Master Data Management: Organizational Change Management as a Driver for Implementation

Master Data Management: Organizational Change Management as a Driver for Implementation

Introduction

MDM is a collection of rules and practices, organizational initiatives, business processes, and technologies that defines and maintains critical business data that can be shared with all essential areas within a business. Quality data accurately communicates the pulse of the business to Decision Makers.

Correct, timely, and reusable data is vital to support organizations as they face information needs in a dynamic business environment that includes:

·      Changing markets

·      Vital real-time Business Intelligence and reporting support

·      Post-merger and acquisition integration

·      Regulatory requirements that change

·      Cost and production pressures

·      System upgrades and transition to the Cloud

Master Data Management (MDM) is the key strategy for addressing these challenges, and Organizational Change Management (OCM) is a vital driver for the MDM transformation to be successful. A robust MDM regime can clean, maintain and change data to support all corporate information requirements. MDM can be the linchpin to bring disparate systems together and maintain them in the future.

People have dynamic and intimate relationships with data, though most take data for granted as they enter text in controlled boxes and use dropdown menus to complete forms and process their work. For successful real time reporting, data needs to be correct and relevant to be useful. Pressure for accurate and new data typically grows faster than data entities, formats and forms can be developed or changed. Most organizations don’t have a reliable, accessible and transparent processes to update data at the same pace that executives, business processes and regulators require.

Accurate and timely information to support excellent decisions can be a differentiator from competitors who rely on legacy systems that input and change data on an ad hoc basis or hardly at all. Inaccurate, incomplete and old data cost time, money and competitive advantage. And that says nothing about data errors or erroneous information in free text boxes. Additionally, data management tools are frequently incomplete or obsolete because organizations don’t want to invest in them as the return on investment is not obvious. In a world screaming for internal investment, data needs are usually a whimper because they don’t have an organization and a process that are demanding sufficient tools, and many MDM initiatives lack executive sponsorship.

Master Data is a vital asset for all successful organizations. Data that can be trusted because of its timeliness, quality, accuracy, consistency and uniqueness is the life blood of a company for:

·      Effective communication and reporting across borders

·      Addressing different languages and their inherent ambiguity

·      Cultural and regional needs for parts, purchasing, and local supplier management

·      Inventory status

·      Production metrics

·      Finance needs

·      Human Resources and payroll accuracy and adherence to government requirements

·      Quality information for investors

Without trusted Master Data corporations experience inefficiencies that sustain bad data, misinformation, incomplete information and time delays that impact internal quality and ease of communication. Bad data compromises services with suppliers and to customers, financial records, and vital and new information and reports to decision makers and other stakeholders. These inefficiencies cascade through organizations and in the end cost money and make them less competitive.

Master Data Management is one of the most important endeavors a corporation can undertake, it is a major organizational shift, and very few do it well. Gartner reports that through 2019, only 33 percent of organizations that initiate an MDM program will succeed in demonstrating the value of information quality.

“An MDM program is not a project but a commitment by the business to leverage information for reuse in order to improve business process outcomes. The real barriers to MDM adoption remain ones of change management, governance process, organizational change and measurement of business value. The creation of effective governance organizations, policies and processes that focus on the Master Data life cycle is key to success with MDM.” -Andrew White, research Vice President at Gartner

Organizations need to create a MDM governance framework, an organizational structure, and a set of roles and responsibilities to fulfill their MDM strategy and policies. This paper will look at the impacts of MDM and present ways to bring success to an MDM program by implementing it through structured Organizational Change Management methods.

MDM Defined

Master Data typically includes consistent data about people, places, organizations, services, and their relationships. It is this emphasis on uniform and consistent data across large, interconnected systems that distinguishes MDM from less rigorous methods of data management. MDM drives performance by putting the most accurate and appropriate information in the hands of decision makers when they need it. IT professionals have long recognized MDM as an ideal IT strategy but implementing it on a large scale is a challenging project beyond the technical requirements, especially for global organizations that have complex methods and many legacy data systems in a setting with divergent language, culture and business processes.

MDM is much more than a single technology solution; it requires an ecosystem of technologies and organizational change to allow the creation, management, and distribution of high-quality Master Data throughout the organization. —Forrester Research

MDM spans many industries, but the common truth is that it pays to have correct data at the point of entry, the beginning of a process, for all businesses. With manufacturing it can be the Bill of Materials (BOM) or a sales order or a receiving intake form. Cost of rework rises exponentially if errors are put into the system initially and flow through the business. Inventory, finance and business intelligence reporting must all use the same information. MDM is not just about data and process, it is about the interactions between the business and IT, it is about people. Looking at the triumvirate of People, Process and Tools the biggest reasons MDM initiatives do not live up to expectations are

  • Failing to plan for organizational and cultural change
  • Underrating the importance of a data governance regime
  • Relying on outdated and incompatible technologies


Businesses need to ensure that their data is relevant, up-to-date, and comprehensive. That’s were MDM comes in—making sure consistent, reliable, and actionable information is available across the business and its IT systems to inform decision makers.

