Big Consulting Firm Smashes the Big Data Conundrum
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
Speculation About Beyond Search
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.
Whitney Grace, June 2, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Old Pals Chatting: IDC Expert Chums Up Cognitive Marketing
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
A Partnership or a Sale for Gartner
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?
I learned:
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
The Forrester Wave Becomes Blobs
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
- Microsoft
- Profisee (like prophecy I assume)
- Software AG
- Semarchy
- Teradata
- Tibco (the data bus folks)
Analytic model, a gray circle:
- Novetta
- Reltio
Logical model, a blue gray ellipse which looks like an egg standing on one end:
- SAS
- SAP
- IBM
- 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?)
- Verato
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
How Many Types of Big Data Exist?
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
Text Analytics: Crazy Numbers Just Like the Good Old Days of Enterprise Search
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
A Thought Leader Embraces Mid Tier Consultant Thinking
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
Gartner and the Business Intelligence Magic Quadrant: Lots of Explaining, Lots of Subjectivity It Seems
March 13, 2016
I read a downright weird article/interview called “Big Data Discovery may put Oracle back in BI Magic Quadrant.” The title contains the magic word “may”, which does not promise to make Oracle a big dot in a Gartner Magic Quadrant, but it suggests that Gartner is doing some explaining.
As I understand the situation, the mid tier consulting firm analyzed the business intelligence sector and figured out which companies were winners and losers. Well, that’s the lingo that the original Boston Consulting Group quadrant used, and that’s how General Eisenhower used his quadrant. So those approaches override the Garnter words like niche players and visionaries. (Is it not possible for a niche player to be a visionary? Does Gartner know “Venn” to check it logic?)
The point of the write up is that Oracle, one of the big dogs in the Department of Defense’s DCGS-A and DCGS-N mash up analytics initiative is not in the Garnter magic square thing. Nope. Deleted.
Why may be a question which some folks at Oracle have been asking. The article/interview appears to be an “explainer” to make the Garnter mid tier method appear more near the top drawer in the cabinet of analytics collectibles.
I noted this passage:
Question: It sounds like the change isn’t coming from something Oracle did, but from Gartner.
Gartner’s R&D Big Dog, Josh Parenteau: Right, OBIEE is still there. It’s still being sold as their platform, but it does not meet the modern definition of the Magic Quadrant right now.
The acronym OBIEE means Oracle analytics. You, gentle reader, knew that.
Oracle was excluded because “they didn’t fully participate,” says Parenteau. He adds:
I do think that they’re late to the game by quite a bit… For Oracle, it’s recognizing the signals a bit earlier. It’s responding to customer needs and, I think, realizing that it’s not just about product. You can have the best product in the world, but if customers don’t want to work with you because they don’t like the relationship, it’s not going to matter.
So what companies of note made the Magic Quadrant? Since I don’t pay Gartner to advise me, I checked Bing and Google to locate the 2016 Magic Quadrant for Business Intelligence. It did not take long, because this MQ report appears to be a marketing item, not a confidential study like a report about the AVATAR program.
Check out these outfits who have met the Gartner criteria, objective and subjective:
- BeyondCore
- Domo
- Logi Analytics
- Platfora
- Sisense
Okay, some names of note.
These outfits made the list as well:
- IBM
- Microsoft
- SAS.
I highlighted this paragraph as particularly suggestive:
But I would say that, if you are a member of the install base of Oracle, know that they do have offerings in the space. They just didn’t have enough traction to get on the quadrant. If you have a big data Hadoop initiative going on, of course look at Big Data Discovery, because that’s exactly what it’s focused on. If you are looking for a tool to do data discovery, of course look at Visual Analyzer, which is part of the cloud service. If you have an initiative to get into the cloud, look at BICS. I wouldn’t say that, just because they’re not on the Magic Quadrant, if you’re an existing Oracle customer that you shouldn’t continue to look at them for solutions. This doesn’t mean that they are gone forever or off the MQ forever. It’s a transition. We’re in a market that is transitioning. Next year, it may be a new ball game.
Very mid tier. I liked the “you shouldn’t continue to look at them for solutions.” Are those words a positive or a negative? Worth watching the interaction of the Oracle folks at the Gartner experts.
Stephen E Arnold, March 13, 2016
Gartner and Business Intelligence Magic Thing
February 14, 2016
I love consultants, especially mid tier consultants. The idea is that folks who are reasonably pleasant can become experts in various market sectors is a signal that optimism is alive and thriving in a sketchy economic swamp.
The mid tier consultants are a fave. These outfits provide more tradition than the webmaster or Visual Basic programmer who is out of a job. The ease with which one can become a consultant lends a certain squishiness to Lone Rangers offering expertise for hire.
The blue chip outfit are just too expensive for many folks who know they need help. Think of the difference between someone who jets to Lyon for lunch and the person who grabs a slice in Midtown.
Thus, blue chip outfits (the top drawer firms), the azure chip firms (companies either on their way up or down in the expertise Great Chain of Being), and the gray chip folks. The gray chip folks are the disaffected middle school teacher who decides to become a self appointed expert in sponsored content for search engine optimization.
The write up “Critiquing the Gartner BI and Analytics MQ” will not elicit much of a response from the mid tier outfit responsible for the “analysis.” Legal eagles slap when the actual quadrant thing is reproduced.
But the write up hits some nerves in the sagging neck of the azure chip services firm; for example:
- Companies excluded for no apparent reason. (Maybe these outfits rejected the azure chip firm’s blandishments to buy services and be better understood?)
- A “kitchen sink” approach. (Maybe this means dumping stuff into a container and binge watching Happy Days on Hulu? Stuff breaks when hasty hands place dirty dishes in a sink.)
- Products are mixed up. The example is Design Studio. (Aren’t these software components pretty much the same? Sure they are, gentle mid tier consultant getting smart by searching Google for info. Sure they are.)
- Inconsistency. (The write up displays actual, high value, super secret, for some eyes only magic thingies. I looked at each graph and was confused in terms of what was presented and how the classifications changed in the span of one fiscal year. Aren’t I the dunce?)
The write up is not about hell fire and brimstone. Here’s the peace offering after the carpet bombing:
To be fair on Gartner, they have made a solid effort at explaining their rationale and, given there are some 500 vendors globally, vying for attention, narrowing down to this selection is a valiant effort. The care with which Gartner has made its understanding known is also commendable, even if some of those explanations are questionable. Another problem with the report is that it is static. It is a snapshot at a point in time that is biased in favor of one constituency and which does not, in my view, adequately recognize the necessary and sometimes difficult tensions that exist between IT and lines of business when it comes to rationalizing or consolidating BI tools in an enterprise setting. I think Gartner has done the industry a major favor by decoupling the reporting element and focusing upon the modern approach to BI. But that’s not enough.
Maybe another azure chip outfit will leap into this opportunity. A mere 500 vendors. The number seems low to me. I eagerly await the next intellectual semi-truck load of insights from the azure chip sector. Yes, eager am I.
Stephen E Arnold, February 14, 2016
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