Predictions about Technology: Digital Retreading
October 7, 2015
I like it when a person tells me that software or a human can predict the future. My question is, “If the predictions are spot on, why is the owner of the prediction system talking? Why not play fantasy football, pick stocks, or hang out at Keeneland during an auction and buy horses whose value will skyrocket?
The answer is, “Err, well, hmmm.”
Exactly. Predicting the future is a bit like imagining oneself putting on soccer boots and filling in for the injured Lionel Messi. Easy to thing. Essentially impossible to do.
The fix is to be fuzzy. Instead of getting into a win-lose situation, there are caveats. I find these predictions and their predictors amusing. Not as enjoyable as the antics of something like IBM cognitive computing marketed by Bob Dylan or the silliness of Hewlett Packard management activities. But close, darned close.
I read “Gartner: Top 10 Strategic Technology Trends For 2016.” I noted this statement from the capitalist tool:
…the evolution of digital business is clearly at the heart of what is covered.
Okay, the trends are going to identify trends which will allow an MBA or a savvy marketer to look at business and understand how “business” will evolve. Darwin to the future, not Darwin from the past I assume.
The question in my mind is, “Are these retread ideas?”
Here are three “trends” which caught my attention. To get the full intellectual payload, you will need to read the article or, better yet, seek out a Gartner wizard and get the trend thing straight from the horse’s mouth. Yep, right, mouth.
Trend 2: Ambient user experience.
I remember hearing about ambient computing years ago. The idea was that one could walk around and compute. I also ran across Deloitte’s identification of a similar trend months ago. But it was in the late 1990s or early 2000s when an MIT person talked about the concept. Obviously if one is computing whilst walking around, there is an experience involved. With mobile devices outselling tethered devices, it seems disingenuous to talk about this trend. According to Forbes, the capitalist tool:
Gartner posits that the devices and sensors will become so smart that they will be able to organize our lives without our even noticing that they are doing so.
I like posit. The word means “to dispose or set firmly, assume or affirm the existence of, and propose as an explanation.” Yep, posit something that academics and blue chip consulting firms have been saying for a while.
Trend 4: Information of Everything
Now these universal statements are rhetorical tactics which make my tail feathers stand up. “Everything” is a broad concept. A critical reader may want to ask, “Will you provide me with information about line 24 million in Watson’s 100 million lines of code?” The “everything” is going to provide this answer. Nope. Logical flaw. But here’s how the capitalist tool, a font of logical thought, presents this “information of everything” trend:
According to Gartner, by 2020, 25 billion devices will be generating data about almost every topic imaginable. This is equal parts opportunity and challenge. There will be a plethora of data, but making sense of it will be the trick. Those companies that harness the power of this tidal wave of information will leapfrog competitors in the process.
I like the plethora. I like the leapfrog. I like the tidal wave. I have a sneaking suspicion that most folks with a computer device have experienced a moment of information confusion. “With every topic imaginable”, confusion is a familiar neighbor. Now how long has this concept of lots of information from lots of devices with communications capability been around? Forbes, the capitalist tool, published in June 2014 “A Very Short History of the Internet of Things.” If the Forbes’ writer had taken the time to look at that article, the concept poked its nose into the world in the early 1930s. Well, that is only 80 years ago. But it is a trend. Hmm. Trend.
Now my favorite.
Trend 9. Mesh App and Service Architecture
The notion that computer systems able to exchange information is a good one. I can’t recall when I learned about this concept. Wait. No, I remember. It was in 1963 when I took my first class in computer programming. The professor, a fine autistic polymath, explained that the mainframe—a 1710—was a collection of components. He said in 1962 that different machines would talk to one another in the future. Well, there you go. A third rate university with dullards like me in class got a prognostication which seems to be true. That was more than half a century ago. Here’s the modern version of this old chestnut:
More apps are being built to be plugged together, and the value of the combination is much greater than the sum of the parts. As Lyft has integrated with comparable offerings in other countries, its ability to expand its offering for traditional customers traveling abroad and the reverse has meant faster growth with minimal cost implications.
Enough of these walks down memory lane. Three observations:
- These trends are recycled concepts
- The presentation of the trends is a marketing play, nothing more, nothing less
- Mid tier consulting firms are trying really hard to sound very authoritative, important, and substantial.
That would work if footnotes provides pointers to those who offered the ideas before. Whether a blue chip consulting firm like Deloitte or a half wild computer science professor in the Midwest, the trends are not trends.
We are, gentle reader, looking at digital retreaded tires. A recap. A remold. Old stuff made fresh. Just don’t drive too quickly into the future on these babies. Want to bet on this?
