In 2027, “we offer data training” will no longer be enough. Organizations will need to show which people hold which data and AI skills, to a recognized standard, and a corporate data certification strategy is the most practical way to do that.
Three forces converge next year. The EU AI Act’s high-risk obligations for systems used in areas such as employment, credit, and education apply from December 2, 2027. AI and big data are the fastest-growing skills employers expect to need. And certification has a growing evidence base: in Skillsoft’s latest IT Skills and Salary research, 97% of IT decision-makers said certified staff add value to their organizations (Skillsoft, via Global Knowledge).
This article explains what a corporate data certification strategy is, why 2027 is the year to put one in place, and how to build one step by step.
What a corporate data certification strategy is
A corporate data certification strategy is a deliberate plan that decides which data and AI credentials each role should hold, how people will earn them, and how the organization will track and use the results. It treats certification as part of workforce planning, not as a personal perk.
Most organizations already spend on data training. The difference lies in structure and proof:
| Ad hoc training | Certification strategy | |
|---|---|---|
| Who decides | Individuals or managers, case by case | Leadership, tied to business goals and roles |
| Standard | Varies by course and provider | A shared, externally assessed framework |
| Evidence | Attendance and completion | Independent exam results and verifiable credentials |
| Coverage | Patchy, often limited to data teams | Mapped across every role that touches data |
| Value to the organization | Hard to measure | Visible skills inventory, audit trail, and benchmarks |
A good strategy has five parts: a skills framework for each role, a chosen set of certifications mapped to that framework, a funded path to earn them, a record of who holds what, and a review cycle as roles and technology change.
Why 2027 is the year to act
Regulation, AI adoption, and skills demand all reach a turning point in 2027, and certifications take months to plan, fund, and earn. Organizations that start now will have certified people in place when the pressure arrives.
1. The EU AI Act’s high-risk rules arrive
The Digital Omnibus on AI (Regulation (EU) 2026/1744), in force since July 27, 2026, moved the high-risk deadlines but did not remove them. Obligations for stand-alone high-risk systems, including AI used in employment decisions, education, credit, and biometrics, now apply from December 2, 2027 (Cooley).
These obligations depend on people. Deployers of high-risk systems must provide human oversight, and the staff who carry it out need the training and support to do so (Travers Smith). The Omnibus also reworded the general AI literacy duty in Article 4, shifting emphasis to the Commission and member states supporting organizations, though Cooley notes that the substance of the underlying obligation remains (Cooley). The AI Act also reaches organizations outside the EU whose AI is placed on the EU market or whose AI outputs are used there.
2. AI is moving from experiment to operations
In Pearson VUE’s 2025 survey of nearly 24,000 IT professionals, 69% said their employer had started or increased investment in AI. The number planning to earn an AI or machine learning certification more than doubled in two years (Pearson VUE). Yet only 39% of employees who use AI at work had received AI training from their company (Microsoft and LinkedIn).
3. Data and AI skills are the fastest-moving target
The World Economic Forum expects AI and big data to be the fastest-growing skills to 2030, and 39% of workers’ existing skills to change or become outdated over the same period. Employers rank skill gaps as the biggest barrier to transformation, and 85% plan to prioritize upskilling (World Economic Forum). When skills change this fast, a periodic, externally assessed check on what people know becomes more valuable, not less.
The Business Case: What Certification Delivers
The evidence points the same way across the largest industry surveys: certified staff report higher quality, productivity, and innovation, and decision-makers see measurable value.
79%
Certified professionals who saw the quality of their work improve
Certification also supports retention. LinkedIn found that 88% of organizations are concerned about retention, and that providing learning opportunities is their number one retention strategy (LinkedIn Learning). A funded certification path is one of the most visible forms that learning can take.
A fair caveat. These figures are self-reported, and the surveys come from organizations that run exams and training. They show strong perceived value, not controlled proof of causation. That is one more reason to measure your own results, as described below.
What a strategy adds beyond individual certificates
How to build the strategy, step by step
A workable strategy can be designed in a quarter and rolled out over a year. These eight steps take you from audit to measurable results.
- 1
Map your data and AI exposure
List where the organization uses data and AI, which systems could fall under the EU AI Act’s high-risk categories, and which roles operate or oversee them.
- 2
Define role profiles
For each role family, describe the data and AI skills it needs, from basic literacy for everyone to deep technical skills for specialists. Aligning these to a common framework such as SFIA makes them easier to link to job levels.
- 3
Assess the baseline
Use self-assessments, manager input, and existing credentials to find the gap between the skills people have and the skills their roles need.
