have heard the term Integrated Marketing. It’s the idea that all your marketing messages should be consistent throughout all your communications channels.

Integrated marketing means more than marketing informally telling different people similar messages. It means formalizing your marketing message, distilling it to the core so that each channel group can work off of the same message to customize it appropriately for each channel. That’s where the positioning document comes in.

The Product Marketing Manager (PMM) is usually the owner for the message for each product, while corporate marketing can have a separate positioning document for the business. As I’m talking about how to better focus on marketing products, I’ll be referring to the PMM. Sadly, there’s not enough understanding of the need for a PMM in many small companies, so that’s one reason the messaging tends not to solidify, but this article will refer to the position.

The market positioning document should be a tool for consistency in all channels. That means it’s an internal tool as long as “internal” also means resellers or any partner who customizes messaging.

The Positioning Document

A positioning document for enterprise software solutions needs to address the following key issues:

  • What is it: If you can’t describe the product simply, you don’t understand it.
  • Why does it matter: What technical problems are you solving for the market?
  • What’s the value: How does that solution benefit the business and the stakeholders?
  • Target Audience: Speaking of stakeholders, who are they?
  • Competition: What issues matter in the competitive landscape and how are they addressed?

While all the issues matter, it’s the middle one that, like any deli sandwich, is the meet. What you have and what the market wants meets at your value. Part of that value section can be the elevator pitch, but it has to make clear why it is somebody wants to write you a check.

There are a number of ways of creating the positioning documents, so there’s no single template to define. What I’ve seen are two typical directions depending on the size and focus of the company.

Startups and early stage companies are typically focused on their technology. They’ve built something they think is needed and that a couple of prospects, typically known personally by founders, think they want. They need to formalize their market positioning, so they should start with what they have. The ordered list of bullets above area a good flow for companies in the early stage to clarify what they have and then figure out the larger market that can boost sales.

However, mid-size and larger companies should have things turned around. They should have changed from finding a market for cool technology to building technology for a market they understand. That means starting with the target audience. What are their pain points? What about their business needs help. Then look at those and understand where you can add value. From there, adjust your products or create new ones. The positioning document should help define products, rather than describing them.

One critical item that should run throughout the positioning document though not mentioned explicitly is the simple fact that product marketing isn’t in a void. PMMs are part of a larger corporation. Do not create a positioning document within the product marketing group but ensure that messaging matches corporate strategy. While that might sound obvious, I’ve seen examples of different PMMs creating positioning documents that contradict each other because of a product focus that doesn’t take into effect corporate needs.

Document Usage

The PMM controls the product messaging for public relations, advertising, analyst briefings and more. To be involved in all of those tasks to a detailed level is a huge strain upon a busy job. If the positioning document can be for basic boilerplate, it can save the PMM time. Whether the corporate marketing team extracts information to combine with other information, then runs it by the PMM or the PMM uses it as a basis for multiple documents to quickly hand off to the team, everyone’s job is made easier and more effective.

An oft overlooked, use of the document is SEO/SEM. Keywords matter. Trying to sit and think those up in a void is often an experiment in randomness. However, if you can distill what you’re doing into a core value statement, your keywords arise naturally. Depending on the search engine and the campaign, terms for the specific target audience can help raise results, as can understanding competitive positioning. The SEO/SEM team can work with the positioning document test keywords and bring them back to the PMM for analysis and refinement.

Don’t forget channel partners. While smaller partners can directly access your collateral or simply add their logo and contact, larger partners have their own processes, standards and templates. The positioning document can provide consistency across organizations, and even more important task than within your own organization.

A final example for all PMM’s can be summed up on one word: Re-use. The PMM is the source of the product message and has to keep abreast of all the places where your products are mentioned. If you can clarify and distill your message into a single document, you not only help the company but yourself as well. You’re no longer remembering the multiple documents where pieces exist or managing a large folder of examples. You have the document. You can boilerplate lots of information. When you distribute it and the rest of the marketing or sales team calls with questions, you can have them refer to the standard usage and messages, then more quickly help them adjust the messages to any unique environment.

