August 23, 2016
Search and retrieval technology finds a place in a “bot landscape.” The collection of icons appears in “Introducing the Bots Landscape: 170+ Companies, $4 Billion in Funding, Thousands of Bots.” The diagram of the bots landscape in the write up is, for me, impossible to read. I admit it does convey the impression of a lot of a bots. The high resolution version was also difficult for me to read. You can download a copy and take a gander yourself at this link. But there is a super high resolution version available for which one must provide a name and an email. Then one goes through a verification step. Clever marketing? Well, annoying to me. The download process required three additional clicks. Here it is. A sight for young eyes.
I was able to discern a reference to search and retrieval technology in the category labeled “AI Tools: Natural Language Processing, Machine Learning, Speech & Voice Recognition.” I was able to identity the logo of Fair Issacs and the mark of Zorro, but the other logos were unreadable by my 72 year old eyes.
The graphic includes these bot-agories too:
- Bots with traction
- Connectors and shared services
- Bot discover
- Bot developer frameworks and tools
The bot landscape is rich and varied. MBAs and mavens are resourceful and gifted specialists in classification. The fact that the categories are, well, a little muddled is less important than finding a way to round up so many companies worth so much money.
Stephen E Arnold, August 23, 2016
August 12, 2016
I read “Microsoft Is a Leader in 18 Gartner Magic Quadrants, Including Cloud Infrastructure as a Service.” Those folks at Microsoft should be darned proud of themselves. Receiving A grades in 18 Gartner Magic Quadrants is remarkable.
I noted this passage in the write up:
Microsoft is the only cloud computing vendor that is a Magic Quadrant Leader in all of the major cloud services categories, including IaaS, Platform as a Service (PaaS), and Software as a Service (SaaS). These ratings place Microsoft in an enviable position above Amazon AWS, Salesforce, and Google. Looking at the following chart, we can see that Microsoft is a Leader in fully 18 different Magic Quadrants.
Yes, Microsoft stomps on Amazon. I can here the chant “We’re number one” now even though I am in Harrod’s Creek, Kentucky.
What are those 18 Magic Quadrants? I think this is the list, but I can be wrong. My view is that Gartner’s experts are never, ever, ever incorrect in their objective analyses of leading vendors. Perish the thought that the Magic Quadrant is influenced by any subjective element. I shudder to think how subjectivity influencing ratings would rock the myriad consultants wherever they may work.
The 18 Magic Quadrants:
Application develop life cycle management or ADLM
Business intelligence and analytics platforms or BIAP
Cloud infrastructure as a service or CaaS
CRM customer engagement center or CRMCEC
Data warehouse and data management solutions for analytics or DWaDMSfA
Disaster recovery as a service or DRaaS
Enterprise content management or ECM
Horizontal portals or HP (Please, do not confuse the leadership outfit Microsoft with the struggling Hewlett Packard)
Identity as a service or IDaaS
Mobile application development platforms or MADP
Operational database management systems or ODBMS
Public cloud storage services or PCSS
Sales force automation or SFA
Unified communications or UC (Not to be confused with Google ooze)
Web conferencing or WC (Please, be careful with this acronym in the UK)
X86 server virtualization infrastructure or XSVI.
Frankly, the best acronym on this list, which is filled with impressive acronyms, is DWaDMSfA. However, I quite like UC which may be pronounced “uck” and WC. But for the connotation of a loo, WC is outstanding. I know that Microsoft is the all time champ of the enterprise.
Perhaps Amazon will pick up its marbles and focus on space travel and selling weird voice activated surveillance devices? Kudos to Microsoft for its stellar and totally objective achievement.
Stephen E Arnold, August 12, 2016
August 9, 2016
I read “Cracking the Data Conundrum: How Successful Companies Make Big Data Operational.” The high level, super sophisticated, MBA quivering report is free. Does that mean that Capgemini Consulting is trying to drum up business? I thought these top level outfits generated 90 percent of their annual revenue from repeat business? Perhaps today’s economic climate is different?
