I recently reached out to data professionals on LinkedIn to ask a series of questions about their work. Results from one of those polls revealed that 58% of data pros reported that they prefer a hybrid approach (on premises and cloud) for their tools and technologies. About a third of respondents preferred the cloud. Only 6% of respondents preferred on premises. See figure below.

What are the differences between data pros who prefer hybrid vs those who prefer cloud? I am attempting to answer that question in study I am currently conducting. Currently, I am conducting a large-scale survey of data professionals by asking them a set of questions about their work in data science and machine learning. If you are a data pro, please consider answering this survey. In addition to the poll question above, the survey will cover such topics as skill sets, solutions used, company’s ethics practices and roadblocks to insights, and more.

To take the survey, please click the link below.

http://bit.ly/dsmlsurvey

The survey will take you about 10-15 minutes to complete. Your responses will remain strictly anonymous. However, if you wish to receive a free executive report of the findings, you will be asked to provide your name and email address at the end of the survey so that we can email you the report when completed (in a few weeks after the survey closes). In this case, if you do provide your email address, your responses will remain confidential.

Thanks in advance for your help!

I recently reached out to data professionals on LinkedIn to ask a series of questions about their work. Results from one of those polls revealed that 46% of data pros reported that they work more collaboratively with their colleagues now than before the COVID-19 pandemic. About a third of respondents indicated that they work less collaboratively with their colleagues now than before the pandemic. Twenty-four percent of respondents report having the same level of collaboration with their colleagues now as before. See figure below.

I found the results unexpected as the world went on lockdown at the start of the pandemic, preventing employees from working face-to-face with their colleagues. What are the differences between those data pros who work more collaboratively now and those who work less collaboratively now? I am attempting to answer that question in study I am currently conducting. Specifically, I am conducting a large-scale survey of data professionals by asking them a set of questions about their work in data science and machine learning.

If you are a data pro, please consider answering this survey. In addition to the poll question above, the survey will cover such topics as skill sets, solutions used, company’s ethics practices and roadblocks to insights, and more.

To take the survey, please click the link below.

http://bit.ly/dsmlsurvey

The survey will take you about 10-15 minutes to complete. Your responses will remain strictly anonymous. However, if you wish to receive a free executive report of the findings, you will be asked to provide your name and email address at the end of the survey so that we can email you the report when completed (in a few weeks after the survey closes). In this case, if you do provide your email address, your responses will remain confidential.

Thanks in advance for your help!

I recently reached out to data professionals on LinkedIn to ask a series of questions about their work. Results from one of those polls revealed that data professionals are generally dissatisfied with their company’s use of analytics in helping it gain a competitive advantage; nearly 6 in 10 data professionals said they were dissatisfied with their company’s use of analytics (22% extremely dissatisfied). See figure below.

While this stand-alone poll is somewhat interesting, the true value polling comes when you can ask multiple questions of respondents to see how responses across questions are correlated with each other. For this particular question, correlating responses to other aspects about the respondents’ work environment would help us identify the circumstances when data pros are satisfied vs when they are dissatisfied, essentially helping us identify best practices to optimize satisfaction with company’s use of analytics.

To identify best practices in analytics, I am conducting a large-scale survey of data professionals by asking them a set of questions about their work in data science and machine learning. As a practicing data professional, you are in a unique position to help the world better understand the data science and machine learning landscape. I developed a survey to give you an opportunity to share your work experiences in data and analytics. The survey will cover such topics as skill sets, solutions used, company’s ethics practices and roadblocks to insights, and more.

To take the survey, please click the link below.

http://bit.ly/dsmlsurvey

The survey will take you about 10-15 minutes to complete. Your responses will remain strictly anonymous. However, if you wish to receive a free executive report of the findings, you will be asked to provide your name and email address at the end of the survey so that we can email you the report when completed (in a few weeks after the survey closes). In this case, if you do provide your email address, your responses will remain confidential.

Thanks in advance for your help!

Data professionals, in all their different forms, play a significant role in how businesses operate today. From business analysts and data scientists to machine learning engineers and software developers, these data professionals extract insights from data and use those insights to drive businesses forward. Finding and using these data-driven insights requires the interplay of data pros, processes and technologies.

While we do know a lot about optimize the value of your data analytics efforts requires that we understand howFinding insights in data and using those insights to drive businesses forward involves the interplay of people, processes and technologies.

