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 (416)

    Thursday
    Jun072018

    Ending 'Upgrades' Once and For All

    About a hundred years ago (ok, it was not actually that long, just feels like it some days), I participated in my first, real 'Enterprise' technology project - helping to implement an Oracle E-Business Suite solution for a foreign division of a massive US-based telecommunications company. Even with that small, early scope, (the project continued for some years covering more and more countries), it was a pretty substantial piece of work.

    We needed DBA's to configure our servers, install the Oracle Applications, and do 'normal' maintenance like patching, load balancing, security, and cloning, (basically creating copies of application setups and configurations to create test, QA, and development environments). 

    We needed Analysts to figure out the business requirements, map these to the application functions, set up the apps accordingly, run tests, identify gaps, and train the end users.

    We needed Programmers to develop custom data loader programs and custom interfaces to other systems that the Oracle Apps needed to feed, or to be fed data by. And of course these programmers needed to develop custom capability that the business needed but the Apps, as least out of the box, could not provide. And don't get me started on all the custom reports that had to be built - even for a small implementation in one country initially.

    Add in to all this documentation, test plans, user process scripts, communication, change management - and the countless other things that need to be done in order, back then, to complete a successful implementation of traditional, (read on-premise), enterprise technology.

    And after all that work, all that time, resource allocation, investment and effort, after it was all done and the system was live do you know what we found? That by then Oracle had released a brand new, better, more capable and upgraded version of the Applications, and if we and the users really wanted the latest and best functionality we had to, wait for it - upgrade.

    But the problem was back then, and to some extent for many companies this is still true today, an upgrade was almost as much work as the initial implementation. The upgrade from one version of a large, on-premise, set of enterprise applications, (with customizations and interfaces), required the efforts of all those same groups of people mentioned above. Upgrades were really manual, required tons of validation and testing, and if the functionality had changed enough, also necessitated significant user training and change management efforts. Frankly, upgrades stunk. And so many organizations running enterprise apps on-premise avoided them as much as they could, preferring to stick with and maintain older versions, (with fewer features and capabilities), as the tradeoff. And that tradeoff has perpetuated. Way longer than most of us thought it might.

    Even in 2018, in this time when we like to assume that every large organization has moved their enterprise systems for Finance, HR, Operations, Manufacturing, Supply Chain, etc. to the cloud, many large organizations have not, and are still running versions of on-premise Enterprise solutions, as the cost, complexity, and resources needed to do a 'upgrade to the cloud' were just as massive and daunting as the old on-premise upgrades were back then. In fact, many of these cloud migrations are not upgrades at all in the classic sense - they are full-on re-implementations - huge technical and functional projects that as I said, many firms have continued to avoid or postpone.

    Sorry for the history lesson, but it's important for the news I wanted to share today.  

    Earlier this week, and in a way that particularly resonated for me given my history with Oracle E-Business Suite, Oracle announced the Oracle "Soar to the Cloud" solution - an automated set to tools and processes to enable customers running older versions of Oracle Applications on-premise to migrate to the Oracle Fusion ERP in the Cloud solutions much faster than ever before, and with the assurance that data will be migrated, configurations will be applied consistently in the cloud, and even any customizations done on-premise will be addressed with a new library of pluggable Fusion ERP integration capabilities.

    If you want to deep dive into the nuts and bolts how this process will work, take a look at this video of the presentation made this week by Larry Ellison, Oracle CTO and Chairman, as he walks through the process. But even if you don't need r want to understand all the technical details - just remember this - the Oracle Soar to the Cloud program promises to make your organization's 'upgrade' to the cloud truly the last upgrade (in the traditional sense), that you will have to undertake.

    Once you make it to the Cloud - your organization can get the benefit of regular, continuous, and frictionless updates to your enterprise apps - making the ability to adopt new capabilities, remain compliant with new regulations, and move more quickly and innovate more rapidly than at any time in the past. 

    Your organization probably knows you want/need to be in the Cloud for these reasons and more.

    But if you are in one of the organizations that for one reason or another has avoided the cloud, avoided the dreaded 'upgrade' - this new Soar to the Cloud program might be just what you need to kick start those plans. 

    Learn more at Oracle Soar

    Friday
    Jun012018

    Five observations from the new Fortune 500

    Dug out from my Feedly 'Read later' list was the announcement a couple of weeks ago of the latest iteration of the venerable Fortune 500 - the annual list of the largest 500 US companies (ranked by annual revenues).

    The Fortune 500 has become a synonym for 'big business' in America, and taking a look through the list, and especially looking at changes and trends in the list, has become an annual exercise for folks like me who like to think about macro trends in the economy, and to think about how these trends suggest what might be coming next.

