Quantcast
Subscribe!

 

Enter your email address:

Delivered by FeedBurner

 

E-mail Steve
This form does not yet contain any fields.
    Listen to internet radio with Steve Boese on Blog Talk Radio

    free counters

    Twitter Feed

    Entries in Technology (236)

    Monday
    Jan192015

    Diversity, testing, and how bugs often go unnoticed

    Recently I was talking to a friend who told me that he was in the market for a new car. My friend, who has been a loyal driver of a particular brand of vehicle for many years, for argument's sake let's call it Lexura (not the actual brand, but since I don't want to get hassled by any PR folks, I am making up this brand). When I asked him if he was considering the latest Lexura luxury model, a brand new one for 2015 that has generated significant buzz and some really stellar initial reviews, my friend surprised me by saying 'No', that he had soured on the Lexura brand.

    When I asked why the conversation went more or less as follows:

    Him: 'I like Lexura, I really do. But the last two Lexura's I owned have had the exact same problem. When it is raining, and I have the windshield wipers on, the water comes right inside the driver's window. I am always getting wet.'

    Me: 'Why would you have the window open if it is raining so much that you have to have the wipers on? That seems a little odd. Most people you know, close the windows when it's raining.

    Him: 'Well, usually I do. But sometimes I am smoking. And I like to keep the window open when I am smoking in the car. And then if it is raining the water comes right in off the wipers.'

    A sort of odd story, and immediately after hearing the 'when I am smoking in the rain the wipers get me wet' take from my friend I starting thinking about software, (and really any other kind of product), that is developed, tested (significantly), and then is released into the wide, strange, and harsh world of customers and users.

    My friend should not, on paper anyway, be driving in the rain, wipers on, driver's side window rolled down, getting wet. It does not really make much sense to most of us. It's raining. Roll up the window, dummy.

    But if you are a smoker, then it is pretty common that when you are smoking and driving that you want/need the window down, at least part of the way. I guess even smokers don't really want to be trapped in a small, enclosed area with their own second hand smoke. So they open the window. And most of the time it works out just fine. Unless it's raining and your Lexura has the bad habit of directing water off of the windshield wipers right into the open window and in your face.

    So let's spin this back to technology and testing and think about how/why 'bugs' like the Lexura spitting water back through the driver's window (let's just assume that it is actually, a bug for now), can make it past the testers and developers and engineers and make it into the world. Could it be, perhaps, that no one on the Lexura design/dev team was a smoker? That driving in the rain with the wipers on and window open was never actually tested, as it would have never occurred to the non-smokers at Lexura that this was actually a thing that would be important to some customers? That this possible lack of 'diversity' in the makeup of the Lexura team led to a bug that was only likely to be experienced by customers whose specific issues were not adequately represented at Lexura?

    This is kind of a odd story, as I mentioned above, but I think there is something important here nonetheless.

    First, it is almost impossible in the design, development, and testing processes of software, hardware, or products of any sort to test everything, every potential use case that is possible. It just cant' be done. Bugs will results, often from customers using the product in a way the builders never considered or even could have reasonably imagined.

    And second, 'diversity', at least the way we usually think about it, is often a very incomplete way to frame the noble notion of ensuring all important and representative voices are heard. Because every time you think you have incorporated ideas and points of view from all the necessary constituencies, one new one you never thought about raises a hand, and wants to be heard.

    Even smokers who drive in the rain with the windows open.

    Have a great week.

    Wednesday
    Jan142015

    SURVEY: Depressingly, Email remains the most important technology at work

    One of my go-to places for news, data, and research on technology adoption, usage, and trends is the Pew Research Internet Project. Towards the end of 2014, the folks at Pew released a short research report titled Technology's Impact on Workers, a look at how and which kinds of technologies are effecting work and workforces. It is a pretty interesting and easily digestible report, but since I know you are really busy and might not have time to read the entire research report, I wanted to call out one data point, and then we can, together, pause, reflect, and lament for a moment.

    First the data point, take a look at the chart below that displays survey responses to the question of which technologies workers (separated into office workers and non-office workers), consider 'very important' to their jobs:

    Two things stand out from this data. First, and the obvious one (and still exceedingly depressing), is that email remains the most important type of technology cited by office workers for helping them perform their jobs. Despite its relative maturity (and that is putting it nicely, as far as technology goes, email at about 30 years old should have been brought out behind the barn and put out of its misery decades ago), email continues to hold its vise-like death grip on modern office work. I hope I live (or at least work) long enough to see email finally disrupted from this position, but so far alternate workplace communication and collaboration options have not been able to accomplish what (ironically), almost everyone desires - the end of being slaves to email all day.

