GITNUXREPORT 2025

Data Science And Statistics

Data science market grows rapidly, transforming industries and boosting competitive advantage.

Jannik Linder

Co-Founder of Gitnux, specialized in content and tech since 2016.

First published: April 29, 2025

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Statistic 1

91.5% of data scientists and analytics decision-makers report that their organization’s data initiatives have increased competition

Statistic 2

65% of organizations use data analytics to improve decision-making

Statistic 3

84% of business leaders say that AI and data analytics are crucial for their organizations

Statistic 4

Business Intelligence (BI) and data analytics tools are used by 73% of organizations to derive insights

Statistic 5

39% of data science projects focus mainly on predictive analytics

Statistic 6

The top three industries utilizing data science are finance, retail, and healthcare, with over 70% in each industry

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6 out of 10 companies say data science has significantly contributed to operational efficiency

Statistic 8

45% of data science projects are concentrated on customer analytics and personalization

Statistic 9

The use of AI and data science in supply chain management increased by 45% over the past three years

Statistic 10

41% of organizations have integrated data science into their core business strategies

Statistic 11

40% of data science projects fail due to poor data quality

Statistic 12

48% of data science projects are initiated without clearly defined goals, leading to higher failure rates

Statistic 13

Data scientists spend approximately 45% of their time cleaning and preparing data

Statistic 14

Only 21% of data science projects fully meet the initial expectations of ROI

Statistic 15

80% of data science practitioners believe that ethical AI is an important priority

Statistic 16

The average time from project conception to deployment for a data science model is 7 months

Statistic 17

58% of data scientists report that they lack sufficient access to high-quality data

Statistic 18

55% of data practitioners believe that explainability of AI models is critical for trust and compliance

Statistic 19

51% of data scientists report that explaining model results to non-technical stakeholders remains a challenge

Statistic 20

68% of organizations consider data privacy one of their top concerns when deploying AI solutions

Statistic 21

57% of data science models deployed in production are updated or retrained every three months

Statistic 22

The global data science market was valued at approximately $37.9 billion in 2020 and is expected to reach $142.9 billion by 2028

Statistic 23

Machine learning and deep learning are the fastest-growing data science subfields, with growth rates exceeding 40% annually

Statistic 24

The use of natural language processing (NLP) has grown by over 35% in the past three years

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44% of organizations plan to increase their data science budgets in 2024

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The global market for AI in healthcare is expected to reach $45.2 billion by 2026

Statistic 27

Data science training programs have grown by 250% globally over the past five years

Statistic 28

The percentage of enterprises adopting big data analytics increased from 17% in 2015 to 54% in 2023

Statistic 29

Python is the preferred programming language for data scientists, used by 76% of practitioners

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52% of organizations leverage cloud-based data science solutions

Statistic 31

The adoption of automated machine learning (AutoML) solutions has increased by over 60% in the past two years

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62% of organizations plan to adopt AI-driven automation in their workflows within the next two years

Statistic 33

69% of data scientists prefer using open-source tools such as R and Python

Statistic 34

33% of organizations use automated data insights, reducing analysis time by up to 75%

Statistic 35

The adoption of blockchain technology for data security in data science workflows grew by 30% in 2023

Statistic 36

Over 65% of data science job descriptions now require proficiency in SQL, Python, and machine learning

Statistic 37

72% of organizations use data visualization tools like Tableau, Power BI, or Looker to communicate insights

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Around 2.7 million data science jobs are projected globally by 2024

Statistic 39

The median annual salary for a data scientist in the US is approximately $120,000

Statistic 40

74% of data scientists work in the tech sector

Statistic 41

37% of organizations report difficulty in hiring skilled data science talent

Statistic 42

The demand for data engineers has increased by 50% in the last year

Statistic 43

The average size of a data science team in a medium-to-large organization is 8 members

Statistic 44

The data science job market is projected to grow by 28% from 2020 to 2030, much faster than the average for all occupations

Statistic 45

29% of organizations have a dedicated data science center of excellence

Statistic 46

83% of data professionals believe that ongoing education and reskilling are vital due to rapid technological change

Statistic 47

The median time to fill a data science position is 62 days, indicating a competitive hiring landscape

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Key Highlights

The global data science market was valued at approximately $37.9 billion in 2020 and is expected to reach $142.9 billion by 2028
91.5% of data scientists and analytics decision-makers report that their organization’s data initiatives have increased competition
65% of organizations use data analytics to improve decision-making
The percentage of enterprises adopting big data analytics increased from 17% in 2015 to 54% in 2023
Around 2.7 million data science jobs are projected globally by 2024
40% of data science projects fail due to poor data quality
Python is the preferred programming language for data scientists, used by 76% of practitioners
The median annual salary for a data scientist in the US is approximately $120,000
84% of business leaders say that AI and data analytics are crucial for their organizations
74% of data scientists work in the tech sector
48% of data science projects are initiated without clearly defined goals, leading to higher failure rates
Machine learning and deep learning are the fastest-growing data science subfields, with growth rates exceeding 40% annually
52% of organizations leverage cloud-based data science solutions

Data science is transforming industries at an unprecedented pace, with the market projected to soar from $37.9 billion in 2020 to nearly $143 billion by 2028, while organizations increasingly leverage AI, big data, and advanced analytics to boost competition, enhance decision-making, and drive innovation across sectors.

