7. Retraining pathways and transitions from declining to thriving occupations
This chapter discusses how to use big data and the information contained in millions of job postings collected from the internet to design retraining pathways for workers employed in occupations in decline who are willing to move into occupations in high demand. Retraining pathways are built by comparing the skill demands across different sets of occupations and leveraging the granular information contained in job postings. This approach allows to identify detailed lists of skills that workers willing to switch career would need to develop in order to reach the competency level required to perform in occupations in high demand. The chapter presents three example of such retraining pathways, describing how such an approach could be extended to a wider range of occupations in order to strengthen labour market matching and retraining programmes.
The adoption of digital technologies requires the workforce to integrate them in the production processes in which businesses operate. Firms and workers, in turn, need to adapt to the new skill needs that the labour market may demand as a result of the adoption of digital technologies. Over the past few years, this type of changes have been very tangible: new jobs (mainly in the digital sphere) have been created, others have disappeared (or risk doing so in the near future) and, overall, the skills required to undertake most occupations have changed significantly, largely as a result of the digital revolution.
Reflective of this is the World Economic Forum’s recent report (World Economic Forum, 2020[1]), which estimates that 85 million jobs may be displaced by 2025 as a consequence of the shift in the division of labour between human labour and machines. Similarly, however, around 97 million jobs may be created as a result of the digital transition.
While the overall impact of digitalisation on employment is still the subject of a lively debate, it is becoming increasingly clear that the gains from the transition to a digitalised world are unlikely to be shared evenly among workers (OECD, 2019[2]). Certain groups of individuals, in particular the low-skilled workers and those with low education attainment levels will have to adjust to new skill demands (see Box 7.1).
Technological progress has the potential to improve the well-being of individuals by increasing productivity and economic growth. Similarly, technological progress can potentially deepen income, gender, age and territorial inequalities if only a subset of individuals is capable of reaping the benefits of new (digital) technologies. Low skilled workers, for instance, have become substantially concentrated in occupations at high risk of automation. Just to give an example, in between 2012 and 2019 (the pre-pandemic year), the average share of low-educated workers in the six riskiest occupations increased by 5.9%. Lifelong learning, training and upskilling of low-skilled individuals play a key role in countries’ ability to make the digital transition inclusive and work for all. Recent work (OECD, 2021[3]) highlights the importance of promoting interest in adult learning as a key tool for low-skilled workers to benefit from the gains stemming from digitalisation. To turn this into reality, governments need to act to boost incentives for low-skilled workers to participate in training by, among others, reducing financial and time barriers to participation in lifelong learning, while promoting training offer that is better aligned to the needs of the labour market.
In today’s rapidly-changing labour market, one key challenge lies in ensuring that workers are able to move from declining occupations to the others that are expected to thrive as labour market demands changes (OECD, 2021[3]). To ensure that these transitions are effective, workers in declining occupations should be able to upskill and retrain to move to roles that are in high demand in labour markets. But what are the specific skills that are needed to effectively transit from a declining occupation to a thriving one?
To investigate these career moves, this chapter starts by describing the concept of “occupation clusters” by identifying sets of occupations that share similar skill requirements and among which career transitions are likely to be relatively easy due to the overlap in their skill demands. To illustrate the idea of occupation clusters, one occupation of reference is placed in the centre of the occupation cluster where other (similar) occupations are found around it at a certain distance. The distance between the centre of the cluster and the rest of occupations represents the similarity between occupations’ skill requirements (see Box 7.2).
The concept of occupation clusters is used here to depict the similarity between a reference occupation and other occupations that are related to it, having similar skill requirements. In the remainder of the analysis, this chapter will build occupation clusters by identifying the occupations that are most similar in terms of skill requirements to the one at the centre of each cluster (the reference occupation). The farther away an occupation is from the centre and the more dissimilar its skill requirements are relative to the reference occupation.
A measure of “occupation similarity” (i.e. the distance between the centre of the cluster and other occupations close to it) is built based on the analysis of the skill demands contained in online job postings which leverages natural language processing algorithms to assess the overlap between skill requirements across occupations. For instance, customer service managers (one of the occupation under exam in the analysis that follows in this chapter) are “close” to call centre managers or sales supervisors as those jobs require similar skills.
