Month: July 2017

Tackling Job Displacement at Scale: My Ideal Solution

On By karentay7 Comments

Job displacement is not new, but the scale and speed will increase in the coming years – a 2013 study by Frey and Osborne suggested that 47% of workers in America held jobs with a high risk of potential automation.

A few months ago, I wrote “Tackling AI-Driven Job Displacement: A Primer”, which argued that current avenues for people to retrain and find new jobs appeal mainly to those who are already motivated. When changing jobs is a choice, we don’t have a problem. However, when changing jobs is a necessity – which it will be as the speed of job displacement increases – we need the whole population to be motivated and proactive about re-skilling and finding new jobs.

How can we achieve this? For countries, this will be one of the defining challenges of our generation. If we cannot get a large proportion of our population to continuously re-skill to fit emerging jobs, we will face economic slowdown, increasing unemployment and most probably political upheaval.

In this article, I give a big picture of my ideal future, some of the outstanding efforts by tech companies and Governments, and the two big challenges that require collaboration.

My Ideal Future: The Big Picture

The job market today is wrought with inefficiencies which create barriers for job-seekers and employers.

  • For individuals, the job search is intimidating because most of us do not have a good understanding of our skills, and how these map onto other jobs. There is also little incentive to obtain new skills if the link between these skills and future employment seems tenuous. It is a simple cost-benefit analysis.
  • Employers are similarly in the dark. They can pay exorbitantly for skills that seem to be in limited supply when they unaware of people with adjacent skill-sets who could easily fill those roles with some investment in training. According to the Manpower Group, 46% of US employers reported that they had difficulty filling jobs.
  • Education providers can profit greatly from the lack of transparency, when people are willing to pay for credentials that have no bearing on employment outcomes. On the other hand, they may struggle to reach a wider audience because only motivated people are seeking out their services. Adult education providers suffer here.

Imagine a different future with me for a few seconds.

If you are a regular person who wants to stay employed

Imagine a day when making plans for your next job is as much a part of your life as making plans for your next purchase: the barriers to your job search are so low.

  • Just as you have a bank account, you have a skills account which elucidates all the skills you have accumulated through your education and previous jobs. These include self-reported skills, as well as skills that are verified by an authority that has studied the relationship between job descriptions and skills across sectors.
  • Just as Facebook and Google push you advertisements based on your click history, you are consistently notified about emerging jobs with a close skills match to your account. “Good morning, we noticed that these jobs, which match your skillset, are trending. Salaries in these jobs are 5% higher than your current salary”.
  • Suppose these jobs are sorted by degree of match to your existing skillset – 90%, 80%, 70%. You click on one and get an assessment of the best next steps – “Hi Karen, looks like you’re an 80% match to this job, and people with your profile have successfully entered this job by [taking this course] [doing a side project in this area] [taking a 1-month internship with X company]. Click to proceed”.
  • You are also notified if your job is at risk, and nudged to take action. “Based on trends, your ‘risk modelling’ skill is increasingly automated by companies. Job listings for this are are decreasing at a rate of 5% a year and this is expected to speed up. Here are some adjacent skills you should pick up ASAP.”

When it’s so easy, won’t more people start to inculcate this as part of their lifestyle?

If you are an employer

Now imagine you are an employer. You need the right talent to serve emerging needs and want to access this talent, fast. Imagine if you not only had access to candidates who applied for their jobs, but candidates who had a >80% match to needed skill-sets, and could be a perfect fit if they underwent prescribed training or certification. You could reach out to these individuals and nudge them towards this, or sponsor training where it makes economic sense.

Rather than firing staff when functions are automated, you may prefer to move them into emerging jobs within the company, as long as the cost is not prohibitive. What if you could assess the degree of match between a potential lay-off with all the emerging jobs in your organization? This could help you make a choice on whether to invest to train the potential lay-off for these roles, or to favour of a new hire.

The information and incentives are better aligned to help you invest in training, rather than to go through rapid cycles of hiring and firing, which could undermine the fabric of your company.

