Category Archives: Marketing

BI Buzzwords for business management: Self-service and machine learning briefly explained

I’ve seen a few company webinars recently. As I have serious problems with their marketing, but don’t wish that to imply a problem with technology, this post will discuss the issues while leaving the companies anonymous.

What matters is letting business decision makers separate the hype from what they really need to look at when investigating products. I’m in marketing and would never deny its importance, but there’s a fine line between good marketing and misrepresentation, and that line is both subjective and fuzzy.

As the title suggests, I’ll discuss the line by describing my views of two buzzwords in business intelligence (BI). The first has been used for years, and I’ve talked about it before, it’s the concept of self-service BI. The second is the fairly new and rapidly increasing use of the word “machine” in marketing

Self-Service Still Isn’t

As I discussed in more detail in a TechTarget article, BI vendors regularly claim self-service when software isn’t. While advances in technology and user interface design are rapidly approaching full self-service for business analysts, the term is usually directed at business users. That’s just not true.

I’ve seen a couple of recent presentations that have that message strewn throughout the webinars, but the demonstrations show anything but that capability. The linking of data still requires more expertise that the typical business user needs. Even worse, some vendors limit things further. The analysts still create basic reports and templates, within which business people can wander with a bit of freedom. Though self-service is claimed, I don’t consider that to approach self-service.

The result is that some companies provide a limited self-service within the specified data set, a self-service that strongly limits discovery.

As mentioned, that self-service is either misunderstood or over promised doesn’t obviate that the technology still allows customers to gain far more insight than they could even five years ago. The key is to take the promises with a grain of salt.

When you see it, ignore the phrase “self-service.”

Prospective BI buyers need to focus on whether or not the current state of the art presents enough advantages over existing corporate methodologies to provide proper ROI. That means you should evaluate vendors based on specific improvements on your existing analytics and the products should be rigorously tested against your own needs and your team’s expertise.

Machine

Machine learning, to be discussed shortly, has exploded in usage throughout the software industry. What I recently saw, from one BI vendor, was a fun little marketing ploy to leverage that without lying. That combination is the heart of marketing and, IMO, differs from the nonsense about self-service.

Throughout the webinar, the presenter referred to the platform as “the machine.” Well, true. Babbage’s machines were analytic engines, the precursors to our computers, so complex software can reasonable be viewed as a machine. The usage brings to mind the concept of machine learning while clearly claiming it’s not.

That’s the difference, self-service states something the products aren’t while machine might vaguely bring to mind machine learning but does not directly imply that. I am both amused and impressed by that usage. Bravo!

Machine Learning and Natural Language Processing

This phrase needs a larger article, one I’m working on, but I would be remiss to not mention it here. The two previous sections do imply how machine learning could solve the self-service problem.

First, what’s machine learning? No, it’s not complex analytics. Expert systems (ES) are a segment of artificial intelligence focused on machines which can learn new things. Current analytics can use very complex algorithms, but they just drive user insight rather than provide their own.

Machine learning is the ability for the program to learn new things and to even add code that changes algorithms and data as it learns. A question to an expert system has one answer the first time, and a different answer as it learns from the mistakes in the first response.

Natural Language Processing (NLP) is more obvious. It’s the evolving understanding of how we speak, type and communicate using language. The advances have meant an improved ability for software to responds to people without clicking on lots of parameters to set search. The goal is to allow people to type or speak queries and for the ES to then respond with information at the business level.

The hope I have is that the blend will allow IT to set up systems that can learn the data structures in a company and basic queries that might be asked. That will then allow business users to ask questions in a non-technical manner and receive information in return.

Today, business analysts have to directly set up dashboards, templates and other tools that are directly used by business, often requiring too much technical knowledge. When a business person has a new idea, it has to go back to a slow cycle where the analyst has to hook in more data, at new templates and more.

When the business analyst can focus on teaching the ES where data is, what data is and the basics of business analysis, the ES can focus on providing a more adaptable and non-technical interface to the business community.

Machine learning, i.e. expert systems, and NLP are what will lead to truly self-service business applications. They’re not here yet, but they are on the horizon.

