Most of the top of the line officials of financial institutions have had data on their agenda for years. However, the proliferation of data coupled with huge fall in storage as well as data processing costs and growing focus on multiple facets such as data quality, governance, policy, models, aggregation, reporting, metrics and monitoring, has promoted a change in focus. A majority of financial units are now busy in driving transformation initiatives designed to revamp their business models by extracting the value of data.
Top-notch financial units that employed many analytics programs to make decisions are now adopting technology-driven strategies in executing processes, services, and multiple front-line activities. And where they once created relational data warehouses for storing structured data from different sources, organizations are now operating data lakes that capture, store, and update all types of data to facilitate faster and easier data access. Also, they fuel their agility and innovative power using myriad next-gen data integration technologies. These technologies cannot only accelerate data transformations but also deliver maximum ROI.
However, many companies are not able to leverage modern strategies to deliver value from data. As per the Mckinsey survey, financial institutions face a host of obstacles that limit their capability to harness the true potential of data: improper data architecture with a lot of legacy IT systems; lack of self-service features for streamlining data transformation; and more. For handling these obstacles, financial organizations can adhere to a systematic process to transform the way they do business with their customer or partner network.
With an effective data-driven strategy in place, companies can use the information to the best of their capability and make confident decisions for driving the business forward. Others who have embarked on ambitious programs to use data without an explicit data strategy, with supremely disappointing results. Any successful data transformation initiative starts by establishing a clear goal for creating the expected value.
While designing the strategy, organizations must pay heed to a lot of factors that can help drive their data transformation initiatives. For starters, they must pay heed to the fact that whether the data integration platform they are using has all the automated features to transform data-driven processes or not. Other than that, they must ensure that whether their in-house staff are capable of using the technology, and also if it provides self-service features to allow easier usage.
For translating the data strategy into important use-cases, companies need a proper data integration platform that is aligned with their transformation journey. With functionalities such as pre-built application connectors, shared templates, monitoring dashboards, etc., these platforms enable all business users integrate voluminous information to generate quick wins and drive change. Companies do not need to deploy their IT teams for various integration scenarios, freeing them to focus on more important innovation-driven tasks.
Data quality is an important parameter for the financial organization as any breach in data can have a terrible impact on business. If the data integration platform does inadvertently allow unauthorized access, the value extracted from it is limited. The problem incurred from human error can have a detrimental effect on the data and the value derived from it. So, the data integration solution chosen must provide an end-to-end encrypted environment that ensures authorized access in any case. By doing so, organizations can restore the quality and management of data without any problem.
In the last few years, data has turned out to be an essential source of business value. Financial units can adopt the steps mentioned above to harness the potential of data and leverage it for simplifying decision-making.