The Need for MDM for Information Systems

The need for MDM for is shown by the many complex processes that depend on accurate data. In the absence of an MDM strategy, many organizations rely on a combination of paper records and electronic information, and the organizational memory of a few key staff members.

Often core business processes were developed before electronic data collection needs were identified and implemented, so legacy business process and workers still use paper driven information. As a result, much historical yet relevant business intelligence is still in paper form or input into a system haphazardly as an additional task whose value was not obvious.

Paper forms can be especially problematic because workers may leave entries blank or write information on the forms in a way that’s inconsistent with corresponding fields in ERP systems. Paper systems lack timeliness because their information must be reentered into a system to be of value to downstream consumers of data, and they are prone to error.

Frequently, employees will maintain paper records during the day and then input information into the system at the end of the week or month, thereby leaving many advantages of real time accurate data on the shop floor. This double entry is inefficient, costly and denies decision makers timely and accurate information that is vital to the real-time information needs in a fast-paced global business.

Data and information needs evolve over time and they are often overlooked until a point where the organization realizes it is communicating vital information poorly, which is causing problems to meet sales needs, delivery to customers, managing inventory and supply chain visibility and measuring financial and HR performance.

Often the state of poor data is experienced at a critical juncture as when a business needs intelligence tools to be competitive and vital reports cannot be produced.  The repair is costly and time consuming and requires considerable expense in new IT products and employee time to clean and reimplement data. A new and expensive system is brought in to solve these problems but relying on the same inconsistent data and data practices will only highlight the inefficiencies faster; in reality, the new technology may just be another add-on to a dysfunctional information quagmire.

In the absence of an MDM strategy, even when electronic methods are used, workers frequently enter information into a several systems ranging from paper to excel spreadsheets and legacy ERP systems with varying formats and in different ways. Common inconsistencies in information arise from:

·      New codes associated with updated parts, and old codes that are reused, changed or are abandoned

·      Misspelled or truncated names

·      Address, phone number, and email changes for suppliers and customers

·      Typographical errors

·      Account, project and other identifying numbers that vary from one facility or process to another

·      Different names or incomplete records for BOM and other processes

·      Inconsistent sales and ordering, receiving and intake information

·      Poor production and HR tracking

Many Professional Associations and interest groups develop what they hope will become industry data standards, but they may conflict with current business practices and cause additional data contradictions. Even if the proposed standards are well-designed, they may also contradict established industry or government requirements, in which case an organization may choose to use both, adding to data complexity and the need for data governance to support adaptation and integration. Regardless of their source, these standards are complex. The US Environmental Protection Agency, for example, has over 400 attributes for pollutants. Data discrepancies make migration to the cloud and data conversion when upgrading or implementing a new ERP system very difficult. As they say, garbage in, garbage out.

The Need for OCM in Planning MDM Deployment

MDM implemented with Organizational Change Management (OCM) guidance resolves these challenges by creating an organization that can manage data and bring consistency to data updates stored in multiple systems, which improves the data’s quality and its usability across functional groups. MDM implemented with organizational change principles is essential for enabling data driven solutions. Quality data helps organizations make more money.

Many MDM initiatives fail because vital organizational issues that impact implementation are not considered as important as the technical difficulties. Two of the main issues for failure are related to people and process. Tools are separate dimensions that are easier to solve with less ambiguity and difficulty than organizational issues. Technical difficulties pale before the organizational dilemmas caused by bad data.

“The real barriers to MDM adoption remain ones of change management, governance process, organizational change and measurement of business value.”-Gartner

Anytime new systems are brought online, or new processes are put into practice, unexpected problems arise, and users will likely make mistakes and resist as they implement changes in the way they work. Taking these risks into account, along with technology adoption issues, is especially important for technology projects, where system problems and human mistakes threaten productivity. There are several reasons MDM initiatives can become derailed, beyond the technical difficulties.

People:

Often the wrong people are involved in the MDM initiative. It seems obvious that the people who know the data best are the right people to sort through the data and make enterprise decisions, but this can be a trap.  Often the person charged with creating a data governance regime is someone who has been with the organization a long time in a technical capacity, such as a Database Administrator (DBA). This DBA is close to the data and has made data decisions as part of their job. Now they are in a senior role, perhaps near the end of their working career. MDM will be their swan song, how they leave an impact and legacy on the organization. They know the data; they care about the data, but they may be so close to the data that they can’t see “the trees for the forest.”  They may be part of the problem with data and lack the business insight, depth and neutrality to advocate for enterprise-wide data decisions that further incorporate business goals. They like the current state of the data because they probably created it and implemented it.

