So many of today’s job roles have been thrown into confusion because of COVID19. Many other roles are being questioned as a result of the progress of AI and automation. But a timely new report from the World Economic Forum (WEF) might just be the blueprint we are looking for in a post-pandemic world. Eric Shepherd reviews its finding.
The new forces shaping jobs in the new world of work will affect millions worldwide. The Fourth Industrial Revolution (4IR) is giving rise to unprecedented prospects for economic prosperity and communal progress. But Coronavirus threatens to undermine this progress in a big way. On the other hand, it may also lead to the acceleration of acceptance of new ways and opportunities. In this rush to embrace ‘the new’ however, timely measures must be taken to ensure that unequal opportunity and income inequality do not arise and lead to unrest in an already shaken up world. Governments, industries, and workers may need to align their efforts to rethink market policies, employment arrangements, and skills development. Bringing about a positive outcome to the pandemic is contingent on all parties being able to espouse a strategic mindset and spirit of lifelong learning.
In a post pandemic world, existing jobs will continue to be augmented by new technologies. New professions will certainly appear. But these changes will entail a challenging transition. It will demand proactive investments in workers and a suitable framework to manage these changes. These endeavors need to be based on reliable data derived from thorough research into these emerging roles. This can then feed into solid action to prevent talent shortages on one side and unemployment on the other.
Prior to the current pandemic, leading thinkers at the WEF put together a report titled ‘Jobs of Tomorrow: Mapping Opportunity in the New Economy’ that serves as a roadmap for this process. Although this does not include an appraisal of today’s situation, the plans are still hugely, if not more, relevant than ever. It includes plans for spearheading a “reskilling revolution” to create new possibilities for almost a billion people over the coming decade. It looks at the way employment is drifting towards emerging professions, the underlying reasons, and the skills needed to thrive under such circumstances. In that sense, this report is a supremely timely call to action for governments and organizational leaders to deal with the challenges of a post-pandemic economic environment.
The WEF report uses innovative metrics together with researchers from three associate firms. The collaboration paints a realistic picture of up-and-coming occupations and the mindsets and skillsets needed for individuals to grasp these opportunities. Amongst the key findings, perhaps the most crucial is that both human talents and technology will drive growth in the future. The adoption of new technologies is imminent, even accelerated because of Covid 19. They will raise the profile of green economy jobs as well as creating new roles in engineering, cloud computing, and AI. But the human element needed for leadership, management, innovation, healthcare, marketing, content production, and cultural undertakings will remain indispensable.
The report names seven professional clusters and 96 jobs of tomorrow most likely to gain prominence. Statistics can gauge the relative significance of the clusters in the broader labor market. It claims that they will feature in 611 out of every 10,000 career opportunities by 2022. We guess that this figure may have increased still further since the start of March 2020. The WEF forecasts that this will translate into over 6 million new jobs worldwide over the course of the next two years. The roles expected to display the highest growth rates within high-volume jobs are artificial intelligence specialists, data scientists, and full-stack engineers. Where lower-volume jobs are concerned, we should expect growth for green marketers, social media assistants, and growth hackers.
The new world of work displays vibrant demand for an assortment of skills that will underpin these professions. This includes both cross-functional and technical skill sets, divided into five distinct categories. They are:
The ability needed for a role depends on the exact requirements and nature of the job. The kind of job opportunities that will arise largely depends on the demographic, economic, business model, and technological evolutions. A sizeable chunk of new jobs maybe created in new occupations or current ones that are undergoing an overhaul in terms of mindset and skills. The potential impact of these developments on future economic activity is considerable. Out of the total job churn precipitated by these events, new roles will account for 27% of all jobs by 2022. As 4IR accelerates change, the new tasks and types of work could chart pathways to upward social mobility.
The consequences of these transformations will affect all sectors of the workforce. The WEF has been tracking these variations for the past five years to assess the scale of labor displacement. The work was performed to enable workers to shift from declining to abundant roles. The Forum also examined ways of mapping outmoded jobs onto emerging ones to ensure a smooth transition for workers. The WEF agreed that prosperity in the changing marketplace would depend on transitioning to a skill-based hiring system focused on continuous improvement.
