How Workera measures skills differently Meaningful, accurate measurement across skill sets is essential to understanding the talent within your workforce.

On March 24th, the Workera domains of Business Analytics and Databases were updated to reflect recent changes in the industry and how we conceptualize these skill and knowledge areas.

Workera Secures $23.5m in Series B funding: What's next for Skills Intelligence?  At Workera, we’re committed to helping organizations like Samsung, Siemens Energy, and the US Air Force better identify and upskill their talent by providing a deep understanding of their workforce. We’re delighted to have secured $23.5 million in our Series B round of funding led by Jump Capital with participation from Sozo Ventures and existing investors NEA, Owl Ventures and AI Fund. With this injection of capital, we’re able to evolve our skills intelligence platform, empowering leaders to make better, more informed talent development decisions at a greater pace than ever before. But what does the future look like for skills intelligence, and how can we play our part in it? The value of skills intelligence Skills intelligence refers to the ability to identify, assess, and develop skills effectively. It involves the capacity to analyze and understand the skills required for a particular task or job and to evaluate the skills of individuals or groups against these requirements. Kian Katanforoosh, the founder of Workera, believes in the ever-growing importance of solutions that embrace and interpret skills data to help individuals and organizations make the absolute most of their potential. “Technology is advancing faster than ever. New tools, like ChatGPT, shake up industries every year. But skills aren't keeping up,” he explains. “Companies capable of upskilling talent at market-speed will reinvent themselves and build the future. Others will risk being disrupted.” Andrew Ng, the chairman of the board for Workera, emphasizes the importance of using increasingly refined data to maximize impact: “Using deep learning and language models, Workera’s technology makes getting accurate and granular data on people’s skills an efficient process. Assessments help you learn what are your strengths and weaknesses, thereby providing deep skills intelligence that leads to actionable insights for both individuals and organizations.” How can businesses leverage skills intelligence? “The first step in workforce transformation is grasping what skills you have,” said Mike McMahon, co-founder and managing partner of Jump Capital, which has led Workera's Series B financing round. “Unlike most inference-based solutions derived from degrees or work experiences, Workera provides a high-fidelity signal and a powerful engine to target and accelerate learning and steer talent acquisition.”  Belcorp, a multinational beauty company going through a digital transformation, has been using Workera since the May 2022. In that time, the organization has accelerated the skills development of their data engineers by an estimated 30%, while their software engineers have improved their skills by 57%. The chief technology, data and digital officer of Belcorp, Venkat Gopalan, says that Workera enabled them boost their team’s confidence in delivering critical projects, and to improve project quality in general. What’s the future of skills intelligence? With the increased use of AI, machine learning and big data analytics to collect and analyze data on workforce trends and skills gaps, organizations will be able to anticipate and prepare for future workforce needs, and to develop targeted interventions to address skills gaps that equip their workforce with the skills they need to stay competitive in the future job market. Here at Workera, we’re future-proofing our offer by increasing our comprehensive ontology of over 7,000 skills to include soft skills assessments, such as leadership and management. Alongside this latest capital infusion, we’re already rolling out major updates to customers that allow them to create skills-based career pathways in just a few clicks – helping their employees upskill faster than ever. The pathways are adaptive and goal-oriented, providing learners with a highly-personalized and motivating experience. It’s all enhanced by a richer skill assessment experience with more frequent check-ins, which gives leaders even better ongoing measurement of their workforce’s skills. For press-related questions, reach out to tberg@workera.ai 

Bringing your AI vision to life with Andrew Ng: How to ensure you have the right skills and talent to succeed In this episode of the Skill Baseline podcast, Andrew Ng gives us his take on a host of critical topics, such as his checklist for success, how to hire a world-class AI team, and measure learning velocity.