Conclusion

The market positioning document should be a key tool used by every product marketing manager. It will help you focus your product message and then improve the effectiveness of working with others to customize that message for each channel of distribution. Good use of product positioning documents can create powerful messages that repeatedly address the key needs of the market across all channels, providing a consistent front that helps your company show value to prospects.

MicroStrategy at BBBT: A BI Giant Working to Become More Agile

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Last Friday’s BBBT presentation was by Stefan Schmitz, VP Product Management, MicroStrategy. This will be a short post because a lot of the presentation was NDA. Look to MicroStrategy World in January for information on the things discussed.

The Company

The primary purpose of the public portion of the presentation was to discuss the reorganization and refocus of MicroStrategy. Stefan admitted that MicroStrategy has always been weak on marketing and that in recent years Michael Saylor has been focused on other issues. Mr. Schmitz says those things are changing, Saylor is back and they’re focusing on getting their message out. In case you’re wondering why a company that claims to be pushing marketing showed up with only a product management guy, they’d planned on also having a product marketing person but life intervened. Stefan’s message clearly had strong marketing input and preparation so I believe the focused message.

When we discussed the current market, Paul te Braak, another BBBT member, asked a specific question about where MicroStrategy saw self-service analytics. Stefan responded, accurately, it was self-service for analysts only and systems are too simple and miss real data access.

One key point was the company’s view of the market as shown below.

MicroStrategy market view

The problem I have is that data governance isn’t there. It’s in some of the lower level details presented later, but that’s not strong enough. The blend of user empowerment and corporate governance requirements won’t be met until the later is perceived as a top criticality by technical folks. MicroStrategy is a company coming from the early days of enterprise business intelligence and I’d expect them to emphasize data governance the way a few other recent presenters have done, and the lack of that priority is worrisome.

The Technology

On the technology side, there were two key issues mentioned in the open session.

The first was a simplification of the product line. As Mr. Schmitz pointed out, they had 21 different products and that caused confusion in the market, slowing sales cycles and creating other problems. MicroStrategy has simplified its product structure to four products: The server, the architect for developing reports and dashboards, and separate products for Web and mobile delivery.

The second is an AWS cloud implementation along with data warehousing partners in order to provide those products as part of a scalable and complete package. This is aimed at helping the company move downstream to address departmental, divisional and smaller company needs better than their history of mainstream IT sales has allowed.

This is still evolving and the company can give more information, as you’d expect.

More was mentioned but, again, it was under NDA.

Summary

MicroStrategy is an established BI vendor, one of the older group of companies and, unlike Cognos and Business Objects, still standing on its own. The management knows that the newer vendors have started with smaller installations and are moving towards enterprise accounts. It is making changes in order to go the other direction. The company wants to expand from the core enterprise space into the newer and more agile areas of BI. Their plans seem strong, we’ll have to watch how they implement those plans.

Yellowfin at BBBT: Visualization and Data Governance Begin to Meet

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Last Friday’s BBBT presentation was by Glen Rabie, CEO, and John Ryan, Product Marketing Director, from Yellowfin. I reviewed their 7.1 release webinar in late August but this was a chance to hear a longer presentation focused for analysts.

Their first focus was on the BARC BI Survey 14. One point is that they were listed as number one, by far, in how many sites are using the product in a cloud environment. That’s interesting because Yellowfin does not offer a cloud version. This is corporations installing their own versions on cloud servers.

A Tangent: Cloud v On-Premises?

That brings up an interesting issue. Companies like to talk about cloud versus on-premises (regardless of the large number of people who don’t seem to know to use the “s”) installations, but that’s not really true. Cloud can be upper case or lower. Upper case Cloud computing is happening in the internet, outside a company’s firewall. However, many server farms, both corporate owned and third party, are allowing multi-server applications to run inside corporate firewalls the same way they’d run outside. That’s still a cloud installation by the technical methodology, but it’s not in the Cloud. It’s on-premises in theory, since it’s behind the firewall.