Calling all data professionals. An excellent way to learn about a subject is to ask people involved in that subject. Specifically, to learn about data science and machine learning, it is good to ask data professionals who work in those areas. A few years ago, I conducted a study by asking data professionals about their data science experience; I basically applied data science to the field of data science. Not surprisingly, that study generated a lot of insights about data science and the data professionals in that field, helping to establish the state of the industry as well as identifying best practices (see: Getting More Insights from Data: Nine Facts about the Practice of Data Science).

Today, I am extending that research and am reaching out to the data community to ask for help. As a practicing data professional, you are in a unique position to help the world better understand the data science and machine learning landscape. I developed a survey to give you an opportunity to share your work experiences in data and analytics. The survey will cover such topics as skill sets, solutions used, company’s ethics practices and roadblocks to insights, and more.

To take the survey, please click the link below.

http://bit.ly/dsmlsurvey

Help Us Answer Two Questions

Your feedback will help us answer two broad questions:

1. What is the state of data science and machine learning? What DS and ML practices, tools and technologies are companies currently adopting? Which industries are excelling?
2. What are the best practices in DS and ML? What specific DS and ML practices will help give companies a competitive advantage? How should companies design an analytics center of excellence that is world class?

The survey will take you about 10-15 minutes to complete. Your responses will remain strictly anonymous. However, if you wish to receive a free executive report of the findings, you will be asked to provide your name and email address at the end of the survey so that we can email you the report when completed (in a few weeks after the survey closes). In this case, if you do provide your email address, your responses will remain confidential.

The survey will be open through the end of April. To take the survey, please click the button below:

If you have any questions about the survey, please contact me.

An excellent way to learn about a subject is to ask people involved in that subject. Specifically, to learn about data science and machine learning, it is good to ask data professionals who work in those areas. A few years ago, I conducted a study by asking data professionals about their data science experience; I basically applied data science to the field of data science. Not surprisingly, that study generated a lot of insights about data science and the data professionals in that field, helping to establish the state of the industry as well as identifying best practices (see: Getting More Insights from Data: Nine Facts about the Practice of Data Science).

Today, I am extending that research and am reaching out to the data community to ask for help. As a practicing data professional, you are in a unique position to help the world better understand the data science and machine learning landscape. I developed a survey to give you an opportunity to share your work experiences in data and analytics. The survey will cover such topics as skill sets, solutions used, company’s ethics practices and roadblocks to insights, and more.

To take the survey, please click the link below.

http://bit.ly/dsmlsurvey

Help Us Answer Two Questions

Your feedback will help us answer two broad questions:

1. What is the state of data science and machine learning? What DS and ML practices, tools and technologies are companies currently adopting? Which industries are excelling?
2. What are the best practices in DS and ML? What specific DS and ML practices will help give companies a competitive advantage? How should companies design an analytics center of excellence that is world class?

The survey will take you about 10-15 minutes to complete. Your responses will remain strictly anonymous. However, if you wish to receive a free executive report of the findings, you will be asked to provide your name and email address at the end of the survey so that we can email you the report when completed (in a few weeks after the survey closes). In this case, if you do provide your email address, your responses will remain confidential.

The survey will be open through April 22. To take the survey, please click the button below:

If you have any questions about the survey, please contact me.

Results of a survey of data professionals show that about 1 out of 5 are women. Women are paid less than their male counterparts yet both women and men have similar levels of education. Ways of improving gender diversity in the field of data science are offered.

Even though women make up about half of the total workforce in the US, those numbers hide the disparities in some occupational domains. As you can see in Figure 1, while women make up about half of the life, physical and social science occupations in the US, they only account for 25% and 17% of the professionals in computer and mathematical occupations and architecture and engineering occupations, respectively.

Look at diversity numbers for some tech giants, and you will see that women make up a small part of the technology industry. In 2020, women made up 30% of the employees at Microsoft, 32% at Google, 45% at Amazon and 37% at Facebook. In fact, these numbers have only slightly improved, if at all, over the past 6 years.

How does gender diversity look in the data science world? Using data from LinkedIn, the World Economic Forum’s 2020 Global Gender Gap Report shows women make up only 26% of professionals in Data and AI.