Stephen E Arnold, October 7, 2015
A New Wave of Old School BI Outfits Are Agile, Maybe Juicy
September 27, 2015
The mid tier outfit Forrester has released another report about enterprise business intelligence platforms” for the third quarter of 2015. These reports cost about $2,500, so you know the information is red hot, spot in, and objective. Always objective. in the write up “The Forrester Wave: Agile Business Intelligence Platforms 2015”, the report is described as “juicy.” Imagine. Juicy applied to IBM, Microsoft, and Oracle. Let me refresh your memory of juicy’s official definition:
1: having much juice : succulent
2: rewarding or profitable especially financially : fat <juicy contract> <a juicy dramatic role>
3a : rich in interest : colorful <juicy details>
b : sensational, racy <a juicy scandal>
c : full of vitality : lusty
I am not sure mid tier consulting firms’ reports are “rewarding or profitable especially financially” for the reader. At a couple of thousand per authorized copy of the report, the mid tier firms are likely to be drenched in juiciness. Will this report be lusty, sensational, colorful, and succulent? Nah. This is marketing pulp, gentle reader.
Which are the companies which make the cut? According to this write up, there are a baker’s dozen of agile, BI vendors:
- Birst
- GoodData
- IBM
- Information Builders
- Microsoft
- MicroStrategy
- Oracle
- Panorama Software
- Qlik
- SAP
- SAS
- Tableau Software
- TIBCO Software.
Scanning this list, I wonder how “agile” IBM, Microsoft, Oracle, SAP, and SAS really are. I know that TIBCO acquired some nifty technology for its analytics functions, and that the founders of Spotfire have moved on to even more interesting analytics at their new company, funded in part by Google and In-Q-Tel. The other firms are ones which have run around the BI bases for years and may have a touch of arthritis; for instance, Information Builders which kicked off its career 1975. Qlik was founded in 1993. MicroStrategy flipped on its lights in 1989 and spawned at least one outfit (Clarabridge) which strikes me as slightly more agile than the mother ship. Tableau, now a publicly traded outfit, hung out its shingle in 2003.
GoodData may be the most spry among this group, not because it was founded in 2007, but because the firm landed another $25 million in funding in 2014.
According to the blurb about the report, each of these companies are agile because of several special features each of these vendors offer their customers. These characteristics are:
First, these 13 vendors’ products allow their business users to be self sufficient. I am not sure I agree, that SAS stuff requires a person to be SAS-sy, which means able to navigate the companies’ programming methods with some skill. IBM, Microsoft, and Oracle provide many different ways to skin the business intelligence cat. In my opinion, these companies’ business intelligence technology require that the business user have the equivalent of a fighter jet maintenance crew to assist them on the flights into analysis and visualization.
Second, each company generates knock out visualizations. My thought is that for zippy visualizations, more specialized tools are required. The companies highlighted in this report can deliver slides and graphs which are niftier than those in Excel, but far short of the Hollywood style outputs which come from Palantir and Recorded Future, among other firms not included in the agile list.
Third, each of the 13 companies offers its licensees and customers options and additional features. This is definitely a must have function. Most of the firms in the list of agile BI companies sells services. Some have partners, lots of partners. The business model may be less to be agile and more to sell billable work, but that’s okay. I am not sure inking a six figure services contract delivers agility.
I assume the complete $2,500 report will become available from the companies listed in the report. For now, think agility. Think IBM, Microsoft, and Oracle, along with the 10 other companies.
Remember, these are 13 juicy and agile outfits. Remarkable. Juicy.
Stephen E Arnold, September 27, 2015
Spark: Another Open Source Game Changer
September 24, 2015
Gentle reader, I know that knowledge about Spark is as widespread as information about the woes of the Philadelphia Eagles. My understanding of Spark is that is is an open source engine for large scale data processing. It is faster than Hadoop. It is easy to use. It is flexible enough to allow the intrepid Spark aficionado the combine structured query language, streaming, and analytics in one software system. Spark runs “everywhere.” For more about Spark, see this Apache project page.
Spark is one of the next big things, poised to ignite innovation, consulting revenues, innovations, and vendor repositionings.
I approached “Game-Changing Real-time Uses for Apache Spark” in order to learn how Spark can change the game for real time data and information work. Game changing means that old school outfits are going to lose because the new game has new rules, new players, and new everything.
The write up identified these ways Spark will change some quite significant markets:
- Credit card fraud detection
- Network security
- Genomic sequencing
- Real time ad processing
- Medical
My goodness, Spark will become the number one enabling technology for some very problematic market spaces.