Time for a new survey. We’re talking about multi-server, parallel processing applications verses single server technology. What’s a good name for that?

Back to Our Regularly Scheduled Diatribe

One bit of marketing fluff I heard is that they claim to be the first completely browser based UI. I’ve heard from a number of other vendors who have used HTML5 to provide pure browser interfaces, so I don’t know or care if they were first. The fact that they’re there is important, as is the usability of the interface. The later matters more. As I mentioned in the v7.1 review, they don’t hide that they’re focused on the business analyst rather than the end user, and for that target audience it is a good interface.

An important issue that points to a maturation of business intelligence in the market place was indicated by a statement John Ryan made about their sales. Yellowfin used to be almost exclusively based on sales to small pilot projects, then working to increase the footprint in their clients. He mentioned that they’ve seen a recent and significant increase in the number of leads that are coming into the funnel as full enterprise sales from the start. That’s both a testament to IT reviewing and accepting the younger BI companies and to Yellowfin’s increased visibility in the market.

“All About the Dashboard” and Data Governance

Glen and John repeatedly came back to the idea that they’re all about providing dashboards to the business user, focusing on letting technical people do discovery and the tough work then just addressing visualization for the end user. The idea that the technical people should do the detailed discovery and the business user show just look at things, slicing and dicing in a limited fashion, might be a reason they’re seeing more enterprise sales.

They seem to be telling IT that companies can get modern visualization tools while still controlling the end users. That’s still a priests at the temple model. That’s not all bad.

On one side, they’ll continue to frustrate end users by limiting access to the information they want to see. On the other side, many newer firms are all about access and don’t consider data governance. Yes, we want to empower business knowledge workers, but we also need to help companies with regulatory and contractual requirements for data governance.

Yellowfin seems to be walking a fine line. They have some great data governance capabilities built in, with access control and more. One very useful function is the ability to watermark reports as to their approved status within the company. It might seem minor, but helping viewers understand the level of confidence a firm has in certain analysis is clearly an advantage.

An interesting discussion occurred in the session and on Twitter about a phrase used in the presentation: Governed Data Discovery. Some analysts think it’s an oxymoron, that data discovery shouldn’t be governed or limited or it’s not discovery. I think it makes a lot of sense because of the need for some level of controls. Seeing all data makes no sense for many reasons, and governance is required. Certainly, too tight governance and control is a problem, but I like where Yellowfin seems to be going on that front.

But What About the Rest of BI?

As mentioned, Yellowfin is working to let analysts build reports and help knowledge workers consume reports. However, the reports are built from data. Where’s that come from? Who knows?

When I asked how they get the information, they clearly stated they weren’t interested in the back end of BI, not ETL, not databases. They’re leaving that to others. That’s a risk.

Glen Rabie pointed out, earlier in the presentation, that many of their newer clients are swap-outs of older BI technologies. For instance, he said two of his more recent clients in Japan had swapped out Business Objects for Yellowfin. Check the old Business Objects press releases from customers in the last couple of decades. The enterprise sales weren’t “Business Objects sells…” but rather “Business Objects and Informatica,” “Business Objects and Teradata,” etc. visualization is the end of a long BI process and enterprises want the full information supply chain.

As long as Yellowfin is both clear about its focus and prepared to work closely with other vendors in joint sales situations then it won’t be a problem as the company grows. They need to be prepared for that or they’ll slow the sales cycle.

Social Media Overthought

The final major point is about Yellowfin’s functionality for including social media within the product to enhance collaboration. While the basic concept is fine and their timeline functionality allows a team to track the evolution of the reports, I have two issues.

First, the product doesn’t link with other corporate-wide social tools. That means if a Yellowfin user wants to share something with someone who doesn’t need to use the tool, a new license is needed. I know that helps Yellowfin’s top line, but I think there should be some easy way of distributing new analysis for feedback from a wider audience without a full license.