“Data isn’t usually treated like an asset. It’s treated like trash, like a waste product.” Such is the frank appraisal of Walid Mehanna, Group Data Officer at MERCK KGaA Darmstadt Germany, regarding the value most organizations place on their information assets. Needless to say, Walid considers this dismissive attitude to data analytics to be a serious problem, because the future success of many organizations depends on the adoption of an efficient, data-driven approach. So how can organizations, and especially large corporations like MERCK–a multinational science and technology company with over 600,000 employees–establish a sustainable and effective data strategy at scale if data is commonly treated like “trash”? Walid’s advice has the ring of hard-won wisdom: an effective data strategy must lower the barrier of entry into the use of data and raise an organization’s ability to create solutions by leveraging data. Data’s False Dichotomy When Walid took over at MERCK, he began by instituting a fully-immersive, 10-week  data analytics educational program for those in the organization who reported high interest and significant space in their schedule. Participants in the program took a Workera assessment before, during, and after their 100 hours of training and mentorship in order to benchmark growth–unsurprisingly, the results revealed how effective an immersion program can be. Of course, ideally, everyone in an organization would be able to spend months becoming an expert data analytics practitioner. However, few companies have the time and resources to carry out a data transformation in this way. Many company leaders, therefore, feel they must approach their data strategy in one of two less-than-ideal ways–either everyone in the company will develop their data skills on an ad hoc basis, or a few individuals, by participating in an immersion program like the one mentioned above, can become experts who will carry the data analytic load for the entire organization. The Optimal Third Option: Create a Data Ecosystem During his time at MERCK, Walid has done his best to destroy false dichotomies of this kind. While continuing to offer an abbreviated (aka more efficient) immersion program, he also began to develop Optimize, a data ecosystem that seeks to make data more accessible to the organization as a whole. Walid's goal for Optimize was to enable people across the company to create data-driven solutions by removing many difficult tasks that often become obstacles, such as identifying data, begging for access, testing models, and combining data. By providing a catalog of the data analytic skills across the organization and introducing technologies that allow individuals to access data more autonomously, Optimize seeks to lower the barrier of entry into the use of data in hopes of creating a company culture that prizes data-driven solutions. “Unfortunately, technology can be easily misunderstood…that it’s the silver bullet. It is not. Technology is useful and it’s helpful, but it’s always the question how you leverage it, how you make it really count for the people.” Walid Mehanna For Walid, making data accessible and upskilling an organization’s personnel are simply two “levers” aimed at the same goal–that is, to use data to solve business problems. Making data accessible lowers the barrier of entry for everyone in an organization, while targeted upskilling allows individuals to make more effective use of the data at their fingertips.

At this point, The Great Resignation is almost a cliche. We’ve been notified time and time again–maybe one too many times–that people are leaving their jobs in droves, dissatisfied with their work and struggling with motivation in their careers. Similarly, it’s hardly a revolutionary idea that continuous learning in the workplace is a worthy pursuit and valuable investment for many companies. However, increasingly over the last few years, workplace learning, or upskilling, has been suggested as a strategy for overcoming many of the attrition and retention issues plaguing organizations.  In a recent conversation with our founder and CEO, Kian, Shelley Osborne, Head of Learning at Modal and author of The Upskilling Imperative, offered insights and recommendations regarding how leaders can develop their organization from within–that is, how they can grow their organization by investing in its people rather than relying on hiring to fill skill gaps and acquire talent. According to Shelley, “We have to realize that talent is human, that these are human beings, and it is really expensive and really inefficient to look for talent outside an organization.” By supporting upskilling, leaders can help their people build rewarding careers within an organization, which will in turn assist the organization in retaining and developing talent instead of spending on unnecessary hiring. The question we have to ask though, is: what does supporting upskilling look like? While it’s easy enough to understand the theoretical benefits of upskilling, how can leaders actually begin “developing from within”? Here are Shelley’s top three insights: 1. Be willing to reskill It is relatively clear how upskilling will help your organization’s efficiency and, ultimately, its bottom line. As an organization’s people become more knowledgeable and skillful, they can complete more tasks and build solutions that are more creative, leading to improved outcomes for the organization as a whole. However, Shelley is adamant that leaders should also embrace “reskilling”, allowing an individual to develop new sets of skills that will qualify them for a role that is different from the one they hold. There is a tendency among leaders to “pigeonhole” their people by keeping them on a career path that reflects the skills on their resume when they were hired. But an individual’s curiosities, self-knowledge, and aspirations develop over time, so embracing internal mobility and career transformation is key. Organizations that want to retain their people need to be willing to let their people change–and provide support for that change. 2: Have career conversations with your people…often Internal mobility counteracts attrition and supports retention among an organization’s people, but it depends entirely upon a consistent line of communication between leadership and employees. Shelley recommends beginning the conversation about upskilling and reskilling during an initial performance review six months after an employee’s hire, and then to continue these conversations on a biannual basis. Leaders should seek to identify three major things in these career conversations: Peaks: When an individual felt proud and successful in their role Valleys: When an individual felt like their work was not meaningful Values: What an individual is working for (for example, financial reward, collaboration, purpose, personal growth, etc.) With the knowledge gained from these conversations, leaders can guide individuals into roles that will allow them to enjoy a long, satisfying, and productive career in an organization. 3. Talk about the learning you are doing A final strategy for “developing from within” is to create a culture of learning by making learning public. Strange as it may seem, learning can be a source of shame, often felt to be something more properly confessed than encouraged. From this perspective, learning means little more than acknowledging a lack of skill. To battle this perfectionistic perspective, Shelley encourages leaders to be honest with their team about what they have been learning and to provide regular opportunities for employees to do the same. For Shelley, this has meant instituting “Wins and Learnings” discussions during team meetings, where each member reports something that has gone well and something learned over the last week. Strong leaders should also participate, admitting where they need and desire to grow in order that the rest of their team can feel comfortable admitting the same.  As Shelley says, “When you think you have to be perfect, you don’t give yourself space to grow and develop.” “Developing from within” requires an openness to change–embracing the flexibility of internal mobility, instability of career changes, and vulnerability of honest conversation. By embracing these (sometimes uncomfortable) contingencies, a leader puts the best interest of the organization and its people first.