- 4
Choose the certifications
Select a small, coherent set that covers every role profile. Favor a stackable scheme so people can progress from foundation to advanced levels.
- 5
Fund and schedule
Set a budget, decide between accredited training and supported self-study, and give people protected time to prepare.
- 6
Deliver through your learning portal
Put each certification path on the portal, assign it by role, and track enrollment, exam attempts, and results in one place.
- 7
Recognize and reward
Link certifications to career paths, internal mobility, and promotion criteria. Celebrate milestones publicly.
- 8
Measure and refresh
Review pass rates, skills coverage, and business outcomes at least yearly, and update role profiles as technology and regulation change.
Timing for 2027
If your organization deploys AI in areas that may be classed as high-risk, such as hiring, education, or credit decisions, aim to have the people who oversee those systems trained and certified well before December 2, 2027. Working back from that date, steps 1 to 5 belong in the first half of 2027.
Choosing certifications: vendor-neutral or vendor-specific?
Most organizations need both, but in different roles. Vendor-neutral certifications build the shared foundation; vendor-specific ones prove skill on the exact platforms you run.
| Vendor-neutral | Vendor-specific | |
|---|---|---|
| What it proves | Understanding of principles, methods, and good practice that apply across tools | Hands-on skill with one provider’s products |
| Best for | Foundations for all staff, analysts, managers, architects, governance roles | Engineers and administrators working on a particular cloud or analytics platform |
| Portability | Stays relevant if you change platforms | Tied to the vendor’s product roadmap |
| Typical refresh | Often longer-lived | Often renewed as products change |
Questions to ask of any certification
- Who accredits it? Look for independent examination and accreditation, not a course provider marking its own work.
- Is it stackable? Can people progress from foundation to advanced levels in one scheme?
- Does it map to a skills framework? Mapping to frameworks such as SFIA helps HR connect credentials to roles and grades.
- How is it delivered? Check for accredited training providers, self-study options, and online proctored exams for distributed teams.
- How long is it valid? Understand renewal rules before you budget.
A DASCIN Certification Pathway by Role
DASCIN’s Enterprise Big Data Framework (EBDF) certifications answer the checklist above: they are vendor-neutral, stackable, independently examined and accredited by APMG International, and mapped to the SFIA skills framework (APMG International).
| Role group | Recommended DASCIN certification | Why it fits |
|---|---|---|
| All employees | Data Literacy Fundamentals (DLFU) | A common foundation in managing, analyzing, visualizing, and communicating data |
| Anyone handling personal data | GDPR Awareness | Data protection principles, data subject rights, breach and consent management |
| Managers, project leads, business and IT professionals | Enterprise Big Data Professional (EBDP) | Big data concepts, roles, architectures, and how data connects to machine learning and AI |
| Analysts and data scientists | Enterprise Big Data Analyst (EBDA) | Statistical and machine learning models applied to business problems, assessed through a case study |
| Data engineers, architects, database administrators | Enterprise Big Data Engineer (EBDE) | Data storage, pipelines, governance, and big data technologies |
APMG lists further DASCIN credentials as coming soon, including AI Awareness, AI Fundamentals, Data Governance Fundamentals, Enterprise Big Data Scientist, and Enterprise Big Data Architect. Those will extend the pathway into AI literacy and governance, the areas the EU AI Act puts in focus.
Practical advantages for a corporate program
- No expiry. The Enterprise Big Data certifications do not expire, which keeps renewal costs predictable.
- Flexible delivery. Teams can train with an accredited training organization or self-study and take online proctored exams around the clock.
- Verifiable proof. Each successful candidate receives a digital badge that HR and clients can check.
Source: APMG International.
References
- APMG International. DASCIN Enterprise Big Data Framework Certification
- Cooley. Digital AI Omnibus Delays Key Deadlines, Introduces New Rules (August 3, 2026)
- LinkedIn Learning. Workplace Learning Report 2025
- Microsoft and LinkedIn. 2024 Work Trend Index: AI at Work Is Here (May 8, 2024)
- Pearson VUE. 2025 Value of IT Certification Candidate Report: Highlights (survey of nearly 24,000 IT professionals)
- Skillsoft. IT Skills and Salary Report, Volume 19, 2024 to 2025 (via Global Knowledge)
- Travers Smith. The EU AI Act’s AI Literacy Requirement: Key Considerations (July 29, 2025)
- World Economic Forum. The Future of Jobs Report 2025 (January 7, 2025)