    The other bit of data from the Pew survey comes from the bottom portion of the chart - the kinds of technologies that workers find not 'very important' to them in getting their jobs done. And in a result that will make the social networking aficionados cringe (and many CIOs who would prefer to block these kinds of things from corporate networks happy), social networking sites like LinkedIn, Twitter, and Facebook were cited as 'very important' by a measly 7 percent of office workers and 2 percent of non-office workers. Now that doesn't mean that these networks are 'not important', based on the way the question was phrased, but certainly the vast disparity in the stated importance of social networks in getting work done compared to email, (general) internet availability, and phones paints a pretty clear picture. For most folks, technology use at work is dominated by email, with web access and phones, (land and mobile), rounding out about 90% of the technology picture.

    I will close with a quote from the Pew report, and then sulk away with my head bowed, dreaming of a better future for our children...

    This high standing for email has not changed since Pew Research began studying technology in the workplace. Email’s vital role has withstood major changes in other communications channels such as social media, texting, and video chatting. Email has also survived potential threats like phishing, hacking and spam and dire warnings by commentators and workplace analysts about lost productivity and email overuse.

    Ugh.

    Happy Wednesday.

    Tuesday
    Jan132015

    What Will Happen if we Move the Company: The Limits of Data

    Some years back in a prior career (and life) I was running HR technology for a mid-size organization that at the time had maybe 5,000 employees scattered across the country with the largest number located on site at the suburban HQ campus (where I was also located). The HQ was typical of thousands of similar corporate office parks - in an upscale area, close to plenty of shops and services, about one mile from the expressway, and nearby to many desirable towns in which most of the employees lived. In short, it was a perfectly fine place to work close to many perfectly fine places to live.

    But since in modern business things can never stay in place for very long, a new wrinkle was introduced to the organization and its people - the looming likelihood of a corporate relocation from the suburban, grassy office park to a new corporate HQ to be constructed downtown, in the center of the city. The proposed new HQ building would be about 15 miles from the existing HQ, consolidate several locations in the area into one, and come with some amount of state/local tax incentives making the investment seem attractive to company leaders. Additionally, the building would be owned vs. leased, allowing the company to purpose-design the facility according to our specific needs, which, (in theory), would increase overall efficiency and improve productivity. So a win-win all around, right?

    Well as could be expected once news of the potential corporate HQ relocation made the rounds across the employee population, complaints, criticism, and even open discussions of 'time to start looking for a different job' conversations began. Many employees were not at all happy about the possible increase in their commuting time, the need to drive into the 'scary' center city location each day, the lack of easy shopping and other service options nearby, and overall, the change that was being foisted upon them.

    So while we in HR knew (or at least we thought we knew), there would be some HR/talent repercussions if indeed the corporate HQ was relocated, we were kind of at a loss to quantify or predict what these repercussions would be. The best we were able to do, (beyond conversations with some managers about what their teams were saying), was to generate some data about the net change in commuting distance for employees, using a simple and open-source Google maps based tool.

    With that data we were able to show that (as expected), some employees would be adversely impacted in terms of commuting distance and some would actually benefit from the HQ move. But that was about as far as we got with our 'data'.

    What we didn't really dive into (and we could have even with our crude set of technology), was break down these impacts by organization, by function, by 'top' performer level, by 'who is going to be impossible to replace if they leave' criteria.

    What we couldn't do with this data was estimate just how much attrition was indeed likely to occur if the move was executed. We really needed to have an idea, (beyond casual conversations and rumor), who and from what areas we might find ourselves under real pressure due to possible resignations. 

    And finally, we had no real idea what remedial actions we might consider to try and stave off the voluntary and regrettable separations (the level of which we didn't really know).

    We basically looked at our extremely limited data set and said, 'That's interesting. What do we do with it?'

    Why re-tell this old story? Because someone recently asked me what was the difference between data, analytics, and 2015's hot topic, predictive analytics. And when I was trying to come up with a clever answer, (and I never really did), I thought of this story of the corporate relocation.

    We had lots of data - the locations of the current campus and the proposed new HQ. We also had the addresses of all the employees. We had all of their 'HR' data - titles, tenure, salary, department, performance rating, etc.

    We kind of took a stab at some analytics - which groups would be impacted the most, what that might mean for certain important areas, etc. But we didn't really produce much insight from the data.

    But we had nothing in terms of predictive analytics - we really had no idea what was actually going to happen with attrition and performance if the HQ was moved, and we definitely had no ideas or insights as to what to do about any of that. And really that was always going to be really hard to get at - how could we truly predict individual's decisions based on a set of data and an external influence that had never happened before in our company, and consequently any 'predictions' we made could not have been vetted at all against experience or history?

    So that's my story about data, analytics, and predictive analytics and is just one simple example from the field on why this stuff is going to be hard to implement, at least for a little while longer.