The approach adopted by this report of ‘real-casting’ employment trends was done in collaboration with two organizations that hold authoritative data on job opportunities: Burning Glass Technologies and LinkedIn. The former organization traces the number of job openings posted online, and the latter keeps track of how many professionals are being hired for new opportunities. They classified emergent professions as those that experienced record growth over recent years. Applying these methods produced seven characteristic occupational clusters: data and AI, cloud computing and engineering, care economy, green economy, product development, people and culture, as well as sales, marketing, and content. It is notable that individual growth rates and extent of employment prospects vary across all seven clusters.
The clusters in which the absolute number of job projections have the highest probability of changing in line with shifting business practices are the care and green economy. The green economy is susceptible to fluctuating government regulations due to upgrading of the services infrastructure to accommodate renewable energy. It has also fared less well during the pandemic outbreak as people’s attentions have turned to more immediate concerns. As far as the care economy goes, we have seen world events focus attention on to these challenges. But influences also include other demographic and society trends such as increasing numbers of women entering the workforce, aging populations.
Occupations registering high growth often require diverse competencies. The skills taxonomies used to delineate these competencies have been standardized by the WEF’s partners but only up to an introductory level. International cooperation is necessary to formulate a common language for the labor market to transcend geographic boundaries. Data scientists at online learning provider Coursera have drawn on users’ insights to devise a taxonomy of skills as a pre-requisite for online assessments. Keeping up with the learning preferences of people working in emerging professions helps to find patterns for upskilling in those fields. Coursera has also created a global skills index to appraise learners across a set of crucial future-oriented skills ordered into the three clusters of computer science, data science, and business skills.
Much of the past decade is defined by the rapid technological disruption that raised the threat of massive job losses and unsustainable skills inflation. Coronavirus has magnified this disruption ten-fold. This has naturally caused real concern and livelihoods are no longer guaranteed. Analysts that have previously tried to solve this puzzle concluded with a call for prudence but they also offered hope. A few analysts surmise that technological advancements will anyway shrink the size of the corporate playing field. But, encouragingly, most propose that many new opportunities will materialize. This stipulates that efficient training mechanisms must be in place to ease the transition of workers to the new world of work.
The World Economic Forum’s 2020 report embraces a quantitative, data-driven method to arrive at professional classifications for the future job market. AI is used to augment human estimations in this regard and offer unparalleled insights into the labor market. The development of standard metrics shared by public and private firms will be a powerful tool to help employers position themselves for a successful upskilling agenda. Exactly the kind of agenda we will need to help us all get back on our feet again once the current disruptions are over. The methodology adopted for matching skills to new jobs can be piloted to create sustainable systems that can supply information on the labor market in real-time. We need more attention to conceive skills taxonomies that reflect the demand for baseline and new disruptive technical skills. Although disruptive technological skills like data science are vital, so is the ability to give personalized care and supply bespoke learning and development. The diverse opportunities set to appear in the market will open multiple avenues for both low- and high-skilled careers.
The progression of these careers will be directly influenced by the decisions that governments make. This report shows that the means to inspire positive change in the labor market is within reach. More than ever, this will be in the best interests of everyone to create new opportunities and efficiencies for tomorrow’s post pandemic environment.
Predictive Analytics helps organizations leapfrog their competitors. Well-informed prediction models, to inform decision making, are essential during times of change.
Assessments and analytics are increasingly used to predict employee behavior, engagement, performance, and risks of attrition. In this article, Eric Shepherd explains what predictive analytics is and how this will revolutionize our thinking.
We see examples of predictive analytics in our day to day lives. Credit scores, product recommendations, dating apps, and supply-chain management systems all use predictive analytics. Credit scores predict how likely we are to repay a debt. Finely tuned algorithms can predict products that we're like to purchase. Dating apps use personal data to connect people that could be a match. And supply-chain management systems can pre-order and pre-position products that are likely to be consumed. Unlike the 2002 movie Minority Report that uses "precogs" to predict crimes, we use machines to crunch data in making predictions.
The social media giant Facebook uses predictive analytics to predict which user is likely to buy a product and place ads, based on the data collected from their profiles and the posts. It is a technology that uses historical data and predicts future events or outcomes.