Business Impact and Industry Applications

91.5% of data scientists and analytics decision-makers report that their organization’s data initiatives have increased competition
65% of organizations use data analytics to improve decision-making
84% of business leaders say that AI and data analytics are crucial for their organizations
Business Intelligence (BI) and data analytics tools are used by 73% of organizations to derive insights
39% of data science projects focus mainly on predictive analytics
The top three industries utilizing data science are finance, retail, and healthcare, with over 70% in each industry
6 out of 10 companies say data science has significantly contributed to operational efficiency
45% of data science projects are concentrated on customer analytics and personalization
The use of AI and data science in supply chain management increased by 45% over the past three years
41% of organizations have integrated data science into their core business strategies

Business Impact and Industry Applications Interpretation

In an era where 91.5% of data-driven organizations cite increased competition and nearly half embed data science into core strategies, it's clear that harnessing AI and analytics isn't just a technical upgrade but a strategic imperative—turning data into a competitive edge across finance, retail, healthcare, and supply chains.

Challenges, Skills, and Best Practices

40% of data science projects fail due to poor data quality
48% of data science projects are initiated without clearly defined goals, leading to higher failure rates
Data scientists spend approximately 45% of their time cleaning and preparing data
Only 21% of data science projects fully meet the initial expectations of ROI
80% of data science practitioners believe that ethical AI is an important priority
The average time from project conception to deployment for a data science model is 7 months
58% of data scientists report that they lack sufficient access to high-quality data
55% of data practitioners believe that explainability of AI models is critical for trust and compliance
51% of data scientists report that explaining model results to non-technical stakeholders remains a challenge
68% of organizations consider data privacy one of their top concerns when deploying AI solutions
57% of data science models deployed in production are updated or retrained every three months

Challenges, Skills, and Best Practices Interpretation

Despite spending nearly half their time cleaning data and navigating ethical and privacy concerns, data scientists often find their efforts hamstrung by vague goals and poor data quality, resulting in only 21% of projects delivering expected ROI after a lengthy seven-month journey.

Market Size and Growth

The global data science market was valued at approximately $37.9 billion in 2020 and is expected to reach $142.9 billion by 2028
Machine learning and deep learning are the fastest-growing data science subfields, with growth rates exceeding 40% annually
The use of natural language processing (NLP) has grown by over 35% in the past three years
44% of organizations plan to increase their data science budgets in 2024
The global market for AI in healthcare is expected to reach $45.2 billion by 2026
Data science training programs have grown by 250% globally over the past five years

Market Size and Growth Interpretation

As data science continues its exponential ascent—catapulting from a $37.9 billion industry to an anticipated $142.9 billion by 2028, fueled by rapid growth in machine learning, NLP, and healthcare AI—organizations worldwide are investing heavily, recognizing that in the age of information, the real wealth lies in data-driven insights and the talent to unlock them.

Technology Adoption and Tools

The percentage of enterprises adopting big data analytics increased from 17% in 2015 to 54% in 2023
Python is the preferred programming language for data scientists, used by 76% of practitioners
52% of organizations leverage cloud-based data science solutions
The adoption of automated machine learning (AutoML) solutions has increased by over 60% in the past two years
62% of organizations plan to adopt AI-driven automation in their workflows within the next two years
69% of data scientists prefer using open-source tools such as R and Python
33% of organizations use automated data insights, reducing analysis time by up to 75%
The adoption of blockchain technology for data security in data science workflows grew by 30% in 2023
Over 65% of data science job descriptions now require proficiency in SQL, Python, and machine learning
72% of organizations use data visualization tools like Tableau, Power BI, or Looker to communicate insights

Technology Adoption and Tools Interpretation

The rapid transformation of data science—from a niche skill to a strategic pillar—indicates that while Python and open-source tools dominate the toolkit, organizations are increasingly embracing automation, AI, and blockchain technology to turn data into decisive action faster and more securely than ever before.

Workforce and Employment Trends

Around 2.7 million data science jobs are projected globally by 2024
The median annual salary for a data scientist in the US is approximately $120,000
74% of data scientists work in the tech sector
37% of organizations report difficulty in hiring skilled data science talent
The demand for data engineers has increased by 50% in the last year
The average size of a data science team in a medium-to-large organization is 8 members
The data science job market is projected to grow by 28% from 2020 to 2030, much faster than the average for all occupations
29% of organizations have a dedicated data science center of excellence
83% of data professionals believe that ongoing education and reskilling are vital due to rapid technological change
The median time to fill a data science position is 62 days, indicating a competitive hiring landscape

Workforce and Employment Trends Interpretation

As data science continues its meteoric rise—with 2.7 million jobs projected and a median salary of $120,000—organizations scramble to hire skilled talent amidst fierce competition and rapid technological change, highlighting both its lucrative potential and the pressing need for ongoing education in this dynamic field.