Introduction

In our first blog, we made reference to the likely causal relationship between data-driven decision-making and the positive effects this has on firm performance (Brynjolfsson, Hitt & Kim, 2011). To this end, the cumulative human capital of data professionals and data-minded employees play a key role in the enablement of data-driven decision-making.  Due to the scarcity of data professionals, it is essential that firms manage job satisfaction levels to ensure that they are well-positioned to live up to their potential. In addition, prior studies have shown that higher job satisfaction in general increases firm performance (e.g., Kessler et al., 2020).

Consequently, research on work redesign – alternatively known as job enrichment – as a strategy to improve job satisfaction as an intermediate step to increased levels of firm performance dates back to the 1960s (e.g., Herzberg, 1966). The latter author has developed the motivation-hygiene theory, stating that intrinsic motivation is ultimately what raises job satisfaction levels. Conversely, the so-called hygiene factors, such as financial rewards and pension plans, only act as means in which to prevent dissatisfaction. In other words: financial rewards, for example, must be satisfactory and meet certain benchmarks, but do not play a key role in long-term job satisfaction.

The central question that subsequent research attempted to answer may be posed along the following lines: ‘’How is one able to manage the levels of intrinsic motivation to increase the job satisfaction of its employees and thereby improve firm performance?’’ Consequently, differing authors from various disciplines have provided multiple perspectives to shed light on this strategic issue. These have been encapsulated in theories that focus on aspects ranging from the extent of cognitive stimuli (e.g., Berlyne, 1967) to the interdependencies between technical aspects of work and the broader context in which it is carried out (e.g., Emery & Trist, 1969).

Ultimately, the job characteristics model devised by Hackman and Oldman in 1976 has emerged as the predominant theory of job enrichment. According to their research, there are five core job dimensions, such as skill variety, that jointly impact three critical psychological states. Improved psychological states, such as the experienced meaningfulness of the work, subsequently heightens the levels of employee motivation and satisfaction, increases the quality of work, and lowers the rates of absenteeism and turnover. The rest of this blog delves further into these core job dimensions and provides practical suggestions on how these may be enriched for data professionals.

Job Characteristics Model

As stated previously, the psychological states lie at the heart of the job characteristics model. In another article co-authored with Lawler (1971), Hackman postulates ‘’that an individual experiences positive affect to the extent that the learns (knowledge of results) that he personally (experienced responsibility) has performed well on a task that he cares about (experienced meaningfulness)’’. The underlying assumption is that this leads to a reinforcing cycle for employees in which increased effort leads to higher rewards. Consequently, the intrinsic motivation of employees is at its highest when all psychological states are managed well through the five core job dimensions.

Experienced Meaningfulness

The first critical psychological state concerns the experienced meaningfulness of the work, which is impacted by three core job dimensions: skill variety, task identity, and task significance. Firstly, skill variety is defined as the degree to which a job requires the use of a number of different skills and talents of the person. Secondly, task identity revolves around the degree to which the job requires completion of a whole and identifiable piece of work. Thirdly, the core job dimension of task significance focuses on the degree to which the job impacts of lives or work of co-employees or other people.

Skill Variety

In our daily conversations with candidates, we have noticed that the degree of skill variety may be experienced as either too low or too high. Some data professionals, for example, note that they are expected to stick to coding and modeling activities (hard or technical skills) and should refrain from getting too involved in other aspects, such as stakeholder communication (soft skills). This may result in skill variety being experienced as too low. Conversely, others mention that they are expected to cover complete project cycles by themselves. Consequently, the latter group may experience the expected skill variety as too stretched.

Task Identity

In essence, task identity is tightly coupled to the extent to which data professionals feel that they are responsible for doing a job from beginning to end with a visible outcome. With regard to this core job dimension, we notice two issues in the interaction with candidates. Firstly, data professionals that are employed by relatively larger firms commonly experience a lower level of task identity due to extensive specialization and tasks and responsibilities that are scoped too narrowly at times. Secondly, data professionals are often far removed from visible outcomes resulting from the highly abstract nature of the corresponding activities.

