There may be no industry that relies on their customer data more than those in finance. With more and more people accessing data in ways that weren’t possible just a decade ago, banks, insurance companies, and other financial institutions are increasingly turning to Data Integration software to gain new ways of deriving insights from their customers. No longer can growing financial organizations rely on anything but automating their data processes to ensure complete control over the data they need to better serve their constituents. NGDATA recently published a really interesting article offering 50 tips financial institutions should use for optimizing customer Data Integration. We’ve taken the liberty to select the 5 we see as most important, explaining why as a result.
1. Utilize Data Integration to enhance customer data security
As a highly-regulated industry, the financial sector already has a strict set of guidelines that must be taken into account. In addition, the trust of the customer is and should be the only master of stakeholders in banking and insurance, as one major incident involving sensitive data could spell doom for even the largest organization. Consumers have a seemingly infinite amount of data available to them at any time, thus, financial institutions need to ensure that they are playing on level playing field or risk succumbing to competing businesses. One way to do this is to deploy modern integration tools that can generate clean, high-quality, and governed data.
2. Data Integration should come before the use of Business Intelligence and Data Analytics
If customer data is truly to be an asset for financial institutions, then they need to deploy their Data Integration solutions prior to running the data against an analytics tool. The goal of Data Integration is to create a single, unified view of business data so that it all may be analyzed. In this way, data must be pulled from the many different sources that organizations pull from, cleaned, organized and transformed. Once this has taken place, a Business Intelligence tool can suck up all of the data to answer the specific business question that is being asked. Without all of the most relevant data in one place, analytics will fail to reveal what the business seeks.
3. Data Integration should be used to bolster customer privacy
As I mentioned earlier, financial institutions are nothing without customer confidence in sensitive data remaining safe outside of their grasp. Customers have grown accustomed to doing anything at any time as a result of the mobile revolution, and financial institutions have been a major beneficiary of this. They’ve enabled this by the proper use of Data Integration; ensuring that data is in the right place on-demand and in real-time so that they and their users have unlimited access. Integration tools with niche feature capabilities enable platforms to share data without revealing personal information, which is an absolute must. As a result, customers can feel secure in knowing their data is safe as a direct result of successful Data Integration.
4. Data Integration is essential for those using Big Data
Big Data has been one of the driving forces behind the widespread adoption of Data Integration tools, no matter which industry the business sits in. Think of integration tools as the keys that unlock data sharing between business applications and users, and enterprises. The migration and integration of data from one place to another has allowed for unparalleled data search and discovery, providing financial institutions with the awareness and visibility they crave so that they may better serve their customers. Nearly every type of business is currently dealing with a data explosion in terms of connected data sources and the sheer volume of data that they are now finding to be valuable. So long as this continues (and it will), integration tools will be technological staples.
5. Data Integration breeds Data Quality for banks
Integration software gives financial institutions a complete view of the customer, helping to migrate data from different places inside the organization to a common location where it can be analyzed. Banks, for example, may also want to explore different relationships if they’ve got the authority, perhaps between interest rates and business types. If integration is not properly run prior to the analytics process, there’s a good chance the data is not quality for that specific job. This means that any “insights” that are generated are likely to lead the bank in the wrong direction. Proper integration techniques ensure that quality is achieved, and that only the data that need be run through a BI tool is present.
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