    Also, it's just fun. If you are a geek like me.

    So for an almost-summer Friday, here's my first five quick observations from looking the new Fortune 500"

    1. For all the talk about technology that dominates most business news cycles and programs, old-fashioned retailer Walmart remains number one on the list - and it isn't really even close. Walmart has double the revenues of the next closest rival for the top spot, ExxonMobil. And while we know all about the massive businesses in retail and in cloud computing, (an odd combination), that Amazon has built over the years, Walmart still has almost 3x the revenue as their competitor from the Northwest. I know I like to think of Amazon as the most interesting and important company in America, but we can't or shouldn't forget the outsize impact of boring old Walmart. And don't forget their 2.3 million (with an 'm', employees).

    2. Lots of 'The future is changing, are you ready' presentations like to talk about how much turnover there is over time in the list of Fortune 500 members. While interesting, I find it even more interesting, given the massive changes in business, technology, society, and more since the list's inception in 1955, that 53 companies (ExxonMobil, GE, Chevron, and GM to name some), have been on the list every year since 1955. That over 10% of the largest companies in American have been there for over 60 years is remarkable to me.

    3. Despite point 1 about Walmart's staggering size, it is true that technology or tech-dominated firms make up large portions of the upper end of the Fortune 500. Household tech names like Microsoft, Apple, Amazon, Alphabet, IBM, Intel, Facebook, Oracle, and Intel all crack the top 100. And further down the list we see Netflix, Qualcom, Nvidia, and Adobe - all companies doing incredible things in their respective markets. And while the Fortune 500 ranks by revenue, if you think about company value as expressed by market cap, (subject to stock prices fluctuations), the most valuable list is also dominated by tech - Apple, Facebook, Amazon, Microsoft,  and Alphabet are five of the top six most valuable companies in America.

    4. There are 30 'mega-employers' on the list - companies with over 200,000 employees as of the date the list was compiled. The above mentioned Walmart leads the employment table, but some other notable massive employers are Amazon, (566,000); Home Depot, (413,000); Starbucks, (277,000); UnitedHealth Group (260,000); JP MorganChase, (252,000); and Ford Motor (202,000). And coming in just below the 200k employee threshhold are big names like Disney, Marriott, Boeing, Oracle, Microsoft, and Apple - each having more than 100K employees. 

    5. There are only 17 new companies on the list this year. The most interesting 'newcomers' to the Fortune 500 are, for me, Molson Coors Brewing, (Coors was my preferred beer once upon a time), Wynn Resorts, (I still need to get to Macau), and Conduent, (I just talked with them this week, look for an HR Happy Hour Show coming soon featuring some folks from Conduent). The last new entrant on the list is corporate supply company Cintas checking in at 500. For perspective, the last company on the list is a giant organization of 42,000 employees and 900,000 customers.

    Ok, that's it from my quick walk down the Fortune 500 this year, I find it interesting every year, hope you do too.

    Have a great weekend! 

    Monday
    May212018

    The challenge of recruiting for a job we think is going away

    If there is one job in the American labor force that presents an incredibly interesting, complex, and important case study on supply and demand, price economics, the impact of automation on work, and the current and future labor force it is the job of commercial truck driver.

    A couple of important statistics to keep in mind before we wade into some of the details that make commercial trucking so darn interesting, (at least to labor market and automation geeks like me).

    According to the American Trucking Association there are about 3.5 million commercial truck drivers in the US. And 71% of all the freight tonnage in the country is moved by truck. Finally, according to the BLS, truck drivers earn an average of about $24 an hour, and have an average age of about 55 years old.

    There are a couple of other factors specific to commercial trucking that tend to make it a difficult job to perform and to recruit for - traditionally new entrants have had to fund their own, expensive training and certification, for new drivers, the hours and time away from home are significant, the job itself is stressful, hard, and tends to foster really unhealthy habits, (poor sleep, fast-food, little exercise), and finally, and perhaps most importantly, commercial truck driving has been increasingly seen as being a job that can and will soon be replaced and disrupted by automation. Estimates of the impact of automation on commercial truck driving vary, but one representative example from Goldman Sachs, estimates that as many as 300K trucking jobs will be lost annually, once self-driving trucks become more widely adopted.

    Factor all of this in, the hard lifestyle, the relatively low pay, the looming threat of automation making many of these jobs redundant - oh, I didn't even mention the federal regulations making most of these jobs not available to workers under 21 and the strong market for alternative jobs in construction and energy luring many of the trucking industry's target candidates - and you would probably bet that the US economy is not producing as many new truck drivers as it has in the past.

