Key pillars of Master Data Management

BANGALORE, INDIA: Financial institutions grapple with massive volumes of data (market data, static data, transaction data and compliance data) which have a profound impact on sustainability and profitability.

Managing and transforming this data differentiates successful organizations.

3rd gen database technology shaking up market: IDC



Lax data management practices leave financial institutions with questions like:

How to ensure consistency of customer data and preferences across data systems?

How to remove duplicate and inconsistent data repositories created by a piece meal legacy approach to automation?

How to ensure reporting system use up-to-date, accurate data to generate compliance reports?

How to ensure uniformity of data during a merger or the introduction of a new product?

Master Data Management: The Key

Master data management helps address these challenges by creating and enabling processes for extracting, aggregating, reconciling, storing and distributing data throughout the organization to maintain information consistency and quality. 

In financial institutions entities such as customers, products, securities, credit ratings and exchange rates are referenced throughout the organization by different lines of business (LOB) for operational reasons.

Most LOBs maintain a copy of their master data and if it is not properly managed will lead to operational inefficiencies.

A majority of financial institutions realize that losses arising from inconsistent data can be minimized by having a robust and well governed master data management process that translates into the deployment of a robust

MDM programme.

Key Pillars of Master Data Management 

Data Strategy

The first step to a sound MDM program is devising a data strategy that aligns master data management initiatives to revenue and profitability enhancement. It provides a roadmap for an effective Master Data Management set-up within the organization.

A data strategy needs to account for various sources and users of information, the processes involved in standardizing the information, the data enrichment mechanisms and protocols for addition of sources and destinations of information.

Metadata Management

Creating a single metadata dictionary is central to building a robust MDM programme. Metadata ensures that information entities are referenced correctly, ensuring access to relevant and accurate data by all applications.

With the application landscape in financial institutions constantly changing, new databases and information keeps getting integrated. This increases the complexity of metadata management. A complete metadata management program needs to include development and maintenance of metadata standards and managing metadata repositories to remove inconsistencies and redundancies.

Data Integration

A proper data integration strategy can lead to substantial improvements by creating a single view of customers to drive revenues. Data integration also plays a key role in compliance as financial institutions have to integrate information to achieve accurate compliance reporting for Federal agencies and Self Regulatory Organizations (SROs).

Data Quality Assurance

Data quality assurance supports enrichment of data integrated from various sources. This enriched information becomes the new master data for an organization.

Superior data quality reduces failed trades, offers better reconciliation, reduces incorrect payments and improves overall efficiency of any business process.

Data Conversion and Migration

Financial institutions constantly strive to enhance operational efficiency through automation. In a majority of cases, this involves introducing new systems. Implementing these require master data conversion/ migration from the existing system to the target platform.

Data migration enables master data management systems to be recalibrated to maintain consistency.

Data Warehousing

Historical information about customers, transactions, trades, prices, and products are stored in data warehouses to support business analytics and reporting. This forms the support system that allows reporting for various recipients.

Data warehousing initiatives need to facilitate reporting and analysis by extracting data from various heterogeneous sources, loading it to a centralized repository and transforming the data to fit operational and analytics initiatives.

Data Governance

It is critical for process pillars that deliver information to be aligned to your MDM strategy. This requires policies, processes, guidelines, monitoring and control mechanisms to maximize the benefits of MDM.

Data governance helps identify an enterprise’s needs and arrive at a data strategy, by establishing data policies, standards and procedures that are constantly monitored and reviewed to ensure adequate supervision and control of data and ensure proper communication to promote the value of data assets.

Conclusion

Increasing emphasis on compliance, risk management, mergers and acquisitions, profitability and operational efficiency has made creating and maintaining master data a business imperative.

What differentiates a successful organization from others is the ability to obtain, create and manage accurate master data. While it's easy to think of master data management as an issue that poses significant development and maintenance challenges, it also offers major long term benefits to an organization.

The author is Head of Global Delivery at Collabera.