Every certification DASCIN® offers carries no prerequisites. This reflects a deliberate approach to how DASCIN® structures learning. The entry point into any certification is determined by an individual’s own readiness, rather than by a formal exam or a mandatory prior credential. This affords considerable flexibility in how a certification path is built, though it also places more responsibility on the individual to choose a starting point that genuinely matches their experience. With sixteen certifications spanning Enterprise Big Data, AI, Data Governance, Data Management, Green IT, Service Automation and Robotic Process Automation, it is reasonable to ask where to begin.

To make that decision easier, this guide groups the certifications into three broad tiers based on prior experience: Foundation, Practitioner and Specialist. This grouping is a guide to readiness rather than a formal DASCIN® structure, and it is independent of the three certification frameworks, Enterprise Big Data Framework®, Service Automation Framework® and Green IT Framework®, that organise the certifications by subject area. What follows outlines what each tier assumes about existing experience, and how to select a starting point that reflects current readiness rather than an eventual goal.

The headline numbers on enterprise Artificial Intelligence in 2026 don’t tell a story about bad technology. They tell a story about bad foundations.

The Foundation tier exists for one purpose: to establish fluency in the language of data, privacy or sustainability before that knowledge is applied in practice. Professionals whose current role touches data, governance or environmental practice only indirectly — for example reviewing reports produced by others or approving decisions without owning the technical detail — should treat this as their starting point, regardless of seniority.

The three Foundation certifications are not sequential; they represent parallel entry points into different subject areas. Selection should be based on the gap most frequently encountered in daily work:

• Data Literacy Fundamentals (DLFU®): for professionals who need to read, question and use data confidently, without yet building or managing it directly. This is the broadest of the three and a reasonable default when uncertain which to choose.
• GDPR Awareness (GDPR®): for anyone handling personal data as part of their role, including marketing, HR, customer operations and procurement. It establishes the compliance vocabulary needed to identify risk before it becomes a problem.
• Green IT Awareness (GITA®): for professionals in operations, procurement or facilities who are being asked to factor sustainability into IT decisions and require the underlying concepts rather than the policy alone.

There is no restriction on completing more than one. Some professionals complete DLFU® and GDPR Awareness together because their role genuinely spans both. The primary risk at this tier is bypassing it entirely without sufficient confidence in the basics. The Practitioner tier assumes that confidence already exists, and entering without it makes the material more difficult than necessary.

The Practitioner tier is where a general foundation becomes a chosen specialisation. This point is typically reached in one of two ways: completion of a relevant Foundation certification, or existing working exposure to data, AI, governance, automation or sustainability practice gained through employment. Both paths are equally valid; the certification itself does not distinguish between them.

Seven certifications sit at this tier, and unlike the Foundation tier, they map closely onto job function. Current or intended role is the most useful deciding factor:

• Enterprise Big Data Professional (EBDP®): a broad, multidisciplinary option for anyone working with enterprise data generally, including analysts, project leads and product owners who require working knowledge across the data lifecycle rather than one narrow area.
• Data Governance Fundamentals (DGFU®): for professionals responsible for data quality, policy or stewardship, including those establishing or maintaining a governance framework internally.
• Data Management Fundamentals (DMFU®): for professionals managing data as an organisational asset across its lifecycle, including storage, quality and integration. A useful companion to DGFU® for those whose remit extends beyond governance into broader data management practice.
• AI Professional (AIPR®): for professionals working alongside AI systems or leading AI adoption within a business function, without necessarily building the underlying models.
• Green IT Foundation (GITFF®): for those progressing from GITA® into applying sustainable IT principles operationally, including procurement decisions, infrastructure choices and reporting.
• Service Automation Foundation (SAFF®): for professionals scoping or overseeing automation initiatives, including business analysts and process owners who work alongside automation teams rather than building solutions directly.
• Robotic Process Automation Foundation (RPAFF®): for anyone directly involved in identifying, designing or supporting RPA use cases, typically alongside a delivery or transformation team.

Holding more than one Practitioner tier certification is common, and often useful, particularly where roles overlap, such as governance and AI, or automation and RPA. There is no requirement to select only one.

The Specialist tier is where the framework moves from general knowledge to role-specific depth. These six certifications are intended for professionals who have either completed the corresponding Practitioner tier certification or bring equivalent practical experience in the domain, sufficient to move past foundational material directly into role-specific content.

• Enterprise Big Data Analyst (EBDA®): for professionals whose role centres on interpreting data and producing insight, including reporting, dashboards and analysis in support of decision making. A natural progression from EBDP®.
• Enterprise Big Data Engineer (EBDE®): for those building and maintaining the data infrastructure and pipelines that analysts and scientists rely on.
• Enterprise Big Data Science (EBDS®): for professionals applying statistical and machine learning methods to business problems, extending beyond general data literacy or AI awareness.
• Enterprise Big Data Architect (EBDAR®): for those designing overall data architecture, including how systems, storage and governance function together at scale.
• Service Automation Practitioner (SAFP®): for professionals delivering automation programmes on an ongoing basis, building on the scoping knowledge established in SAFF®.
• Robotic Process Automation Practitioner (RPAP®): for RPA developers and implementers who move beyond RPAFF’s foundational concepts into build and deployment work.

Because Specialist tier certifications are role-specific rather than sequential among themselves, most professionals hold exactly one certification: the one matching their actual or intended job title, rather than progressing through several.

For professionals still uncertain which tier to enter at, three questions tend to provide clarity:

• Do you build with data, or make use of what others produce? Someone in a role involving direct delivery, such as building pipelines, developing automations or running models, generally has sufficient context to move directly to the Practitioner or Specialist tier, provided the specific domain is familiar.
• Is exposure to this domain recent, or long-standing? If a role has changed recently, or if a professional is moving into data, governance, AI or automation from an adjacent field, beginning at the Foundation tier closes the vocabulary gap efficiently, even for those already experienced in their broader profession.
• Certifying for a specific role, or for broader capability? A generalist role involving oversight of multiple initiatives or reporting to leadership across functions tends to suit the Practitioner tier’s broader options, such as EBDP® or AIPR®, better than a Specialist tier certification tied to one specific job function.

None of these questions has an incorrect answer, and because DASCIN® certifications carry no prerequisites, an inaccurate initial choice costs only the time required to reassess. If a Practitioner tier certification proves more demanding than expected, that is useful information; it indicates the value of completing the corresponding Foundation tier certification first, rather than a sign of being in the wrong field.

Begin at the Foundation tier if the subject matter is genuinely new; there is no advantage to skipping ahead, and each certification is designed to prepare for the next rather than test what is already known. Move to the Practitioner tier once confident with the fundamentals, whether from a Foundation tier certification or direct experience, since this is the point where general interest becomes a chosen focus. Progress to the Specialist tier once the specific job role being certified for is clear, as this tier is built around what a role actually requires.

Because no prerequisites are enforced anywhere in the framework, the only meaningful risk is selecting a tier that does not match current readiness, and revisiting that choice is a straightforward, low-cost adjustment rather than a setback. It is also worth noting that DASCIN® certifications carry no expiry date and, unlike many providers, do not require periodic resitting, so the effort invested at any tier holds its value over time.