Second, and much less important, is the mention of allowing people to vote on the reports. I was amused. It reminded me of a great quote from the late Patrick Moynihan, “Everyone is entitled to his own opinion, but not to his own facts.” I think the basic social tool in Yellowfin is very useful, but voting on facts seems a tad excessive.

Summary

Glen Rabie and John Ryan gave a great overview of Yellowfin, covering both the company’s strategy and the current state of product. Their visualization is as good as most others and they have some of the most advanced data governance capabilities in the BI industry.

There’s a lot of good going on down under. Companies wanting modern visualization tools should take a look, with one caveat. If you think that the power of modern systems means that functionality is clearly moving forward and should allow business users to do more than they have been able to do, Yellowfin might not match up with other firms. If you think that end users only want dashboards and want a good way of providing business workers with those dashboards, call now.

Webinar Review. Lyndsey Wise and Information Builders: Five Ways SMBs are Putting Information to Work

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This morning I attended a TDWI webinar with Lyndsay Wise, Wise Analytics, and Chris Banks, Information Builders, presenting the subject line topic. It was a nice change from the webinar earlier in the week.

Lyndsey Wise began by speaking about the differences between Small-to-Medium sized businesses (SMBs) and enterprises. While SMBs are smaller and have fewer resources than enterprises, they are also more adaptive and can more quickly make decisions that impact corporate infrastructure.

I’ll disagree with what she said in that description on only one point. Ms. Wise mentioned the lack of internal IT resources makes SMBs more comfortable with consultants, the Cloud and information appliances. Two out of three ‘aint bad. Appliances tend to be a capital expense made by enterprises wanting to retain control inside the firewall. They give enterprises more comfort. SMBs don’t typically want that kind of investment and overhead. She’s absolutely right that’s why many external IT consultants and web software vendors have successfully focused on the market.

She then turned to a discussion of the following five ways SMBs are better utilizing information:

  • Embedded BI within operations
  • Web portal expansion and information sharing
  • Mobilization of the workforce
  • Better flexibility in dashboard and analytics design – self-service and data discovery
  • Monetizing of data collected

The first point was that SMBs have had to deal far longer than did enterprises with separate operational and BI software that barely overlapped if you were lucky. Operational software companies have begun to add BI to their packages while BI vendors are better linking to operational systems, both at the data end and up front through single-signon and other interfaces enhancements that aid integration.

The growth of web portals is not new and not really in the pure BI space, but it is a key part of the solution, as Ms. Wise points out for the reason that it helps businesses share with customers, suppliers and other people and organizations in their ecosystems. I’m glad she had the point there because many people in tech sectors get focused just on their niche and don’t look at the full information picture.

Workforce mobilization issues are the same for any size business. The only difference I see is that SMBs have fewer resources for training. People wanting to focus on SMBs need to ensure that mobile apps go through user interface design cycles to present clear and easy understanding of critical information.

The fourth point is critical. It ties into what I mentioned in the previous paragraph. As Lyndsey Wise pointed out, SMBs don’t tend to have programmers, especially not ones with the inflated title of data scientist (full disclosure, “inflated” is my opinion not hers), That means UI, UI, UI. True self-service is a must for SMBs.

The final issue is another on which I’m on the fence as to the importance. Not that monetization is unimportant, just that monetization of information has been important to business for millennia before computers. All BI is ultimately about making better business decisions and that usually means a predominate roll in lower costs or increasing revenue. That’s information monetization. It’s not that the point is unimportant, it just seems redundant to me.

The presentation was then turned over to Chris Banks. Information Builders, one of the big first generation BI firms, seems to be making a big push to remind people it’s there. Given the short time frame, Chris intelligently shortened and breezed through his presentation. He gave a good overview of the breadth and depth of his company’s offerings.

The issue I have with Mr. Banks’ presentation is, no surprise to anyone who knows I focus on marketing, is how he presented the company. Information Builders, Cognos, Business Objects, et al, grew up in a time when enterprises were trying to understand the large amounts of data in multiple operational systems. They spent decades on an enterprise sell. Chris is working to position them to fit Wise’s SMB message, but he’s not there yet.

