AI data governance
Using metadata as the framework and AI as the engine, manage the scattered data in various business systems into standardized, trustworthy, clearly authorized, and reusable enterprise data assets.
- Covering mainstream databases, data warehouses, BI, and interfaces, automatically collecting table structures, blood relationships, and change records to form a data map.
- Unify data standards, code sets, and master data encoding rules to make core objects such as customers, materials, and organizations unique throughout the company.
- The five rules of completeness, uniqueness, consistency, timeliness, and effectiveness can be configured, and the inspection results will be automatically assigned for rectification.
- Organize assets by theme domain, support search, application, evaluation, and popularity statistics, allowing data to be truly found and utilized.
What exactly does this solution solve for you
Do not stack functions, each ability corresponds to a real business problem.
Automatic metadata collection
Covering mainstream databases, data warehouses, BI, and interfaces, automatically collecting table structures, blood relationships, and change records to form a data map.
Data standards and master data
Unify data standards, code sets, and master data encoding rules to make core objects such as customers, materials, and organizations unique throughout the company.
Quality Rule Engine
The five rules of completeness, uniqueness, consistency, timeliness, and effectiveness can be configured, and the inspection results will be automatically assigned for rectification.
Data Asset Catalog
Organize assets by theme domain, support search, application, evaluation, and popularity statistics, allowing data to be truly found and utilized.
Classification and Safety
Data classification, sensitive identification, dynamic desensitization, and access auditing make openness and compliance no longer a binary choice.
AI assisted governance
AI automatically recommends field standards, identifies similarity tables, generates quality rules and data explanations, transforming governance work from manual labor to auditing.
From dilemma to solution, explain it all at once
Common difficulties
- The same customer has three names and five codes in different systems
- The report caliber is inconsistent, and we argued in the meeting first: who should we trust?
- Data quality relies on human flesh verification, overtime until late at the end of the month
- The data asset list is in Excel and will expire after one update
- Sensitive data can be exported by anyone, and security auditing is at a loss
Abbot's approach
- Unified encoding and unique identification of master data, cross system consistency for reconciliation
- Centralized definition of indicators and implementation in the system, with a unified set of indicators for the entire company
- Automatic inspection of quality rules and automatic dispatch of rectification orders for abnormalities
- The asset catalog is automatically updated, searchable, applicationable, and trackable for usage
- Data classification+dynamic desensitization+full access audit
The Four Chapter Story of a Project
From the initial challenge to being truly utilized by the business. We will explain it to you in its entirety.
The same daily report, three departments provide three numbers
A rapidly expanding retail chain enterprise has launched four systems including POS, membership, e-commerce, and finance within three years. At the business analysis meeting, the operator said that the sales revenue was 2.18 million yesterday, the finance department said it was 2.03 million, and the e-commerce backend showed 2.26 million. All three numbers are correct, but the caliber, time cutoff, and return processing are different. The meeting lasted for two hours, and no one cared about the business itself anymore.
We don't know how to analyze, we just can't believe the data——Customer Data Manager
Don't engage in large-scale projects across the entire region, focus on addressing a 'sales revenue' first
Abbett did not implement global governance, but chose the most painful point: to connect the entire process of defining and retrieving the "sales revenue" indicator. First, unify the caliber definition, then locate the 41 fields in the three types of data sources, sort out 23 quality rules, and finally solidify the indicators into the system for automatic calculation. Six weeks later, the daily report will only have one number, and it will be able to drill down to stores, categories, and documents with just one click.
From 'people looking for problems' to' problems looking for people '
The governance platform automatically inspects all data in the early morning every day, generates corrective work orders for any abnormalities, and assigns them to corresponding departments according to their responsibility areas. The processing and results are recorded. At the same time, the data asset directory is open, and business personnel can search, apply for, and evaluate data tables, using heat reversal to tell the data team which assets are worth continuing to invest in. Within three months, the workload of manual verification has decreased by 70%.
The job value of the data team is first seen by the business
After unifying the standards, the monthly business analysis will be shortened from two hours to forty minutes, and the debate will shift from "trust whom" to "what to do next". The data team has also transformed from a "data retrieval tool" to a builder of indicators and assets. The second phase plan extends AI data governance to the supply chain and member domains, and integrates AI intelligent agents to enable business personnel to directly ask questions in natural language.
Now the meeting can finally focus only on discussing business——Customer Operations Director
Different industries, the same sense of certainty
retail chainUnified caliber: Daily reports start with one number
Six weeks to connect the entire "sales" indicator chain, reducing the duration of business meetings by 67%.
Smart ManufacturingMaterial master data governance: BOM clear at once
Unified material coding and attribute standards, design, procurement, and production finally match.
Group type enterpriseData Asset Catalog: Enabling Data to be Found
260+assets registered, self-service application for business, quantifiable usage popularity.
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