HSSCM: Updates from Facebook and Snapchat
October 5, 2018
High school science club management methods are flourishing.
I wanted to highlight two examples of interesting ways to operate publicly traded companies in the spotlight.
The first example of HSSCM comes from Facebook, truly a gold mine of examples. I learned that a Facebook executive sat in a photo op location during the Brett Kavanaugh hearing. My source was “Facebook’s Head of Public Policy Is Supporting the Kavanaugh Nomination, and Some Employees Are Livid.” A Facebook top dog named Joel Kaplan appeared, at least to the Verge, to endorse “his close friend.”
The result, according to the write up, “roiled the social network.” I like the word roiled. The real journalists at the Verge reported:
For Facebook, the controversy over Kaplan represents a new point of division at a company that is still grappling with the Instagram founders’ unexpected departure and the largest data breach in its history. Only when it comes to the Kaplan controversy, it’s not clear to me what the company’s next move should be. The C suite seems to have been annoyed by Kaplan’s attendance, but was initially dismissive of employees’ concerns. (How concerned are employees? My favorite detail in Isaac’s story is that they went into Kaplan’s calendar and learned that he had not in fact taken the 27th as a personal day, as Kaplan initially stated. The calendar was later updated to reflect that it was, indeed, a personal day.)
It appears in the canon of the HSSCM method that it is not necessary to know what senior executives are doing. Furthermore, revisionism in the form of modifying a digital calendar, is supported. I also enjoyed learning that in this particular HSSCM example, the concerns of the employees were not at the top of the to do list.
Implications? I suppose there is a possibility that some MBA might interpret the method as exemplary decision making regarding a chain of command, time allocation, and awareness of the senior staff activities.
The second example concerns Snapchat, a service which I admit I never understood and still do not.
I captured this HSSCM method in the write up “9 Highlights from Snapchat CEO’s 6000-Word Leaked Memo on Survival.” Like leaks at Google and Palantir, the fact that someone released an internal memo suggests that shared values about how to handle confidential information may be lacking. Science club members have to be loyal, right?
My first reaction to the write up was, “6,000 words is a lot of words for a memo.” If I worked at Snapchat, I am not sure I would read that document. Some employees obviously concluded that reading and leaking were proof of the HSSCM approach.
I found a couple of the management principles embedded in the memo semi-interesting.
Item One, I noted the admission that the company moved too quickly. Well, it did break things, including usage of the software. An added plus was social media visibility. Annoyed users expressed their displeasure with a Snapchat design tweak. HSSCM knows how to get publicity. That’s a plus.
Item Two, a service called Discover is good. But the service is a “mess.” Hey, that’s part of the break things. The fix? Make Discover a “lean back” experience. I love the word experience. HSSCM methods deliver experiences, just like Google ads’ redesign which features the word “experience” instead of “increasing revenues”. See the email sent by Google on October 4, 2018, with the friendly, warm experience of the mail address: ads-noreply@google.com.
To sum up, here are the HSSCM methods I extracted from these two case examples:
- It is not necessary to know what senior managers are doing during the day on prime time television for six or seven hours
- It is okay to make it easy to modify digital calendars which is a trendy approach to revisionism
- It is okay to make decision in a hurry, ignore feedback, and then apologize. That is a way to move fast and put broken things back together.
- It is okay to create services which appear to be a mess. Creative destruction, right?
No wonder sign ups for MBA degrees are sinking. Who needs to study management when HSSCM methods are within reach of anyone working at a tech centric company?
Stephen E Arnold, October 5, 2018
COBOL Cowboys. Where Are the Cowgirls? Where Is the Trail Boss?
October 4, 2018
I love ThomsonReuters. Every once in a while, its real journalists craft a gem. I submit that “Banks Scramble to Fix Old Systems as IT Cowboys Ride into Sunset.” I will not point out that eliminating the “the” before “sunset” is a quite trendy touch.
The point of the write up is that when a bank hires an individual to work on systems, that engineer may love python, tolerate C, and maybe invite Java in for coffee once a month or so.
The write up reports that a banker allegedly said:
It [dealing with COBOL based systems] is immensely complex which sells new IT infrastructure to banks. “Legacy systems from different generations are layered and often heavily intertwined.