The question arises, how does it work? Predictive analytics are data-driven and use statistical techniques to forecast and analyze the outcome. A mathematical model or algorithm is created by analyzing the data collected to project potential futures. For organization’s leaders and HR teams, this enables them to build predictive models to understand the possible engagement and effectiveness of its workforce.
Managing a golf club provides a useful example. Playing golf is most enjoyable with perfect weather conditions. If the manager tracks reservations, course usage, weather predictions, and actual conditions, over time, there is a possibility of predicting bookings and usage. Armed with this information, a manager could prepare staff and equipment for the busy times and reduce the facility's capacity based on the data collected.
Does the manager have enough data to predict course usage reasonably? The answer is yes and using a decision tree predictions can be more useful.
A decision tree is a tree-like model of decisions and consequences; it is a powerful tool used in analytics, where each leaf, or node, denotes an attribute, and each branch indicates the potential effects.
In the case of the probability of golf course usage, let's think about two predictors to keep the model simple, i.e., sunny day vs. rainy day. The likelihood of playing golf diminishes if it rains. Whereas the possibility of playing golf increases when the day is sunny. In short, weather forecast data can be used to predict the likelihood of golfers playing golf. Weather forecasts are not entirely accurate and so keeping an eye on the weather is another useful predictor. The leaf is the data, such as its raining, and the brand is the consequence, we need less staff and less equipment. This simple case of managing a golf course helps us understand the principles. Now imagine that we have hundreds of data inputs, and we are trying to predict if someone will enjoy a specific movie. And now imagine that we're trying to provide that service for millions of individuals. The problem becomes exponentially more complicated. Machine learning, which is a technique for computers to produce their own statistical models, can help here by digesting numerous data points and making sense of the patterns to come up with predictions. When organizations apply it to their workforce, it can provide useful insights on engagement, risk of loss, and productivity.
Data stored by organizations in their Human Resource Information System (HRIS) can be used to develop predictive models for employee behaviors. HR predictive analytics can help formulate policies for employee well-being, engagement, and efficiency, and to predict the performance the organization is likely to achieve.
Here are some examples:
All organizations have a level of regretted attrition, that is losing employees that an organization truly regrets losing. An employee's tenure will depend upon their circumstances and the nature of the work. Employee turnover may be benchmarked against similar job roles and industry sectors. Regretted attrition has many negative consequences which include loss in revenue, added cost of hiring, unwanted distractions, and reduced productivity.
Employees that enjoy their work and fit in with the culture and have good managers are likely to stay with organizations. Calculating a "Flight Risk Score" using mathematical models can help to predict the possibility of a worker resigning. Over time these models will improve to help the organization understand additional predictors for attrition.
There are privacy-related issues with accessing flight risk scores. That means access to this data must be restricted on a need to know basis. The power of the flight risk score is to help a manager intervene to avoid a regretted resignation and give them a game-plan in cases where an employee's departure is inevitable.
Statistics play an important role in predicting hiring success; many large organizations have accepted this. When recruiting, there are times of feast and famine. When there are more jobs than people looking, assessments can promote interest by using clickbait on social media to engage a qualified person who isn't yet looking. When more people are looking than jobs available, assessments can screen candidates into, or out of, the process.
Assessments, and the data collected, can streamline the engagement and interview process to find the right candidate. Using data helps determine cultural fit, i.e., does the candidate's values, motives, and preferences, match the organizational culture. Assessments can also predict job fit by testing to see if the person has the abilities required for the job. Assessments can promote better hiring practices but do not guarantee success.
Just as an organization wants to learn about the candidate to determine cultural and role fit, the candidate is making judgments about their potential employer. Using data to drive an active engagement process means an organization has a better chance of winning the heart and mind of the candidate. Predictive analytics can use data from multiple sources to guide the recruitment process and ensure that first-class candidates are properly engaged.
Studies reveal that there will be a decrease in morale and productivity should toxic employees be recruited or retained. Behaviors indicating disrespect, drug use, alcohol abuse, and sexual harassment need to be investigated before they fester. Predictive analytics can help spot the signs of toxic employees to promote an early intervention to discover the root cause of their behaviors.