The report is interesting because the premise is that Capgemini has solved a “conundrum.” This is a nifty word which I learned when I was a wee lad trying to keep my tutor in Campinas, Brazil, happy. I recall that the word was used by one Thomas Nash (no, not a relative of the Nash made famous with the quip “the golden trashery of Ogden Nashery). But that neologistic meaning has a fresh charge of meaning for me; to wit:
A term of abuse for a crank or a pedant.
Today the word is popular among the MBA set as a solvable problem. However, a conundrum can be another word for dilemma. That’s a logical word for illogical statements; for example,
Bruno was gored on the horns of a big, angry dilemma.
What does the Capgemini document suggest is the resolution to the problem of Big Data.
The write up tells the reader that most outfits trying to integrate Big Data into every day work life screw up. The fancy wording is:
Successful Big Data implementations elude most organizations.
That’s bad for the organizations, and I assume really good for consultants who know how to deal with wasted money.
The problem? Organizations’ management are not able to manage. I learned:
Our research revealed that the top challenges that organizations face include: dealing with scattered silos of data, ineffective coordination of analytics initiatives, the lack of a clear business case for Big Data funding, and the dependence on legacy systems to process and analyze Big Data.
Imagine organizations have these flaws. What are they to do?
Step one is to get their act together; that is, organize for Big Data. Sounds good. But what if the organization is set up to do something else; for instance, make men’s shirts or do publicity of a Hollywood motion picture?
Well, these outfits need to have a systematic approach to Big Data. And one size does not fit every organization. Capgemini identifies four ways to put the ponies in the circus wagon. These are:
- Scattered pockets of Big Data stuff
- Decentralized Big Data stuff. (How is this different from “scattered pockets”?)
- Centralized Big Data stuff
- A Big Data business unit. (This is the one that delivers the most “success.” I am not sure for whom however.)
How does an organization move from total loser in Big Data to a successful outfit integrating Big Data into operations? This effort, which will be billed either as a flat fee, a retainer, or time and materials basis, is an “implementation journey.” I have a hunch that this trip will not a 10 walk to the convenient store for a bottle of Big Red soda pop. The trip will be a hike through the Ural mountains in winter.
The write up includes a test. This makes it easy for the shirt maker in Bangladesh or the 20 somethings working from a trailer in Orange County to put their act in the circus’ center ring.
The write up references a survey conducted in 2014. I suppose in the slow moving world of the shirt makers and Hollywood publicists a year and a half is a reasonable time interval.
If you want to test your understanding of the word “conundrum,” you will want to read this free report. Only you can answer this question: Does conundrum reference a crank or pedant or a hapless MBA dangling from a sharp horn? Whenever horns of a bull enter a conversation, other stuff may follow.
Stephen E Arnold, August 9, 2016
June 2, 2016
If you are curious to learn more about the purveyor of the Beyond Search blog, you should check out Singularity’s interview with “Stephen E Arnold On Search Engine And Intelligence Gathering.” A little bit of background about Arnold is that he is an expert specialist in content processing, indexing, online search as well as the author of seven books and monographs. His past employment record includes Booz, Allen, & Hamilton (Edward Snowden was a contractor for this company), Courier Journal & Louisville Times, and Halliburton Nuclear. He worked on the US government’s Threat Open Source Intelligence Service and developed a cost analysis, technical infrastructure, and security for the FirstGov.gov.
Singualrity’s interview covers a variety of topics and, of course, includes Arnold’s direct sense of humor:
“During our 90 min discussion with Stephen E. Arnold we cover a variety of interesting topics such as: why he calls himself lucky; how he got interested in computers in general and search engines in particular; his path from college to Halliburton Nuclear and Booze, Allen & Hamilton; content and web indexing; his who’s who list of clients; Beyond Search and the core of intelligence; his Google Trilogy – The Google Legacy (2005), Google Version 2.0 (2007), and Google: The Digital Gutenberg (2009); CyberOSINT and the Dark Web Notebook; the less-known but major players in search such as Recorded Future and Palantir; Big Brother and surveillance; personal ethics and Edward Snowden.”
When you listen to the experts in certain fields, you always get a different perspective than what the popular news outlets gives. Arnold offers a unique take on search as well as the future of Internet security, especially the future of the Dark Web.