Book Review: The Content Trap

The new books section of my library had a text I almost didn’t check out. Unfortunately, I did. It’s “The Content Trap” by Bharat Anand, and it’s another great example of what academics miss about the real world. The book, from the fly leaf and introduction, presents itself as attempting to say that social networks are important and content isn’t. While the recent presidential election might imply that’s true, the author is supposedly knowledgeable about business and is focused on helping management strategy.

The problem is that I didn’t get twenty pages into the book before Mr. Anand displayed his complete misunderstanding of the business of technology. His chapter three is about “networks” and the first example purports to explain why Apple lost to Microsoft in the 1980s. He provides some semantically nil blather about “direct network effects” and “indirect network effects,” while assiduously avoiding what happened.

There are a number of reasons for Apple’s failure to get a significant market share at that time, among which are:

  • Jobs and Wozniak ran a perfectionist organization while Gates and Allen quickly got “good enough” products to the market.
  • Microsoft’s founders understood what IBM’s off-the-shelf production meant for rapidly entering a market while Apple wanted complete control of hardware, software and networking.
  • Apple went for high-end price and élan rather than the factors that attract a business market quickly looking to move many things off of the mainframe and onto a manager’s desk.
  • While Microsoft quickly adapted to larger screens, more functional mice, and other newer technology easier for business users, Apple stuck with the Mac’s small screen, one button mouse, and other limitations for far too long.

While the author talks about “network effects,” he doesn’t seem to show any understanding of the key products that provided that for Microsoft: The elements that became Microsoft Office: In particular, the spreadsheet. To talk about networks at a high, completely theoretical, level while claiming to give a case study does nothing to display an understanding of the issues involved.

That brings us back to Mr. Anand’s primary, fallacious, point. The PC didn’t create a network. Mainframe reports already provided to the network. His page 13 graphic about the hub and spoke versus multiple connection network has a simplistic accuracy but again misses the point. In the traditional method, most content was centralized. What he misses is that it’s not just users talking to each other around central content, as he presents, but each user having his or her own content that needs to integrate which changed.

The spreadsheet, and so many things since then, allowed individual managers to create their own content and then share it, faster than they previously could do the same. That led to a speed-up of business reactions.

However, it also led to multiple versions of content and the question of “versions of truth” that those of us in business intelligence daily address. We understand the power of networks, but also understand that without content and control over it business will have serious problems.

Content and networks can be seen as two halves of a coin. However, as the Apple example shows, they’re really two faces of a die, with many other factors that also matter. Bharat Anand doesn’t seem to comprehend that, but seems to instead to be quickly taking advantage of a market condition to abuse a network without content. It’s clear that, if you’re only interested in making money, networks will help. For example, an impressive academic title might get a lot of libraries to buy your book. However, to be truly of value, there must be content. The Content Trap lacks content. The author has made money, he’s added another line to his CV, but he’s added nothing of value to the ecosystem.

Nobody in business should pay attention to this book.

DBTA Webinar: Too many cooks, yet again

Sadly, DBTA is becoming known for taking interesting companies, putting them in a blender and having each lose their message. A recent webinar included Cask, Attunity and HPE Security – all in a one hour time slot – again shows the problem. It was a mess.

Cask is a young Hadoop company with an interesting opportunity (Disclosure: As I’m discussing marketing, I need to mention I recently interviewed for a position at Cask). The company is working to put wrappers around Hadoop code to make it easier for IT to use the data platform. One of their products is Cask Hydrator, to help populate the database. That begins to move the message of Hadoop out of the early adopter phase and into a business message, but the presentation was still far to technical.

Attunity then presented and a key point was that they make data ingest easy. If that sounds like a similar message to Cask’s, you’re right. Why the two were together on the webinar when much of what they said sounded like competition wasn’t clear. On the good side, Attunity did a far better job at presenting a business message, both in how the presenter talked about the products and in which case studies were used.

HPE Security made another appearance, tacked onto the end of a presentation. Data security is critical, and HP has put together a very good message on it, but it didn’t vaguely fit the tone and arena of the previous presenters.

When Companies Should Share a Stage

The smaller companies seem to have a problem. It’s simple: Their involvement in webinars might be driven by marketing, but it’s being controlled by bean counters. Each of the three companies had something good to say, and each should have taken the time to say it in a stand-alone webinar. However, sharing costs was made to be the primary issue and so the mess ensued.