Let’s look at what Spark will do to real time ad processing. The write up reports:
One advertising firm uses Spark, on MapR-DB, to build a real-time ad targeting platform. The system looks at user data and decides which ads to show users on the Internet based on demographic data. Since advertising is so time-sensitive, advertisers have to move fast if they want to capture mindshare. Spark Streaming is one way to help them do that.
What strikes me is that Spark requires programmers, software engineering, and then integration of different components. If an error manifests itself, the Spark solution may require those who embrace it to perform some old fashioned work.
In a sense, the game hasn’t changed at all. Open source software reduces license fees and provides a developer with some freedom from license restrictions. On the other hand, the difficult task of getting a complex system to work as intended remains.
My hunch is that Spark is an interesting open source project. The consultants and start ups see Spark as an opportunity. The game changing nature of Spark is potential energy, not a sure thing.
Stephen E Arnold, September 23, 2015
HP: Management Insight from Michigan
September 22, 2015
I love HP. I used to use the firm’s laptops. Sure, the hinges broke, but the gizmo was pretty good. The misstep with Autonomy, however, is more significant than a poor hinge design. The management methods of the company are exposed with the Autonomy matter in my opinion. Perhaps HP should bring back Carly Fiorina. Dual CEOs. Whitman and Fiorina. What could be better.
I read “Michigan Sues HP over $49 Million Project That’s Still Not Done after 10 Years.” My thought was that the Whitman-Fiorina duo would have this resolved quickly.
According to the write up:
A new lawsuit filed by the state of Michigan over a $49 million project the state says is still not completed after 10 years. The contract dates back to 2005 and called for HP to replace a legacy mainframe-based system built in the 1960s that is used by more than 130 Secretary of State offices.
Now Michigan allegedly has paid HP about $33 million. The state, in a moment of wisdom, wants the source code for the project.
The write up includes this statement:
“I inherited a stalled project when I came into office in 2011 and, despite our aggressive approach to hold HP accountable and ensure they delivered, they failed,” said Secretary of State Ruth Johnson in a press release. “We have no choice but to take HP to court to protect Michigan taxpayers.”
How does a project drag on for 10 years?
My hunch is that governments, whether national or state level, have a tendency to create Healthcare.gov type situations. Also, large services firms which also sell printer ink are likely to find the mainframe thing sort of challenging. Toss in other variables like staff turnover, and the result can be darned exciting.
Again. Maybe it is time for dual HP CEOs. One can sue Autonomy. The other can manage Michigan’s state government, make the University of Michigan number one in computer science, and probably fix Detroit at the same time. Seems reasonable to me.
Stephen E Arnold, September 22, 2015
Big Data, Gartner Hype Cycle, and Generating Revenues
September 21, 2015
I read “Big Data Falls Off the Hype Cycle.” Fascinating. A term without definition has sparked ruminations about why a mid tier consulting firm does not define Big Data as hyperbole.
The write up states:
“Big Data” joins other trends dropped into obscurity this year including: decision management, autonomous vehicles, prediction markets, and in-memory analytics. Why are terms dropped?
The article scoots forward to answer this question. The solution for those of you familiar with a multiple choice test include:
Sometimes because they are too obvious. For example in-memory analytics was dropped because no one was actually pursuing out-of-memory analytics. Autonomous vehicles because “it will not impact even a tiny fraction of the intended audience in its day-to-day jobs”. Some die and are forgotten because they are deemed to have become obsolete before they could grow to maturity. And Big Data, well, per Gartner “data is the key to all of our discussion, regardless of whether we call it “big data” or “smart data.” We know we have to care, so it is moot to make an extra point of it here.”
The write up then offers:
When I first took a stab at making a definition I concluded that Big Data was really more about a new technology in search of a problem to solve. That technology was NoSQL DBs and it could solve problems in all three of those Vs. Maybe we should have just called it NoSQL and let it go at that. Not to worry. I’m sure that calling things “Big Data” will stick around for a long time even if Gartner wants us not to.
I have a different take. My hunch is that the hype cycle is a marketing and lead generation vehicle for a mid tier consulting firm. When the leads no longer flow and the “objective studies” no longer sell, a fresh approach is needed.
Big Data as a concept is no longer hype. That’s reassuring. Perhaps progress is retarded by buzzwords, jargon, and thrashing for revenues?
Stephen E Arnold, September 21, 2015
Big Data: The McKinsey Way
September 11, 2015
I read “6 Observations from a New Survey on the State of Big Data Analytics.” The data come from a study underwritten by a magazine outfit, a blue chip consulting firm, and a company selling storage and related bright and shiny things.