Showing a crowded diagram of the breadth of products is scary to SMBs, even with Chris’ caveat that he’d never expect anyone to buy all the products. He should have focused on a simpler slide showing how SMBs can quickly begin using his products.

Then he transitioned to a slide showing a portal, backing Ms. Wyse’s second point. Sadly, it was the Hertz portal. Not exactly an SMB play.

That continued with a NASCAR slide of global enterprise customers and a case study from a large US bank. It wasn’t until the very end that there was a case study on an SMB, a roofing contractor that achieved insight and business benefit from Information Builder’s tools.

Summary

Lyndsey Wise had a good overview of issues facing small-to-medium sized businesses when trying to better gather, manage and understand business information.

Information Builders is working to communicate with the SMB community and made an ok first stab. From the presentation, I’m not really sure how they can help, but I’m not convinced they can’t. There’s much more work to be done to better address and important market.

Webinar Review: Big Data addressed poorly

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I’ve been in computing business for almost thirty five years, but until this year it was always working for vendors or systems integrators. As a newly minted analyst, I’ve stayed away from very negative reviews. I’ve watched a few bad webinars recently and made the choice not to blog about them. However, as I’ve seen more and more, I’ve realized that doesn’t help the industry and I can’t remain silent.

On Tuesday, I watched a webinar by David Loshin, President of Knowledge Integrity, and Ramesh Menon from Cray. It was not pretty.

Let’s take, for instance, David Loshin’s five points for big data:

  • Plan for scalability
  • Go heavy on Memory
  • Free your applications from the Hadoop 1.0 Execution Model
  • Real-time ingestion and integration
  • Feed the SQL need

The first item has been around since client/server application first came to the fore. Big data has grown, in part, because of its ability to scale large volumes of data. This is nothing new.

Memory? It was a great point years ago, with Tableau and others having pushed it for quite a while. However, the last year or two we’ve been hitting the limits of pure memory solutions and I’ve seen a number of presentations from vendors focused on better integrating memory and disk depending on data latency needs. David’s statement that ““We will start seeing more applications using memory rather than disk,” is wrong. We’ll see more applications better leveraging memory, but disk isn’t going anywhere.

The Hadoop organization’s release of Hadoop v2, YARN, is a clear indication of the limitations of 1.0 and why people have also been talking about it for years. However, in the presentation, leading with 2.0 would have been better than again being a laggard about the known issues with 1.0. Either people use Hadoop and already know the issues or haven’t yet used it and will start with 2.0.

It’s not real-time ingestion the critical issue and I would have liked to see him focus more on the second half of the fourth bullet. Real-time extractions of information are moving much more rapidly than the ability to integrate it with the rest of corporate information and to provide analytical to that information.

David’s final point is the only timely one. People have recently begun to remember that evolution is easier than revolution and I’ve seen a number of vendors begin to focus on providing access to the new data sources via SQL. A lot more people providing business insight to corporations know SQL and that needs to be made available. Ramesh Menon said it better, but the point is here.

The biggest problem I had was with Loshin’s forward looking statement. I’ll almost ignore that nonsense about data lake, he’s not the only one busy trying to use a new, supposedly fancier, term for the ODS, but I’ll mention it anyway. The issue was that he claimed he saw data management moving away from the data lake as we move to in-memory. Really? The ODS isn’t going anywhere. It’s nonsense to thing that every bit of corporate information needs to reside in memory, just in case it might be needed. The ODS is becoming the central source of all operational and business data. Individual business intelligence tools and needs will drive in-memory usage for near real-time needs of specific departmental, divisional or corporate level analytics needs, but there will always be a non-memory source for all that information in order to provide consistency, appropriate levels of control and to handle data governance issues.