If you are an education or training provider

Now imagine you are an education or training provider. When your courses are not standalone units but part of a pathway that quantifiably increases someone’s chances at securing an emerging job, your user base will likely increase. With more employers investing in education and skills training, your revenue also increases.

If you are a country Government

Now imagine you are a country government who has realized that the traditional model of centralized labour force planning is far too slow for the rate of job displacement. You can breathe easy because there is no longer a need for this outdated method. Individuals are motivated to reskill by good information and clear outcomes. Employers are willing to invest in training that will bring them the skills they need. Training providers fill real gaps and less people are overspending on education that yields little outcomes. The market is finally working – not just for the motivated few, but for your whole population.

Existing Efforts by Tech Companies are Laudable, but Insufficient

What I have laid out is pretty ambitious. It requires:

  • A closed loop between individuals, training providers and employers (graph below). Having a common language to describe skills and jobs lies at the core – if we continue to describe the same skills and jobs in different words, employers do not know the full extent of their candidate pool, job-seekers do not know where they fit, and educational institutions do not know how their programs really help. It’s the wild, wild west.
  • User-centric design that caters for the full range of our population: from the highly-motivated time-rich youth, to time-strapped parents and people who have worked in the same industry for decades. Some may not even use the internet. Employers similarly have to come on board – from the large multinationals to our mom and pop shops.

skillsarticle

Several companies have already been working on parts of this puzzle. For example:

Google for Jobs, announced in June this year, leverages its powerful search engine to pull jobs which are most relevant to job searchers. To achieve this, Google’s powerful machine-learning model normalizes jobs and skills which are described very differently by employers and users. For example, if you search for “collections call centre job”, google will be able to flag out all the jobs that you will likely be interested in, even those that include none of your search terms, e.g. “Revenue Management and Care Representative.”

In my opinion, no one is better placed than Google to do this because of their experience with relational models, which group relevant search terms and results. Relational models form the fundamental basis for how Google yields relevant results even when you are not sure how to search for it. I wrote about how Google uses relational models in health search here, which has strong parallels to the job search.

Linkedin is well-positioned to create a closed loop between job-seekers, training providers and employers because all these players are in their platform. Linkedin helps individuals develop their skills portfolios through self-reporting and endorsements. It is also starting to map individual skills and jobs. If you have a premium subscription, you can see how your skills compare to other candidates and the percentile of applicants you fall into based on your Linkedin profile (example below). With the acquisition of Lynda.com, Linkedin is increasingly able to recommend courses to help people up-skill towards their desired job outcomes. To provide greater transparency to job seekers and employers, Linkedin’s Economic Graph team is also mapping the demand and supply of various skills across the world. However, Linkedin’s current business model is expensive for users, which could exclude a large part of the population and limit the data it collects.

linkedinpic
Linkedin: initial efforts at mapping skills to jobs

Start-ups are also playing important roles in this ecosystem. Just two examples:

  • Interviewed, an SF-based start-up, helps to close the loop between skills training and jobs. It started out by successfully developing a range of assessments to help employers assess the skills of candidates. It has since developed platforms such as Rightskill, where employers commit to hiring or at least interviewing people who obtain a certain score on hosted course. MooCs like Udacity have also made strides to closely link skills training to job placements – an essential move if they want to motivate a wider population.
  • JobKred, a Singapore-based start-up, uses predictive analytics and data science to connect job seekers to their best opportunities, recommend personalised learning plans, and help employers zero in on their top prospects. They provide a customised user experience in the job search, making the job search less frightening.

Two Big Challenges Ahead

The creativity and experience of these companies will go a long way to achieving my ideal future. However, there are two big challenges ahead that I hope companies, Governments and non-profits will tackle together.

First, aligning the way that everyone describes skills.

Google has made leaps in working out the objective relationship between skills and jobs on the back-end. Their relational model is based on proprietary occupational and skills ontologies that they built in-house.