Studies have uncovered that an increase in employee engagement leads to higher revenues. More engaged workers are more creative, less tardy, and work harder to achieve their goals resulting in greater productivity with higher levels of quality.
Measuring employee engagement and taking actions to create an extraordinary situation for individuals at work will positively impact an organization's performance. Using pulse surveys (a short, frequent survey with simple questions to give a quick insight into an organizations health) and other data sources, organizations can use predictive analytics to improve the workplace, engagement, retention, and performance.
An increasing and vital role for HR is to be an advisor to managers. HR, using data and predictive analytics, can help managers better understand themselves, others, and how to intervene to encourage behaviors required for success. Assessments and analytics are transforming the way HR is helping managers and employees and their work. Predictive analytics is also helping them forecast and optimize policies for employees and organizational growth.
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$We see examples of predictive analytics in our day to day lives. Credit scores, product recommendations, dating apps, and supply-chain management systems all use predictive analytics. Credit scores predict how likely we are to repay a debt.$
When normality returns, we will be facing a different set of challenges and opportunities than we had back in 2019. Martin Belton takes a look at the mindsets we're going to need from a personal and a business stance.
At Talent Transformation, we've been calling attention to the new world of work for quite some time before COVID-19. But the changes to work processes and the speed of those changes, signaled by the virus, have surpassed almost everybody's expectations. A good example, from a member, where they had a significant three-month project scheduled to introduce Microsoft Teams company-wide. With the advent of COVID-19, and the mass introduction of home working, they reduced that schedule down to three days. And so far as we are aware, it is all working well!
Of course, the broader definition of the new world of work is about a lot more than just working from home and taking on-board new tech to do your job. Even before the virus, so much was changing already; businesses are experiencing increased expectations in terms of social responsibility. Many organizations have been experiencing new stresses as millennials enter the workplace. The increased use of AI and robotics have also brought new opportunities but fears as well.
Given how well people have been adapted to new ways of working, it is tempting to say that these other issues will also be dealt with confidently and swiftly. But not everyone can change so quickly. It still requires a genuine change in mindset from us all to embrace such speedy changes. Not everyone is comfortable with such changes. And unquestionably, more changes are on the horizon. When normality returns, it is likely to look a lot different than it did back in 2019.
In the first instance, here is some practical advice from a personal point of view. These thoughts are mostly taken from the advice of experts in psychology and personal enrichment. They are designed to help those still struggling to adapt to working from home and the changes in their environment. We hope that this counsel will help prepare you for what's to come when the 'new normal' hits us:
As well as re-evaluating your personal goals, now is the time to do the same with your business goals. As we've already noted, so much was changing already with new technology and social responsibility becoming center stage. The influence of these factors will almost certainly increase now. So what does that mean for your business?
Here are some thoughts that might help you navigate these new challenges and opportunities:
This is clearly the ideal time to properly understand these new opportunities as new roles, products and ways of working are emerging
The world is in a different place now to where it was in 2019. It is how we deal with the new paradigms that will make the difference in the long run.
Martin Belton checked out there for the best and greatest advice for you all working from home and got some great advice from our Twitter followers.
Transport for London, (with an illustration by Kera Till) started us off well, giving us sound directions for getting to our desks on time. Though as Peter Harrison points out, don’t imagine your journey will always be free of challenges:
Our sporting heroes are have also been quick to remind us that there are still opportunities to make sure you get the recognition you deserve for your hard work:
Planning ahead is also important. Stock answers can be a boon says Marie-Christine:
Of course setting up your laptop at home can be a challenge. Hannah Wetherill is clearly into re-purposing existing facilities:
Paul Nazareth on the other hand has really got into the spirit of things:
That said, Marcel Cardillo is also organised but is still complaining that his international conference calls aren't going too well.
We hope you enjoyed our April 1st post enjoying the creativity of everyone from home. And as we work form home let's take a moment to thank our scientists and healthcare workers that are using their talents to inform us in the short term, cure us in the medium term and keep us safe for the long term. Thank you.
Critical tools and indicators need refreshing. This article from Eric Shepherd suggests new data sets that are needed to evaluate the organization’s health.