No kidding. Who knew? I recall the Year 2000 hysteria which sparked a bit of interest in COBOL. My memory may be fading. Perhaps that money gusher for COBOL professionals was an illusion.
A couple of observations:
First, COBOL has been around for 60 years. Innovations and alternatives have been around for decades. The failure of major institutions to invest in infrastructure is one reason why Amazon could provide a solution. There’s more money in banking than there is in selling eBooks, by the way.
Second, the notion of programmers as cowboys strikes me as odd when the #MeToo movement and its assorted fireworks are in evidence. A modest nod to non male COBOL wizards seems to be an odd omission. I saw the word cowboys and I wondered if the folks running this outfit should be asked to create a more appropriate name; for example, Gender Neutral COBOL Remediation or GNCR. I like it. Perhaps a Twitter storm will erupt.
Third, years ago I assumed Boards of Directors were supposed to provide inputs and help senior management figure out what to do with computers, software, and other business decisions. Have the Boards of Directors remained unaware of technological advances for more than half a century? That’s a question to which the answer seems to be, “Yes.” I am assuming that the TR write up is on the money.
Finally, what’s up with bank regulatory entities? It seems to me that somewhere along the regulatory chain the question, “What should be the minimum for bank technology enhancements?” I wonder if IBM has played a small role in keeping those mainframes humming? No, IBM would not make it difficult (technically or financially) to get free from the mainframe grasp. I assume I could ask Watson, but maybe not.
To sum up, ThomsonReuters’ article is a gem. I wonder if ThomsonReuters is running obsolete computer and network infrastructure hardware? Are these some DEC 20s lurking in Boston? Are banks able to search their documents in a reliable, satisfactory way? Why have the trail bosses lost the cattle?
Yikes, too many questions.
Stephen E Arnold, October 4, 2018
Factualities for October 3, 2018
October 3, 2018
Believe ‘em or not.
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- 56 percent. The number of teens who have experienced cyber bullying. Source: Pew Research Center
- 66 months in prison. Sentence for NSA thief taking work home. Source: Daily Beast
- $80 billion. Amount spent by technology companies to maintain a competitive edge. Source: Bloomberg
- 75. The number of banks joining JPMorgan Chase’s blockchain system. Source: Bitnovosti
- 800 kilograms. The world’s biggest bird. Source: CNet
- $3,499. Starting price of a Microsoft Surface Studio 2 PC. Source: Softpedia
- 90 million. Number of Facebook access tokens lost to hackers. Source: Betanews4
- More than 70. Number of new emoji in Apple iOS 12.1. Source: Apple
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Stephen E Arnold, October 3, 2018
Factualities for September 26, 2018
September 26, 2018
Believe ‘em or not:
- 58 million. The number of new jobs artificial intelligence will create by 2022. Source: Forbes
- 183.9. The top speed a woman reached riding a bicycle. Source: National Public Radio
- 55 percent of millennials prefer learning via YouTube. 59 percent of Generation Z prefer learning via YouTube. Source: Axios
- 71 percent. The percentage of startups in Israel focusing on business to business applications based on artificial intelligence. Source: Forbes
- 557,000. Number of backlogged US security clearances in the last 90 days. Source: FAS.org
- $783. Average monthly p9ayment of an Uber or Yelp driver in 2017. Source: Technology Review
- 2.0 billion euros. Amount raised by startups in France in the first six months of 2018. Amount raised in same period in 2017: 2.6 billion euros. Source: Bloomberg
- Everyone. The number of people Twitter will ask about its policy changes. Techcrunch.
Yep, numbers one can trust. Like “everyone.”
Stephen E Arnold, September 26, 2018
Change Is Difficult: Especially So for the Big Search Folks
September 25, 2018
There is a pretty good reminder that plumbing is an issue. Most users of smart search just assume that systems will continue to work. Hey, it is 2018. This Moore’s Law stuff, free services, and nifty new “old” phones are slam dunks.
Not quite.
I found “The Woes of Incremental Resource Drains in Big Systems” useful because it offers practical information. Most of the content snagged by my monitoring systems return what I consider marketing craziness.