  • Google’s occupational ontology gives a top-down understanding of 1,100 job families, a task typically undertaken by Governments.
  • Google’s skills ontology maps out 50,000 hard and soft skills and the relationships between these skills.
  • The skills and jobs ontologies are then mapped onto each other to paint an objective picture of the hard and soft skills required in each job, regardless of how they are described.

This is an amazing first step – let’s just take a moment to appreciate it. The fact that they make their relational models available through the Google Cloud Jobs API is even more amazing.

However, I believe these ontologies should not be kept on the back-end, used only when someone conducts an internet search for jobs. Many aspects of the job and education journey take place offline. Constraining the benefits to people who make proactive internet searches may miss out on a huge swathe of potential job-seekers.

Public and non-profit organizations should help with nudging individuals, employers and training institutions towards describing skills and jobs in the same language – perhaps Google can consider working with Linkedin, to combine Google’s job and skills ontologies with Linkedin’s data on self-reported skills, and make these a shared resource. However, the business model must make sense for the companies. Instead of users, Governments, charities or philanthropists should pay as this is a public good.

To reach the widest population, we also need to proactively help individuals populate and manage their skills accounts, just as they manage their bank accounts. UX designers need to work on interfaces that give people a bias to action. I covered some ideas in my “ideal future” section.

Second, bringing users on at scale to enable powerful Artificial Intelligence.

AI will play a huge role in enabling my ideal future: it underpins recommender systems and relational models, just to name a few areas. In turn, these AI-powered systems are enabled by data. Here we face a chicken-and-egg issue: unless we bring on the wider population to use the system, the software will not be able to serve the wider population. To collect data, we need to bring employers, individuals and educational institutions onto this system at scale.

This is an area where technology companies will benefit from the help of public and non-profit institutions. Singapore’s decision to give every citizen $500 for skills training and to create a unified skills and jobs database for individuals is a first step towards bringing a larger segment of the population on board. For segments of the population who are not digitally savvy, we need humans to come alongside and help them get onto the system.

Final thoughts

My ideal future is one where everyone – not just the highly motivated, time-rich or digital savvy – is empowered to continually update their skills and move to emerging jobs, rather than get left behind in the wave of job displacement. Finding your next job must be made as easy as finding the next place you want to eat at.

In all likelihood, this vision will not come through 100%. New safety nets must be put in place for people who simply cannot find new jobs. However, I believe that keeping people in jobs must be our utmost priority: not only are jobs the best safety net, there is also something dignifying about work that I do not believe can be replaced by a handout.

The goal of motivating a population at scale and overcoming inefficiencies in the job market is an area ripe for solutions by technology companies. I am encouraged that many – from conglomerates to start-ups – are working hard to achieve this vision. At the same time, I want see more collaborations that will enable large segments of our population to benefit. I would love to hear your thoughts – what would you like to see in this space?

 

 

Smart Cities Connect 2017 – Interview and Takeaways

On By karentayLeave a comment

I was in Austin, Texas, in June to represent Singapore at Smart Cities Connect 2017. I  participated in a panel on Data and Networks with the CIO/Chief Data Architects of San Jose, Orlando and Austin; served as a reviewer for eight Urban Mobility Start-up pitches, and had incredible side-meetings with CTO/CIOs across America. In an interview with Chelsea Collier, Smart City Connect’s Editor-at-Large, I shared some takeaways:

An Interview with Karen Tay, Smart Nation Director, Singapore

By: Chelsea Collier, Smart Cities Connect 

Chelsea Collier [CC]: I’m so happy to have you here at Smart Cities Connect.

Karen Tay [KT]: Thank you, I’m glad to be in your city after you visited Singapore a few months ago.

CC: I was so blown away not only by what you all are doing, but how you’re doing it, and how intentional and collaborative everyone in the government proper is. I was very very inspired by what I saw there.

KT: Thank you, yes it’s not without challenges. I think one of my takeaways from this conference is that we all face the same challenges, and part of it is organizational: how we are set up in a way that gets all the different domains to collaborate on Smart City Projects. That is not something that comes naturally because we are so used to working in silos. By setting up a smart city team (typically within the CIO or CTO’s office), actually many cities in the U.S. are doing similar things to Singapore. 

