Data Strategy and Master Data Management

 

Data Strategy or setting up an enterprise strategy to manage Data and extract benefits from Data is difficult and a never-ending process. One aspect is the ‘master data management’ endeavour that is a part of, and probably the beginning of, a Data Strategy.

Master Data Management: Establishment and on-going operations of an enterprise data service which provides a master data set across the enterprise to all business partners.

Key attributes of MDM:

• Master DataWarehouse
• Single source of truth
• Single source of definitions

An example would be the various customer databases in an organisation. We need one interaction with that client (not many). Key assets such as revenue generated, orders, prospects, future orders would be managed and coordinated.

Why is MDM Important?

A Data Strategy usually resides with the Enterprise Data Architect, or Chief Data Officer. Most of these strategies fail.

1) Relying on only technology and tools without buy-in and support from business units;

2) Focusing on fixing and solving current data issues, without forward thinking.

For MDM to be successful, it needs to be first a business-driven process and embraced by business departments and executives. In many cases, fundamental changes to business processes will be required to establish and maintain unified master data and some of the most difficult MDM issues are not technical at all.

A Data strategy with a future Target Model in mind, based on business outcomes and KPIs, is crucial in placing MDM as an essential part of data management in an organization.

The implementation of MDM is easiest and smoothest when a dataset has just been introduced for ETL and into a business intelligence project. Trying to fix for existing data assets and processes often require high cost and large effort, which also likely leads to big impact on the current deliverables. You still have to do this hard work to fix current databases and processes, but don’t expect a lot of cooperation.

Current Estate Data Issues

1. Duplicate processes in silos and costly development
2. Data quality issues everywhere with no easy way to track down
3. Low customer satisfaction
4. Data asset potentials are not fully realized
5. Difficult to migrate to new data platform

Implement MDM from the Beginning

1. Efficient and faster development with lower cost
2. Fewer data quality issues that are faster to fix
3. High customer satisfaction
4. Generate more revenue opportunity
5. Much easier to migrate to new data platform when needed

With the above comparison, it is clear that MDM should be an essential part of any company’s data strategy, and should be forward-looking with long-term commitment. In other words, MDM needs to be treated as an investment, which will pay off in the long run and establish a solid foundation for a company’s growth and profitability in the areas of big data, analytics and IoT.

First Step: Set up an MDM and Data Strategy for the Enterprise. Data Strategy steps here.

Step 2: Establish Data Governance Embraced by the Entire Organization

This is the most critical and essential piece of MDM, and also the most difficult one. To enforce MDM requires the commitment of a data governance committee, which normally has the following structure:

• Executive and Advisory Information Console — C-Suite and department heads
• Information Stewards — data governance managers/directors usually from IT or CDO organizations
• Data Stewards — domain experts from every business department

The main missions of the committee include the following:

• Establish data governance policies and procedures, and revise them based on business needs or changes in data, operations and technology
• Establish regular communication channels to communicate and reinforce policies clearly
• Establish the right escalation process for data issues, prioritize and make decisions wisely and efficiently.
• Ensure buy-in and ownership from all stakeholders

Below lists some of the key areas that the data governance committee should make decisions for:

• Holistic view of the company’s data sets and what the core data assets are in the enterprise
• Document and define how data assets should be shared or used under the right security and regulatory constraints
• Establish standard definitions and business rules for data elements in a data asset or data object
• Determine the right course of actions or plans to ensure that data policies and procedures are enforced and executed across the organization
• Resolve definition ambiguities or conflicts

Step 3: Policies and Principles

For example, data governance should enforce and propagate its definitions, policies and principles into the following technical implementations:

• Logical and physical design of databases (data modeling)
• Define column/field names and business rules in the ETL process
• Define display names and formulas in the reporting engine
• Configuration and set up when using third-party softwares such as ERP and Salesforce
• Enforced by Quality Assurance (QA) testing and User Acceptance Testing (UAT)

Step 4: Apply MDM to New Data Additions or New Applications

Data governance policies and definitions are implemented throughout 2 channels:

1) via any new projects and application development;

2) by using a data governance software.

Many organizations’ MDM implementations stalled because of the high cost and effort they faced when trying to fix the existing systems and issues; they did not realize that the best way to start with MDM is to apply it for going forward for new projects, which will test it out first and enable the organization to build up expertise and experience.

Step 5: Select the Right MDM Software

An ideal MDM software should have the following functionalities:

1. Referenceing and access to the metadata of master data assets in a company (e.g., RDBMS, Hive, flat files, etc.)
2. Enable information and data stewards to define and modify the definitions easily in the tool
3. Capable of reviewing the data and configure business rules to apply or enforce what has been defined in the data itself

There are many tools on the market that can do 1) and 2), but it is not easy to do 3) with the same tool. This is the reason why a MDM software can be also a data integration tool at the same time, or vice versa. Recent rapid progress in artificial intelligence (AI) has made such software more powerful with enhanced data management, which has a bright future in the coming years.

Step 6: Remediate Existing Data Systems

To make data remediation of existing systems a sucess, careful planning is required to establish a road-map with multiple phases. Sometimes, it may be a better strategy to apply MDM only partially, until the data or system is migrated to the new platform, while focusing on applying MDM to new master data that are being added or new applications and processes that are being built for the enhanced and new data sources to be joined with the master data.