Only a few years ago, many businesses were in the middle of a mobile transformation, with companies hiring transformation managers and instituting Mobile Centers of Excellence to aid in this process. Today, we are in the age of digital transformation, and we can only begin to guess what the next transformation will be. In this world of rapid change, it’s becoming increasingly clear that learning to learn well–to transform and adapt smoothly as an organization–is an essential component of success. In a recent conversation with Kian, Mario Maffie, Corporate CIO and VP at Mars Incorporated, shared what he believes to be the secret of success when leading a transformation: the success of a transformation ultimately depends upon whether an organization has mastered the art of upskilling. While this may seem like common sense, developing a learning and development plan--and, more specifically, identifying metrics to measure that plan's effectiveness--can be a surprisingly difficult process. “There was a time when intelligence and upskilling was really not core to delivering on commitments. But that’s behind us now. You really have to embed intelligence into the design of the things that you do.” Mario Maffie  Outline of a Transformation As Mario explains, in any transformation, there are several phases. At first, no one understands the new technology. After a while, however, many people with only superficial exposure begin to think they understand it. Therefore, for a new technology to really “enter the bloodstream” of an organization–that is, if the technology is going to be leveraged to do work and create solutions–an organization must train their people in the new technology. First and foremost, leadership must clearly spell out the level of dexterity in the new skills that those in various groups across the organization must achieve, and then provide learning resources. Eventually, new hires will arrive having already learned those skills in a previous workplace or during their education. Once everyone in the organization has been trained, and new hires can be expected to have those skills upon arrival, the transformation is considered complete. “The reality of it is that the workforce and our leaders have not kept up with the pace of technology…because technology is such a large part of the solution now for running a business, and intelligence is built-in and embedded within it, the need for digital upskilling is really important.” Mario Maffie  How can an organization be certain that the new technology has been actually adopted by the employees? Traditionally, organizations have relied upon numbers provided by the learning and development department to assess upskilling effectiveness, such as the number of courses held, people trained, and certifications awarded. These are perfectly legitimate ways of measuring whether employees have completed their training, but, as Mario acknowledges, they are not reliable for providing an accurate picture of whether those new skills are being put to use. Measuring a Transformation’s Effectiveness In addition to these traditional numbers, Mario suggests that companies use two specific business markers to gauge the effectiveness of their learning programs. First, they should tally how many company projects are incorporating the new skills and technologies. For example, when Mars had a significant initiative supporting the use of Agile technology, Mario and his team followed up by assessing how many projects used Agile instead of Waterfall in the initiative’s aftermath. If those who participated in the upskilling programs continued to use Waterfall instead of Agile, then company leadership could assume their employees did not understand the benefits or workings of Agile and then design further upskilling opportunities accordingly. “We can’t believe that we’ve done upskilling, or hope. We really need to, like in all the other parts of the business, have data that helps us understand if we have successfully upskilled.” Mario Maffie  A second way leadership can assess how well their organization has internalized new skills or technologies is by charting how many roles in the organization have the new skills and technologies as their main focus. For example, if a company had only a few data scientists five years ago, but today they have dozens or even hundreds, it is fair to assume that the company has fully incorporated the skills and technologies associated with that role. Conclusion As a CIO, Mario knows that technology is always far ahead of most people’s understanding of it. Few people are on the cutting edge of tech, and as such, have to be taught how to leverage new technologies for productive purposes. By identifying key metrics to evaluate upskilling success, organizations can be certain that their people–and their organization as a whole–have the skills necessary for a productive future.

