Business analytics is becoming a measurable part of how organizations operate, but adoption varies substantially by company size, technology, industry, and country. In 2025, 39.85% of EU enterprises used data analytics either with their own employees or an external provider, while 19.95% used at least one listed AI technology. In the United States, operations research analysts and data scientists represented established and expanding analytics occupations, with different pay levels and outlooks.
Table of contents
- Business analytics infrastructure in the EU
- ERP, CRM, and business intelligence adoption
- How EU enterprises use data analytics
- AI-enabled analytics and decision support
- The U.S. analytics workforce
- Analytics pay and industry differences
Business analytics infrastructure in the EU
The technology base for analytics is widespread across EU enterprises, although high-capacity connectivity and cloud use are less universal. Eurostat reported that 95.01% of EU enterprises with at least 10 employees or self-employed persons used a fixed broadband connection in 2025. However, only 17.29% used a fixed internet connection with a speed of at least 1 Gb/s.
Company size is strongly associated with access to faster connectivity. In 2025, 40.69% of large EU enterprises used fixed internet connections of at least 1 Gb/s, compared with 15.32% of small enterprises. That gap matters for analytics workloads that depend on transferring large datasets, using cloud platforms, or connecting distributed teams.
Web presence was more common than high-speed connectivity. A total of 79.01% of EU enterprises had a website in 2025. The share was 95.65% among large enterprises and 76.66% among small enterprises. Paid cloud-computing services were used by 52.74% of EU enterprises.
These figures describe enabling conditions rather than analytics performance. A broadband connection, website, or cloud subscription does not by itself show that an organization has reliable data governance or makes decisions from analysis. They do show that the basic digital environment is present for a substantial share of businesses, with the clearest size-related difference appearing in connectivity capacity.
Source: Eurostat, Digital economy and society statistics - enterprises, 2025.
ERP, CRM, and business intelligence adoption
Enterprise software provides another view of analytics readiness. In 2025, 46.45% of EU enterprises used Enterprise Resource Planning (ERP) software, 28.51% used Customer Relationship Management (CRM) software, and 16.28% used Business Intelligence (BI) software. Eurostat reported that 53.47% used at least one of ERP, CRM, or BI software.
The size gap is especially pronounced for BI. Only 11.45% of small EU enterprises used BI software in 2025, compared with 69.24% of large enterprises. ERP adoption was 41.08% among small enterprises and 88.71% among large enterprises. CRM adoption was 24.69% among small enterprises and 65.43% among large enterprises.
| Software measure, 2025 | Small EU enterprises | Large EU enterprises | All EU enterprises |
|---|---|---|---|
| ERP software | 41.08% | 88.71% | 46.45% |
| CRM software | 24.69% | 65.43% | 28.51% |
| BI software | 11.45% | 69.24% | 16.28% |
| At least one of ERP, CRM, or BI | — | — | 53.47% |
Industry also changes the picture. BI software was used by 41.08% of EU enterprises in information and communication in 2025, while the share in construction was 6.77%. The difference suggests that business analytics adoption is not only a question of enterprise size. The availability of data, the complexity of operations, and the way work is organized can also influence whether BI tools are used.
The software figures should be read as adoption rates, not as measures of active usage, analytical maturity, or return on investment. They indicate whether the enterprise reported using a category of system during the measurement period.
Source: Eurostat, E-business integration, 2025.
How EU enterprises use data analytics
In 2025, 33.02% of EU enterprises performed data analytics using their own employees. The proportion rose to 78.84% among large enterprises and was 27.86% among small enterprises. This is a large difference in internal capability: analytics performed by employees was reported by nearly four out of five large enterprises, but fewer than three out of ten small enterprises.
External support added another route. Data analytics was performed by an external enterprise or organization for 13.85% of EU enterprises. Combining internal and external routes, 39.85% performed data analytics either with their own employees or an external provider. The combined figure is not a measure of how often analysis occurred or how many projects were completed; it identifies enterprises reporting either form of activity.
The most common listed data source was transaction information. In 2025, 26.20% of EU enterprises performed analytics on transaction records such as sales or payment records. Customer data was used by 17.62%, while social-media data was used by 11.44%. More specialized sources were less common: 3.58% performed analytics on satellite data.
The country comparison in the available Eurostat figures also shows variation within the EU. In Denmark, 59.99% of enterprises performed data analytics either with their own employees or an external provider in 2025. That is a country-level observation, not an EU-wide average and not a direct explanation of why adoption differed.