The write up explains that an attempt to implement a necessary change can go off the rails. It’s like magic. One day Gmail or Amazon doesn’t work. Hand waving. Tweets. Then the problem is solved and forgotten.
The write up explains:
Let’s say you have a tier of 100,000 web servers. Every one of them opens one connection to your database. That connection is shared with all of the concurrent hits/code executing on those web servers. It just gets multiplexed down the pipe and off it goes. Then, one fine day, someone decides to write a change that makes the web servers open four connections to the database. They’ve invented this new “pooling” strategy, such that requests grab the least-busy connection instead of always sharing the single one. It’s supposed to help latency by n% (and get them a promotion, but let’s not get into that now).
Do the math. Dead system.
Worth remembering because as certain companies become like the timesharing systems of the past, excitement will be inevitable. Some can be hidden. Some will surface in unexpected ways.
Bing, Facebook, Google, and maybe Amazon may face this type of challenge more often than one believes.
Stephen E Arnold, September 25, 2018
Factualities for September 18, 2018
September 19, 2018
Believe ‘em or not:
- Zero. The change in the average age of the IBM workforce after reductions in workforce. Source: Poughkeepsie Journal
- $115. The cost of a holiday pine tree from Amazon. Source: APNews
- $100. The expected cost of Sony’s forthcoming PlayStation Classic game device. Source: TechSpot
- 63 million. The number of Apple OSX mobile phones sold. Source: Bloomberg
- 621 miles. The distance a Google Loon balloon can beam an Internet connection. Source: Fortune
- 40 percent. The percentage of economic experiments and studies which cannot be replicated. Source: Science Magazine
- 14 percent. How much larger the font Times Newer Roman is than Times New Roman. Source: The Verge
- 15. The number of employers who advertised on Facebook for job candidates of a specific sex. Source: ProPublica
- $9 million US. Amount of new capital injected into the vegan food delivery service Allplants. Source: TechCrunch
Stephen E Arnold, September 19, 2018
Technical Debt: A Bit of a Misunderstanding of the Iron Maiden Effect
September 18, 2018
Let’s go back in time. It is 1979 and Lockheed Martin has nosed into the commercial database business. The system was designed around IBM mainframe technology and due to costs and other factors, the Dialog Information Services outfit embraced Hitachi plug compatible mainframes. Now it is 1982, what’s the technical situation?
The answer is, “Nowhere.”
In fact, one can data the slow degradation and eventual marginalizing of the Lockheed Martin operation and the original commercial online business from the early 1980s. The challenges boiled down to:
An inability to perform tasks customers wanted because the technical architecture made the “changes” to deliver what the customer wanted too expensive, too complex, too time consuming, and too different from what mainframe architectures could deliver. And what did customers want? Reports. Yep, a report that showed who used what database. The trick mainframe architecture managers use to discourage a customer from getting a report was to charge an outrageous fee. In fact, a signal that a technical architecture cannot be bent to the will of the customer is a wild and crazy charge for what seems a simple request.
This is an iron maiden. It doesn’t look like much. Put an MBA, accountant, or lawyer running a high technology company inside and suddenly the technology makes its point or points as it were.
A lack of cash and managerial willingness to recreate a business on a more modern computing architecture. For the mid 1980s, the switch would have been to slightly less restrictive computers from non mainframe manufacturers like Digital Equipment Corporation. The money part is easy to understand. Investing in the future would require abandoning a good but slowly growing line of business to confront the risky world of the start up. That’s because a technical shift is just a start up when stripped of the fancy PowerPoints.
A failure to listen to those who explained that a change was indeed necessary. I worked for a woman who carried this message of change to luminaries in the commercial online timesharing world. Although tolerated, the managers smiled and went about their day to day mainframe approach to commercial online services. There is no fix for a person’s lack of desire and inability to listen and comprehend the message.
Okay, so that’s my view of the built in problem with most online services oriented co9mpanies. Against this set of personal ideas, I read “Too Big to Survive: There Is No Bailout for Technical Debt.” The write up makes a good point; specifically:
The only difference between technical debt and financial debt is that costs are more often known in advance when taking on financial debt. Both types of debt are a tool when used intelligently with purpose and a plan to manage it, and both can take a devastating toll when used recklessly or imposed through misdirection or miscommunication.