Ujwal Chajerla, director of the AI/ML Center of Excellence (COE) at PPG, recently joined Kian to discuss why upskilling is important in his role. He explained that PPG has established the COE to generate value at scale by underpinning key business operations and customer experiences with AI. The Power of People Many companies are undergoing a digital transformation, that is, building AI teams and technologies that will guide their organization into a data-driven future. Yet, despite the power of these technologies, what is it that sets one company apart from another? According to Ujwal, it’s a company’s people. When making a digital transformation, any company can reference use cases, streamline processes, build platforms, and access data. However, the people, with their knowledge and skills, will determine how effectively those resources are used. No matter how powerful AI can be, the extent to which that power is being put to use for a company depends on the abilities of its people. “The unique thing that you can have that will differentiate you in the market is your people” - Ujwal Chejerla The fact is, every individual employee has their own skill limitations and knowledge gaps. That’s why PPG has developed a model of product development that utilizes COEs and cross-functional teams to generate value. As Ujwal says, “It’s very difficult to find unicorns—people who know everything. So you work with what you have.” Yet, he also recognizes that “what you have” can grow and develop, given the right amount and kind of attention…and that’s where upskilling comes in. By upskilling, you can take “what you have” and develop teams of people with much greater knowledge bases and abilities, leading to more innovative ideas and efficient problem-solving. Deep or Wide? Growth doesn’t look the same for everyone, though. Upskilling ought to be guided by the OKRs established by the company, but it should also take into account the unique capacities, goals, and passions of the individual employee. Taking this into consideration, PPG has established a T-shaped career progression system for employees that allows them to either go broad or deep in their skillset. For some career paths, such as those in management, learning horizontally–that is, developing competencies in several domains–will be most advantageous, while those in technical roles will often benefit most by going deep in a single domain or subdomain, which is called vertical learning. “We can do better today than yesterday every day” – Ujwal Chejerla PPG’s T-shaped career progression system is a fascinating application of the more conventional understanding of T-shaped learning, which focuses on the development of the individual. At Workera, we often speak of T-shaped learning on an individual basis: an employee ought to develp a broad base of durable knowledge and skills, while delving deep in areas that are at once more practical in the short term and perishable in the long term. In many cases, horizontal learning can be carried out steadily over time, while vertical learning occurs on an occasional basis in order to accomplish a particular project. PPG’s innovative application of the concept, however, allows members of the team to choose a career path that highlights going deep or wide. Either way, what is certain is this: every successful AI team depends upon growth to remain relevant and creative in this competitive, constantly adapting field. “If we look five years from now, I believe there will be an expectation of every executive to be extremely good at developing skills”  Kian Katanforoosh At PPG, the development of AI technology has paid off. The implementation of AI has developed operations at PPG (specifically in making products in labs and plants), yet its effects have hardly been relegated to the backend. Digital technology has also allowed PPG to simplify and enhance their customer experience while maximizing organic growth through improved speed to market. Here at Workera, we believe that companies that are willing to transform digitally, and invest in the people that make such a transformation both possible and successful, will experience similar successes.

Helping to transform how government agencies assess and train their personnel

Season 1 of The Skills Baseline has come to a close and we are sharing our top-5 takeaways for data and AI leaders looking to make skills their competitive advantage. The industry experts that joined our founder and CEO, Kian Katanforoosh, provided actionable insights and plenty of best practices for developing skills, driving continuous learning, and planning for the future. See our favorites below, then catch up on any episodes you may have missed!

July 2022 product updates - Enhanced learning recommendations Domain feedback and learning plans in Workera will now be enhanced with predicted skills!

Introduction In 1979 the parents of Workera.ai co-founder and CEO, Kian Katanforoosh, were students in Iran on their way to rewarding careers. The destabilizing violence of the Iranian revolution drove them to independently move to France, where they met and together navigated a new country, a new language, and the unfortunate reality of unequal pathways to success. Kian’s father, who wanted to pursue a scientific career, took up whatever work he could to provide for his family.