Task Significance

The priorly mentioned aspect also decreases the experienced levels of task significance, as the impact of models devised by data professionals located at the corporate headquarters may be quite distant from the effects that these models have on frontline personnel. In addition, we notice the structurally increasing demand for jobs that allow data professionals to make their mark on society by deploying advanced analytics and their skills for the common good (e.g., positions within healthcare organizations and NGOs). Along similar lines, firms that are more active in terms of corporate social responsibility may also find it easier to attract talent.

Experienced Responsibility

The second critical psychological state revolves around the experienced responsibility of the outcomes of the work, which is impacted the core job dimension of autonomy. The job dimension of autonomy is defined as ‘’the degree to which the job provides substantial freedom, independence, and discretion to the individual in scheduling the work and in determining the procedures to be used in carrying it out. If autonomy is deemed to be high, an employee is likely to feel a heightened sense of responsibility towards the outcome of his or her activities. This then hypothetically leads to increased levels of intrinsic motivation.

Autonomy

As with other core job dimensions, candidate engagement reveals that autonomy may be experienced as either too narrow or too stretched. On the one hand, too narrow levels of autonomy may result from strict mandates which force data professionals to work with legacy solutions that do not allow for the extensive adjustments to the existing infrastructure and processes of organizations that may be required to implement newer alternatives. On the other hand, being a lone data professional in a large company unfamiliar with data-driven decision-making may imply that you are left with little guidance on how to effectively conduct projects.

Knowledge of results

The third critical psychological state concerns knowledge of the actual results of the work activities, which is impacted the core job dimension of feedback. This fifth core job dimension is defined as ‘’the degree to which carrying out the work activities required by the job results in the individual obtaining direct and clear information about the effectiveness of his or her performance. To this end, it is essential that data professionals receive proper feedback in order to engage in what is known as deliberate practice. For many data professionals, feedback is an important element in the evaluation of potential employers’ suitability.

Feedback

Due to the rapidly evolving of business intelligence and data science, candidates frequently note that they value feedback from peers to improve themselves and in order to stay up to date with the latest technological developments. This may involve both interim feedback and feedback that is provided when a project has been concluded. Junior data professionals may especially value the presence and feedback of talented peers and data professionals with longer tenures. As noted by Provost and Fawcett (2013), firms that attract top talent may find it easier to attract highly skilled data professionals in the future – and vice versa.

Conclusion

At risk of stating the obvious, the job characteristics model – like all models – is not without its flaws. As indicated by the authors, the strength between the relations and its causal effects on job satisfaction may differ per mediating variables, such as the desire of employees to grow in terms of skills and knowledge. Due to the human nature and the wide range of inherent and changing preferences, additional mediating variables have been introduced by prior studies (e.g., Boonzaier, Ficker & Rust, 2001). Moreover, other researchers have noted issues in terms of validity and predictive performance (Fried & Ferris, 1987). 

Nonetheless, the job characteristics model remains a useful starting point to engage in productive dialogue for employees and firms alike. In engaging with employees, the job dimensions may a useful template for firms to be used when managers attempt to discern the reasons that undergird the particularly high or low levels of the job satisfaction of data professionals. Moreover, such data professionals may find the job dimensions useful in their selection of potential employers and the corresponding trade-offs. One may, for example, for a firm with high expected levels of task significance, even though the opportunities for feedback are limited.

As for managers looking to raise the intrinsic motivation of data professionals, the five core job dimensions provide an actionable set of levers with which to do so. Firms looking to hire junior data scientists, for example, may do well by opting to first hire a seasoned professional that is able to guide and coach them throughout their project and increase the opportunities for feedback that are available to them. In addition, firms could increase the number of occasions in which corporate data professionals interact with frontline personnel or its end users to learn about how their activities impact others.

Feel free to reach out to us at Broadwick to discuss how the job characteristics model and the current design of the corresponding job dimensions affect your firm’s ability to attract data professionals. Although the opportunities to do so are limited only by creativity, we would like to inform you about best practices and actionable opportunities on how to redesign these and offer enriched jobs to data professionals. The same also applies if you are a data professionals that feels like one or more of these job dimensions is highly important to you, yet insufficiently present at your current employer.