Being slightly more technical, the measure of occupational similarity is computed as the cosine similarity between the vector representing each occupations which are obtained applying the Doc2Vec natural language processing algorithm to the information contained in online job postings. The algorithm produces a mathematical vector representing each occupation in terms of the relevance of its underlying skill requirements as they appear in the demand of employers who publish job adverts online. Mathematical vectors distant from each other signify that underlying occupations share few or no skill requirements with each other. Conversely, occupations that share a high degree of skill similarity will be represented in vectors that are close to each other in the vector space and where their cosine similarity is high.
It is worth noting that, by definition, occupations within each cluster share similar skill requirements and are natural candidates for career moves within it. Customer service managers, for instance, could easily retrain to become call centre managers as that occupation belongs to the same cluster and share similar skills. While some transitions may be easy from a retraining point of view, this chapter focuses on those transitions between occupations that are declining in terms of employment and those whose demand is expected to increase and that are “digital” in nature.
Note: Each bubble represents an occupation. The one in the centre is the reference occupation of the cluster while those around it are occupations that are similar to it in terms of skill requirements. The closer an occupation is to the centre and the higher the degree of similarity of its skill requirements to the reference occupation. The measure of occupational similarity is built using the cosine similarity of occupation vectors computed using natural language processing algorithms (Doc2Vec) that pool together all skill requirements mentioned for any given occupation across online job postings for the country under exam.
In the remainder of this chapter, the analysis focuses on different pairs of origin-to-destination occupations by first identifying occupations whose employment is projected to decline that will be used as the “origin” occupations and “destination” occupations (whose employment outlook is particularly bright and expected to grow in the future).1
The pairs of origin-destination occupations that are used to illustrate a selection of results have been identified in such a way that the retraining effort that will be needed to move from a declining (origin) occupation to a thriving (destination) occupations is the lowest. This is to ensure that the transition is reasonable in terms of the skill acquisition that would be required to move from one to the other.
Given the scope of this report, the destination occupations are also selected to be “digital” in nature such that, the destination clusters will contain several of the “digital jobs” that have been analysed in previous chapters of this report. By no means, however, the analysed transitions constitute the only career transitions that are possible. They are, instead, examples in this chapter that has to be interpreted as a case study. Finally, to illustrate the results, this chapter focuses on the United States for which granular data on employment projections can be merged with the information contained in online job postings.2 Table 7.1 presents the pairs of occupations that will be analysed in this chapter and that represent the centre of each occupation cluster described in the text below.
The projections presented in this chapter draw from the US National Employment Matrix database produced by the Bureau of Labor and Statistics (BLS). The matrix displays data on base- and projected-year employment and employment change. BLS produces occupational employment projections by analysing current and projected future staffing patterns (the distribution of occupations within an industry) in an industry-occupation matrix. Changes in the staffing pattern for each industry are projected and applied to the final industry projections, yielding detailed occupational projections by industry. This projected employment matrix includes estimates for 790 occupations across 295 industries. The Occupational Projections Data database displays data on employment, employment change, occupational openings, education, training, and wages for each detailed National Employment Matrix occupation.
7.1.1. Occupation clusters for account executives and marketing specialists
The advent of automation and the digitalisation of many routine tasks is affecting a variety of sales and accounting occupations. The role of traditional advertising sales or that of account executives, for instance, are changing rapidly as new automation technologies are replacing workers in a variety of routine tasks. Salesforce management systems, for instance, are nowadays used in customer relationship management marketing, helping to automate sales and sales force management functions. They are often combined with a marketing information system in CRM (Customer Relationship Management) systems.
Similarly, the Bureau of Labor Statistics (BLS) in the United States underlines that while “advertising will continue to grow in digital media, including online video ads, search engine ads, and other digital ads intended for cell phones or tablet-style computers […] the ability to automate digital ad placement and the use of ad blockers by digital users will limit employment demand for advertising sales agents along these channels” (US Bureau of Labor Statistics, 2021[4]).
The digitalisation of services is also affecting the demand for workers in some sales and executive roles indirectly. For instance, the US BLS also point out that “the decline of print advertising will drive an overall employment decrease for advertising sales agents. Both newspapers and magazines have seen circulation declines that are expected to continue. With fewer consumers viewing advertisements in print media, fewer advertising sales agents will be needed to support ads in these media” (US Bureau of Labor Statistics, 2021[4]).
As a reflection of these technological innovations and industry shifts, the BLS projections indicates that the growth in employment for advertising sales agents/account executive (SOC 41-3011) is expected to be significantly lower than the average growth in the United States (approximately 3% by 2030), where most of the new jobs that will open in the future will aim to replace workers who transfer to other occupations or those that retire.