I analyzed data from the 2020 Kaggle Machine Learning and Data Science survey in which they surveyed over 20,000 data professionals. That survey showed that, overall, 20% of the respondents were women (see Figure 2), a result somewhat comparable with the percent of women in high-tech companies in general. Gender disparity varied over job titles of data professionals. Women represented 24% (a high, mind you) of the data professionals who identified as a Statistician or Data Analyst. Women, on the other hand, represented only around 13% of data professionals who identified as a Product/Project Manager, Machine Learning Engineer, Software Engineer or Data(base) Engineer.

Salary Differences

Using data from 2018 and 2019, the US Census Bureau estimates that, for every dollar that men earn, women earn 81.6 cents.

We looked at differences between men and women data professionals in the US with respect to their salary. In line with the Census figures, women’s median salary in the US was $90,000-$99,999 while the men’s median salary was $100,000-$124,999 (see Figure 3). That comes out to women data professionals earning roughly 84 cents to every dollar that men earn.

Educational Background

Women and men in the field of data science do not differ with respect to their formal educational background (see Figure 3). For both genders, a majority of each held advanced degrees (i.e., Master’s: ~41%; Doctoral: ~12%) and around 35% held a Bachelor’s degree. Compared to the results of a similar analysis in 2015, the percent of data professionals holding Doctoral and Master’s degrees has declined while the percent of those holding Bachelor’s degrees has increased. The annual Stanford 2021 AI Index reports that women account for less than 19% of all AI and computer science PhD graduates in North America over the past 10 years. 

In 2015, around 50% of data pros held a Master’s degree, 20% held a Doctoral degree and a quarter of them held a Bachelor’s degree. This shift in the educational makeup of those who are in the data analytics profession could be the result of more people moving into the field of data analytics at a young age. Also, the growth of universities that offer undergraduate degrees in the field of data science has exploded since 2015.

Not only do men and women data professionals possess similar educational degrees, they also possess the same skill set. In a prior study, I found that men and women possessed comparable proficiency in subject matter expertise, technology and programming and math/statistics.

Benefits of Gender Diversity

Gender diversity is good for business. In 2014, Gallup found that more gender diverse business units outperform their less gender diverse counterparts with respect to revenue and net profits. Also, in 2017, McKinsey reports that companies in the top quartile for gender diversity are 21% more likely to outperform companies in the bottom quartile.

It has been argued that improving gender diversity would help ameliorate the bias found in our AI algorithms. Because algorithms reflect the values of data professionals who create them, a diverse workforce would act as a gatekeeper to potential bias.

More simply put, if an organization’s leadership and workforce do not reflect the diverse range of customers it serves, its outputs will eventually be found to be substandard. Because learning algorithms are a part of larger systems composed of other technologies and the people who create them and implement them, bias can creep in anywhere in the pipeline. If the diversity within an organization’s pipeline is low at any point, the organization opens itself up to biases — including ones that are deep enough and, potentially, public enough that they could divide customers and eventually lead to obsolescence and failure. Some customers would stay, but others would leave.

~ Ayanna Howard and Charles Isbell from Diversity in AI: The Invisible Men and Women

What You Can Do to Improve Representation of Women in Data Science

Researchers Francine Berman and Philip Bourne proposed 10 ways you can improve gender diversity in data science. These changes come about through changing of the work culture and advocating for women’s representation in data science. You can see their specific tips in Figure 4.

Summary

The data science, machine learning and AI professions have a long way to go to reach gender equality. The current analysis revealed low rates of women employees in all data-related roles. Gender inequality also revealed itself in salary differences of men and women, with women data professionals making Despite this gender disparity, we did not see any large gender differences with respect to educational background; both women and men achieved similar levels of education. Data professionals can take practical steps to improve the representation of women in data science, including changing the work culture around gender diversity and being an activist for women’s representation in data science. 

Results of a worldwide survey reveal that data professionals overwhelmingly use a personal computer or laptop as their computing platform most often for their data science projects. The next most used computing platform is a cloud computing platform and a deep learning workstation.

The practice of data science requires a variety of different tools and technologies to extract value from data. One piece of equipment that is commonly used is the computing platform. A computing platform is the environment in which a piece of software is executed.