Now we turn to Ramesh Menon. His presentation was better than David Loshin’s, but not by much. I’m sorry, there’s no excuse for someone who puts himself forward as a voice of the industry to not understand the difference between a premise and a premises. Considering he used premise correctly in his presentation, it was terrible that it was used three times before that while describing “on-premise” computing. Everyone in our industry needs to sit down, focus and practice saying the right word.

His customer use case was a very jumbled story and an overcrowded slide with the main point being “the customer had a lot of data.” I wouldn’t have guessed. He needs to talk more about solutions, how Cray address the data.

As mentioned above, Ramesh had a very clear point about the difference between data scientists and business analysts being one reason that Hadoop 2.0 is important. The move from batch to lower latency access is part of the difference between a data scientist, someone wanting to be the priest at the temple, and a business analyst, a much larger group working to provide wider access to business information. Updating Hadoop is critical to the ability to keep it relevant.

That was a key point, the problem is that Ramesh isn’t the analyst, he’s Cray’s spokesperson. The discussion shouldn’t have been about generalities but about how Cray must have focused on Hadoop 2.0 for the Urika-XA appliance – but that wasn’t made clear. It was in the data sheet images plopped into the presentation, but reasons and results should have been openly discussed.

I’ll end with the one very interesting point from Mr. Menon’s presentation. He had a slide where he discussed four phases in the analytics pipeline: ETL, algorithms, analysis and visualization. His point is that there are very different resource requirements for each phase of the pipeline. This could be an entire presentation itself and Ramesh could focus and expand this, explaining how Cray helps to address part or all of those requirements to help present Cray to the industry.

Summary

The analysis got a couple of things right, but was mostly too late or wrong. The corporate presentation didn’t clearly link Cray to the issues involved. Both presentation halves were far to generic and descriptive with almost no descriptive takeaways. Furthermore, you could tell that neither presenter seemed to have put much time and effort into the webinar by both the content and presentation styles.

People need to learn that “there’s no such thing as bad press” is only something said by entertainers. It’s not enough to have a webinar to get your name out there. Lots and lots of companies are doing that. Thought needs to go into the presentation and practice needs to go into delivery.

There were some good tidbits in the presentation, but overall it was a mess. I was very disappointed in the hour that I lost.

WhereScape at BBBT: Another Intriguing Product Without a Clear Message

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Last Friday’s BBBT presentation was by Michael Whitehead, CEO, WhereScape. The company seems to have a very interesting and useful product, but there’s a huge communications gap that needs to be addressed.

What They Do

One marketing issue to start was that I got most of this section from my own experience and WhereScape’s web site, not from Michael’s presentation. When someone begins a presentation by proudly announcing it is ““guaranteed there’s no corporate marketing in the presentation at all” while you’re presenting to a group of analysts, there’s a disconnect and it shows.

WhereScape has two products, Red and 3D, to help build and maintain data structures. The message is focused on data warehouses, but I’ll discuss that more in the next section. One issue was that their demonstration didn’t work as there seemed to be a problem connecting between their tablet and the BBBT display system, so much of what I’m saying is theory rather than anything demonstrated.

Red is their tool to build data warehouses. Other tools exist and have been around for decades, Informatica being just one competing firm.

3D is where the differentiation comes in. Everyone in IT understands that nightmare that is upgrading major software installations such as ERP, CRM and EDW systems. Even migrating from one version to the next of a single vendor can involve months of planning, testing and building, followed by more months of parallel runs to be safe. A better way of analyzing and modifying data structures that can compress the time frame can have a large positive impact upon a corporation. That’s what WhereScape is attempting.

What They Say

However, their message is all “Automation! Automation! Automation!” and the short part of the demo that worked showed some automated analysis but a lot of clicks necessary to accomplish the task. From what I saw, it will definitely speed up the tasks, if as advertised, with clear time and money savings, but it’s not as automated as implied and I think a better message is needed.

In addition, their message is focused on data warehouses while Michael said “We’re in the automation business not the data warehouse business,” which really doesn’t say anything.