    Monday
    Jan122015

    Work is Anywhere: Notes from a Saturday Morning at the Auto Dealer

    Submitting this (brief) dispatch to re-state the obvious: Work has almost nothing to do with place and has less and less to do with time as well.

    I am writing this on Saturday morning from the extremely well-appointed customer service waiting area (can't fairly call this a 'room', it is larger than my first three apartments I think), at my local auto dealer as my sweet ride gets some maintenance/gets a safety recall item fixed.

    A quick look around reveals two wide screen TVs, (one on Fox News, one on ESPN), several sofas and chairs, a massive two-sided fireplace, a cafe area with free coffee, water, soft drinks, cookies, and most importantly for the rest of this story - free and pretty fast Wifi.

    Of course the car dealer waiting area has Wifi. Everyplace has Wifi now. We, many of us anyway, will choose a restaurant or coffee shop simply on the basis of Wifi access itself. So the fact that the auto dealer offers customer wifi is not really a big deal.

    But what is interesting as I look around the room on this Saturday morning (it is about 9:25 AM local time), is what many of the folks waiting here are actually doing.

    I am writing this blog post, (but I am kind of a loser without much going on so maybe I don't count).

    The guy at the table next to me is coding, a side project that he is working on outside of his day job (I asked him what he was working on).

    Another guy on one of the sofas is catching up on email (I didn't ask him, but a casual/nosy glance over his shoulder revealed the unmistakably bland user interface that is Outlook).

    A woman has been off and on her phone for the last 20 minutes in deep discussion and negotiation about some kind of insurance contracts with a supplier of her business.

    And a young-ish couple is seated together at another table staring at the same laptop and are engaged in pretty deep conversation. I am not exactly sure what is going on there, but decided to let them be and not get too weird/creepy in the waiting area.

    Almost everyone here seems to be working on something. At the auto dealer waiting room on a Saturday morning. I am not really sure if that is a good or bad thing. I do think it is wonderful and great customer service that the dealer has provided such a welcoming and accommodating environment so people can work. But I also, and maybe this is because I am old enough to recall when waiting at the auto dealer meant 90 minutes of pure hell in a tiny, dirty room with old issues of Car & Driver the only distraction, wonder if this is really healthy.

    I know that I am a little messed up for spending my Saturday mornings blogging.

    But I thought I was the only strange one. There is an entire roomful of folks with me this morning who are, equally, strange.

    Have a great week!

    Tuesday
    Jan062015

    Learning by watching, something else at which the robots are superior

    This story, Robots can now learn to cook just like you do: by watching YouTube videos, made the rounds over the past weekend. The basics of the story are these: researchers at the University of Maryland and an Australian research center have managed to create a system by which robots can 'learn' to cook, (how to recognize cooking tools, how to grasp and manipulate objects, how to process unfamiliar inputs into cohesive sets of instructiokns, etc), with the raw learning material consisting of a set of 88 YouTube videos of cooking demonstrations.

    The entire paper, Robot Learning Manipulation Action Plans by 'Watching' Unconstrained Videos from the World Wide Web is here, but I will grab the most interesting and telling bit from the abstract, and then shoot a few comments after the excerpt.

    From the paper:

    In order to advance action generation and creation in robots beyond simple learned schemas we need computational tools that allow us to automatically interpret and represent human actions. This paper presents a system that learns manipulation action plans by processing unconstrained videos from the World Wide Web. Its goal is to robustly generate the sequence of atomic actions of seen longer actions in video in order to acquire knowledge for robots.

    Experiments conducted on a publicly available unconstrained video dataset show that the system is able to learn manipulation actions by “watching” unconstrained videos with high accuracy.

    There is a lot to unpack even in that short snippet from the research, but the implications of this research suggests a future state of even more powerful automation technologies - the kinds of technologies that can learn simply by watching. And unlike us puny humans, they won't get tired of watching the same stupid 'life hack' kinds of YouTube videos 73,000 times before getting frustrated that we can't 'get it' and then just giving up.

    Some time back I posted about robot technology replacing or at least augmenting human staff in retail big box stores. In that post I posited that the real advantage, or at least one of the most important (and I think really overlooked for the most part), advantages that robots and technology have over human labor are the robot's incredible ability to learn, store, and share information with other robots.

    When the robot solves, or learns how to solve maybe just by watching a human colleague, a customer's problem, it can instantly share that knowledge with every other robot, who will all then have learned to solve that problem. Information, learned knowledge then becomes an asset for all. Immediately.

    Think about the power of that ability the next time you have to roll out some kind of training program to your entire workforce. How many times do you have to explain the same thing to another person? How long does it take everyone to 'get it?'

    How many never do?