Master
Data Management

• Reduce costs: MDM supports short-term as well as strategic measures that help you work more efficiently and thus reduce operating costs. 
• Increase revenue: Central master data management helps your company identify early on opportunities for competitive products and services as well as new business models. 
• Minimize risks: Master Data Management provides a comprehensive range of tools and methods to analyze your data. Understanding new connections, patterns and anomalies in data sets provides valuable insights and helps businesses take action based on a complete, consistent, and reliable source of master data across their organization and across countries.
• Reduce error rate: Role-based concepts in master data management simplify data management and ensure that your data is complete and reliable. Thus, you reduce errors due to incomplete or wrong manual input.
• Increase your sphere of influence Consolidating high-quality customer, product and supplier data facilitates your customer support's sales activities.
• Improve customer satisfaction: With MDM, you link customer and product master data, and you have a complete view of historical, current, and predictive data sets. Thus, you can deliver a consistent, personalized customer experience across all channels.

• Enhance customer experience: Up-to-date and consistent master data is the key to creating a seamless, omnichannel customer experience across all customer touchpoints.
• Increase efficiency: Central Master Data Management reduces isolated and siloed applications. As a result, employees spend less time searching for data that is relevant to their daily business.
• Maximize process automation: Master Data Management increases the efficiency and reliability of data processing. Smart import and export features, the integration of reliable databases and embedded AI increase the knowledge gained from corporate data. 
• Ensure traceability: An effective system for product traceability requires targeted identification. MDM is the key to accessing accurate information on items across your entire supply chain.
• Improve compliance: Clearly defined roles and responsibilities in MDM ensure compliance with regulations such as GDPR, HIPAA, PCI, CIPA, and other legal requirements.

Management

Challenges:

• Gaining an overview of the business, its figures, and processes.
• Improving strategic planning
• Achieving operational excellence

Opportunities:

• Improved reputation with investors and banks
• Enhanced processes (and thus reducing costs)
• Improved controlling through more accurate metrics

Marketing

Challenges:

• Gaining a 360° view of your customers
• Creating comprehensive and accurate product descriptions
• If applicable, consolidating distributed media assets

Opportunities:

• Targeted communication
• Increased rates (response, click, retention, etc.)
• Improved quality of texts and images

Sales

Challenges:

• Gaining a comprehensive 360° view of customers and products (who has bought what and when)
• Creating new offerings (products and bundles)
• Attracting new customers (based on the behaviour of existing customers)

Opportunities:

• Increased sales (higher prices, new customer segments, cross-selling)
• Reduced sales costs (better targeting with the right products and offers)
• Strategic customer development

IT

Challenges:

• Consolidating IT systems (eliminating redundancies)
• Introducing data governance and data quality measures
• Focusing on new projects

Opportunities:

• Reduced costs by eliminating redundancies (systems)
• Improved quality means less effort for error correction
• Innovation instead of "just keep them alive"

Finance

Challenges:

• Gaining an overview of the business, its figures, and processes
• Reporting (external/internal) and regulatory requirements
• Realization of innovative applications and processes

Opportunities:

• Simpler, faster, and more accurate business and financial planning
• Simpler reporting, improved standing with banks
• Faster integration of Mergers & Acquisitions (M&A)

Logistics

Challenges:

• Gaining a 360° view of the supply chain
• Optimizing routes and tours to reduce costs
• Gaining an overview of products and shippers

Opportunities:

• Visibility into logistics processes
• Reduced costs through optimized routes and tours
• Support for the purchasing and sales departments

Procurement

Challenges:

• Gaining an overview of products and suppliers
• Optimizing the onboarding process
• Placing orders with suppliers in line with market requirements (customers, sales volume, forecasts)

Opportunities:

• Better overview of supplier-product relationships
• Faster onboarding and time-to-market through more accurate information for suppliers
• Improved reporting = Optimization of order volumes and order times (thus reducing costs, preventing redundant orders, and providing accurate delivery times)

HR

Challenges:

• Gaining a comprehensive 360° view of your staff
• Planning training courses according to your company's needs
• Setting up company standards and rules for HR processes (recruiting, exit management)

Opportunities:

• A better understanding of your employees' qualifications (who can do what - who needs what)
• Optimized training offering (tailored to your business needs)
• Overview of internal guidelines (job descriptions, processes, etc.).

Vision

You can only reach your goal if you know what it is. The same applies to data management.  Why should Master Data Management be introduced in the company?  What will MDM look like in the company?  Which overall goals do you want to achieve by harmonizing your master data? And, above all: How will this benefit your business? By clearly answering these questions, stakeholders will be more committed to the project, managers will be more supportive, and the project will be more focused on its goals. 

Strategy

The MDM strategy defines how to achieve the overall vision. It is based on specific objectives, usually set out in terms of sales growth, cost optimization or risk minimization. In this context, you need to identify the application areas, the requirements, and the challenges that Master Data Management cannot solve. Together with the developed roadmap, they provide the strategic backbone for the entire MDM program. For your project to be successful, your data strategy must be aligned with the corporate strategy and be future-oriented. 