In a worldwide machine learning and data science survey by Kaggle in late 2020 of over 20,000 data professionals, respondents were asked a variety of questions regarding the data science tools they typically use. For one of the questions, respondents were asked, “What type of computing platform do you use most often for your data science projects?” The results of that question appear in Figure 1. Nearly 80% of the respondents said that they use a person computer or laptop most often for their data science projects. Fourteen percent of data professionals use a cloud computing platform most often. The list of computing platforms and the percent of respondents who use them are:

1. A personal computer or laptop (78%)
2. A cloud computing platform (AWS, Azure, GCP, hosted notebooks, etc) (14%)
3. A deep learning workstation (NVIDIA GTX, LambdaLabs, etc) (5%)
4. Other (1%)
5. None (2%)

Summary

The top computing platform used most often by data professionals is a personal computer or laptop, followed by a cloud computing platform and a deep learning workstation.

Use of computing platforms varied over job titles, with Machine Learning Engineers, Data Scientists and Data Engineers use cloud computing platforms more often than other data professionals.

Results of a survey of data professionals show that different data roles engage in different activities while at work. Data Scientists indicated that three activities make up an important part of their work, the most across all data roles. The top activities across all data roles were related to analyzing data to influence decisions and building prototypes.

The practice of data science is about extracting value from data to help inform decision making and improve algorithms. As such, data science requires three broad skill sets, including subject matter expertise, statistics/math and technology/programming. Because different data professionals possess diverse and complementary skill sets, the practice of data science requires a collaborative effort across different data professionals. But what exactly do different data professionals do at work? What broad activities make up their respective jobs?

Kaggle conducted a worldwide survey of over 20000 data professionals to learn about the state of data science and machine learning. The survey included the question, “Select any activities that make up an important part of your role at work: (Select all that apply).” They were given a list of six general activities from which to select.

Figure 1. Work activities of data professionals.

Results showed that, on average, data professionals selected one activity. The percent of respondents that selected each activities is (see Figure 1):

1. Analyze and understand data to influence product or business decisions (52%)
2. Build prototypes to explore applying machine learning to new areas (32%)
3. Build and/or run the data infrastructure that my business uses for storing, analyzing, and operationalizing data (27%)
4. Experimentation and iteration to improve existing ML models (25%)
5. Build and/or run a machine learning service that operationally improves my product or workflows (22%)
6. Do research that advances the state of the art of machine learning (19%)
7. None of these activities are an important part of my role at work (14%)
8. Other (4%)

Differences across Job Titles

The number of activities that make up an important part of data professionals’ roles at work varied over job titles. Some job roles include a broader set of work activities compared to other job roles that included a more narrow set of activities. Specifically, most data professionals reported that only one activity that was an important part of their work; they included Business Analyst, DBA/Database Engineer, Data Analyst, Product/Project Manager, Software Engineer and Statistician.

Data professionals who reported two activities included Machine Learning Engineer, Data Engineer and Research Scientist. Data Scientists reported the highest number of work activities with three.

We selected a few job titles to illustrate the different activity profiles of data professionals (see Figure 2). Generally, we see that some job roles are broad with respect to their work activities (e.g., Data Scientist, Machine Learning Engineer) while others are narrow (e.g., Product/Project Manager, Business Analyst, DBA/Database Engineer).

Figure 2. Percent of data professionals say activity is an important part of their role at work. Click image to enlarge. 

While the top work activity was “Analyze and understand data” for most of the job titles (i.e., Business Analyst, Data Analyst, Data Engineer, Data Scientist, Product/Project Manager, Software Engineer and  Statistician), the top activities for other job titles were:

1. Research Scientist: Do research that advances the state of the art of ML
2. DBA/Database Engineer: Build and/or run the data infrastructure
3. Machine Learning Engineer: Build prototypes to explore applying ML

Summary

Data professionals are involved in different work activities. While many data pros are focused primarily on analyzing and understanding data (the top activity among data professionals), a few of them see their primary work around data infrastructure, building prototypes or conducting research to advance knowledge.

Respondents who self-identified as Data Scientists, on average, indicated that they are involved in the most number of activities at work (3), followed by Machine Learning Engineers, Data Engineers and Research Scientists who reported being involved in 2. The remaining data professionals are generally involved in one activity (Business Analyst, DBA/Database Engineer, Data Analyst, Product/Project Manager, Software Engineer and  Statistician.

Not all data professionals are created equal. Results showed that the work activity profiles varied greatly across different data roles. While many of the respondents indicated that analysis and understanding of data to influence products/decisions was the top activity for them, a top activity for Research Scientists was doing research that advances the state of the art of machine learning. Additionally, the top activity for DBA/Database Engineers was building and/or running the data infrastructure. The activity profiles for each data professional (see Figure 2) gives us a sense that some jobs involve several important activities while others involve fewer (a single?) important activities.