Note: The figure shows the two occupation clusters for i) advertising sales agents/account executives on the left and ii) digital marketing specialists on the right. The most similar occupations to the one shown in the centre of each cluster are identified by a dashed line above which the occupational similarity index is placed. Higher values of the occupational similarity indicate a higher degree of skill overlap between origin and destination. BLS employment projections for the period 2020-30 are indicated in brackets within each bubble/occupation. The blue line connects the two occupation clusters, highlighting potential career pathways from declining to thriving occupations.
Source: OECD calculations based on Lightcast data; and US Bureau of Labor Statistics (2021[4]).
If advertising sales agents and account executives are facing a rather gloomy labour market outlook due to technological change, similar occupations that share a high degree of skill overlap with them are also (perhaps not surprisingly) at high risk of decline in labour markets. This is explored in Figure 7.2 that identifies the jobs that are closest to advertising sales agents/account executives in terms of skill requirements and that belong to the advertising sales agents’ occupation cluster (see Box 7.2 above).
The analysis of online job postings for the United States in 2019, for instance, shows that account manager representatives, business development/sales managers, sales representatives and sales assistants are amongst the occupations that share the highest degree of skill similarity with advertising sales agents and account executives. Account managers and representatives or sales agents, for example, perform a variety of tasks that are similar in nature to those performed by advertising sales agents/account executives. Among them there is the traditional knowledge of accounting as well as all tasks that are related to customer relationship management practices.
Notably, as in the case of advertising sales agents, Figure 7.2 shows that the employment of account manager or sales agents is expected to grow at a speed that is below the average in the United States, hinting to the fact that several of those job roles similar to advertising sales agents/account executives are facing an equally worrying risk of automation and of technological displacement.
While technological change is certainly among the main drivers of the expected displacement and decline in employment of the occupations mentioned above, digitalisation represents also an opportunity for many of those workers to develop new skills and transit to different career paths that are digital in nature and are expected to thrive in future labour markets.
The analysis of skill demands in online job postings (Figure 7.2, right panel) helps identifying a series of different career options that advertising sales agents could consider, should they desire to move into thriving digital-related occupations. The digital occupations in Figure 7.2 (right panel) are expected to grow significantly in employment in the future and they are also the closest (in terms of skill requirements) to advertising sales agents. Among those, digital marketing specialists share the highest degree of skill similarity relative to advertising sales agents and are a plausible career switch option.
It is interesting to notice that marketing specialists are a clear example of an occupation that has experienced a significant transformation with the advent of digitalisation and that is nowadays leveraging digital technologies massively from web analytics to online marketing, through the knowledge of sophisticated software such as SemRush or Pardot.
Figure 7.2 ranks the ten most relevant skills for digital marketing specialists by the intensity of the retraining and upskilling that advertisement sales agents would need to undertake to move to that occupation.
Results show that advertisement sales agents would need to boost their knowledge, in particular, on web analytics and online marketing where the distance between the typical tasks they perform in their current jobs and those of digital marketing specialist is the largest.
Similarly, given the digital nature of marketing specialists’ job, retraining or upskilling in the use of SemRush (a SaaS platform that is used for keyword research and to produce online metrics such as search volume and cost per click) will be also typically needed to access the job. Knowledge of online sales (rather than traditional sales) and of SEO Copyright are also key for advertising sales agents to move into digital marketing specialists.
Along with digital marketing specialists, however, Figure 7.2 (right panel) also shows other connected careers pathways, going from marketing managers to copywriters to media planners, all of which share a high degree of skill similarity with marketing specialists and are also occupations projected to grow significantly in the next decade in the United States.
7.2.1. Occupation clusters for satellite / broadband technicians and computer support specialists
Satellite / broadband technicians have already seen their jobs transforming partly due to the arrival of artificial intelligence. Although some of their tasks still require physical presence (e.g. physical installation of equipment), another part of it is being complemented (or replaced) by the advent of artificial intelligence. For instance, telecommunications companies have turned to virtual assistants to offer solutions to customers’ queries. Virtual assistants automate and scale responses to these requests. Another increasing practice relates to the use of virtual assistants to help customers deal with support requests for installation, configuration, troubleshooting, and maintenance, which traditionally was a central part of technicians’ work. With artificial intelligence, operators can teach customers how to install and operate their own devices. In this rapidly changing context, according to the US Bureau of Labour Statistics (US Bureau of Labor Statistics, 2021[4]), employment for telecommunications equipment installers and repairers will decrease by 1.1% between 2020 and 2030, a projection that is far below the expected average growth of employment over the period for the whole economy.
