What makes Data Governance a key element in Product Data Management?

The period 2020-2021 has been the point of inflexion for product data management. Unforeseen disruptions and limited interactions amongst supply chain partners have forced businesses to adopt data-driven models to facilitate the push and pull of information. Organisations quickly realised that data processing and reorganisation consumed a considerable amount of time of the analytics team.

It has never been more important to access, formulate and present accurate and consistent data from one central source. The ability to then pull and push this data through other business systems is key in building data modules, and reporting dashboards has saved considerable time and effort throughout the organisation.

Respondents to a McKinsey Survey stated that an average of 30% of their total enterprise time was consumed by non-value added activities due to sub-standard data and availability.

The large volume of structured and unstructured data in the supply chain network mandates quality-assuring governance. Data governance in product data management is an intrinsic approach to overseeing data throughout the entire life cycle – from procurement to distribution. Effective data governance adds value to conventional supply chain systems through increased comprehension and collaborative capabilities.

The following sections will delve deeper into how data governance can help attain operational excellence in supply chain management.

5 reasons why Data Governance is essential for Product Data Management

With organisations no longer limited by functional boundaries, it gives rise to potential complications such as data quality, data security and data auditing.

Did you know? Only 3% of enterprise data meets quality standards 60-73% of data is never used for any critical activities Only 20% of enterprises that invest in data governance will prosper in 2022

Based on the above, it is clear that only companies who invest in data governance will harness its benefits.

Let’s look at five reasons companies will focus on designing more intrinsic data governance strategies.

Cost Savings

Typically, a supply chain flow involves designing, planning, sourcing, production, logistics, and distribution. Each stage involves significant amounts of data transfer between internal and external stakeholders. Inefficient data management may lead to potential loss of data assets, skewed demand forecasts, increased labour, etc., which may result in long-term cost burdens on revenue.

Interestingly, during the data design phase, a well-structured data plan could reduce 15-20% in developmental costs.

A unified data management platform makes sure all relevant data is stored in a central location for other systems and users to consume this data – in the knowledge, it is both accurate and secure.

Streamlining of Data

Product data management coupled with business integration helps publish a single source of the truth for all stakeholders across the entire supply chain.

Data governance makes sure that the centralised data software stores information appropriately to eliminate multiple or incorrect dataset referencing. Establishing data-centric views and enterprise-wide data quality helps transition an information-heavy industry into a digitally powered one.

Here is how you can benefit from better data management and governance:

• Harmonious and homogeneous data management across the enterprise aids better decision making.
• Well-defined rules for any change in data and its processes help make your enterprise scalable at a technical, business and organisational level.
• Central supervision and management control mechanisms make data management cost-effective despite multiplying data sets.
• Creating synergistic capabilities and better coordination processes help increase overall efficiency.
• Automated updating capabilities help simplify operations and replace outdated information and processes with newer versions.

Early Defining of Metrics and Milestones

Data governance is putting in place processes and tools along with defining the metrics and milestones to ensure the reliability of your data. Evaluating data quality through performance analysis is crucial to data governance as it makes data objectively measurable.

“It’s not enough to manage data and create insights. These activities must deliver measurable business outcomes.”– Debra Logan (Research Vice President, Gartner)

It is crucial that before you devise any business strategy or commence a project, the final publishable data is fit for the purpose.

Without well thought out and defined business ‘rules’, good practice principles and data governance, businesses will work on skewed or flawed data sets. Each department may use different data fields, codifications or attributes throughout the organisation. Sometimes the data across departments may not even be synced appropriately in the absence of a centralised enterprise service hub.

You can define metrics and processes to ensure data quality, accuracy, and reliability through a data governance strategy. This would initially involve collaboration across the supply chain to understand the most common data issues and develop data validation, cleansing, and monitoring control.

Transparency

Data governance promotes an environment of trust and transparency. A work-flow driven Product data management system centralises all the key elements of an asset, configuration and change control. In other words, the technical specifications of product parts, the supplementary documentation and all key technical attributes are all managed centrally in one database. While providing accurate data to employees, the platform is also equipped with role-specific visibility and additional permissions for better governance of data management.

The benefits are:

• Enterprise-wide standardisation of data and its processes help to function as a single unit
• Unambiguous data processes help comply with compliance and legal requirements
• Creates open and transparent communication with all stakeholders
• Self-service capabilities help users carry out independent analysis

In addition to the above, the risk linked to data dramatically reduces while the opportunities multiply. Based on this, it would only be wise to consider data governance as a necessary process.

Risk Identification

Data is an important asset for any company, but a lack of data governance can result in incorrect or sensitive information falling into the wrong hands. The expansion of the supply chain and the inclusion of multiple stakeholders results in an exponential increase in information through authorised and unauthorised networks.

Data management allows organisations to provide internal and external participants access to information relevant to their tasks. However, organisations need to put in place robust security infrastructure to ensure that data remains protected from cyber incidents.

A comprehensive data strategy defines data points, puts in place controls and checks to monitor deviances in data locations and helps mitigate costly errors. With data governance tools in place, you will have access to data audit trails that help monitor and track what data was retrieved, by whom, when, and for what reasons.

Conclusion

Data governance is far from static. In the coming years, every data-driven enterprise has to transform itself to make business sense of its constantly growing data assets.

The innovative and technology powered data systems to collect, analyse and streamline data may seem daunting and complicated. However, today, data management systems have become more accessible, intuitive and easier to use.

Organisations and employees gain access to data insights without prior coding knowledge through business analytics, all fed by technical data from a PDM database. By deploying a strict data governance policy, it offers a range of benefits across the organisation, including regulatory compliance, high data quality, lineage and auditing, consistency and accuracy, increased efficiency, and more.

If implemented comprehensively, data governance can enhance the data-driven aspect of a company right from insights, compliance, analysis, scalability, and more.

Opt for TVS SCS’s product data management to ensure governance, quality, and integrity of your product technical data. Our ‘workflow-driven’ product data management application (Msys.PDM) provides a single source of truth for all product technical data. This application obtains ultimate control over parts, their replacements and release sequence data.

Contact us for better governance of your data!