CC: Good, and I’m so excited that there’s so much progress being made just in the past year. This is the second time we’ve done this conference, the first time was in June here in Austin in 2016, and just in that span of one year I’ve seen so many cities go from intention, and more of an ethereal concept to really launching into strategic conversations and into pilots, and talking about scaling. So I’m impressed by how quickly it’s moving. It might not feel that way on the city side because you’re there day in and day out but from the outside world it’s really exciting.

KT: Definitely and I think it’s also driven by compelling use cases. One of the great people I met at this conference was Rosa Akhtarkhavari, the Orlando CIO. She talked about how the Orlando shootings really brought to the fore some of the technology needs that needed to be met and how they’re now going to build video analytics capabilities. I think it’s always driven by the use case. You cannot build too far ahead without the use case in mind. And I think that’s what driving the speed of progress.

[NB: during the Orlando nightclub shooting in 2016, Rosa’s ideal scenario was if she could pump the secondary shooter’s picture into the system, and analytics at the edge could flag out which locations the secondary shooter was seen. (Rather than to bring all the video feeds in and analyze in the cloud/data center, which would slow things down). However, she found that current capabilities did not allow for that.]

CC: Perfect. So any big lessons learned these past couple of days, or what you’ve learned that can benefit you back at work?

KT: I think one of the dominant themes of a smart city conference in the U.S. is how are you going to pay for this digital infrastructure.

In Singapore we are prudent with how we spend but I think we are fortunate that we have the resources to build some of these things. But what I really took away is that even if you have the resources, you have to have discipline of thinking about the economics of it.

I really appreciated the discussions like Chicago collaborating with the university, or San Jose working with a company that’s willing to sponsor a lot of these sensor deployments, or even companies like Civic Connect, which are saying “well, our funders are okay for us to pay for this free of charge to the city as long as we have a business model which will reap the benefits”. I think in Singapore we will benefit a lot from thinking about this economic discipline, just as the U.S. is forced to do. I think that was one of my main takeaways.

However I also think that the government cannot run away from paying for some of these services. It cannot completely be left to the private sector because of this idea of digital inclusion. Fundamentally, the government needs to be able to ensure that use cases which will not yield economic returns will still be accounted for. Who else could do that in society? 

[Another key takeaway that I did not mention in the interview concerned privacy in smart cities. All cities – especially American – want to change the narrative that lumps “surveillance” with “data collection”. CIOs recognize that if there is the capacity to identify people for serious crimes such as terrorism, there is the capacity to identify anyone else. Hence the issue is not about limiting our capacity to do identify people through video footage, but ensuring predictability, accountability and transparency in how the data is used.

Cities need to give citizens utmost assurance in this regard. I believe Seattle has a good model to learn from: Seattle put in place a privacy “self review” process, where every department seeking to launch a technology solution has to undergo a “self review” according to the 6 privacy principles (which were established a few years ago). Any technology project that collects data also has to pass through the City Council’s approval. “At-risk” cases are flagged up by the city’s Chief Privacy Officer in the CTO’s office. She advises on precautions and typically pushes them to conduct a public consultation.]

CC: I think as the public sector and the private sector get more comfortable working together they can have strategic and very honest conversations about that and everybody can own their piece of it. And there’s just no time to waste, the problems aren’t getting smaller. They’re only escalating and technology has the potential to really help make some headway there.

KT: I think so. I think there are problems like homelessness, inequality, access to healthcare – all these are big problems waiting to be solved and technology can. It’s a matter of having those conversations.

CC: Perfect. So glad you’re here. Thanks for joining us.

KT: Thank you.

====

 

 

scc2017

What If My Team Members Are Unmotivated?

On By karentay1 Comment

Since publishing my last article “Three Harmful Ideas About Leadership and Shifts You Can Make”, I received lots of messages from folks with further questions. Two themes emerged:

  1. Does your advice apply to team members who are unmotivated? If not, how should I go about managing them?
  2. How do I balance being a coach, collaborator and challenger with urgent demands to deliver?