May 4, 2016
I recall a fellow named Dave Schubmehl. You may recall that name. He was the IDC wizard who ingested my research about open source outfits and then marketed it via Amazon without my permission. Since that go round with my information used without a written agreement with me, I have taken a skeptical view of IDC and its “experts.” I won’t comment on its business practices, administrative acumen, and general ineptitude with regard to publishing a bit of my research as an eight page, $3,500 “analysis.” Yikes. Eight pages at $3,500 for work pumped out on Amazon, the WalMart of the digital world.
I read, therefore, with considerable skepticism “Interview with Rich Vancil: Group VP, Executive Advisory of IDC.” I was not disappointed. Perhaps I should say, my already low expectations were just about met.
The interviewer, according to the interview text, has been an acquaintance of the IDC wizard for decades. Furthermore, the interviewer (obviously an objective type of person) will “meet up to catch up on life outside business.” The article is “old pals chatting.”
What a chat?
I learned that:
The IDC 3rd Platform is a broad term for our present IT industry and economy. It is where 100% of WW IT revenue growth is coming from and it includes the product categories of Mobile; Social; Cloud, and Big Data. The 3rd Platform is eclipsing the 2nd Platform – described broadly as the “last 30 years” of IT, and this has been mainly enterprise computing: Lan / Internet; Client / Server; and premised based infrastructure such as servers, storage, and licensed software.
A third platform. “Platform” is an interesting word. I get the idea of a Palantir platform. I suppose I can get in sync with the Windows 10 platform. But an IDC platform? Well, that’s an idea which would never have floated from the pond filled with mine drainage here in Harrod’s Creek.
A consulting firm is in the business of selling information. A platform exists at outfits like Booz, Allen, McKinsey, and Bain. But the notion that a mid tier outfit has had three platforms intrigues me. When I looked at some of the 1917-1918 reports at Booz, Allen when Ellen Shedlarz ran the information center, the format, the tone, the approach, and the word choice was incorporated in the charm school into which new hires were herded. I could, in a moment of weakness, call Booz, Allen’s systems and methods a platform. But are the words “systems” and “methods” more appropriate?
The other interesting point in the write up was a nifty new diagram which purports to make clear the third platform confection. I know you won’t be able to read the diagram. Buy the report which hopefully is less than the $3,500 slapped on eight pages of my research.
Source: IDC 2016 at this link. If you find the link dead, just buzz up IDC and order document 01517018. The reports based on my research were 236511, 236514, 236086, and 237410. Buy them all for a mere $14,000.
Notice the blobs. Like another mid tier outfit, blobs are better than numbers. The reason fuzziness is a convenient graphic device is that addled geese like me ask questions; for example:
- What data are behind the blobs
- What was the sample size
- Where did the categories come from like “cognitive marketing”?
I have a supposition about the “cognitive” thing. The IDC wizard Dave Schubmehl pumped out lots of tweets about IBM cognitive computing. One IDC executive, prior to seeking a future elsewhere, wrote a book about “cognitive” processes. Both of these IDC experts guzzled the IBM Watson lattes somewhere along the cafeteria line.
Back to the interview among two friends. I learned:
MarTech is a big deal. IDC is doing a very careful accounting of this area and we now account for 78 separate product / service categories and literally thousands of vendors. Like any other emerging and fast growth IT category, consolidation will be inevitable. But in the meantime, it makes for a daunting set of choices for the CMO and team.
I like the word daunting. There is nothing like a list of items which are not grouped in a useful manner to set IDC neural pathways abuzz. But the IDC mavens have cracked the problem. The company has produced a remarkable 2015 technology map. Check this out:
Source: Expert Interview, 2016
I moved forward in the write up. The daunting problem has contributed to what the interviewer describes as “an awesome conference.” I like that “awesome” thing. How does the write up conclude? There is a reference to golf, the IDC professional’s medical history, and this statement:
The best analysts can simplify, simplify. Analysts who try to impress by using big words and complex frameworks…end up confusing their audience and so they become ineffective.
Remarkable content marketing.
Stephen E Arnold, May 4, 2016
May 1, 2016
I read an article that confused me. Its title is “Mirum Partners with Forrester to Help Brands Compete with Disruptors like Airbnb and Alibaba.” Forrester is a mid tier consulting firm. The outfit lights up my radar with the lightning over waves and the confusion of blobs in its analyses.
