As data is widely seen as an asset of an organization, it needs to be managed with the same rigor as any of the other assets, such as intellectual property, people and money.
- In most organizations, data architecture refers to models that describe data at an application level. However this may not be the right approach and could lead to an incomplete understanding of data. Large transformation programs that deliver business capabilities across multiple value streams require a business oriented, system agnostic understanding of the data across the organization. Therefore, a lack of a business oriented understanding of data curtails the agility of growth.
- Due to the focus on application, data architects get involved too late in the IT investment lifecycle. By this time, decisions that span across the enterprise are already taken. As a result, the data architect has a limited influence on decisions concerning the right source of data, prevention of duplication of databases etc.
- Data quality initiatives are locally focused. There are metrics at an application or a business area level but there are problems in meeting business expectations. This reduces the level of trust imposed by business on the quality of data available to support timely decisions
- Excessive effort is spent in establishing compliance to regulatory requirements
HCL’s data architecture services, with roots in the Enterprise Architecture practice, provides a top-down solution to these problems. Some of the services include:
- Definition of information domains
- Business oriented definition of information entities
- Publication of gloassaries using a variety of indistry standard platforms
- Providing a 360 degree view by mapping to business functions and applications
- Definition of end-to-end lifecycle for key information domains
HCL’s data architecture services provide the architectural foundation essential to support a variety of enterprise level programs:
- Ensuring the success of data governance programs
- Ensuring that large transformation initiatives spanning multiple capabilities have a comprehensive view of the data
- Facilitating the consolidation of data and applications after mergers and acquisitions
- Establishment and finetuning of IT architecture governance programs to help prevent problems related to data duplication and database sprawl
- Increasing the value that is derived from data quality initiatives that support business operations
- Implementation of MDM platforms and associated data quality programs