DIGITAL TRANSFORMATIONS: THE TOP KEY TALENT FACTORS

 DIGITAL TRANSFORMATIONS: THE TOP KEY TALENT FACTORS

INTRODUCTION

Digital transformations— Laying a performing digital and advanced-analytics organization is hard. Decisions about talent and technology will often determine the transformation’s success. The problems start from choosing the correct  method whether digitalizing an entire company or setting up a digital and advanced-analytics (DnA) start-up within the organization are a crucial challenge. We have frequently seen these transformations stagger along the way, and leaders often have pressure sustaining any developments over time. Across the transformation journey, talent and technology are critical to success, from planning and inducting to managing and developing. After observing several large-scale digital transformations across a range of industry sectors, each conducted within the past three years. Our aim was to gain a greater understanding of the talent and technology decisions that strive (or blocked) the success of these programs.

1. Prioritize inducting digital leaders to captivate talent      and improve value intention

Finally, performance is defined by our talent and technology strategies and the capabilities of leaderships, like lead data scientists, spurring the transformation. It is possible based on leaders’ courageous decision the variability may be attributable 50% in group or unit outcome. These people are torch-bearer in shaping  the future organization in multiple ways: screening and hiring candidates, founding technical standards, and setting the tone for ways of working like collaborating, innovating, failing or learning quickly, and maintaining high quality. Selecting the right human talents for these roles will define the success of the digital transformation. Therefore, it is vital to invest the time needed to conduct a broad search.

Significantly, the right appointment also sets a Standard Operating Procedure (SOP) for future subsequent hires: the chief digital officer (CDO) is a key enabler to the company’s employee value proposition to attract follow-on talent. It is important to allow the CDO to hire from the top down, starting with senior roles and moving to junior roles, to facilitate the search for the right lead data scientist, data engineers, software engineers, and technical architects. The CDO’s expertise and domain knowledge will greatly help convince top-tier talent to join the company after taking all other elements of the organization into consideration—such as whether the brand and story make sense, whether there is adequate compensation, and how the company addresses lifestyle issues. All of these factors are critical to effective change management. The quality of reports to the CDO will play a crucial role in achieving the initial leads to become pioneer and gain traction, such as successfully developing digital products or setting up technical infrastructure. In general, organizations risk the overall reputation and viability of their programs if they attempt to take shortcuts with early hiring. In fact, our experience shows that taking shortcuts can delay transformations by six months to a year or more. Thus, organizations should go further and be proactive in setting up the digital leader for success—ensuring that the CDO has influence and a seat at the table and that the program is large enough to require commitment and conviction from the C-suite.

2. Reassess our value proposition for digital talent

Although, the CDO is an important proactive participation. Factor in shaping the organization’s value proposition as it relates to talent, there is only so much that a leader can do alone. It is important to consider the local hiring market and talent pool, besides factors specific to our industry sector, and strive to improve our own work environment in a local context. There is no need to assume that we will always be competing for digital talent with big tech companies, such as Google, Amazon, Microsoft, Apple, etc. In fact, companies incline  to compete with other local companies to attract key technical roles. For instance, a mining company may be in a remote area where big tech doesn’t operate, so the competition for digital talent would be primarily with other mining businesses and oil and gas companies. That said, digital skills are not industry specific, which means that local players from other sectors, such as financial services, may also compete for the same digital talent.

After meeting minimum requirements such as salary or respected tech leadership, consider how our industry can appeal to each individual candidate’s specific needs. To stand out, companies need to be committed to a modern technology stack. Understand the factors that motivate particular categories of candidates and adjust our pitch—and work environment—accordingly. It is helpful to recognize that some industries can readily offer what a candidate may be looking for. For example, in automotive, manufacturing, or energy sectors, there may be opportunities to address challenging or novel problems, such as the transition to a net-zero economy, as well as to work with cutting-edge technology. We must be able to offer development opportunities, including top-tier training programs or access to educational conferences. At one mining player, the company value proposition was revised to focus on what matters to digital talent; at an airline player, the high levels of data available and solutions at stake were key components of an attractive value proposition for top talent. That said, companies still have roadblocks if  seeking to attract digital talent, such as remote work locations—but these are solvable. Beyond this, it is important to develop an accurate picture of wer tech culture: a mix of skills, mindsets, and work preferences is necessary to build a successful organization and is preferable even within a given technical role. In this context, employers should hire for specific profiles as part of the overall organizational mix, and a clear definition of expectations aligned to different profiles can minimize confusion.

Historically, culture has been the number-one barrier to delivering impact from digital initiatives. Organizations need to understand where they are today, set the vision across both strategy and culture, and hire employees based on gaps and culture fit. Finally, team dynamics within the cultural landscape are important: employers should pay close attention to their collaboration models, carefully considering the importance some employees attach to working with groups outside of their own specialties, such as data engineers working with the business function. Based on the business problems that need to be solved and the governance model that will steer the work, it is important to understand what individuals experience on a day-to-day basis and to appeal to them accordingly. Peer groups and the expected level of responsibility are equally important. Make sure we’re accurately pitching the level of a given team. A colleague who signs up for a “tier one” team and finds that they are working on less supercilious problems may lose interest and look for an exit.