When should firms share the spotlight? That should happen when the item missing from the top of my presentation is there. The missing piece is having a joint story to tell. None of the case studies mentioned the companies working in partnership. None. When multiple vendors work to provide a complete solution to a client, even if the vendors might sometime compete, there’s a strong case for multiple companies in a webinar.

This webinar was not that. It was companies not feeling strongly enough about themselves for the other executives to overrule the COO’s or CFO’s and push a solid webinar about themselves.

All of these companies are worth looking at within the big data arena, just not in such a forced together setting. Stand on your own or show a joint project.

DBTA Webinar Review: Leveraging Big Data with Hadoop, NoSQL and RDBMS

A presentation last week, hosted by Database Trends and Applications (DBTA), was a great example of some interesting technical information presented poorly. As that sentence implies, this column is one about the marketing of business intelligence (BI), not about the technology – well, not much…

There were three presenters: Brian Bulkowski, CTO and Co-founder, Aerospike; Kevin Petrie, Senior Director and Technology Evangelist, Attunity; Reiner Kappenberger, Global Product Management, HPE Security – Data Security.

Aerospike

Brian was first at the podium. Aerospike is a company providing what they claim is a very high speed, scalable database, proudly advertising “NoSQL!” The problem they have is that they are one of many companies still confused about the difference between databases and SQL. A database is not the access method. What they’re really focused on in loosely structured data, the same way Hadoop and other newer databases are aimed. That doesn’t obviate the need to communicate via SQL.

He also said that the operational in-memory market is “owned by NoSQL.” However, there were no numbers. Standard RDBMS’s, columnar and NoSQL databases all are providing in-memory storage and processing. In fact, Information Management has a slide show of Gartner’s database analytics vendor report and you can see the breadth there. In addition, what I constantly hear (not statistically significant either…) is that Hadoop and other loosely-structured databases are still primarily for batch. However, as the slide show I just mentioned is in alphabetical order, and Aerospike is the first one you’ll see. Note again that I’m pointing out flaws in the marketing message, not the products. They could have a great in-memory solution, but that’s doesn’t mean NoSQL is the only NoSQL option.

The final key marketing issue is that he kept misusing “transactional.” He continued to talk about RDMS’s as transactional systems even while he talked about the power of Aerospike for better handling the transactions. In the later portion of his presentation, he was trying to say that RDBMS’s still had a place, but he was using the wrong term.

Attunity

Attunity’s Kevin Petrie was second and his focus was on Attunity Replicate. The team of Aerospike and Attunity again shows the market isn’t yet mature enough to have ETL and databases come smoothly together. Kevin talked about their 35 sources and it seem that they are the front end in the marketing paring of the two companies. If you really need heterogeneous data sources and large database manipulation, you’ll need to look at the pair of companies.

My key issue with this section was one of enterprise priorities. Perhaps the one big, anonymous reference they both discussed drove the webinar, but it shouldn’t have owned the message. Mr. Petrie spent almost all his time talking about Hadoop, MongoDB and Kafka. Those are still bleeding edge tools while enterprise adoption requires a focus on integrating with standard and existing sources. Only at the end, his third anonymous case, did Kevin have a slide that mentioned RDBMS sources. If he wants to keep talking with people running experimental and leading edge tests of systems, that priority makes sense. If he wishes to talk to the larger enterprise market, he needs to turn things around.

The other issue was a slide that equated RDBMS, Data Warehouse and Hadoop as being on equal footing. There he shows a lack of business knowledge. The EDW, as an old TV would declare, is the one of these things that is not like the other. It has a very different purpose from the two database technologies and isn’t technology dependent.

HPE Security

Reiner Kappenberger gave a great presentation but it didn’t belong. It seems the smaller two firms were happy to get HP to help with the financing but they didn’t think about staying on message.

Let me make it very clear: Security is of critical importance. What Mr. Kappenberger had to say was very important for people to hear. However, it didn’t belong in this webinar. The topic didn’t fit and working to stuff three presenters into forty minutes is always tough. Another presentation where all three talked about how they work to ensure that the large volumes of data can be secure at multiple levels would have been great to hear – and I hope the three choose to create such a webinar.

Summary

This was two different webinars stuffed into one, blurring the message. In addition, Aerospike and Affinity either need to make sure they they’re not yet trying to address the mass market or they need to learn how to stop speaking to each other and other leading edge people and begin to better address the wider enterprise market.