Michael did talk for a bit about the bigger data picture that includes data warehouses as part of the full solution, but again there’s no clear message. While saying that he doesn’t like the term Data Lake, he’s another that can’t admit that it’s just the ODS. There’s also a discussion of the logical data warehouse, also not something new.

One critical and important thing Mr. Whitehead mentioned was something I’ve heard from a few people recently, the point that Hadoop and other “unstructured databases” aren’t really unstructured, they support late binding, the ability to not have to define a structure a priori but to get the data and then understand and define a useable structure for analysis.

What They Need to Say

This is the tough one and not something I’m going to solve in a short column. The company is targeting a sweet spot. Data access has exploded and that includes EDW’s not going away, the misnamed concept of Big Data and much more. Many products have been created to build databases to manage that data but the business intelligence industry is still in the place packaged, back-end systems were in the 1990s. Building is easier than maintaining and upgrading. A firm that can help IT manage those tasks in an efficient, affordable and accurate way will do well.

WhereScape seems to be aimed at that. However, their existing two-fold focus on automation and data warehousing is wrong. First, it doesn’t seem all that automated yet and, even if it was, automation is the tool rather than the benefit. They need to focus on the ROI that the automation presents IT. Second, from what was discussed the application has wider applicability than just EDW’s. It can address data management issues for a wider area of business intelligence sources and the message needs to include that.

Summary

Though the presentation was very disjointed, WhereScape seems to have focused on a clearly relevant and necessary niche in the market: How to better maintain and upgrade the major data sources needed to gain business understanding.

Right now, while there is a marketing staff at the company, WhereScape’s message seems to be solely coming from the co-founder and CEO. While that was ok in the very early days, they have some good customer stories, having led with Tesco’s success in this presentation, and it’s time to leverage a stronger and clearer core message to the market.

Where the issue seems to be is the problem I’ve repeatedly seen about messaging. The speed of the industry has increased and business intelligence is, on a whole, crossing Jeffrey Moore’s chasm. That means even younger firms need to transition from a startup, technically focused, message to a broader one much more rapidly than vendors needed to do so in the past.

While WhereScape has what seems to be the strong underpinnings of a successful product, they need to do some seriously brainstorming in order to clarify and incorporate a business oriented messaged throughout their communications channels – including in presentations by founders.

An ODS by any other name still smells like data

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Data warehouse theory originally posited extracting data from systems, performing transformations on them and loading the resulting schemas into the data warehouse. It was a straight flow of information. However, the difference between theory and practice quickly reared its head. Today, people are talking about Data Lakes and Data Swamps. They’re not new, they’re just the ODS updated for modern data.

Data Warehouses and the ODS

Academics don’t have to deal with operational systems. In the 1980s and 1990s, those systems were growing, with ERP, CRM and other systems increasing the complexity and volume of data. Those mission critical systems, however, weren’t designed for extraction of information. They were primarily running on RDBMS systems that had locking schemas that could grind process and transactional systems to a halt while and extraction program kept open large blocks of records while transforming basic data in star schemas. Something needed to be done.

There was also a secondary effect that was very important to some people. IT, just as with ever other department in a large enterprise, isn’t monolithic. The people managing the operational systems knew their systems were mission critical and also knew how, in reality, those systems were big but fragile. They weren’t happy with opening their operational systems to other IT folks who were interested in non-operational things. Those folks answering other business problems? They were viewed as intruders, getting in the way of the “real work.”

For both reasons, intrusions into the operational systems were something to be kept to a minimum. IT organizations began using an Operational Data Store (ODS) to quickly open the operational systems, suck all the data out, willy-nilly (yes, I decided to use that term in a tech article…), and then go back to prime performance in an isolated system.  ODS 1

It was then the ODS that was the source of the data warehouse ETL process. On a tangent, this is why the people now arguing about ETL v ELT amuse me. It’s been ELETL for decade, if we want to be honest; but who cares? I’d rather have a BLT than spend so many cycles over slightly different acronyms for concepts that ETL handily describes, even in permutations.