Truth is, Google, Linkedin and Facebook, as premium employers, can hire all their staff for strict criterion such as high motivation, capacity and adaptability. I have worked in some places like that, where it is easier to make the shifts I suggested. Everything seems to flow.

On the other hand, I have also worked in places where motivation, capacity and attitudes were varying. This is the more common experience of the two. What do you do when you are a new manager, your boss is breathing down your neck for a deadline that was yesterday, and there are team members who simply cannot do the job “up to standard” or simply do not want to do it?

These are extremely complex and painful questions that many managers face when working with a team of varying motivation, capabilities and attitudes. Besides what my previous article advocated –  building a network of peers who can provide sounding boards and coaching – I would give three buckets of advice:

  1. Self management
  2. Boss management
  3. How to address “less motivated” team members

Very briefly on self- and boss-management: more later

On self-management: if you are a high performer, chances are verything seems “urgent” to you because your frame of reference is how fast it can be done. You need to stop and think about what is truly urgent, and whether the standards you are applying are truly necessarily (a silly example but some of you may identify: do I really need people to write in perfect English ALL the time, or is it just my preference??) Think hard, because cost is alienating your team by trying to deliver everything that 10 clones of yourself could do. More on self-management later, but I define this field as understanding your own triggers and tendencies that make your over-react, so that you can catch yourself before the damage is done. We all have these: I have many.

On boss management: If you are a high performer who recently became a new manager, perhaps you think your boss hired you to make his life easier.You want your boss to have the perception that everything is under control, so you try to settle as many things without knocking on his door. Pick your bosses carefully, and if you find a decent one, he/she will be more sympathetic than you think. Having honest conversations with your bosses about priorities and staff development should be inbuilt to your monthly routine. Sometimes, stepping out and having your boss work directly with your team members helps bring issues into focus. More on boss engagement here.

How to help “less-motivated” staff

Now for the hard part: how to help “less-motivated” staff, especially when your team is under pressure to perform. My friend Sandra Soon, who has had more than a decade of management experience, mentally sorts her team along two dimensions: capacity and alignment.

  • Capacity refers to the ability to do the job as the organization requires.
  • Alignment refers to the closeness between the individual’s motivations and the organization’s motivations.

The matrix below shapes her developmental strategies.

HIGH CAPACITY LOW CAPACITY
HIGH ALIGNMENT Coach Coach
LOW ALIGNMENT Try to persuade them briefly and if that fails I exit them Work on exiting them immediately

My last article works better for the top half of the table. For the groups in the bottom half of the table, the challenging part is not making value judgments immediately; Sandra says: “I try to control my natural urge to be critical and make an effort to assume the best of people at the beginning — often lack of motivation stems from quite benign reasons eg they are distracted due to their many other interests, or they may not actually realise that they can do so much better.” In other words, don’t jump to the exit option too early.

My friend Pearlyn Chen further points out: “all of this is framed first by seeing each team member as individuals with their own potential and not just a management issue.”

Aaron Maniam, another respected leader with years of experience under his belt, provides elaboration on strategies you can take to help “less motivated” team members before considering exiting them. These are particularly pertinent in work contexts where exiting staff may be very difficult (another difference from Google/Linkedin/Facebook) – bureaucracies and family business are examples.

  1. Unearth external and seasonal factors.

    Aaron writes: “If people *seem* unmotivated, we need to talk to them and find out why. The answer could be something as simple as stuff going on at home, or a bad spell. Our job as leaders is then to help them through this bad patch or transient valley. This might involve some reallocation of work, having them work from home for a while so they can take care of kids, etc”.

  2. Unearth reasons for disengagement.

    “If the problem is not external, then perhaps they may not feel engaged by their work. Then our job is to help figure out what makes them tick – and not everyone understands themselves, sometimes, so having conversations about personality type, natural preferences, sources of energy and motivation, etc can be helpful. We might then be able to help them reframe their work to find stuff in it that is motivating. Eg not seeing writing minutes as a chore, but a way of learning the deep language of an organisation so that a person internalises its thinking process and hones his/her own analytics. Or helping a fresh graduate who enjoyed learning in uni, to see how there are also learning opporunities at work, even if those look like merely routine tasks.”