In the series ‘Where Data Professionals Work’, we discuss the findings of the market research Broadwick conducts with concern to the labor market of data professionals in the Netherlands. We publish these findings as we believe these to be informative to data professionals and data-driven organizations alike. To this end, we primarily use public data provided by LinkedIn and its users. Although we try to be as accurate as possible, please do feel free to contact us at info@broadwick.nl if you are of the opinion that any findings are (partially) incorrect or if you would like to request more information.

Introduction

In earlier editions of WDPW (‘Where Data Professionals Work’), we looked at the largest employers of data professionals in general and data engineers specifically, as well as the employers of data scientists that apply analytics internally to advance their own objectives or for the benefit of clients. In this fifth part, we aim to provide you with new insights into the labor market of the approximately 13,500 data analysts that are currently active in the Netherlands. These data analysts perform the essential role of collecting and processing data, performing statistical analyses, and clearly communicating the results to the relevant stakeholders.

Findings

Firstly, Table 1 below denotes the largest 10 employers of data analysts in the Netherlands. The ranking process is based on absolute figures and excludes consultancy firms, without any sample restraints with regard to size. As in earlier editions of WDPW, the banking trio Rabobank, ABN AMRO, and ING lead the pack. The City of Amsterdam, however, is an organization that we have not seen in any of the previous articles. In terms of the distribution of data analysts across organizations, this once again appears to be quite skewed as these 10 organizations employ approximately 10% of all data analysts.

Table 1 – Top 10 employers of data analysts in the Netherlands (absolute figures, excluding consultancy firms, all organizational sizes)

RankOrganizationData Analyst Intensity
1Rabobank1.1%
2ABN AMRO1.5%
3ING1.0%
4Booking.com2.3%
5Belastingdienst0.8%
6ASML0.7%
7KPN0.5%
8Shell0.7%
9Philips0.5%
10City of Amsterdam0.4%

Although the organizations listed above employ the most data analysts in absolute terms, the average Data Analyst Intensity (number of data analysts employed by an organization divided by its total workforce) is less than 1%. Hence, we redid the ranking process on the basis of this latter metric. We, however, also limited the sample criteria to only include ‘large’ organizations (defined by the Chamber of Commerce as organizations that have 250 or more employees) to help enable an apples-to-apples comparison. The results are shown in Table 2 below, with these organizations having an average Data Analyst Intensity of over 2%.

Table 2 – Top 10 employers of data analysts in the Netherlands (relative figures, excluding consultancy firms, 250+ employees)

RankOrganizationData Analyst Intensity
1CACEIS2.5%
2Uber2.4%
3eBay2.4%
4Booking.com2.3%
5Eneco2.3%
6Bol.com2.0%
7CZ1.8%
8FedEx1.8%
9IQVIA1.8%
10De Nederlandsche Bank1.8%

Thirdly, Table 3 below limits the sample criteria to only include consultancy firms. This ranking is once again based on absolute figures, unlike the Data Analyst Intensity metric that were used for Table 2. As we discussed in the fourth part of WDPW, nevertheless, the intensity metric provides a means with which the level of diversification can be measured. Digital Power, for example, is purely focused on the analytics value chain and features a Data Analyst Intensity of almost 50%. Conversely, Cognizant is a well-diversified consultancy firm which is correspondingly marked by a Data Analyst Intensity of less than 1%.

Table 3 – Top 10 employers of data analysts in the Netherlands (absolute figures, consultancy firms only)

RankOrganizationData Analyst Intensity
1Digital Power48.9%
2Atos0.8%
3YGroup Companies16.7%
4Infosys1.4%
5Newzoo16.9%
6Itility5.7%
7Tata Consultancy Services0.6%
8Cognizant0.7%
9SoliTrust80%
10Accedis18.9%

Conclusion

We hope this article provided you – whether you are a data analyst (employer), or just interested in the realm of data – with new insights concerning the labor market of data analysts in the Netherlands. If you would like to discuss the impact on your career or organization of any of the above findings, feel free to give us a call. For now, this will be the last edition of ‘Where Data Professionals Work’ – although we’ll certainly do a new edition in 2021. In addition, we have got some quite interesting ideas for future articles. Thanks for reading and stay tuned!

