E020 Data and AI Strategy carries banner number ITEC 6410 and is worth 2 competency units. It places data science and artificial intelligence inside a technology leader's role, asking you to analyse existing technology strategies and how data is actually used in an organization. E020 and ITEC 6410 are one requirement. It is a short course with a demanding standard, because strategy in this area is mostly about telling apart what is possible from what is being sold.
Data maturity decides what is possible
The most useful diagnostic in this course is asking what an organization can actually do with its data today. Many cannot answer basic questions consistently because the same measure is calculated differently by three departments. An organization in that position cannot deploy predictive models usefully, not because the models are hard but because there is no agreed version of the truth to train on or to act upon. Recommending advanced analytics to an organization that cannot reconcile its own reporting is the classic error this course exists to prevent.
That leads to the sequencing insight worth carrying: definitions and ownership come before pipelines, pipelines before analytics, analytics before prediction, and prediction before automation of decisions. Each stage depends on the one before it, and skipping a stage produces impressive demonstrations that never reach production.
Value framing is the second discipline. A data or AI initiative is worth doing when a specific decision would be made better, more often, or faster, and someone can say what that is worth. Initiatives justified by capability rather than decision are how organizations accumulate dashboards nobody opens and models nobody uses.
Governance and ethics belong in the strategy rather than beside it. Who owns a data definition, who may access what, how automated decisions are explained to the people they affect, and what recourse exists when the system is wrong are strategic questions with legal and reputational weight. A technology leader who treats them as someone else's problem has misjudged the role.
Outcomes are logged as Competent or Not Competent, without letter grades, and WGU keeps no ordinary grade point average, with 2 competency units describing its share of a flat-priced six month term. Two competency units inside a flat-priced six month term makes this a course worth closing early alongside heavier work.
Turning a compact rubric into a strategy
If your version of E020 uses a performance assessment, expect a small aspect count with high expectations per aspect. WGU requires a score of 2 in each aspect for a task to pass and judges each aspect alone, so on a four or five aspect rubric a single thin answer represents a large share of your risk.
Budget carefully. Take a rubric with four scored aspects and a target near 1,400 words. Reserve 120 words for the organization and its current data position, and 90 for the close, leaving 1,190 across four aspects, or roughly 297 each. Weight by demand: an aspect requiring analysis of the existing strategy takes 380, an aspect requiring recommendations takes 360, and two remaining aspects covering governance and measurement take 225 each. That totals 1,190.
Anchor every recommendation to a named decision. "Improve customer analytics" is a capability. "Give the retention team a weekly list of accounts likely to lapse so they can call them, replacing the current quarterly report" is a decision changed, with an owner and a frequency, and it can be evaluated.
Reserve budget for what the organization is not ready for. Naming the initiative you would defer until the data foundations are in place, and why, is frequently the strongest paragraph available in a short document.
Shape for a data and AI strategy
E020 deliverables usually assess an organization's data position and set direction. These proportions fit a compact document.
| Section | Content | Share |
|---|---|---|
| Organization and decisions | What the business does and the decisions that data could improve, named specifically. | 15 percent |
| Current data position | What data exists, who owns definitions, and whether the organization can answer basic questions consistently. | 19 percent |
| Existing strategy analysis | What the current technology strategy says about data and where it does not match practice. | 17 percent |
| Recommendations | Sequenced initiatives, each tied to a decision, with what has to exist first. | 21 percent |
| Governance and ethics | Definition ownership, access, explainability of automated decisions and recourse. | 17 percent |
| Measurement | How you would know the strategy is working, and what would tell you to stop something. | 11 percent |
Sourcing in a field with more marketing than evidence
Capability claims about tools and services belong to primary documentation, dated, because this field's practical facts change within months. Claims about what organizations achieve with data and AI are empirical and belong to research or survey data with a stated method and year, treated sceptically because reported success in this area is heavily selected.
Vendor material is the dominant source in this subject and needs handling accordingly. A provider describing what their platform enables is a capability claim you can use; the same provider describing the business results customers achieved is marketing. Separating those two uses of the same document is a distinction worth making explicitly.
Where you discuss data governance or automated decision-making, published guidance and regulation exist and should be cited directly rather than paraphrased, since obligations around explanation, fairness and personal data are specific and consequential.
Your own arithmetic remains the most persuasive evidence available. If a decision is made four hundred times a month and each improvement is worth a stated amount, the value of a proposed capability follows from the scenario's own numbers, and that calculation is worth more than any external claim.
Follow the citation style named in your task and place each reference beside the claim it supports.
Competent strategy and returned work
Competent submissions assess data maturity honestly, tie every recommendation to a named decision, sequence initiatives by prerequisite, and treat governance and ethics as part of the strategy rather than as an appendix.
Returns follow four shapes. Recommendations assume capabilities the organization has no foundation for. Value is expressed as capability rather than as a decision improved. Governance is a closing paragraph. Or the document repeats industry claims about transformation without testing any of them against this organization.
Build versus buy versus partner is a decision this course expects you to reason about rather than assume. Most organizations should not be building analytics platforms, and many should not be hiring specialist teams either, because the capability is available as a service and the scarce resource is people who understand the business well enough to ask the right questions. Saying which parts of the capability the organization should own, and which it should rent, is a strategic judgement with real cost consequences.
Data quality deserves an honest paragraph as well. Every analytics initiative eventually discovers that the data is worse than anyone believed, and projects that did not budget time for that discovery lose months to it silently. Naming who is responsible for quality, how it will be measured, and what the plan does when a source turns out to be unreliable is an unglamorous section that makes the rest of the strategy plausible.
A quick test: for each recommendation, name the person whose work changes and what they will do differently. If nobody's work changes, the initiative produces a capability and not a benefit, which the aspects will notice.
Revision and resubmission carry no grade consequence at WGU, so send it in the moment each aspect is genuinely addressed, and let the evaluator find the last gap faster than you would. If your section also carries an objective assessment, WGU objective assessments are proctored and our boundary is fixed: preparation only, with strategy frameworks, practice questions and a candid read on your preassessment result. Nobody here sits an assessment, are absent for the whole of it, and we would refuse portal credentials if they were offered.
Recommending AI to an organization that cannot reconcile a report?
Send the E020 rubric and scenario. We assess data maturity, sequence by prerequisite and tie every initiative to a decision, with word targets.
Eight mistakes that cost time in E020
- Skipping data maturity. An organization that cannot agree a measure cannot use a model built on it.
- Capability instead of decision. Name the decision that improves, who makes it and how often.
- Ignoring sequence. Definitions, pipelines, analytics, prediction, automation. Each depends on the one before.
- Vendor results as evidence. Capability claims are usable; reported business outcomes from the same source are marketing.
- Governance as an appendix. Definition ownership and access rules are what make the rest possible.
- Ethics without recourse. If an automated decision affects a person, say how it is explained and how it can be challenged.
- Undated sources. Practical facts in this field expire quickly. Date everything.
- No stop condition. Say what would tell you an initiative is not working and should be ended.
Three questions students ask about E020
Do I need data science skills?
How current do my sources need to be?
Is 2 CUs realistic for a subject this broad?
Where E020 sits in WGU's programs
The July 2026 catalog places this code in 1 current WGU program. Open a program page for the complete standard path and term positions. The live Degree Plan remains authoritative after transfer credit, substitutions, and mentor planning.
The assessments, one by one
The public catalog does not publish this course's PA/OA identity or task count. WGU Tutors publishes at most one PA manual per course and only from a WGU-controlled public rubric. Until that source exists, PA help begins from the student's real Course of Study and OA support remains preparation only.