Data quality and governance
Solid data governance is essential to get the full value out of an ERP. With FiveForty’s expertise, you structure, secure and standardize your data to ensure its reliability, consistency and availability in Dynamics 365.
Data governance challenges in Dynamics 365
- Insufficient data quality and consistency across departments
- Multiple data sources and no unified master data
- Risks related to compliance, security and access
- Business processes hampered by incomplete or outdated data
- Difficulty using data for management and analysis
Our approach to Dynamics 365 data governance
FiveForty helps companies establish clear, operational data governance. We define the roles, responsibilities and management rules that ensure the quality of your data in Dynamics 365. Our approach is built on solid master data, standardized structures, controlled access rights and a managed data lifecycle. We also secure data flows, document processes and optimize data entry practices. The goal is to deliver data that is reliable, usable and aligned with your business needs. Good data governance improves efficiency, streamlines management and prepares your teams to take advantage of AI and advanced analytics.
Our data governance services for Dynamics 365
- Definition of data governance and responsibilities
- Building and standardizing business master data
- Setup of business rules and quality controls
- Structuring access rights and security
- Documentation, best practices and team training
- Aligning ERP, BI and business processes
The key benefits of Dynamics 365 data governance
- Reliable, consistent data across the organization
- Better use of Dynamics 365 and BI tools
- Fewer errors and less manual rework
- A solid foundation for AI, automation and advanced analytics
Data quality, the foundation for everything else
A high-performing ERP fed with wrong data produces wrong decisions, faster.
Identify recurring defects
Duplicate customers and suppliers, inconsistent item master data, outdated addresses, approximate cost allocations: these flaws are measured before they are corrected.
Deduplicate and improve the reliability of master data
Customers, suppliers, items: shared master data is what pollutes the most downstream uses. Cleaning it up produces the most visible gain.
Define data entry rules
Mandatory fields, standardized formats, blocking checks at creation: preventing an error costs infinitely less than correcting it afterward.
Appoint data owners
Each master data domain must have an identified owner, empowered to decide on rules and disputed cases. Without an owner, quality inevitably degrades.
Measure and track over time
Completeness rate, duplicate rate, record age: a few indicators monitored regularly make it possible to detect drift before it turns into a major project.
Preparing for advanced use cases
Business intelligence, automation and artificial intelligence amplify data quality as much as its flaws. Data cleansing comes before these projects, not after them.
This is exactly what is at stake with Copilot in Dynamics 365: AI is only as good as your data, and unmanaged master data produces wrong answers stated with confidence.
A question? An answer.
Frequently asked questions
Without clear rules, data becomes inconsistent, hard to use and a source of business errors. Robust governance ensures quality, reliability and consistency, while improving operational and analytical efficiency.
Ideally before, but it can be put in place at any time. Well-defined governance streamlines design, secures processes and improves the quality of existing data.
Because every use depends on it: reporting, automation and artificial intelligence. A high-performing system fed with wrong data does not correct errors; it industrializes them and spreads them faster.
With the one that pollutes the most downstream uses: generally third parties (customers and suppliers), then items. These are the master data sets shared by most processes.
Through blocking controls at creation, explicit data entry rules, an identified owner for each master data set and a few indicators tracked regularly: completeness, duplicates, age.
Yes. These uses amplify both the value of accurate data and the effect of inaccurate data. Launching an analytics or AI project on degraded master data produces wrong conclusions with great confidence.