The top work activity for data professional roles appears to be very practical and necessary to run day-to-day business operations. These top work activities included influencing business decisions, building prototypes to expand machine learning to new areas and improving ML models. The bottom activity was more about long-term understanding of machine learning reflected in conducting research to advance the state of the art of machine learning.

Different data roles possess different activity profiles. Top work activities tend to be associated with the skill sets of different data roles. Building/Running data infrastructure was the top activity for Data Engineers; doing research to advance the field of machine learning was a top activity for Research Scientists. These results are not surprising as we know that different data professionals have different skill sets. In prior research, I found that data professionals who self-identified as Researchers have a strong math/statistics/research skill set. Developers, on the other hand, have strong programming/technology skills. And data professionals who were Domain Experts have strong business-domain knowledge.

Remember that data professionals have their unique skill set that makes them a better fit for some data roles than others. When applying for data-related positions, it might be useful to look at the type of work activities for which you have experience (or are competent) and apply for the positions with corresponding job titles. For example, if you are proficient in running a data infrastructure, you might consider focusing on Data Engineer jobs. If you have a strong skill set related to research and statistics, you might be more likely to get a call back when applying for Research Scientist positions.

Data science and machine learning work really is a team sport. Getting data teams with members who have complementary skill sets who are capable of performing their specific job activities will likely improve the success rate of data science and machine learning projects.

A recent survey by Kaggle revealed that data professionals used a variety of different ML algorithms in their work. On average, data professionals used two (median) algorithms. The most frequently used algorithms were 1) linear/logistic regression, 2) decision trees/random forests and 3) Convolutional Neural Networks. The total number of and use of specific algorithms varied across job titles, with ML engineers using the most (4) and DBA/Database Engineers using the least (1).

Machine learning algorithms are employed by data professionals to predict important outcomes as well as find patterns and structure in their data. The application of machine learning reaches across industries (e.g., healthcare, education) and professions (e.g., marketing, content management). Kaggle conducted a worldwide survey in October 2020 (2020 Kaggle Machine Learning and Data Science Survey), asking over 20,000 data professionals about the work they do, including the ML algorithms they tend to use.

Figure 1. Top Machine Learning Algorithms Used in 2020. Click image to enlarge.

The survey included a question for data professionals, “Which of the following machine learning algorithms do you use on a regular basis? Select all that apply.” On average, data professionals reported that they use 2 (median) machine learning algorithms. The top 10 machine learning algorithms used were (see Figure 1):

1. Linear or Logistic Regression (59%)
2. Decision Trees or Random Forests (49%)
3. Convolutional Neural Networks (33%)
4. Gradient Boosting Machines (xgboost, lightgbm, etc) (29%)
5. Bayesian Approaches (20%)
6. Recurrent Neural Networks (19%)
7. Dense Neural Networks (MLPs, etc) (19%)
8. Transformer Networks (BERT, gpt-3, etc) (7%)
9. Generative Adversarial Networks (6%)
10. Evolutionary Approaches (4%)

Data Professionals Use Different Algorithms

Figure 2. Machine Learning Algorithm Usage Across Different Job Titles for Data Professionals. Click image to enlarge.

Not all data professionals are created equal. Comparing data professionals by job title, we found that the data professionals differed with respect to the number of ML algorithms used and the types of ML algorithms used. Specifically, the survey results revealed that data professionals who used greatest number of algorithms were:

1. Machine Learning Engineer (4 algos used)
2. Data Engineer (3 used)
3. Data Scientist (3 used)
4. Research Scientist (3 used)

The job title with the lowest number of algorithms used was DBA/Database Engineer (1 used).

Figure 2 includes information about ML algorithm usage for specific job titles. While Linear or Logistic Regression and Decision Trees or Random Forests were clearly the top algorithms across all data professional, for some data pros, many of them use additional algorithms as part of their work; specifically, many Machine Learning Engineers and Research Scientists use Convolutional Neural Networks (62% and 47%, respectively). More than half of Data Scientists (57%) say they use Gradient Boosting Machines. Finally, more than a third of Machine Learning Engineers (39%) say they use Recurrent Neural Networks and Dense Neural Networks (MLPs, etc) on a regular basis.

