How Does Your Data Quality Challenges Compare?
National Oilwell Varco (NOV) worked with Birlasoft on a data transformation project that took their data quality to the next level. They were able to turn NOV's data quality challenges into data quality success.
NOV's challenges were discussed by Melissa Haught, Manager at NOV and Deepak Gupta, Practice Manager & Solution Specialist for the Oracle MDM Practice at birlasoft during a recent webinar
"This video clip discusses the specific data challenges NOV sought to overcome by deploying Enterprise Data Quality for Product (EDQP)."
Do these data quality challenges sound familiar?
- Fragmented data is manually replicated without common definition or framework
- Data quality is managed independently with no consistency
- Highly manual data quality processes with limited governance
- Data from new acquisitions come with their own systems and data standards
- Data needs to be normalized, classified, cleansed and de-duplicated for integration into JDE and other applications
- Data consolidation projects take 3-6 to complete
- Product hierarchies change frequently due to corporate restructuring
- Manual, difficult and expensive methods for product classification and attribution
Setting out on a Data Quality Journey Means Having the Right Tools
As with any important project, knowing what tools you need is the first step. NOV shares the requirements NOV used to select Data Quality for Product (EDQP).
"This short video clip highlights the requirements NOV used to drive their selection of Enterprise Data Quality for Product (EDQP) as their tool of choice. Watch to see what EDQP features addressed their requirements."
NOV used these requirements to drive their selection process
- Ease of you
- Ease of integration
- Rapid installation and deployment
- Scalable performance
- Embedded governance
- Iterative development
A Peek into a Phased Approach to Data Quality
NOV needed to transform their product data from dysfunctional to a quality data set that helped them save money and add value to their operations. NOV discusses their phased approach.
"Watch this clip to gain insights into how NOV used a phased approach to deploying EDQP."
NOV's phased approach included the following activities
- Install and configure EDQP environments
- Standardize product data for 400 product categories and build automated maintenance processes
- Standardize product data for another 300 product categories
- Integrate EDQP with legacy systems and JDE
- Develop additional business rules in EDQP rules
- Develop processors for other systems and integrations
- Perform data consistency checks between systems
Govern Your Data Before It Governs You
One of the aspects of NOV's Data Quality transformation initiative included establishing a strong data governance process. Watch NOV describe how NOV built their data governance organization.
"This clip examines NOV's approach to building a data governance organization and how they established a rules methodology in Enterprise Data Quality for Product (EDQP) to support it."
A data governance organization includes the following roles
- Data governance lead
- Data quality lead
- Data stewards
- Category owners
How Data Stewards Keep Your Data Looking Good
It takes a strong team and excellent tools to execute a large organization's data quality strategy. Though there are many important players, data stewards have the biggest role in the implementation and maintenance processes. Learn what NOV believes about the role of data stewardship in their organization.
"This clip highlights the data stewardship workflow employed by NOV. Watch to see how data flows through their systems on the road to quality."
In summary, the data stewardship process for NOV features
- Well-defined ownership of product data at various stages of data processing cycle
- Data quality metrics to track product data quality
- Data tracking and monitoring dashboards for governance lead to track processes and continue improvement
- Roles and security to govern data integrity
- Alerts and notification at various touch points
Data Quality Translates to Business Benefits
Good data quality supports growth objectives, reduces data management costs associated with data remediation and reduces operational inefficiencies. NOV realized these benefits and many more when they teamed up with Birlasoft for their data discovery project.
"This clip demonstrates the before and after state of data at NOV. Tune in to see the possibilities."
Here are some of the data quality benefits your enterprise organization can look forward to with a superior enterprise data management tool and smart processes
- Validated data and known data quality metrics
- Complete, accurate and classified product data
- Automated workflow and processes
- Classification for conversion takes weeks instead of months
- Enterprise-wide data governance
- Business insight and intelligence
The Journey to Data Quality Maturity
Birlasoft MDM Practice Manager Deepak Gupta