Master Data Governance – A Complete Guide

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By: Manish Shewaramani

Master Data Governance – A Complete Guide

In 2017, The Economist presented a captivating proclamation: “Data, Not Oil, Is Now the World’s Most Valuable Resource.” In the present era, businesses are fueled by the power of data. This intangible asset serves as the bedrock of analytics, propelling organizations to new heights and granting them a significant advantage over their competitors. Master data governance helps companies to manage the data effectively.

Obtaining valuable insights into the overall trajectory, consumer demand, operational efficiency, and sustainability of your company can be the key determinant of success or failure. To enable management to make informed decisions, data analysts dedicate substantial time and effort to meticulously process and analyze vast amounts of information daily.

In the world of data acquisition, companies strive to answer crucial questions: How exactly do they acquire data? And, more importantly, how do they guarantee the integrity and accessibility of that data?

The master data governance function has emerged as a highly valuable resource in this regard.

What is Master Data Governance?

Master data governance establishes a framework of rules, policies, and procedures aimed at ensuring the quality and consistency of data. It encompasses various components, including definitions, policies, workflows, and the delineation of roles and responsibilities for data stewards and users.

The primary objective of MDM data governance is to maintain the accuracy, completeness, and interconnectedness of data across the entire organization.

It is an ongoing process, which involves aligning new data with existing records, validating its accuracy, and structuring it appropriately to derive valuable insights and achieve business objectives.

Although complex in nature, certain master data management (MDM) platforms, such as Reltio Connected Customer 360, leverage the power of machine learning to streamline MDM governance and simplify the process.

Why Do You Need Master Data Governance?

The saying “garbage in, garbage out” is a familiar refrain, emphasizing the critical importance of data quality. Numerous studies consistently highlight that poor data quality poses the most significant obstacle to the success of any big data initiative within a business.

When crucial business decisions are driven by data that is disconnected, disjointed, and riddled with inaccuracies, the outcome is highly likely to be erroneous. The true extent of these errors may only become apparent after losing customers and market share, leading to severe consequences for the organization.

Given that, master data permeates every facet of your business, the presence of data silos or subpar data quality yields unfavorable outcomes across the board:

  • Marketing fails to capitalize on valuable customer insights.
  • Financial reporting becomes compromised and lacks accuracy.
  • Supply chains experience disruptions and inefficiencies.
  • Customer orders suffer from inaccuracies or delays in fulfillment.
  • Operational costs exceed their optimal levels.

The absence of master data governance introduces inefficiencies into your business, leading to financial setbacks and hindering your agility. Master data management and data governance serve as fundamental requirements for harnessing cutting-edge technologies such as IoT and Big Data within your MDM platform.

Without proper data governance, the ability to establish the essential connections required to thrive in the experience economy is compromised.

Effective master data governance offers a range of valuable benefits, including:

  • The enterprise is striving to establish a single, authoritative version of the truth that encompasses all stakeholders.
  • Standardized definitions and business rules are being implemented for creating and categorizing data in a comprehensive manner.
  • To ensure accuracy and transparency, all changes to the master data are meticulously documented through comprehensive audit trails.
  • To help your organization achieve its business goals and extract valuable insights, our team delivers accurate and high-quality data that is precisely aligned with ours.

Problems that Businesses Face with Ungoverned Master Data

MDM governance, including all forms of data governance, is frequently perceived as the responsibility of the Chief Data Officer (CDO) and is sometimes regarded as a mere administrative and compliance burden.

However, it is crucial to recognize that master data governance is far more than a checkbox exercise. It directly influences an organization’s capacity to scale and attain new objectives.

Moreover, it serves as a proactive measure to prevent financial losses in the present. Neglecting proper governance leads to the wastage of valuable time on manual processing and reactive problem-solving.

By implementing robust data governance practices, businesses can streamline operations, optimize resource allocation, and proactively address challenges, ultimately enabling them to thrive and stay ahead of the competition.

When master data management governance is left ungoverned, it can lead to instances where goods are erroneously dispatched to incorrect addresses, or customers receive items that do not align with the advertised descriptions. These occurrences result in negative customer experiences, damage to reputation, and a loss of customer loyalty.

Poor data quality can have subtle yet impactful consequences, such as:

  • Missed opportunities for upselling to customers due to the inability to accurately identify their preferred product categories.
  • Wasted time spent on correcting and reprocessing erroneous data.
  • Inability to negotiate purchasing discounts due to duplicate supplier information, making it difficult to determine the total expenditure.
  • Loss of web sales caused by inaccurate sizing data, leading to a negative portrayal on comparison sites.
  • Limited visibility into the supply chain, including sourcing, manufacturing methods, and subcontractor usage, resulting in expensive recalls and damage to the brand.
  • Inability among manufacturers to create and share accurate information between distributors and retailers because of ungoverned master data.
  • Lack of defined rules on setting data compliance standards and meeting the regulations set by government authorities.
  • Inconsistency of data across systems and processes erodes confidence in analytics, rendering intelligence subjective and decision-making based on opinions rather than factual insights.

