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QUESTION & ANSWER

Enterprise AI Data Governance First Step

The first step is not to aggregate all enterprise data, nor to purchase data platforms first, but to select an AI task for preparation of the operation. It is to identify who uses, what enters, how the results are checked, how the error consequences and the manual bottom-ups are, and then to list the required business objects, documents, fields, systems, authority and responsibilities. The first issue is to manage only the data and knowledge that this task chain relies on, and to validate the governance effects with a fixed task set.

Answer the question.

First, give conclusions that can be used for decision-making

The scope of AI data governance must be determined by operational tasks. Using contractual questions and answers, for example, governance contract documents, client projects, templates systems, versions, authority and validity periods are required; for the sale of Copilot, for example, customer, contact person, business, product, price policy and historical communication is required. Both scenarios are called “enterprise AI”, relying on data objects and error risks.

DECISION FACTORS

What conditions need to be identified before judgement is made?

The same question may have different answers under different business, data and project phases. It is suggested that the following conditions be checked and that the common findings on the web be incorporated into their own projects.

Is there a clear user and business results for the first AI missions?What structured data, documentation knowledge and multi-modular information are neededWhich system and post is responsible for maintaining the dataWhat are the operational consequences of errors, expiry and excess data
ACTION STEPS

Suggested order of advance

01

First, we'll be clear about the target and the border.

Select an AI task that is of high value and can be manually plowed.

02

Validation Key Dependence

Mapping tasks, data, knowledge, systems, authority and accountability relationships.

03

Development of assessable outcomes

Establish small-scale treatment of current lines and fixed assessment collections.

04

Make sure you decide the next step with the real results.

Extensions are determined by the quality of the tasks and operational evidence.

PRACTICAL EXAMPLE

How do you understand it in the actual business?

Example used to illustrate the method of judgement

The team does not import all the shared disks first, but rather selects the high frequency failure of the last three months, checking the product model, manual version, service announcement and worksheet results, designating the product and the person responsible for the sale, and testing the true questions for quotations, no answers and excesses. More product lines are expanded after the first phase is passed.

COMMON RISKS

The easiest pit to step on.

Replace operational mandate with the number of documents and tables

No source, version and responsible person after data import

Only check empty duplicates, do not verify AI tasks and permissions

ACCEPTANCE

How should we end up receiving and confirming?

The first issue is the scope of the mandate, the list of data knowledge, the primary accountability system, the rules of authority, quality questions, assessment and updating mechanisms.

When preparing to communicate with suppliers or internal teams, it is recommended that current processes, representative samples, existing systems, planning time and budget levels be brought. First, the unknown items are clearly marked, and then the decision is made to use diagnostics, PoC, fixed-range projects or ongoing research and development, which is usually more reliable than a direct demand for a price and duration without borders.

Your project conditions are different from the examples above?

Operational objectives, existing systems, sample and planned time could be collated before consultants could make preliminary judgements in relation to actual boundaries.

Associate project consultants