Mirum, on the other hand, is a “global digital agency.” This evokes images in my mind of ad professionals on the beach in Half Moon Bay watching videographers shoot footage of people riding horses through the surf in an inspired attempt to sell a consumer product. Mirum tells me “never to lose my sense of wonder.” Okay. Noted.
Now back to the write up.
Both Forrester and Mirum provide services to other organizations. That means both are consultants moving information around for money. That’s a noble pursuit, but the question is, “Who is paying whom?” Did Forrester sell consulting to Mirum? Did Mirum sell consulting to Forrester? Are both just teaming up in order to pump up their revenues with the idea that a small B&B in Camden, Maine, can compete with Airbnb?
J. Walter Thompson digital agency Mirum in APAC has announced that it’s bringing on board Forrester’s Digital Maturity Assessment Tool. The agency believes using the tool will help support its work in trying to help more traditional brands modernize and digitize their businesses, in order to better compete against the new breed of disruptors like Airbnb and Uber.
Ah, ha. A tool. And for which prospects? The answer is Asian markets. Too bad for the B&B in Camden.
My hunch is that Forrester has a service and Mirum is going to try to sell it. I further assume that if and when Mirum makes some sales, both Mirum and Forrester will chop off some of the prime rib.
Why doesn’t Forrester market its own products? Why does Forrester use blobs instead of hard analytics in its gentle waves? From Harrod’s Creek, it appears that making sales directly might be too hard for the mid tier folks. Hence, a partnership.
Stephen E Arnold, May 1, 2016
April 4, 2016
I want you to know that I read this statement attached to the illustration in “Master Data Management: Which MDM Tool Is Right For You?”; to wit:
Unauthorized reproduction, citation , or distribution prohibited.
Okay, none of that, gentle reader. The Forrester Wave has morphed from a knock off of the Eisenhower grid which was reinvented by Boston Consulting Group. The new look is like this:
Remember Psych 101? What do you see? How do you feel about that? What do you mean it looks like a dog’s breakfast? Do you love your mother?
Each tinted region denotes a type of Master Data Management classification. The classifications which the mid tier consulting firm generated from a rigorous statistical analysis of the data available to the wizards working on this report are:
- Integration model vendors
- Logical model vendors
- Contextual model vendors
- Analytic model vendors.
I am not sure what the differences in the categories are because I am familiar with some of the outfits in the Master Data Model space and it seems to me that outfits like IBM, Oracle, and others offer a range of Master Data Model services and capabilities. Hey, I don’t want to assemble the bits and pieces on offer from IBM into a functioning solution, but I suppose one can.
What companies deliver what function in this Rorschachian analysis?
Integration model, a pale blue horizontal elliptical blob:
- Dell Boomi
- Information Builders
- Profisee (like prophecy I assume)
- Software AG
- Tibco (the data bus folks)
Analytic model, a gray circle:
Logical model, a blue gray ellipse which looks like an egg standing on one end:
- Tibco (yep, in two places at once like an entangled particle)
- Software AG (only the “ware AG” makes it into the logical egg thing)
- Information Builders (the “builders” component is logical. Go figure.)
- Teradata (yikes, just the “ata” is logical. Makes sense to the mid tier crowd I assume.)
Contextual model, which looks like a fried egg to me with a context of breakfast:
- Informatica (another outfit which is like a satyr, half one thing and half another)
- Liaison Technologies
- Magnitude Software (the outfit is another entangled MDM provider because it is included in the logical model. Socrates, got that?)
- Orchestra Software (also part of the logical category like a Rap musician who fills in when the the first violin at the London Philharmonic is on holiday).
- Pitney Bowes (the postage meter outfit?)
I wish I could reproduce the diagram, but there is that legal threat. A legal threat is one way to make sure that constructive criticism of the blobs is constrained. I suppose my representations of the geometry of the analysis connects the dots for you, gentle reader. If not, the mid tier wizards will explain their “real” intent.
I love the fried egg group. How about some hot cakes with that analysis? Also, no half baked biscuits with that, please.
