02 · Enterprise Data Management

Data Management

SHEPHARD helps companies map their existing data resources, establish unified data management standards and integrate information from different business systems, improving the completeness, consistency and usability of their data. We care not only about how data is stored, but about how it creates value in day-to-day operations.

Why it matters

Plenty of data, but little you can rely on.

Data has become a key business resource, but having data does not mean a company can use it effectively.

As a business grows, its data may end up scattered across different systems, Excel files, internal databases and employees' working records. Disconnected systems, inconsistent data standards and hard-to-trace historical information all make it harder for a company to understand and manage its own business.

Does this sound like your company?

The same customer has three different names in different systems
The figures each department brings to meetings don't match
Getting one report means asking someone to export data from several systems and piece it together
No one can say how to use the historical data in the old system

Data problems rarely disappear on their own. The more systems and the longer the history, the more costly they are to fix.

What we offer

01 · Data Assessment
Data asset inventory
Understand the company's existing data resources, sources and uses, and clarify where data sits and how it is managed today.
- Enterprise data inventory
- Data source and structure analysis
- Assessment of existing databases
- Data usage analysis
- Identification of data management issues
02 · Data Integration
Data integration
Connect data across internal systems and business functions to reduce information silos.
- Multi-system data connection
- Data interfaces and integration
- Cross-department data integration
- Historical data migration
- Unified data management
03 · Data Governance
Data governance
Establish unified data standards and management mechanisms to improve data quality and ensure data can be used reliably over time.
- Data cleansing and standardization
- Data quality management
- Data classification and management standards
- Data access management
- Data retention and update mechanisms
04 · Data Management Framework
Data management framework
Help the company build long-term data management practices so that data keeps accumulating and creating value as the business grows.
- Data management policy design
- Data lifecycle management
- Data maintenance mechanisms
- Data security and access control
- Data management process design

How we work

How a project unfolds

First take stock, then integrate. Every step is documented, so data rules don't leave with the people who know them.

01Weeks 1–2
Data inventory
We review the company's existing systems, spreadsheets and databases to establish where each type of data comes from, who uses it and how good it is.
Deliverables
- Data resource inventory
- Data quality assessment report
02Weeks 3–4
Standards definition
Together with business teams, we define unified definitions and coding rules for core data such as customers, products and suppliers.
Deliverables
- Master data standards
- Data dictionary
03Weeks 5–8
Integration and cleansing
We connect data interfaces between systems, clean up duplicate and incorrect data, and migrate valuable historical data.
Deliverables
- Data interface and synchronization plan
- Cleansed master data
04From week 9
Ongoing governance
We assign data owners and maintenance processes and set up quality checks, so data stays clean in day-to-day operations.
Deliverables
- Data management policy
- Data quality monitoring dashboard

The value for your business

What changes afterwards

01
Fewer data silos between systems.
02
More accurate and complete data.
03
A more consistent data management framework.
04
More value from historical data.
05
A reliable data foundation for AI applications and management decisions.

Typical scenarios

The following scenarios are based on situations we commonly see in companies and illustrate the kinds of problems this service can solve.

Wholesale and distribution
One customer, three systems, three names
BeforeThe sales system, the accounting software and the warehouse spreadsheets each keep their own records, so working out customer contribution means matching entries by hand.
AfterA single customer master record is created, all systems share one customer code, and customer contribution is available instantly.
Manufacturing and production
Messy material codes, inventory that never adds up
BeforeThe same material has several codes, purchasing places duplicate orders, warehouse records don't match physical stock, and month-end stocktakes take days.
AfterMaterial codes are cleaned up and merged, approval rules are set for new materials, and inventory data becomes reliable again.
Retail and e-commerce
Data scattered across platforms, no clear view of real profit
BeforeEach sales platform has its own back office, and every week the team downloads reports by hand and combines them in Excel.
AfterOrders and fees from all platforms are consolidated automatically, so margins by product and channel no longer depend on manual spreadsheets.