Get MDM buy-in with this 3-step approach + readiness checklist

Data is the new currency, powering business growth and innovation. But in order to take advantage of it and turn it into an asset, you have to deal with larger-than-ever volumes of complex records that are usually siloed and of poor quality. This makes implementing a robust Master Data Management (MDM) strategy an obvious choice for modern businesses struggling to drive value out of their data.

Here at Ataccama, we usually see two types of personas tasked with implementing this type of Master Data Management strategy:

At a certain point, however, for both these personas, the real obstacle is getting organizational buy-in, securing a budget, and having the right levers for mass adoption. It’s questions like these that keep data leaders awake at night:

• Do I have a solid use case for implementing MDM?
• Can I illustrate how MDM will help tackle business challenges and increase ROI?
• Can I help enough business leaders inside the organization to secure a budget?

We will outline a step-by-step approach to help you through your MDM adoption journey and share an MDM readiness checklist to help assess and keep track of your organization’s readiness.

The 3-step approach to MDM buy-in

Getting buy-in is essential when initially adopting any tool, but it doesn’t mean it stops there. In time, you might define additional use cases that MDM can help with. That will require additional buy-in and following the same process. Here’s an overview of each step:

Step 1: Building the business case

Most MDM projects fail when creating requirements, selecting vendors, or after the implementation starts. That’s because key business stakeholders aren’t involved from the beginning of the process, creating a lack of alignment between business and data initiatives.

Align business goals with data initiatives:

Begin by establishing a clear connection between business objectives and the data initiatives you’re responsible for. Identify who would be directly and indirectly affected by an MDM implementation. Look at key internal or external stakeholders that make use of data in their day-to-day activities both actively and passively:

• Business department heads (CFO, CMO, CCO, COO, etc.)
• Clients (B2B resellers of your services, etc.)
• Suppliers and partners
• Data stewards and business analysts
• End-users (if active in a B2C industry)

Highlighting the cost of inaction:

Engage with stakeholders to understand how poor-quality or siloed data hinders their ability to meet departmental objectives or deliver business initiatives. By presenting real-life scenarios, you can paint a vivid picture of the risks associated with inaction. Here are some examples we regularly see with our clients:

• More cases of breaches and regulatory non-compliance lead to hefty fines & a tarnished reputation
• Long customer service waiting times trigger higher customer churn rates
• Supply chain disruptions cause inconsistent delivery timelines and unhappy customers
• Recalls and quality control issues resulting in lower profit margins and wastage

Key deliverables:

• A “council” comprised of the most impacted business and technical people. It will facilitate easier access to key decision-makers, put a stricter focus on the conversation at hand, and give you the chance to ask the right questions.
• Cross-functional workshops to map out business goals against data challenges. Use this forum to take the pulse of the organization when it comes to bad-quality data and quality-of-life improvements. These insights will help you articulate how MDM can address specific business needs, such as improving customer experience or streamlining operations.
• Cost-benefit analysis (Business value assessment) that compares the current state’s inefficiencies and risks against the potential benefits and savings of implementing MDM. Include metrics around growing market share, expanding your customer footprint, reducing costs & improving efficiency, and mitigating risks.

Step 2: Assess the scope. Address concerns & objections

Before adopting an MDM tool, defining the project size based on critical decisions with all involved stakeholders is essential. To that end, having a holistic view of your organization’s data landscape and processes will help immensely.

Clarify the use cases:

By understanding the major pain points during Step 1 and how they impact the organization, you’ll determine if you need MDM to streamline operational processes, enhance analytical capabilities, or both. Focus on critical use cases for the MDM pilot project by:

• Determining the scope of the MDM initiative: What data domains, systems, and applications will it encompass? This is easier to measure if you already synced with your business counterparts.
• Actively involving stakeholders to refine use cases beyond the initial scope of the MDM initiative. This collaborative approach ensures that the MDM strategy remains aligned with evolving business needs and clarifies expectations.
• Define success metrics: Agree with key stakeholders on what the organization defines as success and the metrics used to measure it. This will help manage expectations and allow you to expand the project’s use case and scope.
• Determine the integration needs: Will the MDM solution need to integrate data in real-time for customer-facing applications, or is the focus more on downstream integration for internal reporting and analysis?