Master Data Management vs Data Governance: What are the Key Differences?

Data governance plays a critical role in the world of master data management vs data governance. MDM serves as a comprehensive framework that unifies master data, establishes meaningful connections, structures the data to unveil valuable business insights, and ensures its accessibility for users. However, to achieve these objectives, the underlying data must be organized and defined consistently.

  • Master Data Management encompasses a comprehensive set of processes that span from data creation to its ultimate delivery.
  • Master Data Governance encompasses a robust framework of rules and policies aimed at ensuring the cleanliness and consistency of data.

Effective management of master data necessitates the implementation of proper data governance practices. Master data governance establishes a set of rules to govern the quality, consistency, and security of master data.

It governs the process of data changes and ensures that incoming data adheres to predefined rules, thereby enhancing its usefulness. An advanced MDM platform incorporates built-in tools that streamline data governance vs master data management, enabling businesses to seamlessly integrate data from multiple domains.

What is Master Data Management?

Master data management (MDM) is far more than a mere checkbox during the SQL Server installation process. It represents a crucial practice aimed at establishing a singular and reliable source of truth for vital business data, such as customers, products, and locations.

At the heart of MDM lies the concept of constructing a trusted master data store, which other systems can query to ensure consistency and accuracy. This central repository contains data at a detailed level, catering to varying levels of granularity required by different systems.

For example, the finance department may prioritize tracking the cost of goods sold, while the shipping department focuses on optimizing pallet capacity.

MDM initiatives should be driven by the business rather than solely by the IT organization. Firstly, IT teams often lack the domain expertise necessary to fully comprehend the intricacies of business data. Secondly, these projects typically demand significant investment and go beyond the scope of traditional technology resources.

However, despite the required investment, the benefits of MDM projects are substantial, delivering exceptional business value. Consider the potential consequences of an inaccurately calculated margin across thousands or millions of units—the cost implications can be substantial.

To explore more about MDM, don’t miss visiting What is Master Data Management (MDM)? that helps you understand the technology effectively. Also, read Master Data Management – An Ultimate Guide to explore all the different aspects of master data management.

What is Data Governance?

Master Data Management (MDM) is an integral component of a broader solution that works collaboratively with data governance.

Data governance encompasses the processes involved in managing the integrity, security, usability, and availability of data within enterprise systems, based on the standards and policies established by your organization. The significance of robust governance processes cannot be emphasized enough.

For instance, the initial step of data governance, which involves cataloguing your data, can provide vital protection in the event of a data breach and greatly simplify reporting projects by enabling a clear understanding of the location of mission-critical data.

While various tools can facilitate data cataloging, their true value is diminished without the foundational principles of data governance in place.

To truly appreciate the value of data, organizations must prioritize and allocate resources toward data governance efforts. Typically, this entails establishing a dedicated governance team whose primary responsibility is to ensure that data sets align with the business objectives of the data governance project.

These desired business outcomes encompass the following:

  • Mitigating the existence of inconsistent data silos across different departments.
  • Establishing agreement on common data definitions to foster shared understanding and interpretation of data.
  • MDM ensures data compliance matching with the existing privacy laws.

Effectively dismantling these data silos poses a significant challenge without the support of a dedicated enterprise architecture organization. Assembling such a team requires a diverse range of expertise. Your data quality teams should consist of both IT and business professionals who possess a deep understanding of the organization’s data and common data structures.

Both data governance and master data management share similar objectives, necessitating close collaboration between the two projects to ensure the consistent and secure management of data.

Master Data Management and Data Governance: Together They Create Magic

Alignment between the two processes, Master Data Management (MDM) and data governance, is crucial for achieving business goals. MDM’s effectiveness is compromised when data accuracy is uncertain or when changes can be made without following a defined process. On the other hand, data governance establishes data stewards across the organization to ensure that data changes adhere to a predetermined process.

As the maturity of these processes progresses, it becomes essential to introduce dispute resolution and escalation mechanisms to address inevitable conflicts that may arise between different departments. Data stewards must have a deep understanding of both the primary objectives of the MDM process and the specific business needs of the organization, which may present resistance to change.

Investing in these skilled individuals will significantly contribute to the fulfillment of your data quality objectives. Moreover, it is essential to coordinate these projects harmoniously to deliver high-quality business intelligence to the organization as a whole.

10 Steps to Follow for Effective Master Data Governance

Master data governance cannot be approached with a one-size-fits-all mentality. However, by adhering to the following steps, which are applicable to any business, you can construct a methodology that is tailored to your specific needs and requirements.