It’s not difficult to imagine that the use of ML algorithms is likely related to the type of work activities in which each data professionals are involved. We have seen before that different data roles possess different job activity profiles; work-related activities will necessarily dictate the types of algorithms that are needed to effectively perform each activity. Building and running data infrastructures requires different algorithms than doing research to advance the field of machine learning. Next week, I’ll look at the these activities across the data roles studies in this blog.

When applying for data-related job, it could be useful to understand the typical types of machine learning algorithms you will need to know to be successful in that position. If you’re competent in a variety of ML algorithms, you might be able to successfully broaden your job search to include different data roles.

A worldwide survey of data professionals showed that adoption of machine learning methods in their company is 45%. Twenty-one percent of survey respondents said their employer is exploring ML methods. ML adoption rates varied by country with Israel (63%), Netherlands (57%) and the United States (56%) showing the highest and Egypt (31%), Morocco (24%) and Nigeria (23%) showing the lowest adoption rate. ML adoption also varied by company size, with larger companies having higher adoption rates (61%) than medium (45%) and small (33%) companies.

Businesses are leveraging the power of machine learning methods to help them extract better quality information, increase productivity, reduce costs and extract more value from their data. As the amount of data continues to grow along with the processing power of technology, businesses will continue to incorporate ML into their business. Researchers have found different AI / ML adoption rates. In one study, adoption rate of ML Methods was 10%; in a 2020 study by McKinsey, adoption rate of AI was 50%. Still, another study found that 42% of companies were currently using AI and 40% of companies were planning on using AI in the next two years. Another 2020 study found that 59% of enterprises have machine learning initiatives either in production or at a proof-of-concept stage.

Current Analysis on Machine Learning Adoption

Kaggle conducted a worldwide survey in October 2020 of 20,036 data professionals (2020 Kaggle Machine Learning and Data Science Survey). The survey sample consisted of data professionals, including men (~79%) and women (~19%), from a variety of job titles (e.g., data scientist, business analyst, machine learning engineer, software developer) and company sizes. The survey asked a variety of questions, including “Does your current employer incorporate machine learning methods into their business?”

Figure 1. Machine Learning Adoption Rates across Countries. Click image to enlarge.

Machine Learning Adoption by Country

Results of the survey appear in Figure 1 for the overall sample as well as countries that have 50 or more respondents. Overall, results show that the adoption rate of machine learning methods is 45%. Twenty-one percent of respondents indicate their company is exploring ML methods. Twenty percent of respondents indicate their company does not use ML methods.

Countries that are early adopters of ML methods include:

1. Israel (63% adopt ML)
2. Netherlands (57%)
3. United States (56%)
4. UK and Northern Ireland (54%)
5. Germany (54%)
6. Australia (53%)
7. France (52%)
8. China (52%)
9. Taiwan (51%)
10. Greece (49%)

Countries with the lowest adoption rate of ML methods include:

1. Nigeria (23% adopt ML)
2. Morocco (24%)
3. Egypt (31%)
4. Philippines (31%)
5. Argentina (32%)

Countries with the highest percent of companies exploring ML methods include:

1. Chile (36% are exploring ML methods)
2. Sweden (35%)
3. Malaysia (32%)
4. South Korea (31%)
5. Peru (29%)

Figure 2. Adoption of ML Methods Across Company Size

Machine Learning Adoption by Company Size

We also looked at adoption rates by company size. Those results appear in Figure 2. Supporting prior studies, we found that larger companies have higher adoption rates about ML methods. The largest enterprise companies (10,000+ employees) reported ML adoption rates of 61%. The smallest companies (0-49 employees) reported adoption rates of 33%. Of the smallest companies, a little over a quarter of them (27%) indicate that they are exploring the use of ML methods.

Summary

Survey of data professionals showed that adoption rates of machine learning methods among businesses is 45%. About 21% of respondents indicated that their company is exploring machine learning methods with the hope of putting a model into production one day.

ML adoption rate varies by country and company size. Survey results reveal that early adopters come from large enterprise companies (adoption rate of 61%) and some countries including the United States, Israel, Netherlands and the UK and Northern Ireland.

Machine learning vendors, looking for inroads into businesses, could focus their marketing and sales efforts on small businesses as they have the highest percentage of companies who are exploring the use of ML methods.