Taken together, the data points to a layered analytics market. Some enterprises have internal staff, some rely on external organizations, and some use both. Transaction records lead the listed sources, while customer, social-media, and satellite data represent progressively narrower or more specialized use cases.
Source: Eurostat, Digital economy and society statistics - enterprises, 2025.
AI-enabled analytics and decision support
AI adoption was lower than basic digital connectivity but high enough to be a significant business analytics indicator. In 2025, 19.95% of EU enterprises with more than 10 employees or self-employed persons used at least one listed AI technology. The rate was 55.03% among large enterprises and 17% among small enterprises.
The specific applications show that language-related uses were prominent. AI technologies for analysis of written language were used by 11.75% of EU enterprises. A further 9.55% used AI to generate pictures, videos, sound, or audio, and 8.76% used AI to generate written or spoken language or programming code.
Country-level adoption varied considerably in the reported figures. AI technologies were used by 42.03% of enterprises in Denmark and 5.21% of enterprises in Romania in 2025. These percentages are national observations and should not be treated as a causal comparison without additional information about industry mix, company size, definitions, or implementation conditions.
AI adoption is also not identical to AI-powered decision support. The statistics identify use of at least one listed technology and selected application types. They do not establish whether AI outputs were used for forecasting, planning, pricing, resource allocation, or executive decisions. For business analytics readers, the distinction is important: adoption indicates exposure to a technology, while decision impact requires separate evidence.
Source: Eurostat, Digital economy and society statistics - enterprises, 2025.
The U.S. analytics workforce
U.S. labor-market statistics provide a different measure of the business analytics field: the number of people employed in analytics-related occupations and the expected direction of employment. Operations research analysts held about 112,100 jobs in 2024. Employment in that occupation is projected to reach 136,200 jobs by 2034, an increase of 24,100 jobs.
The U.S. Bureau of Labor Statistics projects 21% growth in operations research analyst employment from 2024 to 2034. About 9,600 openings are projected each year on average during that period. Annual openings include opportunities created by employment growth and other labor-market needs; they are not the same as a net increase in total jobs.
Data scientists represented a larger U.S. occupation in the supplied BLS figures. About 275,600 data scientists held jobs in 2025. Employment is projected to reach 371,000 jobs by 2035, an increase of 95,400 jobs. The projected growth rate is 35% from 2025 to 2035.
| U.S. occupation | Employment base | Forecast employment | Forecast period | Projected growth |
|---|---|---|---|---|
| Operations research analysts | 112,100 jobs in 2024 | 136,200 jobs in 2034 | 2024–2034 | 21% |
| Data scientists | 275,600 jobs in 2025 | 371,000 jobs in 2035 | 2025–2035 | 35% |
The two series use different base years and forecast periods, so their job counts and growth rates should not be read as a single standardized ranking. They do show two distinct workforce signals: operations research is a smaller occupation with a strong projected expansion, while data science has a larger employment base and a higher projected growth rate in the cited forecasts.
Sources: BLS, Operations Research Analysts: Occupational Outlook Handbook, 2024 base and 2024–2034 forecast; BLS, Data Scientists: Occupational Outlook Handbook, 2025 base and 2025–2035 forecast.
Analytics pay and industry differences
U.S. wage statistics show that compensation varies by occupation and industry. The median annual wage for operations research analysts was $91,290 in May 2024. Among the listed industries, the median was highest in the federal government at $136,700. Manufacturing followed at $107,360, while professional, scientific, and technical services recorded $99,600.
The median annual wage was $96,310 in management of companies and enterprises and $82,790 in finance and insurance. These industry medians describe operations research analysts, not every analytics worker or every employee who uses analytical tools.
Data scientists had a U.S. median annual wage of $120,230 in May 2025. The lowest-paid 10% earned less than $67,240 annually, while the highest-paid 10% earned more than $199,130. Those percentile figures describe the distribution of reported wages and should not be interpreted as guaranteed entry-level or senior compensation.
Among the listed industries for data scientists, the May 2025 median annual wage was $142,240 in publishing, broadcasting, and content providers; $132,380 in computer systems design and related services; and $128,050 in management of companies and enterprises.
The pay figures are measured at different times: May 2024 for operations research analysts and May 2025 for data scientists. Direct comparisons should therefore be treated as period-specific rather than as a precise current wage gap. They nevertheless show why both occupation and industry are necessary when interpreting business analytics compensation statistics.
Sources: BLS, Operations Research Analysts: Occupational Outlook Handbook, May 2024; BLS, Data Scientists: Occupational Outlook Handbook, May 2025.