1. Identify the range

To embark on a successful governance initiative, it is crucial to first identify the range of your endeavor. This involves defining the scope and understanding the specific needs that drive the necessity for governance.

By establishing clear boundaries and objectives from the outset, you lay the foundation for a focused and effective governance initiative.

2. Develop a mission statement

Crafting a well-defined mission statement for your data initiatives is essential. This statement serves as the guiding beacon that aligns the entire organization toward a common goal.

It clearly communicates the purpose and significance of the work being undertaken, fostering a shared understanding of the desired outcomes and the underlying motivations driving the data-related efforts.

By establishing a compelling mission statement, everyone involved will have a clear understanding of the collective objectives and the rationale behind their pursuit, creating a sense of purpose and unity within the organization.

3. Build your team

Achieving successful data governance requires equal attention to both people and the components of technology and process. It is crucial to assemble a dedicated team that will serve as champions of the data cause throughout the entire business.

This team will play a pivotal role in driving the data governance initiative, advocating for its importance, and ensuring its successful implementation.

By bringing together individuals who are passionate about data and possess the necessary expertise, the team can effectively lead the organization in embracing and prioritizing data governance as a key strategic initiative.

4. Identify your stakeholders

Understanding the roles of data owners and data stewards is crucial in establishing effective data governance. Data owners are responsible for the overall management and accountability of specific data domains or set within an organization. They possess the authority and knowledge to make decisions regarding data governance, ensuring its completeness and accuracy.

5. Run data quality assessments

Conducting this analysis will provide you with a clear snapshot of your current position, serving as a foundation for establishing data quality metrics and defining the rules that govern data storage and maintenance.

By gaining a comprehensive understanding of your existing data landscape, you can identify areas that require improvement and implement effective strategies to enhance data quality and integrity. This process empowers you to make informed decisions about what data to retain, how to ensure its accuracy, and the measures needed to sustain its quality over time.

With well-defined metrics and robust governance principles in place, you can optimize the management of your data, ensuring its reliability and usability for driving successful business outcomes.

6. Draw a data map

Analyze your data access points to determine the generated data, authorized users, data flow, utilization, and purpose.

7. Implement a business risk register

Conducting scenario planning and developing contingency plans based on different potential situations can enhance your decision-making capabilities, enabling swift and effective responses to changing circumstances.

8. Define business processes

Create data models and document business processes to maintain consistent and traceable governance levels.

9. Share responsibility

Incorporate master data governance into employees’ tasks and objectives to enhance awareness and foster a data-driven culture.

10. Provide education

Consistently communicate the mission and vision behind the implementation of new policies, highlighting the significance of master data governance and its potential positive impact on the organization and individual roles. Elaborate on the specific benefits and elucidate how each individual can actively contribute to the success of master data governance.

What is Data Governance Framework?

A data governance framework comprises a comprehensive set of data rules, roles, processes, and policies that establish a shared understanding and alignment across the organization. It serves as the blueprint for effective master data governance and guides leveraging MDM platforms to achieve optimal data quality.

Implementing a robust data governance framework can be a complex endeavor, but advanced tools like the Reltio platform can simplify the process and streamline operations.

Who Are All Responsible for Data Governance?

Every individual involved in creating or updating your business’s master data bears the responsibility for MDM and data governance. The greater the adherence of stakeholders to the guidelines of master data governance will reduce the burden of data stewardship.

Consequently, it is prudent to engage your business stakeholders in establishing the data governance framework, particularly during the initial phases of developing a data strategy. Their participation ensures alignment and ownership, fostering a culture of data-driven decision-making throughout the organization.

To empower business stakeholders as effective stewards of data quality, it is essential to incorporate a business glossary and data dictionary within your data governance framework.

  • Data dictionary: The data dictionary defines master data elements and elucidates their relationships, including how they are used in conjunction, their origins, and their comprehensive descriptions.
  • Business glossary: The business glossary bridges the gap between the elements outlined in the data dictionary and business terms, enhancing the business’s comprehension of how data is utilized.

Despite achieving user alignment on the data governance strategy, data errors are bound to occur, and the analysis, matching, and merging of external data sources are necessary. Reltio’s platform streamlines this process, but adhering to MDM governance best practices entails assigning key roles and responsibilities:

  • Data Owner: The data owner possesses extensive knowledge about specific data sets within your business and is capable of making governance decisions. Typically, data owners are strategic individuals within their respective functions who understand the desired state of the data and have the authority to be accountable for its quality.
  • Data Steward: Data stewards act as policy enforcers, ensuring that records adhere to data standards. They are typically subject matter experts within their functions and engage with the data on a daily basis. Data stewards shoulder the responsibility of maintaining data quality and making judgment calls when data deviates from policy norms.
  • Data custodians: Data custodians play a crucial role in post-processing tasks. They handle updates and maintenance of data assets, ensuring that records are properly updated across all sources and facilitating the onboarding of new data assets.