In the series ‘Where Data Professionals Work’ we discuss the findings of the market research Broadwick conducts with concern to the labor market of data professionals in the Netherlands. We publish these findings as we believe these to be informative to data professionals and data-driven organizations alike. To this end, we primarily use public data provided by LinkedIn and its users. Although we try to be as accurate as possible, please do feel free to contact us at info@broadwick.nl if you are of the opinion that any findings are (partially) incorrect or if you would like to request more information.

Introduction

In earlier editions of WDPW (‘Where Data Professionals Work’), we looked at the largest employers of data professionals in general and data engineers in particular, as well as those that employ data scientists and apply data science to their internal processes. A considerable portion of data scientists, however, also work at consultancy firms. Making good on our promise, we cover these consultancy firms in this special edition of WDPW. Firstly, Table 1 below provides a general overview of the consultancy firms employing the most data scientists. Subsequently, Table 2 and Table 3 then distinguish between diversified and niche consultancy firms

Findings

Taking the number one spot in this top 10, Capgemini is a diversified IT consultancy firm that we have also seen in previous editions of WDPW. Interestingly, the difference between the Data Scientist Intensity (the number of data scientists employed by an organization divided by their total workforce) is quite large between Capgemini and runner-up Xomnia, a niche consultancy firm. On average, the Data Scientist Intensity of these 10 organizations is approximately 13.5%. In terms of employee growth, the same 10 organizations have grown by almost 30% on average. In combination, these consultancy firms employ about 270 data scientists.

Table 1 – Top 10 employers of data scientists in the Netherlands (consultancy firms only)

RankOrganizationData Scientist Intensity
1Capgemini0.5%
2Xomnia44.2%
3Atos0.7%
4CGI1.6%
5Itility11.0%
6Sogeti1.0%
7EY VODW22.5%
8Ordina0.9%
9Building Blocks40.0%
10VIQTOR DAVIS14.2%

As mentioned earlier, Table 2 revises the ranking process by limiting the inclusion criteria to diversified consultancy firms. The consultancy firms that belong to this category carry out activities that include those related to the analytics value chain, but also offer general IT-related services or processes related to digital marketing. This may also be gauged from the Data Scientist Intensity, which drops from the 13.5% of Table 1 to below 5% in Table 2 (or below 3% if we exclude EY VODW). In addition, these 10 consultancy firms jointly employ slightly more than 230 data scientists (or 23 on average).

Table 2 – Top 10 employers of data scientists in the Netherlands (diversified consultancy firms)

RankOrganizationData Scientist Intensity
1Capgemini0.5%
2Atos0.7%
3CGI1.6%
4Itility11.0%
5Sogeti1.0%
6EY VODW22.5%
7Ordina0.9%
8IG&H6.8%
9Ilionx1.3%
10Tata Consultancy Services0.4%

Lastly, Table 3 provides an overview of the largest ‘pure players’ in the domain of data science consultancy. These pure players are niche consultancy firms that exclusively focus on the analytics value chain. (Although in practice this distinction is not always as black and white, as this may also necessitate IT-related processes.) As shown above, Amsterdam-based Xomnia is the largest employer of data scientists within this category. In addition, the average Data Scientist Intensity and employee growth over the past 24 months are approximately 37% and 47%, respectively. Furthermore, these 10 consultancy firms combined employ more than 170 data scientists.

Table 3 – Top 10 employers of data scientists in the Netherlands (niche consultancy firms)

RankOrganizationData Scientist Intensity
1Xomnia44.2%
2Building Blocks40.0%
3VIQTOR DAVIS14.2%
4Data Science Lab60.7%
5GoDataDriven29.6%
6MIcompany18.8%
7Pipple50.0%
8Bright Cape28.0%
9Anchormen19.7%
10Vantage AI65.0%

Conclusion

Previously, we noted that just 10 organizations employ well over 10% of the 7,500 data scientists currently active in the Netherlands. In this case, the 20 consultancy firms of Table 2 and Table 3 employ about 5% of the total pool of data scientists – pointing to a more fragmented market of organizations that apply data science for client purposes than those that apply analytics as a means to advance their own objectives. If you wonder whether consultancy would fit your next career move, please do feel free to have a chat with us. Thanks for reading and until next time!