Process:

Effective MDM requires a process to manage data, to make timey decision on solving data discrepancies and methods to create new data elements or to evolve existing standards. Often there are conflicts between different parts of the organization as to who determines a specific standard. Does purchasing, finance or supply determine the data elements of a part? How is a part number updated or retired? Where is the authority to solve conflicts? Often it is the same retiring DBA who deals with these issues, but leaving the decisions to an individual who represents a parochial perspective and not the entire enterprise can have dire consequences downstream.

Tools:

MDM tools rely in part on a foundational data warehousing concept known as Extract, Transform, and Load (ETL), in which raw data is extracted from systems across the enterprise, transformed into a uniform format, and loaded into a central destination database. Modern enterprise-class database software, such as Microsoft SQL Server, Oracle Database, MySQL Enterprise, IBM DB2, SAP and Amazon AWS are all supported by ETL tools, which helps with implementing MDM in a heterogeneous IT environment.

“Organizations need to create an MDM governance framework, an organizational structure, and a set of roles and responsibilities to suit their MDM strategy and politics.”-Gartner

Recommendation:

Data Stewardship and Data Governance groups should be established to manage data conversion and consolidation elements effectively. The Stewardship group consists of the right representatives of the business units within the organization, such as finance, materials, supply, manufacturing and purchasing as well as HR and sales. They make recommendations to Data Governance where there is a conflict in a data format or its definition, i.e., what is a customer? Is it a proper name with a limited number of text characters; is it a numeric code, a combination of both or two separate entities covering the same thing? How is a new customer added or updated? Who is a patient, the child or parent? How are names changed through marriage, divorce and name changes for idiosyncratic reasons? Who makes the decision for the enterprise? These basic data elements can have huge impacts on all areas of the organization as well as on downstream business intelligence and reporting needs. Data Governance considers the ramifications and makes the enterprise data decision to establish a competent, vital and timely solution for data needs.

How are these groups selected, formed, trained and managed? Customized OCM has the answers with a thoughtful approach, methodology and tools. Tried and true OCM methodologies can unlock the people and knowledge power of an organization to achieve a perfect balance of people, process and tools as they relate to MDM implementation and governance. Additionally, an international perspective brings success to multicultural global business concerns with expert cadres of experienced OCM professionals that cover many disciplines that are vital to a global company.

MDM Achieved Through OCM

MDM, as noted, has three core dimensions-people, processes, and tools. While many companies focus primarily on tools and processes, the role of people is the most important because they possess the institution’s collective knowledge and are responsible for implementing information tools and processes. Proper management of the dimensions of Data Stewardship and Data Governance is necessary to get the most value out of MDM Technology. All of these must be considered to enable an organization to undertake a project to discover its Master Data and design, build, and implement its MDM solution.

In order to get people to change their behavior you must get them to act their way into a new way of thinking rather than think their way into a new way of acting. —Michael Hammer

To achieve an MDM cultural change

·      Gather and analyze data at aggregate levels across pertinent functions to understand people, process and tools and to identify and mitigate risks

·      Use assessment tools to examine norms of behavior to measure and provide 

understanding of the current culture to facilitate MDM adoption

·      Deploy analytical tools to assess the gap between the actual and desired culture and operations

·      Conduct workshops and developed planning roadmaps for the implementation of best practices and the organic future state impact on each unique area of corporate culture

“An MDM program is not a project but a commitment by the business to leverage information for reuse in order to improve business process outcomes,” said Andrew White, research Vice President at Gartner.

To make this happen, the initial tasks to support MDM for manufacturing organizations are like those for organization change in any vertical market:

·      Identify the options for immediate improvements

·      Assess the options accurately by developing effective criteria

·      Define and communicate the tradeoffs for specific options

·      Develop effective implementation plans for “quick wins”

The quick wins are particularly important because they help functional areas recognize the importance of MDM and accept it quickly.

Data Stewardship (People)

An organization’s most important asset is its people, and in the context of MDM, the most important people are the Data Stewards who already understand the nature and scope of the information that the organization manages. Data Stewards are not the owners of the data, with complete control over its use. Instead, they are trustees of organizational information and standards; ensuring that adequate quality is maintained so the data can support business processes and achieve organizational goals. The search for quality data is known as a search for “the truth,” and it is a difficult undertaking.

Data Stewards are also key liaisons with other MDM project team members and work with these members to:

·      Manage the organization’s data

·      Define and apply best practices

·      Change how data is controlled

·      Make helpful recommendations regarding data

·      Implement these measures into production

A robust Data Stewardship program with key executive support is vital to any data quality initiative to establish, maintain, and achieve the business and organizational goals of possessing accurate and current data.

