Most organizations today follow a data-silo model with disparate systems catering to specific business segments with nil or very less correlation between them. These individual systems often engage different data integration and management frameworks and toolsets to cater to its segment-specific data interface and transportation requirement, which results in
Under these circumstances, Unified Data Management(UDM) framework lays down a process to consolidate data from disparate data sources of an organization by identifying the integration factors among those and storing the unified data in a common data repository within a data warehouse. This in turn, initiates rationalization of system-specific data integration and management framework into a single organization-wide framework and optimization of multiple Data Integration (DI) teams into a single Data Center of Excellence (CoE) using an optimized DI tool landscape.
UDM drives interdepartmental cooperation by providing a common storage-base where data across different applications of an organization is cleansed, parsed and transformed using unified data dictionary. In a UDM platform, different data governance processes (Data Quality, Transformation, Metadata Ingestion, Data Lineage mapping, Data Discovery etc.) work in cohesion to retrieve the maximum business insights and to drive organizational transformation and regulatory compliance through enterprise wide data assets (See Figure 1).
Figure 1: Unified Data Management platform
What are the business drivers of UDM?
UDM includes both strategic and technical aspects. The success of UDM is determined by its ability to effectively align the upgradation of the organizational data management framework with the agreed business goals.
UDM should cater to these two requirements:
1) Enable coordination of disparate data management principles:
This revolves around collaboration and integration of development efforts among different data management teams and interoperability of the corresponding server, network and code artifacts. It also enables sharing of data management architecture and infrastructure components among different teams. The final product will require the amalgamation of the individual data management team for each of the disparate source systems/applications into a single lean one that can perform the data quality, integration, management and governance activity on the resultant unified centralized data repository.
2) Support strategic business objectives:
UDM should enable an organization to utilize its corporate datasets for extracting business insights and channeling the information obtained to support business goals. A business should first identify and prioritize the goals and communicate to the technical data management team the data-driven requirement for targeting those goals. Thus, UDM has evolved from being the technical data integration framework for organization-wide data to the alignment framework between data management and information-driven business goals. UDM stands vindicated and holistic only when the unified data framework caters to the strategic business goals of the enterprise.
What does an efficient UDM framework look like?
1) UDM is a best practice framework for data integration of enterprise wide data
2) UDM is a solution framework, not a specific tool
3) Adoption of UDM framework is benefit driven
4) UDM can be a discreet program in the data consolidation part of a company or it can be included as a subprogram for a larger program of IT landscape centralization, application rationalization, enterprise data catalogue building, and IT-to-business alignment
5) UDM integrates data as well as teams
What does UDM enable?
UDM is the driver of success for the organization that wishes to benefit from the enormous dataset (master and transactional) being hosted in any of its larger application landscape. UDM brings to the table business insights of the enterprise data by consolidating, integrating and standardizing the data across disparate and sometimes, siloed systems. It also drives business goals and contributes in the success of data-driven business initiatives (including BI, CRM, legal and regulatory compliance). It also lays down the platform for people optimization by consolidating multiple data management teams into a single and leaner data competency team, thus saving the OpEx of the company.
Lead Consultant - Data, Analytics & AI, Wipro Ltd.
Sugata has 14 years of experience in the field of data warehouse, data modeling, data integration and business intelligence architecture. He is currently working as a Data Architect and Automation Consultant for a leading client. From IM Practice perspective, he is also involved in solutions focusing on data integration and cloud data warehouse.
Practice Director - Information Management, Data, Analytics & Artificial Intelligence, Wipro Ltd.
Krishna has over 19 years of business and IT experience in the areas of information management and analytics solutions for global organizations. In his current role, he is responsible for practice vision and strategy, solution definition, customer advisory, consulting, competency development, and nurturing of emerging trends and partner ecosystem in the areas of data and information management.