In today’s digital-first business landscape, data is every organization’s most valuable resource – making proper data management and governance essential. According to one report, the global data governance market size is expected to reach at an estimated value of USD $34.8 billion by 2029. Technology is a tremendous enabler of achieving and scaling data governance, with tools like SAP Master Data Governance (SAP MDG) offering a powerful solution to consolidate and centrally govern master data for consistent master data. When done well, data governance can accelerate organization-wide data value creation and true digital transformation, and provide consistent high quality master data.
So why do so many organizations have trouble getting their MDG implementations right the first time?
Creating a robust data governance framework is a laborious exercise that requires action from both business and IT for the most optimum configuration, implementation, and maintenance. Because of this, organizations implementing Master Data Governance can fall victim to a number of pitfalls, one of the most common being delayed time to value.
This slow time to value can be caused by various factors, including:
- Complexity of the data: Master data is often complex and resides in multiple systems and applications, making it difficult to consolidate and standardize. This complexity can lead to lengthy data mapping exercises to map and model the data across all systems to identify common data elements and relationships, which can take significant time and resources to complete.
- Data modeling and configuration: Before MDG can be deployed, the technical team must configure the solution, generate validation rules, create and extend data models, and design workflows. These time-consuming development tasks can take anywhere from several days to two weeks to implement, depending on project complexity.
- Data quality issues: Master data can have a significant impact on business processes and decisions. Poor data quality, such as incomplete, inconsistent, and inaccurate data, can create significant data complexity. Before data can be governed effectively, it must be cleansed and standardized, which can be a time-consuming process.
- Data standardization: One of the key objectives of MDG is to standardize master data across an organization. This process involves identifying common data elements, defining data standards, and ensuring consistency across all systems. In complex data environments, data standardization can be challenging, requiring extensive data analysis and mapping to identify common data elements.
- Outdated approach: Most organizations implementing MDG take a waterfall approach, spending extensive time on the solution design and prototyping phases before finalizing and deploying MDG. This approach not only delays deployment timelines, but can lead to missed requirements and poor alignment to business goals, given the lack of visibility throughout each phase.
As valuable as an investment into SAP MDG can be, many enterprises would agree that getting the project off the ground without delay can be a challenge. By combining their SAP MDG implementations with the expertise of a trusted partner, organizations can unlock the full value of their master data governance deployments in a more agile and efficient manner. Solutions like Deloitte DGOAL (Data Governance Accelerator) with Syniti RDG (Rapid Data Governance) streamline MDG implementation to fast-track solution deployment without the risk of project delays.
RDG and DGOAL empower organizations to realize faster time to value with an agile approach that includes real-time prototyping and solution development in plain sight. With a methodology that enables organizations to execute while still in the planning phase, teams can configure the solution in real time to start delivering value more quickly. Time-consuming technical steps like data model extension, business rule generation and workflow maintenance are all automated through APIs on RDG and DGOAL. The solution leverages an elegant UI that auto-generates configuration entries, helping organizations accelerate delivery timelines while reducing reliance on technical resources. Users also spend less time performing manual data entry with a visual rules editor that automatically enables teams to add validation and derivation rules to MDG configuration tables. With Deloitte’s industry-leading data governance practice and DGOAL’s preconfigured workflows, organizations can leverage predefined rules and validations to slash time to deployment by up to 40%.
In today’s data-driven economy, your organization can’t afford to base critical business decisions on disorganized, poorly managed data. Investing in a robust data governance strategy will provide high-value benefits to your organization and ensure your data is consistent, accurate, and governed effectively. Master Data Governance implementation can be slow due to a combination of technical, organizational, and resource-related challenges, but it is essential to understand these challenges and take proactive steps to mitigate them to ensure a successful implementation.