Stephen E Arnold, April 4, 2016
March 18, 2016
Navigate to “The Five Different Types of Big Data.” If you are a student of classification, you will find the categories set forth in this write up an absolute hoot. The author is an expert, I assume, in energy, transportation, food, and data. Oh, goodie. Food.
I have not thought too much about the types of Big Data. I usually think only when a client pays me to perform that function. An example is my analysis of the concept “real time” information. You can find that write up at this link. Big requires me to understand the concept of relative to what. I find this type of thinking uninteresting, but obviously the editors at Forbes find the idea just another capitalist tool.
When I learned that an expert had chased down the types of Big Data, I was and remain confused. “Big” describes something that is relative. “Data” is the plural of datum and refers to more than two facts or statistics, quantities, characters, symbols, etc.
I am not sure what Big Data is, and like many marketing buzzwords, the phrase has become a catchall for vendors of all manner of computer related products and services.
Here are the five types of Big Data.
- Big data. I like the Kurt Friedrich Gödel touch.
- Fast data. “Relative to what?” I ask.
- Dark data. “Darker than what? Is this secret versus un-secret or some other yardstick?” I wonder.
- Lost data. I pose to myself, “Lost as in unknown, known but unknown, or some other Rumsfeldesque state of understanding?”
- New data. I think, “I really don’t want to think about what ‘new’ means? Is this new as in never before seen or Madison Avenue ‘new’ like an improved Colgate Total toothpaste with whitener.
I like the tag on the article “Recommended by Forbes.” Quite an endorsement from a fine example of capitalistic tool analysis.
Stephen E Arnold, March 18, 2016
March 16, 2016
Short honk: Want a growth business in a niche function that supports enterprise platforms? Well, gentle reader, look no farther than text analytics. Get your checkbook out and invest in this remarkable sector. It will be huuuuge.
Navigate to “Text Analytics Market to Account for US$12.16 bn in Revenue by 2024.” What is text analytics? How big is text analytics today? How long has text analytics been a viable function supporting content processing?
Ah, good questions, but what’s really important is this passage:
According to this report, the global text analytics market revenue stood at US$2.82 bn in 2015 and is expected to reach US$12.16 bn by 2024, at a CAGR of 17.6% from 2016 to 2024.
I love these estimates. Imagine. Close out your life savings and invest in text analytics. You will receive a CAGR of 17.6 percent which you can cash in and buy stuff in 2024. That’s just eight years.
Worried about the economy? Want to seek the safe shelter of bonds? Forget the worries. If text analytics is so darned hot, why is the consulting firm pitching this estimate writing reports. Why not invest in text analytics?
Answer: Maybe the estimate is a consequence of spreadsheet fever?
Text analytics is a rocket just like the ones Jeff Bezos will use to carry you into space.
Stephen E Arnold, March 16, 2016
March 16, 2016
I read “The Hype of Big Data Revisited: It’s About Extracting Value.” I am not particularly interested in “how big” discussions. What I found interesting was that a through leader reproduced a mid tier consulting firm’s Hype Cycle for Emerging Technologies, 2015. I thought mid tier outfits were not too keen on having their proprietary charts reproduced. Obviously I am off the beam on this assumption.
I did note this statement:
In between 2013 and 2014, Big Data reached the Peak of Inflated Expectations in Gartner’s Hype Cycle for Emerging Technologies. By mid 2014, Big Data was sliding into the Trough of Disillusionment, and by 2015, the term was removed from the hype cycle altogether.
More mid tier goodness.
Here’s what I learned about the source of this write up:
Bob E. Hayes, PhD is the Chief Research Officer of Analytics Week and president of Business Over Broadway. At Analytics Week, he is responsible for directing research to identify organizational best practices in the areas of Big Data, data science and analytics. He is considered a thought leader in the field of customer experience management. He conducts research on analytics, customer feedback programs, customer experience / satisfaction / loyalty measurement and shares his insights through his talks, blogs and books.
Perhaps the notion of thought leadership and recycling a mid tier consultant firm’s viewpoints is the future of deep insight and analysis. Wow, the mid tier consulting firm is a significant influence on some thought leaders.
Too bad the intellectual force does not reach to my part of rural Kentucky. It obviously skips me and works its magic in Bowling Green, the home of the Corvette hole.
Stephen E Arnold, March 15, 2016