E020: Data and AI Strategy

E020 is a two-unit graduate course in WGU's Master of Science, Information Technology. It sits in the standard program, and WGU lists no prerequisites.

The guidebook says the course familiarizes students with data science and artificial intelligence within a technology leader's role. Students analyze two technology strategies, one for data and one for AI. They are not asked to develop their own strategies or set up any data or AI technologies.

That boundary matters. The task is critique and recommendation, so strong answers show you can read a strategy, find where it falls short and suggest realistic improvements, rather than showing you can build a model.

What E020 Covers

The course description names these skill areas: data management needs, how data is used in an organization, data collection, AI organizational strategies and alignment, how organizations evaluate AI, and how AI affects technologies within business operations.

Key Concepts Explained

Data Strategy

A data strategy explains what data the organization needs, where it comes from, who owns it, how quality is kept up and how it supports decisions. Weaknesses often lie in unclear ownership, inconsistent definitions or collecting data nobody uses.

AI Strategy and Alignment

An AI strategy should start from business problems. Alignment means each AI initiative connects to a goal, has data to support it and has a way to judge whether it works.

Evaluating AI

Organizations judge AI by accuracy, cost, fairness, user acceptance and effect on the process it supports. A strategy without evaluation criteria is a warning sign.

Typical Assignments and How to Approach Them

A Critique Checklist

Use the same questions on both strategies so your two analyses feel parallel. For each, record what the strategy says, what it leaves out and what you would change. The checklist below helps keep recommendations specific.

A Study Approach for Each Competency

Start with the data strategy, because it is usually the foundation for the AI strategy. Read it with a simple question in mind: if I were running this organization, would I trust the data to make decisions? Note the places where the answer is no, and turn them into recommendations.

Then read the AI strategy and test each initiative against three points: the business problem it addresses, the data it needs and the way success will be judged. Where any of the three is missing, you have found an improvement to recommend. Finish by checking that your two sets of recommendations do not contradict each other.

Weak and Strong Recommendations

The difference between an average and a strong answer is usually specificity. Compare how the same finding can be written.

Where Students Get Stuck

• Building instead of critiquing. The guidebook says no strategies or technology are to be developed; recommend improvements instead.
• Generic advice. Say which part of the given strategy needs to change and why.
• Treating AI as separate from data. Many AI weaknesses start as data weaknesses.
• No measure of success. Each recommendation should come with a way to tell if it worked.

Study Tips for E020

• Learn the vocabulary of data quality and governance well enough to use it precisely.
• Read each strategy twice: once for what it says, once for what it omits.
• Rank your recommendations and explain the order.
• Check the rubric against your headings before submitting.

How We Help with E020

Send the strategies, task instructions and rubric. A writer can prepare a model critique, explain data and AI governance in plain language, or review your draft for gaps. The work is study support; submit your own work under WGU's academic integrity policy.

GradeEssays is independent of Western Governors University. Orders are written from scratch, include free revisions within the scope of your original request, and are refunded in full if late. See also our business paper help guide.