In the series ‘Where Data Professionals Work’ we discuss the findings of the market research Broadwick conducts with concern to the labor market of data professionals in the Netherlands. We publish these findings as we believe these to be informative to data professionals and data-driven organizations alike. To this end, we primarily use public data provided by LinkedIn and its users. Although we try to be as accurate as possible, please do feel free to contact us at info@broadwick.nl if you are of the opinion that any findings are (partially) incorrect or if you would like to request more information.

Introduction

In earlier editions of ‘Where Data Professionals Work’ we looked at the largest employers of data professionals in general and data engineers in particular. This third part covers data scientists specifically. Currently, there are about 7,500 data scientists active in the Netherlands that apply analytics, modeling techniques and quantitative analysis to gain new insights and actionable recommendations from data. Although many of these data scientists are employed by consultancy firms, we will cover such agencies in a next part of ‘Where Data Professionals Work’. For now, however, we are limiting to scope that apply data science to their internal processes.

Findings

Firstly, Table 1 below displays a top 10 of the largest employers of scientists in the Netherlands. The average Data Scientist Intensity (the percentage of employees that carry out the role of data scientist within their respective organizations) comes in at an estimated 0.8%. As we discussed before, however, the correlation between organizational size in terms of employees and the number of data scientists exceeds 0.5. Hence, Table 2 below revises this ranking process based on the relative figures (as measured by the Data Scientist Intensity). To facilitate a fair comparison, we limit the sample to large organizations (250+ employees).

Table 1 – Top 10 employers of data scientists in the Netherlands (absolute figures, excluding consultancy firms)

RankOrganizationData Scientist Intensity
1Booking.com3.0%
2ABN AMRO0.5%
3ING0.4%
4Rabobank0.2%
5Belastingdienst0.4%
6Philips0.4%
7Ahold Delhaize0.2%
8VGZ2.0%
9ASML0.3%
10Shell0.4%

This revisal of the ranking process paints quite a different picture. Booking.com still takes the number one spot, although the three large banks have been substituted by eBay, the Netherlands Forensic Institute, and online payment processor Adyen. Additionally, VGZ has climbed from the eight to the fifth position, with the second half of the top 10 consisting of new names, such as Statistics Netherlands (CBS) and TomTom. The average Data Scientist Intensity has also more than doubled, climbing from 0.8% to slightly under 2%. Lastly, we will take a look at the fastest-growing employers of data scientists in Table 3.

Table 2 – Top 10 employers of data scientists in the Netherlands (relative figures, 250+ employees)

RankOrganizationData Scientist Intensity
1Booking.com3.0%
2eBay2.6%
3Netherlands Forensic Institute2.1%
4Adyen2.1%
5VGZ2.0%
6Statistics Netherlands1.8%
7IKNL1.5%
8Port of Rotterdam1.4%
9TomTom1.2%
10Knab1.1%

To measure the pace of growth, we use the 24 month employee growth based on LinkedIn data. Please take into account that this data is only available for organizations that 30 or more employees, although including micro-sized firms might also have distorted the results. Based on this metric, the average growth of these organizations has exceeded 65% over the past 2 years. Prosus Group only has grown its workforce by more than 500% in this time frame. In addition, the average Data Scientist Intensity is 7.2% (although the intra-group differences are also significantly higher than in the previous top 10s).

Table 3 – Top 10 fastest-growing employers of data scientists in the Netherlands (24M employee growth, 30+ employees)

RankOrganizationData Scientist Intensity
1Prosus Group5.5%
2Celonis23.5%
3My Jewellery0.5%
4Pacmed28.9%
5Adyen2.1%
6Floryn3.7%
7Bol.com1.0%
8Charly Cares0.2%
9Knab1.1%
10StuDocu | StudeerSnel.nl5.4%

Conclusion

In line with our previous findings concerning the skewed distribution of data professionals in general, the organizations listed in Table 1 jointly employ about 10% of all the data scientists that are currently employed in the Netherlands. For data scientists that enjoy working with other data scientists or like to work in fast-paced environments, Table 2 and Table 3 might also prove informative. If you are a data scientist looking for your next career opportunity, please do feel free to reach out to us via the Contact page to further discuss your preferences. Thanks for reading and until next time!

