(US & Canada) | Have a Framework First for Successful Master Data Governance — Stibo Systems, Director, Product Marketing and Solutions Strategy

Matthew Cawsey, Director of Product Marketing and Solutions Strategy at Stibo Systems, speaks with Lauren Maffeo, CDO Magazine Editorial Board Member and Author, in a video interview about his professional journey, the difference between traditional and master data governance, building a master data governance framework, and an example of successful implementation of the framework.

Stibo Systems offers a comprehensive platform to govern different data domains and drive insights, agility, and transformation.

Cawsey has been in the data management domain for around 25 years. What started with doing database development and design to earn extra money in college led to a career as an Oracle Database Administrator.

As he found it challenging to keep track of technology and learn programming languages, Cawsey shifted into a pre-sales technical role in software sales and eventually sales and marketing. However, throughout all the roles, data migration, and data integration played a significant role.

Adding on, Cawsey states that he understood early on that to make data migration successful, one must understand data. This led him to do data profiling and quality work, which gradually got him involved in master data management, including data governance.

When asked to differentiate between traditional data governance and master data governance, he clarifies that the former focuses on making data available across the organization. This encompasses various types of data, such as analytical, operational, IoT, and unstructured data.

In contrast, master data governance specifically concentrates on ensuring the accuracy and consistency of core data elements, known as master data. Further, it is the cornerstone data essential for business operations, including customer, product, employee, supplier, and location information.

Continuing, Cawsey explains that the master data remains relatively stable over time, unlike other organizational data that constantly changes. He adds that master data governance requires applying policies and rules to this critical data with the master data management (MDM) platform, ensuring its integrity and consistency.

Moving forward, Cawsey highlights that the first step to making a master data governance framework successful is having a framework. Next, he stresses the five critical components of building a successful framework:

  1. Data governance council and executive sponsorship

  2. Policies, processes, and KPIs

  3. Data quality management

  4. Data definitions and cataloging

  5. Educating and training business staff

Elaborating further, Cawsey stresses the need to have a core MDM group that defines roles, ownership, and executive sponsorship to drive the framework forward despite resistance. Policies contain the measures that must be implemented along with KPIs to ensure governance meets business needs.

Regarding data quality management, Cawsey shares that it is critical to have data that is understood, clean, accurate, consistent, unambiguous, trustworthy, and can drive business decisions.

Thereafter, he relies on the necessity of data dictionaries and data catalogs for broader governance. While these help provide clear technical and contextual data definitions, there is also an operational side to them, including data security and compliance with regulations.

The final critical component, according to Cawsey, is educating, training, and communicating with the business staff while involving them in the governance journey, since that buy-in is crucial.

Speaking of successful master data governance implementations that drove tangible organizational value, Cawsey mentions understanding what defines success. He continues that there are various models, and different organizations would implement governance to different degrees.

Then, Cawsey refers to working with a global company that implemented a global multi-domain master data management project with product, supply, and location data. This spans across many business units and different organizational silos

The planning phase was challenging due to numerous stakeholders with conflicting views on data definitions, ownership, and usage, says Cawsey. To ensure the project’s success, the company received business advisory services from Stibo Systems.

Alongside technological implementation, the data governance operating model must be implemented in collaboration with the customer. Referring to Gartner, he states that 60–70% of the master data management projects fail due to change management, adoption, data governance, and organizational collaboration.

However, the partner organization understood and worked on the challenges, leading to a significant reduction in data errors, smoother processes, and improved decision-making, says Cawsey. More importantly, it was aligned with clear ownership, definitions, and accountability.

Concluding, he affirms that successful implementation also brought positive business impact, and the governance program gained more acceptance. Cawsey asserts that while having a data governance operating model may not be easy, not having one will be worse.

CDO Magazine appreciates Matthew Cawsey for sharing his insights with our global community.

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