    And you would be right. But the problem of many US companies, (and consumers), is that while we wait for Elon Musk's fleet of autonomous semi-trucks to take over American highways, and in the age of increasing demand for shipments (driven by the strong economy and Amazon Prime), the industry is seeing an increasing shortage of commercial truck drivers.

    Here's a chart from the American Trucking Association illustrating the problem facing the trucking industry shown as the estimate of unfilled truck driver jobs:

    According to the ATA's estimates, there could be as many as 180,000 trucking jobs unfilled within 10 years. And that kind of a shortfall, should it indeed play out that way, will have a pretty significant ripple effect throughout large swaths of the economy.

    Wages and benefits for truckers, which have been increasing steadily, will have to continue to rise. The transportation companies will have to pass these costs to their customers - manufacturers and retailers and commodity producers - who will past them on to their customers, who will pass them on to you and I. And the development timeline for the kinds of autonomous trucks that might stand in for the human truck drivers will have to accelerate.

    But in the meantime, at least the next 5 or 10 years, if the current trends hold, the US economy and labor market is going to have to find a way to recruit and retain more truck drivers. And lately, it seems like the transportation and other companies have not really cracked the code on just how to do that.

    A tough job, with lots of stress, with relatively poor to average pay, that we keep writing breathless stories about how it will soon be made obsolete by technology, with an aging cohort of workers currently in place, might represent the toughest recruiting challenge in recent memory.

    Sure, everyone likes to think 'tech' recruiting is hard, and it probably is. But I would wager a good commercial trucking recruiter would be worth their weight in whatever it is their company needs to get from one side of the country to the other.

    Anyone out there doing this kind of recruiting? Would love to hear how it is going on the front lines.

    Have a great week!

    Monday
    May142018

    Questions to ask before letting an algorithm make HR decisions

    Nearing the halfway mark in 2018 and I am ready to call it right now - the topic/trend that has and will continue to dominate the HR and HR technology discussion this year is Artificial Intelligence or AI.

    I will accept my share of the responsibility and blame for this no doubt. I have hit the topic numerous times on the blog, I have programmed at least seven sessions (or more) featuring AI topics for the upcoming HR Technology Conference, and the subject comes up on just about every HR Happy Hour Podcast at one point or another. In fact, one of my favorite HR Happy Hour Shows this year was the conversation I had with author and professor Joshua Gans on his new book titled Prediction Machines: The Simple Economics of Artificial Intelligence.

    So if you are thinking that everyone in HR and HR tech is all in on AI you'd probably be right. And yet even with all the attention and hype, at some level I still wonder if we are talking about AI in HR enough. Or more specifically, are we talking about the important issues in AI, and are we asking the right questions before we deploy AI for HR decision making?

    I thought about this again after reading an excellent piece on this very topic, titled 'Math Can't Solve Everything:Questions We Need to be Asking Before Deciding an Algorithm is the Answer' on the Electronic Frontier Foundation site. In this piece, (and you really should read it all), the authors lay out five questions that organizations should consider before turning to AI and algorithms for decision support purposes.

    Let's take a quick look at the five questions that HR leaders should be aware of and think about, and by way of example, examine how these questions might be assessed in the context of one common 'AI in HR' use case - applying an algorithm to rank job candidates and decide which candidates to interview and consider.

    1. Will this algorithm influence—or serve as the basis of—decisions with the potential to negatively impact people’s lives?

    In the piece on EFF, the main example or warning cited when AI-driven processes might negatively impact people's lives is in the use of an algorithm called Compas, which has been used to predict convicted criminals likelihood to become repeat offenders. The potential danger is when the Compas score influences a judge to issue a longer prison sentence to someone the algorithm suggests is likely to repeat offend. But what if Compas is wrong? Then the convicted offender ends up spending more time than they should have in prison. So this is a huge issue in the criminal justice system.

    In our HR example, the stakes are not quite so high, but they still matter. When algorithms or AI is used to rank job candidates and select candidates for interviews, those candidates who are not selected, or are rated poorly, are certainly negatively impacted by the loss of the opportunity to be considered for employment. That does not mean the AI is 'wrong' or bad necessarily, but just that HR leaders need to be open and honest that this kind of AI will certainly impact some people in a negative manner.

    With that established, we can look at the remaining questions to consider when deploying AI in HR.