However, the idea that a manager can avoid the problem I described with the commercial online services business in the mid 1980s strikes me as falsely optimistic. The recommendation that a person should go through the MBA hand waving when the problem is identified within an organization is not particularly useful. The problem resides within the usually small group of executives who have the most to lose when a major shift is required to survive.
To sum up, in my experience, technology based companies are not trapped in an innovator’s dilemma? High technology companies are victims of the technology used to build the business. Many business school students learn about the problem of the buggy whip manufacturer. I have a different view of a high technology company’s getting kicked to the side of the road only to die an agonizing death while waiting for a self driving Tesla to stop and offer the near death outfit a ride to a crowd funding meet up.
The high technology company does not want to get in the Tesla. The outfit’s management prefers to wait for a better offer. Maybe the better offer arrives. Maybe not.
The result is the same. High technology companies become disconnected from what’s happening around them. Examples range from the tone deaf actions of Facebook to the oddly skewed failure of Microsoft in its mobile device business to Google’s social failures like leaking and protesting employees. Amazon is allegedly mounting PR campaigns to explain how well employees fare in a giant warehouse where robots seem to have more fun at work.
My point is that when one creates a business based on a technology and that technology becomes increasingly complicated, the technology will resist being changed. The humans are essentially along for the ride, modifying their world view about change when the implications of failing to make a change are sensed.
In short, we are not dealing with technological debt. We are dealing with an inherent characteristic of technology which seems liberating at first and then becomes a digital iron maiden. When the door begins to close, the pain begins.
Those moving to the side of a road don’t need a lift from a Tesla driver. An emergency vehicle is a better bet, but the likelihood of survival decreases with time. That’s not debt, that’s a far more grim outcome. When one is inside a technology, the door closes. Point made, right?
Stephen E Arnold, September 18, 2018
Machine Learning Frameworks: Why Not Just Use Amazon?
September 16, 2018
A colleague sent me a link to “The 10 Most Popular Machine Learning Frameworks Used by Data Scientists.” I found the write up interesting despite the author’s failure to define the word popular and the bound phrase data scientists. But few folks in an era of “real” journalism fool around with my quaint notions.
According to the write up, the data come from an outfit called Figure Eight. I don’t know the company, but I assume their professionals adhere to the basics of Statistics 101. You know the boring stuff like sample size, objectivity of the sample, sample selection, data validity, etc. Like information in our time of “real” news and “real” journalists, some of these annoying aspects of churning out data in which an old geezer like me can have some confidence. You know like the 70 percent accuracy of some US facial recognition systems. Close enough for horseshoes, I suppose.
Here’s the list. My comments about each “learning framework” appear in italics after each “learning framework’s” name:
- Pandas — an open source, BSD-licensed library
- Numpy — a package for scientific computing with Python
- Scikit-learn — another BSD licensed collection of tools for data mining and data analysis
- Matplotlib — a Python 2D plotting library for graphics
- TensorFlow — an open source machine learning framework
- Keras — a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano
- Seaborn — a Python data visualization library based on matplotlib
- Pytorch & Torch
- AWS Deep Learning AMI — infrastructure and tools to accelerate deep learning in the cloud. Not to be annoying but defining AMI as Amazon Machine Learning Interface might be useful to some
- Google Cloud ML Engine — neural-net-based ML service with a typically Googley line up of Googley services.
Stepping back, I noticed a handful of what I am sure are irrelevant points which are of little interest to a “real” journalists creating “real” news.
First, notice that the list is self referential with python love. Frameworks depend on other python loving frameworks. There’s nothing inherently bad about this self referential approach to shipping up a list, and it makes it a heck of a lot easier to create the list in the first place.
Second, the information about Amazon is slightly misleading. In my lecture in Washington, DC on September 7, I mentioned that Amazon’s approach to machine learning supports Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, Chainer, and Keras. I found this approach interesting, but of little interest to those creating a survey or developing an informed list about machine learning frameworks; for example, Amazon is executing a quite clever play. In bridge, I think the phrase “trump card” suggests what the Bezos momentum machine has cooked up. Notice the past tense because this Amazon stuff has been chugging along in at least one US government agency for about four, four and one half years.