Technological change and automation is negatively affecting the jobs of satellite and broadband technicians but this also extends to those occupations that share with it a high degree of skill similarity which face negative employment projections with the exception of alarm / security system technicians.3 Figure 7.3 shows the occupation cluster for satellite / broadband technicians (that is the occupation sharing the highest degree of skill similarity with them). Out of the five closest occupations in terms of skill requirements, employment is projected to grow at slower pace than average in four of them. Negative employment growth is projected for cable technicians / installers (-0.7%) and television / satellite / television installers (-3.8%). Employment for radio technicians and repair / service technicians is expected to be positive but lower than average (3.9% and 5.9%, respectively).
Note: The figure shows the two occupation clusters for i) satellite broadband technicians on the left and ii) compute support specialists on the right. The most similar occupations to the one shown in the centre of each cluster are identified by a dashed line above which the occupational similarity index is placed. Higher values of the occupational similarity indicate a higher degree of skill overlap between origin and destination. BLS employment projections for the period 2020-30 are indicated in brackets within each bubble/occupation. The blue line connects the two occupation clusters, highlighting potential career pathways from declining to thriving occupations.
Source: OECD calculations based on Lightcast data; and US Bureau of Labor Statistics (2021[4]).
Technological change represents a challenge for workers employed in many routine and medium-skilled occupations, that is also an opportunity to be leveraged to move to safer careers in the future through adequate retraining and upskilling.
The right panel of Figure 7.3 identifies the 5 digital occupations that are closest to satellite and broadband technicians in terms of skill requirements and that could potentially represent interesting career moves. The analysis of skill demands contained in online job postings shows that the closest career transition towards a digital occupation would be to computer support specialists whose employment is projected to grow by 8.9% between 2020 and 2030.
In ways similar to satellite and broadband technicians, computer support specialists are in charge of providing technical support to companies, organisations and customers on computer software and equipment. They also supervise computer systems and work on repairs when needed. Their skills entail knowledge of telecommunications, engineering and technology.
Table 7.3 ranks the most relevant skills for computer support specialists by the intensity of the training that satellite and broadband technicians should undertake to move into that occupation. Results indicate that satellite and broadband technicians would be required to upskill significantly in Helpdesk support and IT management, boosting their ability to assist and inform user on electronic or computer-related issues and to manage IT resources according to a firm’s needs and priorities.
Along with the above skills, satellite and broadband technicians will need to boost their knowledge of technical aspects such as the knowledge of internet and border gateway protocols (i.e. the network layer communications protocol in the Internet protocol suite for relaying datagrams across network boundaries). Similarly, some training will be needed in hardware asset and enterprise mobility management as computer support specialists focus are typically requested to support the management of mobile devices, wireless networks, and other mobile computing services in a business context. Providing broad technical support is also an area where some minor upskilling would be needed, but the effort needed in this area is minimal compared to the rest of skills as satellite and broadband technicians are already typically required to provide such support in their daily tasks.
While computer support specialists represent the shortest transition to a digital occupation for satellite and broadband technicians, other occupations (Figure 7.3, right panel) are also possible career moves into digital professions, where the retraining would be still acceptable and the skill distance relatively manageable.
Occupations that share a high degree of skill similarity with computer support specialists (and that are not too distant from satellite and broadband technicians) are, for instance, network / systems administrators, network / systems support specialists, network / engineer architects or computer systems engineers / architects. All those occupations are expected to grow positively. Computer systems engineers, in particular, are expected to grow by more than 22% in the next decade in the United States, potentially creating skill gaps and bottlenecks that could be filled by workers in other parts of the economy undertaking retraining and upskilling in their core skills and competences.