Metrics/KPIs 

Setting up a Master Data Management program is not only a complex undertaking involving many parties; it also requires considerable investment. All the more important, therefore, to be able to track performance and measure success. Specific metrics or KPIs that assess the impact of measures on customer satisfaction, revenue and/or costs will increase the acceptance of the MDM initiative within the company.

Governance (Directive)

Governance is about ensuring that you have an authority framework in place with clearly defined roles and responsibilities that assigns clear tasks to all stakeholders. The data governance guideline covers the entire life cycle of master data, from creation, update, and quality assurance, to use and deletion. To achieve optimum results, it should be drawn up early in the MDM project.

Organization and Roles

Effective change management and cross-functional training measures are among the most important success factors of Master Data Management programs. To achieve the objectives of your MDM initiative, you need to optimize and adapt processes and organizational structures.  New roles emerge, responsibilities change, new skills are required. This unsettles many employees, which frequently leads to a hostile attitude towards the entire project – something that must definitively be prevented.

Information Life Cycle

In Master Data Management, information is considered a product that has a life cycle. Different types of data require different processes to collect, validate, enrich and publish data, and for data quality management. You need to be fully aware of these information life cycles across different applications and platforms when defining your future Master Data Management processes. 
Do you know what processes are involved, which steps are "nice to have", and which are "must-have"? An in-depth understanding of all Master Data Management processes in place ensures that future information life cycles are optimally structured and take all relevant requirements into account.

IT Infrastructure

There are hardly any IT systems that affect as many departments and data domains in a company as Master Data Management systems. The extreme importance of this type of software within the entire corporate IT infrastructure means that you need to select a system very carefully and strategically. The Master Data Management solution must be aligned with the existing and future infrastructure and provide adequate integration options.
No Master Data Management system will ever meet all the requirements of all domains. Nevertheless, in most cases, relying on established software solutions is still the best option. Whether it is best to implement a multi-domain solution or to deploy multiple systems depends on the company's individual requirements, such as, for instance, the underlying data model, the required scalability, or the business process management.

How Can I Improve the Quality of My Master Data?

To ensure the quality and integrity of your data, you must start right at the source, when creating and entering the data, and thus attack the root causes of errors. Setting up a corporate strategy to collect and maintain master data - and thus ensuring quality throughout the process - is essential. Cleaning up and putting things in order once is not enough. Without proper processes and quality checks in place, the quality of your master data will undoubtedly run into more data quality issues soon enough. You need an approach that addresses not only technical and data-related issues but also organizational and strategic aspects to make sure that the quality of your master data is continuously and sustainably improved.

Organizations that implement a centralized master data management system will benefit from a reliable solution that enables them to establish and maintain high data quality over time. 
A workshop should help clarify the following issues: What kind of data do I have, where is it stored, which data do I need for my business processes? What is the quality of my data? What is my definition of “good data quality”? What is the minimum (data volume/quality/scope) I need to achieve the desired customer satisfaction or increase my business model's competitiveness? Who/which departments are responsible for maintaining master data? Is there any data governance process in place that defines how master data is created and maintained?
Only when all of these questions have been answered within the company can you initiate a program to improve the quality of the master data.

How Do I Harmonize My Master Data?

The classical approach is to centralize master data from the outset. In the case of product data, for example, this entails storing information and other assets in a central system rather than in multiple systems. You do not need to harmonize data if it is stored in a central location.
But harmonizing master data also requires integration: in many organizations, different teams work with different systems and get frustrated when data is not shared and linked. Interfaces between the systems can help in this respect. This allows employees to access all data, even though it is stored in different data silos. 

A workshop should help clarify the following issues: Where do I keep which data? Who is actively involved in the process (creates or maintains master data)? Is there a strategy (global/local)? Why would I like to harmonize my data?
In the long run, data complexity will continue to increase. Storing all master data in a central system is imperative to avoid costly delays in processes. You also eliminate the need for redundant technologies or inconsistent processes. 

How Can I Automate My Data Management Processes?

Businesses need to optimize every stage of the life cycle of master data - from creating to updating and archiving - to meet their specific organizational needs. This is an expensive and time-consuming process that is also prone to errors. 
Therefore, the first step in automation is to find out whether it is possible to avoid data maintenance altogether. For instance, many companies streamline master data that they could simply retrieve from business partners, e.g., product data from suppliers. Retailers should rather focus on integrating high-quality product data from their suppliers than on creating, maintaining, and optimizing their own master data. 
Methods such as AI, analytics, ML, text mining or big data processing automate content generation (texts, for example) or the extraction of attribute values from existing long texts. Furthermore, maintenance of master data can be automated with suitable data models and metadata management.

A workshop should help clarify the following issues: How relevant is (master) data for my business-critical processes? Is it subject to specific approvals? What would I gain by automating processes? What would I risk?