Third, Google brings up dead last. What about IBM? What about Microsoft and its CNTK. Ah, another acronym, but I as a non real journalist will reveal that this acronym means Microsoft Cognitive Toolkit. More information is available in Microsoft’s wonderful prose at this link. By the way, the Amazon machine learning spinning momentum thing supports the CNTK. Imagine that? Right, I didn’t think so.
Net net: The machine learning framework list may benefit from a bit of refinement. On the other hand, just use Amazon and move down the road to a new type of smart software lock in. Want to know more? Write benkent2020 @ yahoo dot com and inquire about our for fee Amazon briefing about machine learning, real time data marketplaces, and a couple of other most off the radar activities. Have you seen Amazon’s facial recognition camera? It’s part of the Amazon machine learning imitative, and it has some interesting capabilities.
Stephen E Arnold, September 16, 2018
Factualities for September 12, 2018
September 12, 2018
Believe ‘em or not. The “facts”, that is.
- $7,800 per month and up for the cloud version of IBM Information Server. Source: Datamation
- 280,000. The number of routers infected with crypto jacking. Source: Next Web
- 250 million. The number of Pinterest users. Source: Engadget
- $131 trillion. The amount of money artificial intelligence will add to global production. Source: Axios
- 44 percent. The number of people who have deleted the Facebook app this year. Source: Reddit
- Less than one percent of start ups fail due to competition. Source: Techcrunch
- Five times an hour. How often Americans check their mobile phones. Source: San Francisco Chronicle
- Zero percent. The use of open source in 1993. Source: Silicon Angle
Stephen E Arnold, September 12, 2018
New Technology: A Problem Solver or Problem Generator?
August 23, 2018
Noodling about the k-epsilon model is probably not popular at most cocktail parties. In brief when flows occur, chaos usually turns up at the party.
Consider a large company anchored in technology from IBM, SAP, and some home brew applications. Toss in a mainframe, an AS/400 legacy system, and run-of-the-mill desktops.
Technological change is difficult, and when a switch is needed from Windows 3.11 to Windows 95, the shift may take years. The mainframe keeps on chugging along with CICS, MVS TSO, and green screens. The SAP system gets updated, but after a three year install process, who wants too make changes.
Today, the world of enterprise computing is different. Even the US government wants to move to the cloud. Virtualization is the big thing, not hardware down the hall behind a keycarded door.
When I read “Fragmenting Budgets and Rapid Pace of Change Creates Perfect Storm for IT Decision Makers.” The write up explains a situation which I thought most computer centric folks knew and understood.
The write up explains:
IT decision-makers are increasingly tasked with the difficult decision of choosing technology within business operations and finding the correct IT solutions for business needs. This extra link in the chain combined with the ever-accelerating pace of technological development is creating a perfect storm. In fact, a recent survey of IT decision-makers found that more than half are struggling to keep up with the pace of new technology. Most (84 per cent), acknowledge that they are not currently running the most optimum IT systems and significantly, 28 per cent admit that their organization has actually fallen behind the rate of technological change.
Nothing is as compelling as fear in an organization.
What’s happening is that the friction brakes of old school systems and methods are being replaced with the equivalent of dragging a sneaker on the pavement to slow down a bicycle. For some young at heart managers, the sneaker brake is great fun.
The downside is what I call a chaos problem. Semi stable flows become chaotic or, in more colloquial language, pretty darned crazy.
IT managers now find themselves in a technology environment less stable than those that existed a scant 10 years ago. The decision to embrace fast changing innovations can be a significant. Not only will the competitiveness of the organization be affected but the work environment may no longer match what must be accomplished to remain a viable entity.
Examples include the well publicized engineer revolts at Facebook and Amazon. The technical waffling from chip vendors when flaws are discovered. The presence of point of sale units at fast good chains and grocery stores which employees cannot operate.
The write up documents an accelerating opportunity for consultants. For those crushed with the fragments from technical chaos, the future may require rehab.
Stephen E Arnold, August 23, 2018
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