  3. Fix the structure and infrastructure of work.

    “I remember having introverts on my team who really found open offices difficult (this was an open office that wasn’t well designed and there weren’t enough quiet spaces for people to go to if they need to do extended thinking/writing). I just let them telecommute as much as they needed, and suddenly their productivity went through the roof. We had to do some norm setting in terms of how everyone on the team would stay connected if some were “off site”, and we did this collectively so everyone bought into the norms. Once the norms were set, they generally worked well.”

  4. Accept, and figure out a strategy towards unambitious team members.

    Aaron writes “I personally think it’s ok for people to make decisions that they don’t want to be particularly ambitious, and are content to work at a steady pace, go home at a regular hour on most days, etc. They of course need to be realistic and accept that this means they aren’t going to get promoted super soon or get an enormous performance bonus — but as long as they keep their expectations realistic, I think this is fine and a perfectly legitimate life decision. They might change it later on, or someone who was once a high performer might decide to reallocate their energies a bit after a few years, and good leaders/bosses need to know how to work around this. If the expectations are unrealistic, then of course a clear and firm conversation needs to be had.” Adding to this, Sandra points out that if you choose to retain an average performer, you will need to manage the perceptions of the rest of the team, who might feel they are shouldering an unfairly large share of the work. 

What is a good exit?

Sandra and Aaron also address what it means to exit team members well. Exiting team members must be done with a sense of responsibility towards both the individual and the organization. It is not about passing them to other teams as quickly as possible, but helping them find a better job fit: “for example, some people who are motivated by short-term, quantifiable targets will do better in sales jobs than in policy jobs”, Sandra writes.

Aaron shares that “Leaders have a duty to the overall system/organisation, not just their immediate teams, and can help facilitate linkups or meetings with other managers, who might be able to take the person on. I’ve seen instances where a lateral transfer has resulted in total transformation for a person.”

Personal thoughts 

I love their advice. Personally, in trying to balance the need for speed/quality versus the desire to nurture team members, I try to take a structured approach. I map out the mission-critical priorities and may assign high performers to those as a start. Especially as a new manager, it’s best not to fail on mission-critical priorities early on. However, it is easy to get into the cycle of only giving priority work to high performers. One must have a systematic way of reviewing work assignments, and ensuring that  “stretch” projects go to team members who initially demonstrate less potential or motivation. (For sanity, I do this at a time when I have bandwidth to provide coaching).

I also want to speak to those of us who are managers of managers: we need to be particularly mindful of what new managers are going through, and assure them that they have our trust as they strive to gain the trust of their team. That can make all the difference, as new managers often feel torn between their bosses and teams. I personally think it is wise to give a new manager a 30-90 day period where they function as a peer to their team, rather than a boss – learning the ropes of the job before they are asked to supervise it.

Conclusion

If you are working with less-motivated team members, this article provides some exercises that you can undertake. If you do this, and there is no fruit, in good faith move towards the exit option. Make a journal of the steps you took, for personal accountability, and also because some organizations require robust evidence when exiting a person.

I also want to end off by saying that you cannot please everyone – learning to face opposition graciously is a huge part of the leadership journey. You also need to take care of yourself and actively find support: bring in your boss to the process; hire a coach or ask a trusted advisor to walk with you for this season. Acknowledge your shortcomings and forgive yourself for them; it’s a painful part of growing up but it will eventually set you free (of trying to be impossibly invincible).

Reach out and I’d be happy to chat more too: the advice above may be too blunt for your situation; or you may feel overwhelmed by the complex emotions that arise if you are a new manager, and perceive yourself to be failing at it. This is a topic close to my heart as I have been through this phase and am deeply empathetic to those of you going through it!

unmotivated_motivated-copy
image credit: http://lauraberginc.com/top-10-tips-to-stay-motivated-as-an-entrepreneur