Seven Steps to Master Data Management for Your Enterprise

 With the increasing demands of customer-centric brand presence and digitally broadcasted product launches, enterprises today often face hurdles stemming from poor data handling. Many businesses today, especially global enterprises, have many separate applications and systems (i.e., ERP, CRM and SCM) where data can quickly get fragmented, duplicated and outdated.

If not managed carefully, the fragmented data can lead to divisional misalignment and suboptimal decision-making. In such a scenario, enterprises find it increasingly difficult to provide accurate answers to most basic questions on performance metrics or KPIs. This causes significant roadblocks in the business growth path. Master data management (MDM) can help address this need through information consolidation and thereby providing a "single source of truth." 

How can MDM help?

MDM supports a number of use cases for enterprises across products, customers, stores/locations, employees, suppliers, digital assets and more. It centralizes and administers critical data assets like product data, asset data, customer data, location data, etc. and improves their consistency and quality. MDM gives capabilities for data governance, data modeling, data stewardship, hierarchy management, quality/semantics, workflow and more. It is also instrumental in analyzing data effectively, securing stakeholders' information and streamlining the infrastructure for results. MDM initiatives lead to the elimination of data silos, improved data quality, easy data edits, better data compliance, cost and time saving, and informed decision-making.

However, carrying out an MDM project is no plug-and-play game. It needs foresight and precision to integrate MDM strategy and adopt respective solutions into the existing organizational framework to realize business value and accelerate growth. 

Business leaders must venture into the MDM journey since it caters to all internal and external data fluxes of the organization for an extended period. It needs to be executed with clarity and precision at all stages, including strategizing, determining KRAs, implementing and scaling with minimum transition ripples. Therefore, it is advisable to have a stronger foundation of data handling and check a few prerequisites to make sure your MDM project genuinely delivers value.

These seven steps of groundwork are a must for MDM success.

1. Evaluation and objective establishment. Since MDM implementation is more a program than just a project, it benefits from having a strategy. Begin with establishing a clear vision and goals you aim to achieve with MDM and then analyze the technology stack required to do so.

Tips: Answer questions like:

• What needs to be done and in what sequence should it be done? 
• What resources would be required?
• How much budget required to keep things on track?

2. Planning with organizational culture, technology and budget in mind. Once you have determined the objectives and returns you expect from MDM, the next step is to finalize a roadmap of implementation that takes into account organizational culture, current technology framework and cost optimization concerns. That involves defining MDM business cases (preferably tool-agnostic) and focused on potential benefits.

Tips:

• Collect all scattered data. Start by identifying data sources and gather valuable information that is critical for your organization. 
• Transform the data. Like any data-related initiative, MDM is achievable when size of your datasets increasing continuously. You can consolidate records and eliminate silos with data modernization efforts such as migrations from mainframe to cloud applications. By dropping incorrect or duplicative datasets during the migration, you reduce the administrative and processing workload required for your MDM initiative on the new platform.
• Create a standard metadata layer. This facilitates sharing information across all your management and analytics platforms. It makes your data gathering and application process more efficient.

3. Onboarding across the system. The success of all MDM initiatives depends heavily on continuous collaboration. Since the MDM platform itself interacts with people, teams and divisions, interconnectivity is a must. Onboarding the people involved with MDM, such as business owners, master data stewards and software architects, can catapult the program towards success. 

Different departments must be on the same page so that data is not skewed at any point in the value chain. MDM provides a central, consolidated view among units like logistics and supply chain, sales and marketing, HR, finance, admin, etc., and allows continuous collaboration between these teams. That said, easy access to data and training on MDM guidelines is of paramount importance to increase productivity and keep everyone informed.

4. Selecting technology architecture. A compilation of business cases should be used to determine critical capabilities to be delivered by any proposed tool. Moreover, there are multiple MDM platforms and solutions available in the market with different variants and features. There is Customer Data Integration (CDI) tools for managing customer master and Product Information Management (PIM) tools for managing the product master data. With clarity on objectives and returns, you can quickly identify the ones that best fit your requirement. Blending MDM technology with existing IT architecture and business ecosystems can provide technical leverage that supports seamless integration.

Tips:

• Either buy or build tools to create the master lists by cleaning, transforming and merging the source data. The toolset should support the process of finding and fixing data quality issues as well as maintain different data versions and hierarchies.
• You can use a single vendor's solutions, but it is advisable to take the best-of-breed approach (since different data require different treatment). 

5. Addressing data governance and quality. As more and more data move in and out, governing the data flow becomes mandatory. So, it pays to set ground rules and policies to effectively analyze, secure and streamline your data infrastructure for results.

Also, while mature MDM tools suffice this need, sustainable data-mastering requires executive sponsorship and organizational commitment to change and navigate through politics. Data governance directives can manage organizational policies, principles and qualities to promote access to accurate and certified master data. Essentially, this is the process through which a cross-functional team defines the various aspects of the MDM program. A model complete with formalized policies and procedures of data governance can help reduce the time of implementation, data exports, audits and scaling by eliminating chances of errors.

6. Implementation. Once you have clean, consistent master data and the required toolsets to manage it, you need to expose them to your applications and provide processes to manage and maintain them. This process involves proper superimposition of business needs and technology constraints. You need to customize the features as per your existing architecture, integrate the solution seamlessly into it, and then train every concerned team on its usage.

A lot of organizational data and system-related challenges must be addressed to have a successful MDM implementation. Even post-implementation, you will have to monitor, test and ensure smooth communication of new applications with others like ERP and CRM. As a result, reliability and scalability are important considerations to include in your design. 

7. Starting small — and growing big. It is also advisable to take a modular approach and not dive head-on into MDM. There are many vendors and solution features available in the market, and you have to zero down to the one that best suits your requirements. That is why one should always take a phased approach to ensure design scalability, track progress and measure ROI. The sooner you streamline, protect and apply the data you have, the faster your business will grow.

Tips:

• You can realize “quick wins” by applying MDM first to smaller, simpler and more stable datasets. 
• Have a long-term plan for what you’d like to see a year or so down the line from the information you have now.s