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An extremely vital aspect of the banking and finance sector is their ability to manage exposure to losses & risk and to protect the value of its assets.

With the rapid growth of FinTech organizations, the banking and finance sector are chomping at the bit to figure out a way to accelerate their data initiatives.

Just like most industries, banking and finance have forever changed due to the explosion of big data and the infrastructure required for processing such data.

At Data Ideology, we have made a concerted effort to recognize and achieve high levels of gender equity in both leadership and employee level positions.

Applying for jobs can be challenging in any situation, but the task can be especially stressful for women in IT considering their underrepresentation in technology fields.

The World Economic Forum recently released the Global Gender Gap Report for 2021. The United States isn’t even in the top 20 for gender equality. Women only make up 14% of cloud computing jobs and 32% of data and AI positions.

Data-driven organizations understand that their data is a strategic asset and to properly harness its power it must be supported by a sophisticated data strategy.

Siloed data hinders collaboration and can cause serious downstream issues affecting multiple departments such as enrollment, claims and reimbursement. This data fragmentation stops the payer’s ability to get a 360° view of their customer, making it difficult to provide a more personalized experience.

In order to implement automation applications and other machine-based processing, data must follow an agreed-upon structure and standardization. The global healthcare community believes they are close to achieving this with Fast Healthcare Interoperability Resources (FHIR).

It is important that payer organizations continue to seek ways of improving their HEDIS scores year-to-year. One certain way of doing this would be to implement a proper data management system.

Many c-suite executives are perplexed at the amount of money their organizations spend implementing and integrating data and software applications with limited evidence that they will even align with desired business outcomes.