7.3.1. Occupation clusters for customer service managers and data engineers
The advent of digitalisation and the vastly increased ability of automated AI-powered algorithms to provide precise information to customers, is also affecting several customer-service occupations that, up until now, have been traditionally carried out by human labour. For some of those roles, automation – triggered by digitalisation – is likely to lead to a significant reduction in employment. One such example of automation affecting the customer service sector is the use of chatbots. It is common today, in fact, that customers who navigate a firm’s webpage in search of information interact with a software that is capable of providing useful assistance and precise answers to customers’ questions. This is possible thanks to the development of machine learning techniques, which allow computers to learn information without being explicitly programmed and enable them to ask questions to customers and answer to customers’ queries using artificial intelligence. The US BLS, for instance, points out that “There is expected to be less demand for customer service representatives, especially in retail trade, as their tasks continue to be automated. Self-service systems, social media, and mobile applications enable customers to do simple tasks without interacting with a representative. Advancements in technology will gradually allow these automated systems to do even more tasks. Some companies will continue to use in-house service centers to differentiate themselves from competitors, particularly for complex inquiries such as refunding accounts or confirming insurance coverage” (Bureau of Labor Statistics, n.d.[5]).
In part as a consequence of these trends, also jobs for customer service managers are projected to decline in the next decade4 by -1.5%. The tasks carried out by customer service managers mainly involve overseeing teams of people responding to inquiries from customers but since most of those teams are shrinking in size as AI is deployed to respond automatically to customers, the relevance of customer service managers is also expected to decrease significantly in the future.
Customer service workers therefore may soon need to consider retraining and upskilling pathways with a view to experiment career changes and transition to jobs that are safer in the labour market. The left panel of Figure 7.45 presents the five occupations that share the most similar skill requirements with Customer service managers and that belong to its “occupation cluster”. The right panel of Figure 7.4, instead, presents those digital occupations towards which customer service managers could transit to with some moderate retraining.
Note: The figure shows the two occupation clusters for i) customer service managers on the left and ii) data engineers on the right. The most similar occupations to the one shown in the centre of each cluster are identified by a dashed line above which the occupational similarity index is placed. Higher values of the occupational similarity indicate a higher degree of skill overlap between origin and destination. BLS employment projections for the period 2020-30 are indicated in brackets within each bubble/occupation. The blue line connects the two occupation clusters, highlighting potential career pathways from declining to thriving occupations.
Source: OECD calculations based on Lightcast data; and US Bureau of Labor Statistics (2021[4]).
It is worth noting that the occupations that share the highest level of skill similarity with customer service managers (i.e. call centre managers, sales supervisors, office managers, recruiters and marketing managers) belong to a “declining occupational cluster” as employment in most of those jobs is projected to decrease in the next decade.6
Call centre managers, for instance, share skills of very similar nature to those required by customer service managers, in particular those related to communication and interpersonal skills needed to manage teams and aimed at providing solutions to customers but are projected to decline by 1.5% in the next decade. Sales supervisors are also relatively close to customer service managers in their underlying skill requirements, being responsible for the activities of sales representatives in promoting and selling a product by phone or email. As in the case of customer service managers, they are also required to exhibit managerial skills to train their staff in communicating with customers. Similarly, employment in this occupation is expected to shrink considerably, (-5%) by 2030.
Office managers responsible for organising and co-ordinating office operations, as well as providing administrative support also share similar skills with Customer service managers namely those related with problem solving, initiative and relationship-building skills and are also expected to decline by 1.5% in the next decade.
Call centre managers, for instance, share skills of very similar nature to those required by customer service managers, in particular those related to communication and interpersonal skills needed to manage teams and aimed at providing solutions to customers but are projected to decline by 1.5% in the next decade. Sales supervisors are also relatively close to customer service managers in their underlying skill requirements, being responsible for the activities of sales representatives in promoting and selling a product by phone or email. As in the case of customer service managers, they are also required to exhibit managerial skills to train their staff in communicating with customers. Similarly, employment in this occupation is expected to shrink considerably, (-5%) by 2030.
Office managers responsible for organising and co-ordinating office operations, as well as providing administrative support also share similar skills with Customer service managers namely those related with problem solving, initiative and relationship-building skills and are also expected to decline by 1.5% in the next decade.
While the employment in many of the jobs that share a high degree of skill similarity with customer service managers is expected to decline, other options could be to transit to similar jobs such as recruiters or marketing managers (see Box 7.4) or to boost one’s digital skill to move towards digital occupations that are thriving in the labour market and expected to grow significantly in the future.
Recruiters and marketing managers share a high degree of skill similarity with customer service managers and, contrary to other occupations in that “skill neighbourhood” they are instead projected to grow in the future. When examining their tasks, recruiters (whose employment is projected to increase significantly – 10.4% – in the next decade) are responsible for finding qualified candidates for a job opening by ensuring that the person meets the demands of employer and employee throughout the hiring process. Recruiters are, therefore, required to have strong communication skills and listening skills, which are also important for customer service managers and justify the closeness between the two occupations.