    2. Can the available data actually lead to a good outcome?

    Any algorithm relies on input data, and the 'right' input data, in order to produce accurate predictions and outcomes. In our AI in HR example, leaders deploying these technologies need to take time to assess the kinds of input data about candidates that are available and that the algorithm is considering, when determining things like rankings and recommendations. This is when we have to ask ourselves additional questions on correlation vs. causation and whether or not one data point is a genuine and valid proxy for another outcome.

    In the candidate evaluation example, if the algorithm is assessing things like educational achievement or past industry experience of a candidate, are we sure that this data is indeed predictive of success for a candidate in a specific job? Again, I am not contending that we can't know which data elements are indeed predictive and valid, but that we should know them, (or at least have really strong evidence they are likely to be valid).

    3. Is the algorithm fair?

    At the most basic level, and the one that has the most applicability for our AI in HR example, HR leaders deploying AI have to assess whether or not the AI is fair - and the simplest way is to review if the algorithm is treating like groups similarly or disparately? Many organizations are turning to AI-powered candidate assessment and ranking processes to try to remove human bias from the hiring process and attempt to ensure fairness. HR leaders, along with their technology and provider partners have the challenge and responsibility to validate this is actually happening. 'Fairness' is a simple concept to grasp, but can be extremely hard to prove, but one that is inherently necessary in order for AI and algorithms to drive organizational and even societal outcomes. There is a lot more we can do to break this down, but for now, let's be sure we know we have, in HR, to ask this question early and often in the AI conversation.

    4. How will the results (really) be used by humans?

    If you deploy AI and algorithms for the purposes of ranking candidates, how will you use the AI-generated rankings? Will they be the sole determinant of which candidates get called for interviews, advance in the hiring process, and ultimately have a chance at an offer? Or will the AI rankings be just a part of the consideration and evaluation criteria for candidates, to be supplemented by 'human' review and judgement?

    One of the ways the authors of the EFF piece suggest to ensure that human judgement is still a part of the process, is to engineer the algorithms in such a manner that they don't produce a single numerical value, like a candidate ranking score, but rather a narrative report and review of the candidate that a human HR person or recruiter would have to review. In that review, they would naturally apply some of their own human judgement. Bottom line, if your AI is meant to supplement humans and not replace them, you have to take active steps to ensure that is indeed the case in the organization.

    5. Will people affected by these decisions have any influence over the system?

    This final question is perhaps the trickiest one to answer for our AI in HR example. Job candidates who are not selected for interviews as a result of a poor or lower relative AI-driven ranking, will almost always have very little ability to influence the system or process. But rejected candidates often have valid questions as to why they were not considered for interviews and seek advice on how they could work to strengthen their skills and experiences in order to improve their chances for future opportunities. In this case, it would be important for HR leaders to have enough trust and visibility into the workings of the algorithm in order to precisely understand where any given candidate was ranked poorly. This ability to see the levers of the algorithm at work, and be able to share them in a clear and understandable manner is what HR leaders have to push their technology partners on, and be able to provide when needed.

    As we continue to discuss and deploy AI in HR processes, we have to also continue to evaluate these systems and ask these and other important questions. HR decisions are big decisions. They impact people's lives in important and profound ways. They are not to be taken lightly. And if some level of these decisions are to be trusted to an algorithm, then HR leaders have to hold that algorithm (and themselves), accountable.

    Have a great week!

    Thursday
    May102018

    PODCAST: #HRHappyHour - Oracle Spotlight: Innovation in HCM Technology

    HR Happy Hour - Oracle Spotlight - Episode 2: Innovation in HCM Technology

    Hosts: Steve BoeseTrish McFarlane

    Guest: Gretchen Alarcon, Group Vice President, Product Strategy, Oracle

    Listen HERE

    This week on the HR Happy Hour Show, hosts Steve Boese and Trish McFarlane continue a special series of podcasts with our friends at Oracle HCM. On Episode 2, we are joined by Gretchen Alarcon from Oracle to talk about innovation in HCM technology, and how HR leaders can best position themselves and their organizations to take advantage of these innovations. On the show, we talk the importance and impact of migrating HCM solutions to the cloud, the emerging influence of AI and machine learning in HCM technology and what that means for HR, and how user focus and user experience are driving much of the most exciting innovations in HCM technology.

    This was a really interesting conversation and one we will build on in upcoming episodes of the Oracle Spotlight series.

    You can listen to the show on the show page HERE, on your favorite podcast app, or by using the widget player below:

    Thanks to Gretchen for joining us and thanks to our friends at Oracle HCM for making this series happen.

    Subscribe to the HR Happy Hour Show on Apple Podcasts, Stitcher Radio, or wherever you get your podcasts - just search for 'HR Happy Hour'.