Master Data Management

Data governance and MDM: Building an effective strategy

Data is a goldmine. It has immense potential but works only when you know how to clean, channelise, and monetise customer data effectively. A well-designed data management strategy ensures that businesses can secure, standardise, and optimise their data assets. A clear and effective master data management strategy can drive growth and enhance business decision-making by providing a single, consistent source of truth.

Every enterprise knows the value of data management in driving growth and ensuring data integrity. Yet, only 10% of companies have an organisation-wide master data management strategy, and 30% function on a limited data strategy, utilising data from only a few key areas.

Data management is critical in making data secure, consistent, trustworthy, and usable, creating a single source of truth. For the best business outcomes, businesses need a master data management strategy that integrates data governance with master data management (MDM) tools and practices.

What are (MDM) Master Data Management vs Data Governance?

Data governance and MDM, along with data integration, are crucial aspects of data management. Although different, they are closely aligned with governance policies. MDM is the process of ensuring the uniformity, and products, to ensure that data is, accuracy, and semantic consistency of ‘master data’, i.e., data describing core business entities such as customers, vendors, and products, to ensure that data is managed effectively. A strong master data management strategy ensures that master data is accurate and available for business decision-making at all times. Data governance refers to the policies and processes for ascertaining the privacy, security, quality, and availability of master data.

MDM is responsible for maintaining a centralised repository of up-to-date master data, often called the ‘golden record’. Data governance ensures that master data is optimally utilised to grow the business.

Building a Successful Master Data Governance Framework

The traditional view of data management as solely an IT responsibility is inadequate and flawed. Master data derives from multiple departments, such as sales, marketing, procurement, and supply chain links. While drawing up a data governance strategy, it is crucial to obtain cross-functional input from executives and end users familiar with specific data domains. Having business leaders and employees on the team alongside IT professionals will bring the right competencies and skill sets to the table.

The traditional ‘one-size-fits-all’ approach to data management, too, is obsolete in the era of tailored mdm strategies. Building teams with diversified roles and responsibilities is key to ensuring efficiency and agility in digital ecosystems. Some essential roles and responsibilities of data governance teams are:

• Data administrators

  Data admins oversee master data repository management, policy implementation, and data-related conflict resolution. The administrative team may include data modellers, architects, and quality analysts focused on data security.



Optimize Your Data Management Strategy and Unlock effective governance and MDM solutions.

• Data stewards

  Data stewards create data assets, ensure overall data quality and security, and define access policies. Good data stewardship is crucial in data collaboration and sharing across the enterprise.

• Data owners

  Data owners are domain experts who control and optimise data from specific datasets. They leverage deep domain knowledge to draw the right insights from master data, gain competitive advantage, and drive market innovation.
  An efficient data governance team helps in –

  • Defining strategic goals and priorities
  • Architecting a sustainable governance model
  • Identifying the right technologies and master data management solutions
  • Getting buy-in from stakeholders

Getting the best out of your data governance strategy

Apart from building the right team, your data management programme needs to establish best practices for data integration to achieve its productivity goals. Here are some ways to optimise your data governance strategy:

• Invest in data architecture

  Data governance requires a seamless collaboration of teams, workflows, and processes to eliminate data silos. Data architecture provides the technology and infrastructure to enable good governance. Ensure that your data architecture aligns with governance goals and includes concrete deliverables such as:

  • Data models, catalogues, and lineage
  • Source inventories
  • Master data definitions
  • Master data metrics
  • Usage policies

• Define ownership and accountability

  Assign clear ownership of specific data assets to the relevant data owners, users, and stakeholders within the data management strategy framework to streamline the data management process. This helps to establish accountability and ensure authorised access, thus enhancing security, efficiency, and conflict resolution processes.

• Collaborate with security and compliance departments

  Ensuring data privacy, compliance, and cybersecurity are among the core responsibilities of the governance team in establishing robust data governance policies that also address data silos. With cyberattacks growing in sophistication and compliance regulations changing often, staying on top of security and compliance is a challenge. Collaboration of IT teams with the security and compliance departments is essential.

• Apply security policies close to the source

  It is best to apply security and access policies as close to source systems as possible to manage data effectively. This mitigates the possibility of sensitive data passing through unsecured channels and eliminates unnecessary data before it reaches the warehouse, ensuring data integrity. This streamlines data governance and MDM.

• Automate workflows

  Manual processes in data management are time-consuming and highly error-prone. AI-driven master data management solutions solutions are instrumental in achieving effective data integration and high-quality data and enhance the management of product data and can automate all possible workflows, such as data asset creation, cataloguing, and lineage mapping.

  Automation augments the visibility, compliance, and auditability of master data, enhancing data integration efforts. It also enables master data sharing across siloed datasets and facilitates better stewardship through data cleansing processes.

For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like living organisms will be imperative for business excellence. A comprehensive yet modular suite of services is doing precisely that for effective master data management. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.

How can Infosys BPM help with effective master data management?

Infosys BPM offers an end-to-end master data management solutions that includes consultancy, data quality management, which is vital for maintaining a robust governance framework and digital transformation, as well as ongoing support. We guarantee that our MDM programme provides the most value to our clients by tailoring our services to their specific company requirements. Know more.