When it comes to MDM data governance, business owners, data stewards, and customers have defined roles. These roles are from the MDM user and owner environment, and it is uncommon for stakeholders to solely assume these responsibilities. In contrast, organizations embarking on comprehensive digital transformation often establish data governance offices and/or data governance committees as dedicated business roles.

A data governance office typically includes the following roles:

  • Manager of Master Data Governance: Leads the data governance team in implementing the MDM governance framework and overseeing its execution.
  • Data Strategists & Analysts: Analyze data trends to develop innovative governance strategies and provide insights on business implications.
  • Compliance Specialists: Monitor data quality to ensure adherence to regulatory standards and compliance requirements.

Master Data Management Solution Empowers Your Data Governance Strategy

Having an MDM solution, along with an excellent data governance strategy will pave a path for businesses to achieve their goals easily and quickly. In fact, the presence of MDM solutions will enhance the accuracy of the business data and improve the operational efficiency of a business.

Moreover, with the presence of MDM solutions businesses can easily dispute resolutions between the departments and help data stewards to develop highly efficient data governance strategies. But to leverage such aspects from MDM, business leaders should be very careful in picking the right MDM solution for their business according to their requirements.

1. Tips on Choosing The Right MDM Solution That Suits Your Strategy

When selecting a master data management (MDM) solution for your organization, there are several important factors to consider. It’s crucial to evaluate the capabilities of the tools available, ensuring that they align with your specific requirements.

It’s not uncommon for some tools to have overlapping capabilities or integrations with your existing Extract, Transform, Load (ETL) processes. While a certain level of overlap is expected, it’s essential to avoid duplicating functionalities across your toolset.

In addition to core capabilities, there are other beneficial features to look for in an MDM solution. Automated approval workflows can streamline the process of approving changes to customer master data, ensuring that updates go through the appropriate channels efficiently.

It’s also important to ensure that the solution enables subject matter experts to review and approve data changes, guaranteeing that updates are complete, accurate, and free of errors.

When it comes to the format of master data, hierarchical structures can often be encountered, such as the hierarchy from continents to countries, states/provinces, postal districts, and streets.

While traditional MDM tools can support these data structures, custom development may involve utilizing a graph database. A graph database consists of nodes (representing entities) and edges (representing relationships), making it particularly well-suited for representing hierarchies. This can be challenging to model in a relational database, but a graph database can handle it effectively.

2. Consolidation and Data Integration

A master data management (MDM) project encompasses multiple objectives, one of which is the consolidation of diverse data sources into a centralized hub that serves as the ultimate source of truth.

A common challenge encountered in many business intelligence initiatives is the tendency for projects to experience delays, not necessarily due to technical difficulties, but rather due to the involvement of numerous individuals and groups.

Any measures that can alleviate the associated overhead and streamline the workflow can significantly reduce the overall project duration. Integration of Master Data processing is one such measure that is essential for downstream operational systems.

Getting Started with Master Data Management (MDM)

In today’s data-driven business landscape, ensuring the quality of organizational data has never been more crucial. Establishing data quality practices, conducting validation testing, and aligning data with the organization’s master data (including products, customers, assets, and locations) are essential to maintain consistent and accurate reporting.

Whether you are developing cutting-edge container applications or constructing a data warehouse, data consistency, and high quality are paramount. While data quality may not be the most glamorous topic in technology, it holds immense significance for any data-driven organization.

Data quality serves as the foundation for reliable insights, informed decision-making, and successful business operations. It safeguards against risks associated with erroneous or incomplete data, enabling organizations to confidently rely on their data assets for strategic initiatives, operational efficiency, and regulatory compliance.

Must visit How to Get Started with Master Data Management? to explore about the MDM implementation process.

Conclusion

Having a well-defined MDM strategy is one of the essentials for modern-day businesses to witness success in the current evolving market condition. It serves as a cornerstone for obtaining high-quality data that is consistent, easily accessible, and properly categorized.

Without such data, any benchmarking or analytical endeavor would be rendered meaningless. In today’s fiercely competitive business landscape, companies that fail to embrace business analytics risk falling behind their rivals.

The value of actionable data insights cannot be overstated. They have the power to enhance operational efficiency, mitigate business risks, foster competitiveness, and streamline overall business operations.

However, harnessing the full potential of data analytics requires a combination of expertise in business analytics and specialized knowledge tailored to your organization’s unique needs.

Say goodbye to data chaos and embrace the power of PIM/MDM to supercharge your business growth!