3. Hire digital talent internally, but keep a high bar on technical skills, and be realistic about reskilling

Not all digital talent comes from outside the organization. In fact, companies often have untapped pockets of digital talent. To begin, appraisal—and hiring—processes for technical roles should include technical-competency assessments rather than just résumé reviews and evaluations of leadership skills. At the same time, not all digital products require sophisticated skill sets. Companies with strong nondigital talent can aspire to cover up to 70 % of their digital needs by upskilling some of their current employees. Being able to spot these people is vital and can be achieved via techniques such as skill surveys. A decision to train an existing employee versus hire externally needs to be based on measurable criteria, such as the time for a candidate to become fully independent in a role. It is important to be realistic about both the number of employees who can be upskilled and the time required to undertake the training and development journey. The first employees to upskill would be those with high data and technical readiness and who benefit from strong business sponsorship.

Beyond this, companies should place internal hires in positions where they can learn and grow while working alongside more experienced engineers, whether these are hired externally or staffed via a third party. In addition, a sometimes-overlooked benefit of hiring from within is that internal hires can strengthen the link between development or product teams and operations. Executives tend to overstate how quickly their existing talent can be converted. If we choose to upskill internally, we should consider the speed with which we are trying to deliver on use cases. Retraining people within IT is not easy, and some roles are too specialized for reskilling (such as cybersecurity engineers and system architects). In such cases, it’s better to hire specifically for that role from the marketplace. For example, it can take up to or more than 100 hours of weekend work for an internal employee to complete the online coursework to pass an Azure data engineer certification. That approach won’t fill teams very quickly. If we decide to do most of it ourself, it makes sense to convert internal talent, but don’t imagine that a process engineer can be converted with two months of training. As an example, it can take years to become a high-quality data scientist or technical architect. Unless we hire highly proficient people initially, we are unlikely to obtain the early wins program needs to gain traction. We risk the overall reputation and viability of the program if we attempt to take shortcuts with early hiring.

4. Build a learning and development program specifically for digital talent

Skills development needs to extend beyond training because the sheer pace of technological change can make setting up formal training programs difficult. A combination of on-the-job training and structured learning programs to round out skill sets can best foster the development and embedding of DnA skills. In this context, an apprenticeship model can work well, which is why it’s important to hire senior leaders first. When matched with more junior employees, who are often eager to work with senior employees, flagship hires and expert temporary contractors can provide powerful on-the-job learning. This push toward ongoing learning also applies to senior employees, including executives and senior technical leadership. Ideally, they should spend half to two-thirds of their time actively doing day-to-day work. This way, everyone is involved in developing the final product, improving both upskilling and retention.

Notably, many successful organizations focus on creating the types of environments in which workers can teach themselves. For example, at Google, the vast majority of tracked trainings happen via an employee-to-employee network called “g2g” (Googler-to-Googler). Members of the network, which includes more than 6,000 people, offer their time to help peers develop. Leading companies are much more inclined than laggards to reward higher skill levels with better compensation (67 % versus 41 %), greater benefits (64 % versus 23 %), and more responsibility (78 % versus 58 %). Employees understand that they must upgrade their skills continually, and there are numerous ways to do so—especially online, where free or affordable courses are available for certification in high-demand technical skills such as machine learning, Python, or R, which they can take at work or during personal time. For people with potential, the key is to provide opportunities and incentives, but they do not need to be spoon-fed further education. Of course, structured learning helps round out new skill sets and fosters a longer-term learning journey.

It is essential that cohort- and role-specific learning journeys are in place across the enterprise from the top all the way down. Learning journeys for different cohorts—for example, the chief experience officer’s team, data engineers, translators, and those operating the products being developed—include components of online courses as well as in-person cases to achieve a mix of self-paced (fundamentals) and group (interactive) learning.

Finally, it is important to be realistic. Best-in-class data scientists spend many years in school and then more years in a working role before being hired by leading firms. It is not possible to re-create this with an internal six-month training program.

5. Evaluate trade-offs between immediate results and long-term capability building, leveraging temporary contractors to supplement delivery

Implanting the new skills and culture is vital for the success of any transformation. However, there are trade-offs to be considered between quick wins and sustainability. All companies need these skills as per periodic assessment.

Contractors can help expedite the early pace of the transformation, but a strong transition plan must ensure successful capability transfer and ownership. A number of actions can help achieve this:

Ensure early product ownership by internal teams—for example, by allocating product ownership internally very early in the transition process (even if teams are not ready yet) and by looking to full-time external experts for coaching. Involve employees on teams from the start. One transformation leader at a mining company explained that the organization never had teams that were 100 % external. For the first six months, 70 % of staffing was external; six months later, it was 50 %; and six months after that, it was 20 %. Though we can expect a higher proportion of external to internal talent early on, aim for the ideal scenario of 60 to 80 % internal talent. Encourage employees to get out of their comfort zones. Internal team members need be encouraged to take ownership. But expectations need to be adjusted: top external talent can be up to twice as productive as internal talent who are still learning the ropes. This performance gap needs to be factored into expectations of pace; especially as external staff begin transitioning the work to their internal counterparts.

Finally, seek to establish strong norms from the start. To do this, we will need to establish protocols and solid ways of working. For one mining company, the first six months were all about codifying ways of working in a playbook in collaboration with the external consultant. This approach paid off: as new people joined, the onboarding process and working patterns were clear.

In general, balancing speed, sustainability, and degree of innovation requires trade-offs. Successful iteration necessitates a culture that can fail fast and learn from those failures. Without this, it’s important to ensure that plans work out in the long term. Appointing strong senior leadership will serve as a catalyst for growth. Carefully crafted value propositions, key performance indicators, and career pathways will attract, motivate, grow, and retain talent. A crucial scale-up consideration is to regularly revisit key trade-off decisions, such as achieving the right blend of training for er employees and the optimal mix of talent sources to balance culture, pace, and quality. Thinking about these themes and iterating on them will help DnA organization grow to its full potential.