MDM as an Enterprise Initiative

This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "Building a Business Case for Master Data Management." The speaker is Abhishek Gupta, product manager at InetSoft.

While MDM is an enterprise initiative, I think in the end it should be a horizontal kind of platform solution for the organization. I think it’s best, and most of the success will come by looking at the problem a little bit more vertically and trying to solve actual business problems.

When should vendor technology evaluations and selections come into the process? Are there any tips for evaluating and sorting out MDM technology options?

Well, there are multiple definitions of MDM out there. I think that’s one issue that I know everybody is dealing with because we often still get questions what is MDM? What exactly do you mean by that? So I think that there are a lot of rival definitions.

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I think there is also a general distinguishing feature between analytical and operational MDM, and that is one that we see as well where analytical is more about supporting BI and reporting and analysis activities, including things like reporting hierarchies, whereas operational MDM is more about establishing a gold copy or reference data that can be used by multiple transaction-oriented applications.

So that’s one distinction that people should look at when they are looking at tools and looking at technology to combat the problem. And then you do have both small vendors and large talking about MDM, and of course, as you get to the larger vendors, it often includes a lot of services approaches that go with the software. So again, organizations have to look at what they really want to take on in terms of build versus buy and bringing in tools themselves that their IT staff can work with versus outsourcing a lot of the effort to more service-oriented organizations.

I think the first step is to see what assets and processes are already in place to handle some of the key activities involved in MDM such as data integration and the profiling and discovery and business rules management that will be involved. Then you can see what’s missing from your current set of tools and current set of technology expertise in the organization to accomplish a specific business purpose identified.

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Another key here I think is to make MDM efforts repeatable so you don’t want to have to reinvent the wheel as you go from business function to business function to address MDM problems, and that’s where I get to the idea that you really need that enterprise view ultimately with MDM.

So that way as you are consolidating systems throughout the organization, particularly after mergers and acquisitions, you are able to move from one scenario to another and set up rules and processes and workflow that will be working again and again and can be refined and continuously improved as you go along.

Then a final recommendation, I think IT and the business side need to be in agreement over goals, no question about that, so that both can be happy with the deliverables and also that filters into how the technology acquisition works and whether those are successful. Since master data management supports all different kinds of departments and initiatives, how does funding for master data management work? Who pays for it, and how should it work?

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What Are Some the Problems That Can Arise When There Is Poor Master Data Management?

Poor master data management (MDM) can lead to a myriad of problems across various aspects of an organization's operations, impacting efficiency, decision-making, compliance, and customer satisfaction. Master data refers to the core data entities that are essential to the operations of a business, such as customer data, product data, employee data, and supplier data. Effective management of this master data is crucial for ensuring data accuracy, consistency, and reliability. When MDM practices are lacking or inadequate, several problems can arise:

1. Data Inaccuracy and Inconsistency: Without proper MDM processes in place, master data can become inaccurate, incomplete, or inconsistent. This can occur due to data entry errors, duplicate records, or outdated information. Inaccurate and inconsistent data can lead to confusion, mistrust, and inefficiencies across the organization, as different departments may rely on conflicting data for decision-making.

2. Poor Decision-Making: Inaccurate master data can significantly impair decision-making processes at all levels of the organization. Leaders and managers rely on accurate data to make informed decisions regarding strategic planning, resource allocation, product development, and customer relationship management. When master data is unreliable, decision-makers may base their judgments on flawed or outdated information, leading to suboptimal outcomes and missed opportunities.

3. Operational Inefficiencies: Poor MDM can result in operational inefficiencies throughout the organization. For example, employees may waste time searching for the correct version of a customer record or product information, leading to delays in processing orders or resolving customer inquiries. Inconsistencies in supplier data may lead to procurement delays or errors in inventory management. These inefficiencies can increase costs, decrease productivity, and harm the organization's competitiveness.

4. Compliance Risks: Many industries are subject to regulatory requirements regarding data management and privacy, such as the General Data Protection Regulation (GDPR) in the European Union or the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Poor MDM practices can expose organizations to compliance risks, as inaccurate or incomplete master data may lead to violations of data protection laws or regulatory requirements. Non-compliance can result in financial penalties, legal liabilities, and damage to the organization's reputation.

5. Customer Dissatisfaction: Inaccurate or inconsistent customer data can have detrimental effects on customer satisfaction and retention. For example, sending marketing communications to the wrong address or addressing customers by the wrong name can create a negative impression and erode trust. Inaccurate product information or pricing discrepancies can lead to customer complaints and lost sales opportunities. Poor MDM can damage the organization's reputation and brand image, ultimately driving customers away to competitors.

6. Difficulty in System Integration: Many organizations rely on multiple systems and applications to manage different aspects of their operations, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and supply chain management (SCM) systems. Effective MDM is essential for ensuring seamless integration and interoperability between these systems. Poor MDM practices can create compatibility issues, data conflicts, and synchronization errors, hindering the organization's ability to leverage technology effectively and achieve its business objectives.

7. Lack of Data Governance: Data governance refers to the framework of policies, processes, and responsibilities for managing and protecting data assets within an organization. Poor MDM often goes hand in hand with a lack of robust data governance practices. Without clear roles, responsibilities, and accountability mechanisms in place, master data may be neglected or mismanaged, exacerbating data quality issues and undermining trust in the organization's data assets.