Quality data equals quality service delivery, so Master Data Management is an organizational issue whose solution is supported by technology; but without organizational commitment, structure, and rigor, data improvement initiatives are doomed to fail. Indeed, the technical issues pale in comparison to the organizational dilemmas of implementing MDM.

At the core of data quality is a realization that organizations need to merge their thought leadership and harmonize the way they think and approach issues through several data sources, applications, and organizations. An enterprise approach is required, though, each functional area usually believes in the superiority of its data, and will be reluctant to change business practices and processes or adopt new technology unless they understand the benefits. A single source of data that is “the truth” rarely exists. Departments usually need to merge and harmonize data from several sources through automation and organizational initiatives involving Data Stewards who manage conflicting values and enterprise needs and administer the most accurate and current data.

Data Governance (Processes)

Inconsistent data renders business intelligence useless.

Data Governance refers to the processes that manage the overall availability, usability, integrity, quality and security of data used in an enterprise. Governance typically includes a council, a defined set of procedures, and a plan to execute those procedures. The purpose of the Data Governance council is to:·      Establish a policy to account for all data and information

·      Create processes to change or add data elements

·      Define and enforce processes regarding how data is stored, archived, backed up and protected

·      Develop standards and procedures that define how data is to be used by authorized persons

·      Manage controls and audit procedures to ensure ongoing compliance with government regulations while enhancing business needs

·      Enforce data decisions when there is a conflict or impasse

The result is a collection of Master Data that represents the most accurate, timely, and trustworthy data in “the best record” about content areas within the business. Data Governance considers and aligns the organization’s processes with internal best practices and other industry and regional standards to stay on track with regulatory issues, as well as with mandated changes and evolving business needs. It is also important to audit current process and provide workarounds to generate accurate and timely reporting to executive management on these invisible processes and their effectiveness.

Data Technology (Tools):

Technology, which includes applying ETL and data cleaning tools, helps departments overcome their negative reaction and avoid these pitfalls.


There are many effective tools to profile, clean, test, load, and manipulate data into a useful form that MDM requires. Additionally, MDM software can exist in a “hub” that supports the ability to:

  • Develop and manage the information models for content domains
  • Support quality data through record profiling, cleansing, and matching in both automated and manually stewarded modes
  • Create user interfaces needed to support those functionalities, especially the Data Stewardship processes
  • Establish operability between new interface and existing applications so that they can contribute to and use new sources of Master Data


To achieve organizational collaboration across all business units, IT functions and applications must be supported. Many departments initially take a reactive position towards data quality issues. Without the right tools and technology in place, an organization will experience the pitfalls of undisciplined and reactive data regimes, such as:

  • Few defined rules and policies regarding data
  • Redundant data in multiple applications and systems
  • Enterprise applications that are not integrated
  • Disparate solutions that solve only one problem (point solutions)
  • Addressing data issues after they occur rather than proactively
  • Confusion over data ownership and authority

Proper technology helps departments overcome their negative reaction data normalization and avoid these pitfalls.


Added Value

Companies can use MDM to develop a strategic view of their data and unlock their organizational truth. Such an effort should be approached with a fervent belief that the knowledge and skills to solve most issues already lie within your organization, rather than a canned approach to solving MDM issues. Respect your people and the knowledge they possess. Leverage the expertise and experience of employees who have dedicated many years of their professional lives to providing Manufacturing service excellence. These knowledge banks can provide insights into the organization and more efficient ways to conduct business.

Organizations should build on their legacy processes and develop thought leadership. Existing processes will reveal much of the organization’s “truth,” including ad hoc processes that even management may know nothing about. An audit of all relevant process and workarounds can reveal their hidden value and provide accurate and timely reporting to executive management on these invisible processes and their effectiveness.

From MDM to ETL and the related field of Business Intelligence, there are experts in the tools that can help businesses gather data from systems across their organizations and convert it into a usable, manageable, and actionable form. A MDM regime will ensure that data is designed and maintained for flexibility with proper attributing that accounts for the organization’s current state, with a keen view of the potential futures the business will face in an evolving Manufacturing landscape. With conformed data and separate dimensions for additional business attributes, a view of data can be obtained and maintained.


Master Data Management Implementation with OCM methodologies can guide an MDM initiative. Melding legacy and market-sector knowledge with best practices results in agility and flexibility to adapt to changing business needs, supports ERP systems and BI activities, strengthens security and privacy, and maximizes operational efficiency while enhancing your differentiators in your industry. MDM impacts the bottom line of competitive corporations



About the Author:

Terry Drent, MBA, MS, MpA, is a Business Transformation Consultant focusing on HR, IT and M&A integration.


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