The unnamed reference seemed to be a company that needed help with credit card transactions and fraud detection, and all three companies worked to provide a full solution. However, from a marketing standpoint I don’t think they did proper service to their project by this 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.

Summary

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 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.

Diyotta: Data integration for the enterprise

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

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

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

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

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

The three main markets John said Diyotta aims at are:

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

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

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

Diyotta markitechture slide

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

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

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

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

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

Summary

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

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

IBM, you BM, we all BM for … Spark!

IBM at BBBT

A recent presentation by IBM at the BBBT was interesting. As usual, it was more interesting to me for the business information than the details. As unusual, they did a great job in a balanced presentation covering both. While many presentations lean too heavily in one direction or the other, this one covered both sides very well.

The main presenter was Harriet Fryman, VP of Marketing, IBM Analytics Platform. Adding information during the presentation were Steven Sit, Director of Product Management, Open Source Based Analytics Systems, and Steve Beier, Program Director, Spark Technology Center.

The focus of the talk was IBM’s commitment to Apache Spark. Before diving deep into the support, Ms. Fryman began by talking about business’ evolving data needs. Her key point is that “we all do data hording,” that modern technologies are allowing us to horde far more data than ever before, and that better ways are needed to get value out of the data.

She then proceeded to define three key aspects of the growth in analytics:

  • Applying analytics in more parts of business.
  • Understand the time value of data.
  • The growth of machine learning and cognitive systems.

The second two overlap, as the ability to analyze large volumes of data in near real-time means a need to have systems do more analysis. The following slide also added to IBM’s picture of the changing focus on higher level information and analytics.IBM slide - evolving approach to data

The presentation did go off on a tangent as some analysts overthought the differences in the different IBM groups for analytics and for Watson. Harriet showed great patience in saying they overlap, different people start with different things and internal organizational structures don’t impact IBM’s ability to leverage both.

The focus then turned back to Spark, which IBM sees as the unifying layer for data access. One key issue related to that is the Spark v Hadoop debate. Some people seem to think that Spark will replace Hadoop, but the IBM team expressed clear disagreement. Spark is access while Hadoop is one data structure. While Hadoop can allow for direct batch processing of large jobs, using Spark on top of Hadoop allows much more real time processing of the information that Hadoop appropriately contains.IBM slide - Spark markitechture

One thing on the slide that wasn’t mentioned but links up with messages from other firms, messages which I’ve supported, is that one key component, in the upper left hand corner of the slide, is Spark SQL. Early Hadoop players were talking about no-SQL, but people are continuing to accept that SQL isn’t going anywhere.

Well, most people. At least fifteen minutes after this slide was presented, an attending analyst asked about why IBM’s description of Spark seemed to be similar to the way they talk about SQL. All three IBM’ers quickly popped up with the clear fact that the same concepts drive both.

While the team continued to discuss Spark as a key business imitative, Claudia Imhoff asked a key question on the minds of anyone who noticed huge IBM going to open source: What’s in it for them? Harriet Fryman responded that IBM sees the future of Spark and to leverage it properly for its own business it needed to be part of the community, hence moving SystemML to open source. Spark may be open source, but the breadth and skills of IBM mean that value added applications can be layered on top of it to continue the revenue stream.

Much more detail was then stated and demonstrated about Spark, but I’ll leave that to the more technical analysts and vendor who can help you.

One final note put here so it didn’t distract from the main message or clutter the summary. Harriet, please. You’re a great expert and a top marketing person. However, when you say “premise” instead of “premises,” as you did multiple times, it distracts greatly from making a clear marking message about the cloud.

Summary

IBM sees the future of data access to be Apache Spark. Its analytics group is making strides to open not only align with open source, but to be an involved player to help the evolution of Spark’s data access. To ignore IBM’s combined strength in understanding enterprise business, software and services is to not understand that it is a major player in some of the key big data changes happening today. The IBM Spark initiative isn’t a marketing ploy, it’s real. The presentation showed a combination of clear business thought and strategy alongside strong technical implementation.

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

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

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

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

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

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

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

LASR, VAE and a cast of thousands

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

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

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

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

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

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

Great, But Not Yet End-To-End

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

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

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

Summary

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

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