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QUESTION & ANSWER

AI Business Analysis Data Consistency

The large model cannot be allowed to speculate directly about the indicators or generate SQLs at will. Enterprises should define the calibration of the indicators and data rights, such as income, customers, orders, profits, etc., and then use the controlled semantic layers, search templates, white lists and results to verify the data generated. Answers should show time frames, filter conditions, calibres and sources, and allow users to drill.

Answer the question.

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

The credibility of AI's business analysis is largely derived from data and query governance rather than model language skills. Main data and indicator calibrations determine what “client” “orders” “income” refer to, respectively; semantics map business words to controlled fields, dimensions and calculations; access layers limit the organization and range of data that users can query; executive layers limit SQL, scans and sensitive fields; and answer layers display calibres, time, source and anomalies.

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.

Availability of single calibre, responsible and valid versions of key indicatorsHow natural languages are mapted to the controlled semantics and queriesHow data privileges are implemented for different roles and organizationsCan you show the filter, the source and the bottom drill?
ACTION STEPS

Suggested order of advance

01

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

Select 10 to 30 real business issues to establish a baseline.

02

Validation Key Dependence

Collating indicators, dimensions, synonyms, permissions and query templates.

03

Development of assessable outcomes

Limit the scope of the query and verify that the results are consistent with the existing report.

04

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

Add the questions of no answers, ambiguity and error to the regression test.

PRACTICAL EXAMPLE

How do you understand it in the actual business?

Example used to illustrate the method of judgement

Users ask “New clients this month” whether the system will first confirm the new entry on first-time or first-time slots, then search for the user’s organizational privileges, show the time and calibre, and allow for client details. If the company has two calibres, the assistant will be asked to choose instead of to decide.

COMMON RISKS

The easiest pit to step on.

Hand over the database structure to the model for free generation query

The only number of different sectors still returned.

Compares text flow only and does not reconcile with authoritative statements

ACCEPTANCE

How should we end up receiving and confirming?

Check semantics, SQLs or search schemes, privileges, numbers, calibres and sources using fixed set of questions; the ambiguity, excesses, super-high inquiries and missing indicators should be rejected or clarified correctly.

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