C-Suite executives are used to managing organizations with the help of Key Performance Indicators (KPIs) and Objective and Key Results (OKR’s). But, during a crisis like we’ve never seen, these critical tools and indicators will need refreshing.
Information and data are the lifeblood of decision making especially during times of crisis. But the data, KPIs and dashboards the C-suite need to manage during this pandemic, will look very different from those of the past. After engaging in calls for the last few days, we have compiled a list of crucial questions to tease out data that the C-suite can use to help with decision making during and after the pandemic.
Data is never perfect. But the more detailed information you can collect, the better the chances of the analytics and projections being accurate. Medical data is sensitive, and most countries restrict access to such data. A sick employee can volunteer their diagnosis, but it is not good practice to ask. In many countries, employers cannot even ask for a projected ‘return to work date,’ which can complicate analytics and predictions further.
There are several ways we can empower the C-suite with useful data. One useful indicator for our model is to take is the average number of days out sick. Given laws and good practice, this data could be sourced from:
Analyzing 2019 data can provide a useful indication of the norms for your organization. When analyzing more recent data, you can factor historical norms into your models to help determine the effects of the coronavirus.
However, historical norms must be used with care as they will not reflect the current norms for non-coronavirus related illnesses as more hand washing will reduce colds and flu infections as these are transmitted in the same way as the coronavirus.
The actual questions and data collected will be specific to your organization. We present the following list to help you brainstorm through the process of identifying the data and information that your C-Suite will need to review to make decisions and perhaps add to the organization’s KPI’s and OKRs.
1. Historical trend data segmented by geography and team for how many and the percentage of people are employed and:
3. Population trend data (from trusted sources) by geography and age, for the number of individuals and percentage of the population:
4. How ready are we for remote working?
5. Historical trend data and predictions of revenue-generating capacity considering:
Data harvested from this data will help the c-suite understand:
Predicting is never accurate, especially when it is about the future! However, we hope that this article provides a stimulus to help you use data-driven-decision making during this pandemic.
And please let us take a moment to thank all the scientists and healthcare professional that are using their talents, to inform us in the short term, cure us in the medium term and keep us safe for the long term.
Working from home is an essential part of the new world of work. But COVID-19 has seriously accelerated this practice. Experienced homeworker Martin Belton gives managers 10 tips to help them successfully manage the transition.
Homeworking is nothing new. But for reasons we all recognize, the concept is now being extended to new groups of employees, managers, and leaders. For all of them, this brings a new raft of responsibilities, actions, and tools to learn. Specifically, it falls on our managers and leaders to create an environment to support these new workers.
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As well as increasing the efficiency of organizations, Martin Belton explains how AI is helping us in the fight against coronavirus.
As fears over the Covid-19 coronavirus continue to grow, scientists are turning to artificial intelligence to help them understand more and combat it at every level.
Do we trust robots and automation? In this article, Eric Shepherd discusses the studies that help us understand the changing relationship between employees, managers, AI, robots, and automation.
Employees, managers, and executives around the world now realize that artificial intelligence (AI) is real and here and emerging into the work environment. Attitudes towards AI are changing. Be they positive or negative; people are starting to recognize the power of these technologies.
Understanding college, career, and work readiness is more important than ever in the new world of work. In this article Eric Shepherd highlights distinctions and explains why.
Why is it that students, leaving schools or college, so often seem to lack so many of the essential skills for the roles they want? Of course, organizations prefer someone that has already had a couple of years’ experience. But why is this so important – and so necessary? In seems that our students are not ‘job-ready.’ College graduates are smart and have plenty of potential. But they often fall short in terms of soft skills and behaviors required for the workplace.
There was so much more to Davos 2020 than Donald Trump and Greta Thunberg. In this article, Eric Shepherd takes look at this year’s conference on capitalism.
It’s easy to imagine that attendees to Davos 2020 may be losing a little sleep after the event. In addition to the eye-watering ticket prices, they were confronted with presenters scaring them to death with tales of climate disasters and humans losing jobs as they are being replaced with machines in the workplace as the Fourth Industrial Revolution powers on.
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