The IAF is a complete lifecycle structure for successful analytics work. Its seven domains help individuals, teams, and organizations frame the right business or mission questions, coordinate work across stakeholders, select and use appropriate methods, deploy solutions effectively, and sustain value over time.
The seven domains are connected and iterative, not sequential. Work in a later domain often reveals the need to revisit an earlier one. Click any segment to learn what happens in that part of the lifecycle.
Click any segment to explore that domain
Select a domain on the wheel to see its description, key tasks, and who does this work.
Each domain represents a distinct phase of analytics work. Together they form a connected, iterative system. Uncovering issues in a later domain often requires circling back to refine an earlier one.
This is not a one-way process. Work often loops back.
Free download with your email address. Includes all seven domains with detailed task guidance.
Any application of analytics must begin with a clear, concise statement describing the business problem, question, or opportunity, not a technical one. The goal is to ensure that analytics is being applied to solve the right challenge for the organization. Not every analytics initiative is sparked by a problem to fix or a question to answer, and many begin with an opportunity to explore. This domain is where many analytics initiatives fail before they begin: teams that skip problem framing often invest significant resources solving the wrong challenge with great precision. It is also the first structured communication checkpoint of the IAF, where stakeholders reach explicit agreement on the scope and direction before any analytics work begins.
"Who does this work: Management analysts, strategy consultants, operations research analysts, VP and Director of Analytics, Chief Data Officers
"
This domain translates the agreed-upon business problem, question, or opportunity into an actionable analytical structure. The work happens at a business level, not a technical one: defining constraints, decisions, and objectives without writing any mathematical formulas or developing any software. A key task here is identifying what type of analytics will address the challenge: diagnostic (why did something happen), descriptive (what is happening), predictive (what is likely to happen), or prescriptive (what should we do). Getting this translation right is what separates analytics efforts that deliver value from those that produce technically impressive but organizationally irrelevant outputs. Like Domain I, this domain closes with a structured communication checkpoint: stakeholder agreement on the analytics approach before any data work or modeling begins.
"Who does this work: Operations research analysts, data scientists, analytics consultants, quantitative analysts
"
Once the business and analytics problem are framed, disciplined data work becomes essential because data quality issues can invalidate even the best models. Data preparation is the most time-consuming domain and frequently where most of the practical effort in applying analytics is spent. This domain covers everything from understanding what data is needed to acquiring, cleaning, and documenting it in a way the entire team can rely on.
"Who does this work: Data engineers, statisticians, market research analysts, data scientists, computer systems analysts
"
With the problem defined and the data understood, the next step is selecting the appropriate analytics methodology. This domain is where the IAF's method-agnostic character matters most: the right method is determined by the problem, the data, the team's capabilities, and the deployment environment, not by habit or tool availability. This is also where the solution architecture is defined, before a line of code is written.
"Who does this work: Operations research analysts, data scientists, industrial engineers, computer systems analysts
"
Although technically the core of analytics work, model development is often only 10 to 15 percent of the total time spent on an analytics initiative when applied within a full lifecycle approach. The IAF emphasizes that building the model is not enough: the model must be validated, its performance documented, and trust built with the people who will ultimately use its outputs to inform decisions. Communication remains central here: user interfaces that let business users evaluate results and provide feedback are not optional extras but core deliverables of this domain.
"Who does this work: Data scientists, statisticians, operations research analysts, financial analysts, machine learning engineers
"
Deployment is a two-pronged effort: technical implementation and organizational adoption. Many analytics initiatives that are technically successful fail at this stage because adoption is treated as an afterthought rather than a core part of the work. The IAF recognizes that a model nobody uses delivers no value regardless of its technical sophistication. Deployment is where analytics connects to the business decisions it was built to support, and where structured communication with stakeholders, including training, validation reports, and explicit sign-off, is what converts a technical deliverable into an organizational capability.
"Who does this work: Logisticians, computer systems analysts, operations managers, analytics practitioners in implementation roles
"
A successful analytics solution requires perpetual care to continue delivering value. This domain is where the IAF most clearly differs from frameworks that end at deployment. Models drift as business conditions, data streams, and external factors change. Organizations that do not actively manage their analytics solutions after go-live will find those solutions degrading in relevance and accuracy, often without realizing it until significant damage has been done.
"Who does this work: Financial managers, operations leaders, analytics directors, anyone accountable for sustained business value from analytics investments
"
Download the complete framework document, free with your email address.
The IAF was developed through a formal Job Task Analysis, informed by expert input from analytics and operations research professionals, and serves as the foundation for CAP exam blueprints. It reflects how analytics work actually happens across industry, government, and academia.
INFORMS is the world's largest professional association for operations research and analytics. The IAF and CAP certification are grounded in a community of more than 10,000 members across industry, government, and academia.
Access publications, networking, discounts on conferences and certification, and a professional community that takes analytics work seriously.
Three tiers: Essentials, Pro, and Expert. Each maps directly to the seven IAF domains. Recognized by employers across Fortune 100 companies and beyond.
The INFORMS Analytics+ Conference and the Annual Meeting bring together thousands of analytics and O.R. professionals each year to share research, practice, and tools.
Analytics magazine and O.R./MS Today cover the intersection of research and practice. Academic journals advance the science behind the work. All relevant to the IAF lifecycle.
Download the full IAF document with detailed domain descriptions, key tasks, and practitioner guidance. Free with your email address.
Your information is handled in accordance with the INFORMS Privacy Policy. We will follow up with resources relevant to your analytics practice.
Ready to validate your analytics competence against the IAF? The Certified Analytics Professional (CAP®) certification is built on this framework.