I found the write up suggestive. The first finding was a bomb shell.
The hype gone, big data is alive and doing well.
Aside from the subject-verb error coming from data is when data is the plural of datum, the information is revolutionary. Big Data is no longer subject to hyperbole. I did not know that. Topsy.com tallied 3,154 tweets about Big Data in the the 24 hours of September 8, 2015. For comparison, Big Data is in a dead heat with the tweets about the Bentley Bentayga SUV. Good company. FYI: Katy Perry managed only 1,468 tweets in the same time period. Nevertheless, in Harrod’s Creek, Big Data, expensive autos, and a musical 30 year old are buzz machines.
The write up reports:
No matter how many times you say “data-driven,” decisions are still not based on data. Sounds familiar? 51% of executives said that adapting and refining a data-driven strategy is the single biggest cultural barrier and 47% reported putting big data learning into action as an operational challenge.
Yikes. More consulting is needed to get this cultural change thing underway.
Other findings that underpin the article are:
- If the CEO is into Big Data, the company is into Big Data…mostly. If the CEO is like the airline executives in the news, the CEO may have other interests
- I love this: “Even if you have top leadership sponsorship and the right culture, getting data to drive action and strategy is a challenge. 48% of executives surveyed regard making fact-based business decisions based on data as a key strategic challenge, and 43% cite developing a corporate strategy as a significant hurdle.” Maybe Big Data is not the slam dunk consultants and journalists wish it to be?
- Brontobyte data. Hey, we have perfectly useful words to suggest unimaginably large quantities. I like yottabyte. The study sponsors seem to be okay with the brontobyte coinage. Very hip, but I would have created a variant of Diplodocus. More colorful for sure.
- There is a shortage of “big data miners.” Okay, I understand. The user friendly analytics tools are just not too helpful unless a company has someone who actually paid attention in statistics classes.
The only thing missing from this write up is links to the sponsors’ product pages. By the way, the article pumps up Big Data. Amusing stuff.
Stephen E Arnold, September 11, 2015
Who Will Be the IDC Cognitive Guru?
September 10, 2015
You remember Dave Schubmehl. He is the IDC “search” expert who recycled some of my content. He then sold a report based on my work via Amazon. My meek attorney was able to get the document removed. Not even a legal eagle could fathom how eight pages of analysis could command $3,500. Mr. Bezos knows that $9.99 is a sweet spot. I suppose the masters of management at IDC thought that $3,500 report would sell like hot cakes to the consumers of romance novels, streaming video, and household commodities.
I noted a tweet (show below) that suggests his unit at IDC is going like gang busters. New staff with undergraduate degrees are needed. Uber drivers, are you paying attention?
When a consulting firm adds headcount, that’s news. I hope that the Renaissance men and women at IDC can make sense of “cognitive computing.” Google pushes “deep learning.” I suppose there will be more buzzwords as the “experts” in enterprise search flail for purchase on the slopes of Mount Make a Sale.
Most of the experts have answers to the most difficult problems in business. The only hitch in the git along occurs when the “experts” do not have certain knowledge. Oh, another modest problem is recycling another’s information without taking the time to issue a contract.
I am confident that the customers of Amazon really did want to buy that $3,500 report. Mid tier consulting firms are the cat’s pajamas. I wonder if that comes from asking IBM Watson questions.
Stephen E Arnold, September 10, 2015
IHS Mines Gartner for Goldfire
September 10, 2015
If you want to read a “pat Goldfire on the back” statement, navigate to “IHS is Recognized as a Visionary in Gartner’s 2015 Magic Quadrant for Enterprise Search.”
I assume you, gentle reader, are up to speed on Invention Machine. No, strike that. You are up to speed on Goldfire, the IHS (Information Handling Service) enterprise search system.
If not, you can dig into the news release for details about how Goldfire can, and I quote:
- Decrease product development cycle time
- Increase the innovation pipeline
- Accelerate market share and competitive position
- Drives top-line margin expansion, and
- Increase productivity gains
There’s not much about search and retrieval, but that’s a minor detail.
What’s important in the news release is this statement which appears in the IHS Magic Quadrant write up about Goldfire:
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
There it is. The “research” is opinion based.” I think I understand that a Gartner report is not to “be construed as statements of fact.” Okay. I did not know that because I understood (obviously incorrectly) that a rigorous analytic process formed the foundation of the vendor selection and categorization.
Guess not. Wow. Who knew? Oh, I know. The PR firms issuing news releases from enterprise search vendors who are leaders, visionaries, etc.