In the series ‘Where Data Professionals Work’ we discuss the findings of the market research Broadwick conducts with concern to the labor market of data professionals in the Netherlands. We publish these findings as we believe these to be informative to data professionals and data-driven organizations alike. To this end, we primarily use public data provided by LinkedIn and its users. Although we try to be as accurate as possible, please do feel free to contact us at info@broadwick.nl if you are of the opinion that any findings are (partially) incorrect or if you would like to request more information.

Introduction

In the first part of ‘Where Data Professionals Work’ we provided you with an overview of the general labor market of data professionals. For this second edition, we exclusively focus on the organizations that employ the estimated 4,000 data engineers that are currently active in the Netherlands. Responsible for laying the foundations for both initial and ongoing data analysis and experimentation, data engineers fulfill an essential role in the overall analytics value chain. Based on the relatively smaller number of data engineers compared to data scientists and data analysts, they act to leverage the analytics value chain as a whole.

Findings

Firstly, Table 1 below ranks the top 10 organizations based on the absolute number of data engineers that they employ. Consistent with our findings in Part 1 of ‘Where Data Professionals Work’, these jointly employ an estimated 400 data engineers – or 10% of the total pool of available data engineers. In addition, the ‘Data Engineer Intensity’ displays the percentage of data engineers employed by organizations as compared to their total workforce. With an average Data Engineer Intensity of 0.3%, this attests to the important role that they play by leveraging their skill sets to benefit data-driven decision-making across their organizations.

Table 1 – Top 10 employers of data engineers in the Netherlands (absolute figures, excluding consultancy firms, all organizational sizes)

RankOrganizationData Engineer Intensity
1ING0.3%
2Rijksoverheid0.1%
3ABN AMRO0.3%
4Rabobank0.2%
5KPN0.3%
6ASML0.3%
7Aegon0.6%
8VodafoneZiggo0.5%
9De Volksbank0.6%
10Shell0.2%

We then redid the ranking process on the basis of the Data Engineer Intensity metric, the results of which are shown in Table 2. As all organizations in Table 1 are viewed as large (defined by the Dutch Chamber of Commerce as having 250 or more employees), we took this into account by tweaking the results to only include large-scaled organizations and ensure a fair comparison. In this case, the average Data Engineer Intensity came in 1.5%. Even after excluding HERE Technologies (which may be viewed as an outlier), the Data Engineer Intensity is still three times higher at 0.9%.

Table 2 – Top 10 employers of data engineers in the Netherlands (relative figures, excluding consultancy firms, 250+ employees)

RankOrganizationData Engineer Intensity
1HERE Technologies6.9%
2eBay1.8%
3Takeaway.com1%
4NN Investment Partners1%
5Adyen0.9%
6NIBC Bank0.8%
7APG0.8%
8Elsevier0.8%
9Wärtsilä0.7%
10Eurofiber0.7%

We also found that many data engineers have also opted for a career within the realm of consultancy to make their knowledge available to various clients. Hence, Table 3 below denotes the top 10 consultancy firms that employ the highest number of data engineers. Jointly, these 10 organizations employ over 200 data engineers – or over 5% of the total pool of data engineers that are employed in the Netherlands. Distinguishing between diversified and niche consultancy firms, Capgemini and Xomnia employ the most data engineers, respectively. In addition, the average Data Engineer Intensity of these firms comes in at over 9%.

Table 3 – Top 10 employers of data engineers in the Netherlands (absolute figures, consultancy firms only)

RankOrganizationData Engineer Intensity
1Capgemini0.5%
2Hot ITem14%
3Tata Consultancy Services1%
4Xomnia28%
5Cognizant1.2%
6LINKIT9%
7Atos0.4%
8Itility7%
9Sogeti0.6%
10GoDataDriven28%

Conclusion

As noted in Part 1, the labor market of data professionals in the Netherlands appears to be quite concentrated with relatively few organizations employing most of the data engineers. In this case, the 20 organizations of Table 1 and 3 jointly employ over 15% of all data engineers. Once again, we hope that this article helped data professionals and data-driven organizations alike to gain more clarity on the labor market of data engineers. In the third part of ‘Where Data Professionals Work’, we will take a look at the landscape of data scientists. Thanks for reading and until next time!