The ODS comes into its own

The IT folks who were working to provide reports for mid- and high-level managers were always trying to tweak enterprise software reports, trying to extract nuggets of value. The data warehouse was a step forward and helped build a bigger picture. However, the creation of star schemas and other DW techniques aggregated data and lots a lot of detail. A manager would see an issue and want to backtrack, to drill-down into the data to know more.

The ODS became the way to do so. Very quickly, the focus changed from ODS in front of the data warehouse to both working side-by-side. Having all that raw data available gave the business analysts a way of providing much more detail and information to the business user. The first big BI companies, those such as Cognos, Business Objects and more, leveraged the two data stores to provide an ability to drill down past the aggregate information into the more detailed data.ODS 2

Having that large volume of data from multiple operational systems also intrigued people who weren’t data warehouse focused. They wanted to sift the raw data for technical or performance trends, things that weren’t of interest to the typical DW designers and users, but were important to mid-level management in manufacturing, marketing and other departments. Business analysts supporting those people began to turn to more and more analysis directly on the ODS data

The ODS comes to the fore – by another name

That was happening in the 1990s, at the same time another key phenomenon was growing: The Web. The growth of the web meant a lot more data about a lot more things. Web sites are operational systems to marketing in just as critical a way as an assembly line is to manufacturing. People became interested in ensuring that what visitors to web sites did was captured and available for analysis. However, as the volume of web traffic grew exponentially, new issues had to be looked at to handle that data.

Columnar databases were one solution, a way to speed up analysis of dimensions of information across individual records. The vastly larger amount of data also helped push emerging MPP technologies and drove creation of Hadoop and other technologies that could manage much larger data sources much faster and more cost efficiently than could individual Unix servers.

However, the web folks were new to IT and grew up in a different generation than the folks who designed and drove data warehousing. It’s natural to ODS 3want to take ownership of concepts, especially those on the edge. So the folks working with these new data sources began talking about Big Data as somehow completely different than what came before. If that was the case, they needed to think of some term for the database where they dumped all the data extracted from web sites. Data Lakes became one term. We’ve heard data swamp and other attempts to create unique terms so a company can differentiate itself from others. However, there’s already a name.

The ODS exists. It’s evolved. It’s moved forward. But it’s still the ODS.

Yes, really

“But,” you say, “an ODS is operational information and the data lake is so much more!” Well, not quite. There are two main problems with that argument.

First, times change. When the ODS was coined, the focus was on the back-end systems such as ERP, CRM, accounting and other fairly closed systems. It was before the web, before the ubiquity of mobile devices, before the wall between back-end and customer-facing systems was destroyed.

As mentioned, not just web sites are but even the internet is an operational system for your business – and not just for ecommerce companies. From lead generation, to maintenance and training, the internet is a key tool for providing operational support and generating business critical operational details.

Second, just as ETL can mean a number of things, so can ODS extend past a pure theory while still being relevant. CRM systems are considered operational but still contained sentiment and other information in comments fields. Just so, the vast volume of data from a call center’s voice recording system being dumped into the ODS have two components. There are basic details about the operation of the call center, things such as number of calls, call length and other details that are purely operational. There are also additional details about customers that can be distilled for strategy purposes, including the ability to provide sentiment analysis. Just because an operational system captures data that can be used for more than purely operational decision making doesn’t obviate that the information extracted resided in an ODS.

Summary

It’s a need of information technologists from all generations to realize that things change but retain context. The ODS isn’t what it was thirty years ago, but the data lake also isn’t some new creation born full blown from the web. There are few truly revolutionary technologies. You can be a brilliant person and contribute much to technology and business and still not be a revolutionary.

The ability to manage the vastly larger amounts of data than we had twenty years ago is critical. There are many innovative things being done. However, I consider the first expert systems, the first MPP algorithms and other similar technologies to be revolutionary. The fact that what is being done to allow business to gain insight combining more and more data from even more diverse sources is no less valuable to the industry because it is instead an evolutionary change.

The ODS has evolved. It doesn’t need a new name, just a tad more respect.

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.