Stephen E Arnold, September 10, 2015
Bar Exam Brouhaha
September 7, 2015
We cannot resist sharing this article with you, though it is only tangentially related to search; perhaps it has implications for the field of eDiscovery. Bloomberg Business asks and answers: “Are Lawyers Getting Dumber? Yes, Says the Woman who Runs the Bar Exam.”
Apparently, scores from the 2014 bar exam dropped significantly across the country compared to those of the previous year. Officials at the National Conference of Bar Examiners (NCBE), which administers the test, insist they carefully checked their procedures and found no problems on their end. They insist the fault lies squarely with that year’s crop of law school graduates, not with testing methods. Erica Moeser, head of the NCBE, penned a letter to law school officials informing them of the poor results, and advising they take steps to improve their students’ outcomes. To put it mildly, this did not go well with college administrators, who point out Moeser herself never passed the bar because she practices in Wisconsin, the only state in which the exam is not required to practice law.
So, who is right? Writer Natalie Kitroeff points out this salient information:
“Whether or not the profession is in crisis—a perennial lament—there’s no question that American legal education is in the midst of an unprecedented slump. In 2015 fewer people applied to law school than at any point in the last 30 years. Law schools are seeing enrollments plummet and have tried to keep their campuses alive by admitting students with worse credentials. That may force some law firms and consumers to rely on lawyers of a lower caliber, industry watchers say, but the fight will ultimately be most painful for the middling students, who are promised a shot at a legal career but in reality face long odds of becoming lawyers.”
The 2015 bar exam results could provide some clarification, but those won’t start coming out until sometime in September. See the article for much more information on Moeser, the NCBE, the bar exam itself, and the state of legal education today. Makers of eDiscovery software may want to beef up their idiot-proofing measures as much as possible, just to be safe.
Cynthia Murrell, September 7, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Subjective Big Data: Marginalized Hype from a Mid Tier Outfit
September 4, 2015
I read “Why Gartner Dropped Big Data Off the Hype Curve.” The article purports to explain why Gartner Group, a mid tier consulting firm, eliminated Big Data from its hype cycle. Let me ask, “Perhaps Big Data reports do not sell to executives who have zero clue what Big Data means to a struggling business?” The write up is an analytics and data clean room. Facts are tough to discern.
The article included a chart without numbers to help knowledge hungry folks figure out what technology is an innovation trigger, a technology which is at the peak of inflated expectations, what technology have fallen (gasp!) into the trough of disillusionment, which are on the slope of enlightenment, and which have reached the plateau of productivity.
The write up fills the empty vessel of my mind with this insight from a mid tier wizard, Betsy Burton. She allegedly revealed:
There’s a couple of really important changes,” Burton says. “We’ve retired the big data hype cycle. I know some clients may be really surprised by that because the big data hype cycle was a really important one for many years. “But what’s happening is that big data has quickly moved over the Peak of Inflated Expectations,” she continues, “…and has become prevalent in our lives across many hype cycles. So big data has become a part of many hype cycles.”
I like that observation about Big Data becoming part of many hype cycles.
That’s reassuring. I don’t know what Big Data is, but it is now part of many hype cycles.
I like subjective statements about what is moving through a hype cycle. When one hype cycle is not enough, then put the fuzzy wuzzy statement into many hype cycles. Neat.
The article explains that other “notable subtractions” took place; for example, drop outs include:
- Prescriptive analytics, which I presume are numbers which are not used in this article’s graphics. Numbers are so annoying because one must explain where the numbers came from, figure out if the numbers are accurate, and then make decisions about how to extract valid outputs from numerical recipes. Who has time for that?
- Data science. I am not sure what this means, but it’s off the hype cycle hit parade.
- Complex event processing. Sounds great but it too is a victim of the delete button.
I view the listing as subjective. Subjectivity is useful, particularly when discussing which painting in the Wildenstein Collection is the best one or which of Mozart’s variations is the hot one.
Objective analyses, in my opinion, to make a case that virtual reality is on the slope of enlightenment or that affective computing is lifting off like a hyperbole fueled rocket.
Am I the only one who finds these subjective lists silly? My hunch is that the reason concepts get added to the list is to create some demand for a forthcoming study. The reason stuff disappears is because reports about the notion do not sell.
I wonder if there are data available from mid tier consulting firms to back up my hypothesis. Well, we can argue whether pale ivory is more attractive than honey milk.
Interior design professionals will go to the mattresses tinted white wisp to defend their subjective color choice. Do mid tier consultants share this passion?
Stephen E Arnold, September 4, 2015
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