Part 1: An overview of the analytics labor market in the Netherlands

In the series ‘Where Data Professionals Work’ we discuss the findings of the market research we conduct with concern to the labor market of data professionals in the Netherlands. We publish these findings as we believe these to be informative to data professionals and data-driven organizations alike. To this end, we primarily use public data provided by LinkedIn and its users. Although we try to be as accurate as possible, please do feel free to contact us at info@broadwick.nl if you are of the opinion that any findings are (partially) incorrect or if you would like to request more information.

Introducing: ‘Where data professionals work’

In this first part of ‘Where Data Professionals Work’, we provide you with an overview of the labor market of data professionals. Although the analytics market is maturing, leading to increased levels in terms of specialization and job titles, we focused on three roles for this piece: data engineers (4,000), data scientists (7,500), and data analysts (13,500). In sum, this implies that there are approximately 25,000 data professionals that are currently employed in the Netherlands.

The ranking of employers is based on the absolute numbers of data professionals they employ, although we have also calculated a ‘Data Professional Intensity’ metric. The Data Professional Intensity metric is calculated by taking the number of data professionals employed by an organization and dividing this by its total number of employees. In other words: if an organization has a Data Professional Intensity of 5%, it employs 5 data professionals out of every 100 employees.

So, where do they work?

Firstly, Table 1 below provides the top 10 employers of data professionals in the Netherlands. Afterwards, the subsequent tables provide a top 10 of employers of data professionals within certain ranges of organizational sizes in terms of total employees. These size classifications are based on the search criteria of LinkedIn.

Table 1 – The Top 10 Employers of Data Professionals in the Netherlands (all organizational sizes)

RankOrganizationData Professional Intensity
1ING2.6%
2ABN AMRO2.5%
3Rabobank1.6%
4Booking.com6.2%
5ASML2.0%
6Belastingdienst1.4%
7KPN1.1%
8Shell1.5%
9Philips1.0%
10VodafoneZiggo2.1%

As shown in Table 1 above, this top 10 is spearheaded by the famous Dutch banking trio of ING, ABN AMRO, and Rabobank. In fact, these three banks jointly employ well over 5% of the data professionals that are currently active in the Netherlands. Moreover, the organizations that make up this top 10 in combination employ an estimated 10% of the total pool of data professionals. There are considerable differences, however, in terms of the underlying Data Professional Intensity – which points to the importance of redoing the ranking process for different size classifications. Let us look at Table 2 next.

Table 2 – Top 10 Employers of Data Professionals in the Netherlands (>10,000 employees)

RankOrganizationData Professional Intensity
1ING2.6%
2ABN AMRO2.5%
3Rabobank1.6%
4ASML2.0%
5Belastingdienst1.4%
6KPN1.1%
7Philips1.0%
8KLM0.7%
9CBS0.5%
10City of Amsterdam0.6%

As you may have noticed, Booking.com, Shell, and VodafoneZiggo have given up their spots in the ranking (for now). As such, this has allowed ASML, KPN, and Philips to climb upwards. In addition, three new names have surfaced on the list: KLM, CBS (the Central Statistical Office) and the City of Amsterdam. Furthermore, the average Data Professional Intensity of these 10 organizations decreased from 2.2% to slightly less than 1.5%.  This is somewhat peculiar, as the correlation between organizational size (in terms of employees) and the number of data professionals employed by organizations lies above 0.5. Up next: Table 3.