Address common concerns and objections:

Anytime you need to make a critical business decision, you must convince your peers that the rationale behind it is sound. From what we’ve seen with our clients, acquiring buy-in from the beginning is critical. Try to leverage:

• Proactive communication: Proactively address potential objections by highlighting the direct benefits of the MDM initiative before widespread concerns arise. These will vary depending on the use cases and metrics you agreed on but can include improved segmentation and targeting, fraud prevention and regulatory compliance, AI & ML ready infrastructure, etc.
• Stakeholder workshops: Conduct workshops to discuss common concerns, such as data privacy, implementation costs, and potential disruptions in live systems. Use these sessions to present case studies, demonstrating how similar challenges were successfully managed in other organizations.
• Risk mitigation plans: Develop and share detailed risk mitigation strategies. Outline the steps to maintain data privacy compliance, provide cost-benefit analyses to justify investments, and plan phased roll-outs to minimize operational disruptions.

Assess the complexity of your data landscape:

How many data domains, systems, and applications do you have across your data landscape? It can be a challenging question to answer, especially for large enterprises with distributed data landscapes.

Start by understanding where critical data is stored and what systems are obsolete. This helps craft a solution that is neither too narrow nor overly ambitious. Tailor the MDM tool to the organization’s specific needs by considering its flexibility, scalability, and integration capabilities. The two main options you can choose from are:

• Operational MDM: An expansive landscape with multiple systems, possibly spread across on-premise and cloud deployments, requires a robust, operational MDM. The tool should be capable of handling complex data integrations and large volumes of records.
• Analytical MDM: A more contained data environment with fewer systems and lower data volumes might be more suited to an analytical MDM. The ultimate goal is highlighting business insights, leveraging analytics, and providing quick wins.

Step 3: Implement data governance and leverage best practices

This last step is not just preparatory; it’s a strategic move that can determine the success and efficacy of your MDM initiative. A data governance strategy coupled with industry best practices can be the difference between success and failure with MDM implementations.

Enforce a robust data governance strategy:

A data governance framework is more than a set of policies; it’s the blueprint for managing and utilizing data across your organization. It involves understanding who owns the data, how it’s classified, and how it’s used in decision-making processes:

• Comprehensive governance framework: Beyond establishing ownership and classifications, make sure your data governance framework encompasses data privacy and compliance standards. This approach guarantees that MDM aligns with regulatory requirements and enhances trust and reliability in data processes.
• Engagement and training: Implement an organization-wide engagement and training program to help employees understand their roles within the data governance framework. Promote a culture where data quality and integrity are everyone’s responsibility.

Implement data management solutions (Optional):

Integrating existing data governance, quality, and analytics solutions with MDM can significantly enhance its effectiveness. These solutions act as gatekeepers, verifying that only high-quality data is fed into the MDM system:

• Take advantage of data quality tools with a quality firewall and automated DQ monitoring. They safeguard the ongoing integrity and quality of data within the MDM environment, preventing the ripple effect of poor-quality data on business decisions and customer experiences.
• Leverage advanced analytics and AI to predict and mitigate potential data quality issues before they impact your MDM system.

Leverage success stories and best practices:

Incorporate learnings from successful MDM implementations. Analyze case studies to identify critical success factors, common pitfalls, and innovative strategies that could be adapted to your use case. Look at other companies’ journeys as a practical guide on what to do (and what not to do).

Use your connections or leverage analysts and MDM vendors to set up meetings and engage in benchmarking exercises with peer organizations. This way, you can set realistic goals and KPIs for your MDM initiative.

Key deliverables:

• Data stewardship team: Assign data owners and start by implementing a data catalog solution. Classify all master, reference, system, and authentication data inside the catalog.
• Develop a governance dashboard: Track compliance, data quality metrics, and stewardship activities. This will provide visibility and accountability, enabling continuous improvement in data management practices.
• Establish a knowledge-sharing platform within your organization. Use this platform to share success stories, best practices, and lessons learned from the MDM implementation.

MDM readiness checklist

Now that you've gotten everyone in your organization excited about the potential of MDM, it's time to ensure you’re on the right track. For that, we created this MDM readiness checklist you can use and share with your colleagues.

In it, we highlight the most important points from each of the three steps with a summary of the action points and key deliverables.

Key takeaways

Every step is crucial for ensuring the successful adoption of an MDM solution and can dramatically enhance the effectiveness of your initiative. MDM is not just about technology; it’s about transforming how your organization views and manages its most valuable asset: data. You can do so by:

• Assessing your organization’s readiness for MDM with our 3-step approach
• Implementing robust data governance with Ataccama ONE
• Leveraging industry best practices from a leader in the data management space.

Break free from data chaos by adopting a strategic approach to MDM.