As competition grows, there is increased pressure to comply with regulatory requirements. There are various ways in which financial institutions are preparing to better manage data to meet their business needs and comply with regulatory mandates and one the most sought after ways is turning to data quality to help them make informed strategic decisions.
The challenges in managing data quality are multi-dimensional and can be attributed to factors such as geographical spread of the organization, magnitude of the IT infrastructure and diversification of the business. Though some financial institutions already have specific solutions to address the issues related to bad data quality, most of them are in fact finding mode.
Some of the key challenges that the financial services industry are striving to resolve are:
- Data governance
- Lack of transparency
- Data Inconsistency
- Manual Adjustments
While the industry is looking forward to implementing sustainable and strategic enterprise data quality eco-systems, most financial institutions are performing due diligence by analyzing existing data sets and infrastructure before taking up larger initiatives. The objective is to develop an inventory of systems, processes and issues to assess the present day data quality and identify break points in order to develop a quality remediation roadmap.
The benefits of data quality management are distinctively visible and measurable. Legacy data clean-up could save more than 20% of operational costs and reduce substantial operational risk. Cleaner data helps in establishing the relationship between multiple entities. This opens up avenues to save costs by cross leveraging data between Investment Banks and Wealth Management firms that is 30%-40% common for client data and more than 90% common for product data.
Automation of data processing leads to lower errors and increased quality, leading to a potential reduction in operating costs by 40%-60%. Further, single data hub, unified processes, enterprise licenses eliminate group wise management and acquisition costs, resulting in major budgetary savings. Over and above the quantitative benefits, user confidence on quality is the key for a successful data initiative.
The process of creating a data quality roadmap is an opportunity for the industry to re-look at its data architecture for IT simplification, preparedness for future requirements, alignment with industry trends/initiatives, and move to newer technologies and rewrite operational processes. An enterprise-wide data quality initiative is an occasion to bring internal business groups together to address their data quality issues and agree on a policy to use data from a single source, raising their convenience and confidence factor for the greater interest of the organization.