Table 3 – Top 10 Employers of Data Professionals in the Netherlands (5,001-10,000 employees)

RankOrganizationData Professional Intensity
1Booking.com6.2%
2Shell1.5%
3VodafoneZiggo2.1%
4Capgemini1,5%
5NN1.6%
6Achmea1.5%
7PostNL0.8%
8NS0.9%
9UMC Utrecht0.7%
10FrieslandCampina0.8%

We see Booking.com, Shell, and VodafoneZiggo reclaim their spots, in addition to the arrival of plenty of new names – leading to considerable underlying differences. Booking.com, for example, was established in 1996 as a natively digital company, which may have laid an early foundation for the later application of analytics. Conversely, the dairy operations of FrieslandCampina go back to the 19th century. And for companies with long histories, strategic change and renewal generally become more difficult over time – a phenomenon known as ‘organizational inertia’, which we will discuss in a later article. But for now, let us turn to Table 4.

Table 4 – Top 10 Employers of Data Professionals in the Netherlands (1,001-5,000 employees)

RankOrganizationData Professional Intensity
1Bol.com3.9%
2De Volksbank2.7%
3Sogeti3.4%
4Ordina3.7%
5Atos1.8%
6Vanderlande3.4%
7Nike2.0%
8Unilever2.1%
9Eneco3.0%
10Stedin2.7%

Online retailer Bol.com takes the number 1 spot in this classification. (Disclaimer: Albert Heijn and Bol.com are viewed as distinct companies, although both are part of Ahold Delhaize. This also applies to similar situations.) At this point, the average Data Professional Intensity has also climbed back up to almost 3%. As stated before, this paints somewhat of a mixed picture. Whereas an increase in organizational size is generally associated with an increase in data professionals that are employed by an organization, the correlation between size and Data Professional Intensity is slightly negative. Nonetheless, let us move on to Table 5.

Table 5 – Top 10 Employers of Data Professionals in the Netherlands (501-1,000 employees)

RankOrganizationData Professional Intensity
1Adyen5.9%
2Ilionx5.4%
3eBay7.7%
4Ericsson3.5%
5NN Investment Partners3.5%
6Exact2.8%
7Ortec5.1%
8NIBC Bank3.9%
9Takeaway.com3.3%
10VIVAT2.2%

In this case, Adyen comes in at 1st place in terms of absolute numbers. For those who are unfamiliar with the company, Adyen is a player in the online payment processing industry and has worked its way towards achieving a preeminent market position since its founding in 2006 – with its stock price more than tripling since its listing in June 2018. In addition, we once again see an increase in the Average Data Professional Intensity. eBay, for example, is characterized by having almost 8 data professionals out of every 100 employees. Lastly, let us take a look at Table 6.

Table 6 – Top 10 Employers of Data Professionals in the Netherlands (<500 employees)

RankOrganizationData Professional Intensity
1Digital Power62.8%
2Itility21.7%
3Xomnia61.4%
4VIQTOR DAVIS28.9%
5YGroup Companies33.3%
6Hot ITem14.0%
7HERE Technologies11.7%
8EY VODW31.4%
9GoDataDriven60.4%
10MIcompany37.5%

Except for HERE Technologies, which was acquired by a consortium of German car makers in 2015, all the organizations listed in Table 6 are consultancy firms (we will cover these in more detail in a future article). At this point, the top 10 features an average Data Professional Intensity of 36.3%. This metric also allows one to measure the extent of diversification of these consultancy firms, with the more focused players generally scoring higher on this metric. Digital Power is purely focused on the analytics domain, for example, as is evident by its high Data Professional Intensity of over 60%.

The wrap-up

As noted earlier, the distribution of data professionals across organizations appears to be extraordinarily skewed. Seemingly, the initial top 10 employs over 10% of all data professionals – with the top 3 firms employing approximately 1,350 (or over 5%) of all data professionals. Looking at the Data Professional Intensity of organizations that apply analytics internally, companies like eBay, Booking.com, and Adyen stand out as well.  As studies (e.g., Brynjolfsson, Hitt & Kim, 2011) have pointed to the likely causal relationship between data-driven decision-making and the positive effects this has on firm performance, this certainly bodes well for the future of these companies.

We hope that this first article proves to be informative and look forward to taking a deeper diver into the data. In the second part of ‘Where Data Professionals Work’, we will take a specific look at the role of data engineering. In subsequent articles, we will also cover the labor market of data analysts and data scientists – with the latter including a dedicated edition to consultancy firms. Thanks for reading and until next time.