Similarly, the analysis of jobs postings suggests that marketing managers are relatively close to customer service managers as several of their skill requirements overlap. Marketing managers plan programs to generate interest in products or services, and their customer-oriented and managerial skills explain why this occupation is relatively close to customer service managers. Employment for marketing managers is also projected to rise by 10% and they could constitute interesting career options for workers currently employed as customer service managers.
Technological change is responsible for some of the trends leading to a decrease in demand for customer service managers. Retraining and upskilling in digital competences, however, could allow workers employed in those declining jobs to move to careers that are safer in the labour market and with a bright employment outlook.
Among the digital occupations analysed in this report, the shortest transition from customer service managers to a digital occupation would be towards data engineers7 whose employment is expected to grow significantly (approximately 8%) by 2030 in the United States.
A career transition from customer service managers to data engineers requires the acquisition of a number of digital skills, many of which may require some significant upskilling or retraining. Amongst the pairs of occupations analysed in this chapter, the transition between customer service managers and data engineers is the one that will typically require the highest intensity of retraining as the two occupations are relatively far apart in terms of underlying skill requirements.
Table 7.4 shows ranks the most relevant skills for a data engineer by the intensity of the training needed to a customer service manager to transit to it. Results show, for instance, that significant training would be needed in the knowledge of distributed computing8 for customer service managers to catch up with the level required to access a profession as a data engineer.
Similarly, significant retraining would be needed in data warehousing and big data, as those represent core skills and knowledge areas for data engineers, but are less frequently used by the typical customer service manager.
Some skills, however, show a stronger overlap between the origin and destination occupation. The knowledge of HiveQL and Microsoft Certified Professional Azure technologies is very relevant for data engineers but the same are also sometimes used in particular tasks performed by customer service managers, in particular when managing large quantities of customer data, justifying a somewhat relatively less intense (though still considerable) need for retraining in case of a career transition between the two roles.
References
[5] Bureau of Labor Statistics (n.d.), Job Outlook, https://www.bls.gov/ooh/office-and-administrative-support/customer-service-representatives.htm#tab-6 (accessed on 2022).
[6] IBM (2021), What is distributed computing?, https://www.ibm.com/docs/en/txseries/8.2?topic=overview-what-is-distributed-computing.
[3] OECD (2021), OECD Skills Outlook 2021: Learning for Life, OECD Publishing, Paris, https://doi.org/10.1787/0ae365b4-en.
[2] OECD (2019), OECD Employment Outlook 2019: The Future of Work, OECD Publishing, Paris, https://doi.org/10.1787/9ee00155-en.
[4] US Bureau of Labor Statistics (2021), US Bureau of Labor Statistics website, https://data.bls.gov/projections/occupationProj.
[1] World Economic Forum (2020), The Future of Jobs Report 2020, https://www.weforum.org/reports/the-future-of-jobs-report-2020.
Notes
← 1. Employment projections come from the US Bureau of Labor Statistics which provides granular employment data that can be used jointly with the information coming from online job postings.
← 2. Similar dynamics are expected, however, to hold for other countries.
← 3. Differently from other occupations that share a high degree of skill similarity with satellite and broadband technicians, employment for alarm / security system technician is projected to grow significantly in the future (16.4%) and this, then, represents interesting career options for workers in the cluster.
← 4. Customer service managers belong to an occupational group (SOC 43-1011) that is projected to decline by 1.5 in employment terms, according to the US Bureau of Labor Statistics projections for 2020-30 (US Bureau of Labor Statistics, 2021[4]). It should be noted that BLS projections are made at the 6th digit level, while customer service managers is the job title used by Lightcast at the 8th digit level within SOC 43-1011).
← 5. The numbers next to the dashed lines in the figure indicate the closeness of the occupation with respect to customer service managers.
← 6. Employment projections used in this chapter refer to the United States in between 2020 and 2030.
← 7. Data engineers “administer, test, and implement computer databases, applying knowledge of database management systems. Co-ordinate changes to computer databases. May plan, co-ordinate, and implement security measures to safeguard computer databases” (US Bureau of Labor Statistics, 2021[4]).
← 8. Distributed computing consists of enabling multiple software components that are on a number of computers to run as a single system, with computers being either physically close together and connected by a local network, or geographically distant and connected by a wide area network (IBM, 2021[6]).