Data distilleries: CIOs turn to new efficient enterprise data platforms

In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise. However, a significant challenge persists: harmonizing data systems to fully harness the power of AI. According to a recent Salesforce study, 62% of large enterprises are not well-positioned to achieve this harmony, with 80% grappling with data silos and 72% facing the complexities of overly interdependent systems.

For chief information officers (CIOs), the lack of a unified, enterprise-wide data source poses a significant barrier to operational efficiency and informed decision-making. To overcome this, many CIOs originally adopted enterprise data platforms (EDPs)—centralized cloud solutions that delivered insights quickly, securely, and reliably across various business units and geographies.

Now, EDPs are transforming into what can be termed as modern data distilleries. These distilleries refine raw data while also embedding pre-built and custom components directly into the data lifecycle. This shift streamlines operations, enhances business insights, and unlocks the full potential of data.

Why data distilleries are a game-changer: Insights from the insurance industry

Traditionally, managing data in sectors like insurance relied on fragmented systems and manual processes. For instance, in claims management, insurers would assess claims based on incomplete, poorly cleaned data, leading to inaccuracies in evaluating claims.

Take, for example, a recent case with one of our clients. They had an AI model in place intended to improve fraud detection. However, the model underperformed, and its outputs showed discrepancies compared to manual validations. An analysis uncovered that the root cause was incomplete and inadequately cleaned source data, leading to gaps in crucial information about claimants. This issue resulted in incorrect risk assessments, where high-risk claims were mistakenly approved, and legitimate claims were wrongly flagged as fraudulent.

Today, the introduction of data distilleries represents a significant departure from this conventional approach. These distilleries streamline data management by providing a unified, high-quality source of enterprise data. By integrating and refining data through these modern solutions, insurers can enhance the accuracy of risk assessments, reduce claims payout time by over 50%, and boost operational efficiency by more than 30%.

To understand the impact of data distilleries, it’s essential to first recognize traditional data management challenges. Historically, insurers struggled with fragmented data sources, leading to inefficient data aggregation and analysis. Integrating advanced technologies like genAI often requires extensively reengineering existing systems. This approach consumed considerable time and resources and delayed deriving actionable insights from data.

Today, data distilleries revolutionize this process by providing a centralized platform that streamlines data aggregation, facilitates access to genAI modules, and supports self-service data consumption in the cloud. Instead of overhauling entire systems, insurers can assess their API infrastructure to ensure efficient data flow, identify critical data types, and define clear schemas for structured and unstructured data. Incorporating custom knowledge graphs, enriched with domain expertise, further optimizes data consolidation.

This shift allows for enhanced context learning, prompt augmentation, and self-service data insights through conversational business intelligence tools, as well as detailed analysis via charts. By focusing on these key factors, insurers can leverage the benefits of data distilleries, transforming raw data into valuable business intelligence and modernizing their operations.

Driving operational efficiency and competitive advantage with data distilleries

As organizations increasingly adopt cloud-based data distillery solutions, they unlock significant benefits that enhance operational efficiency and provide a competitive edge. Consolidating data and improving accessibility through tenanted access controls can typically deliver a 25-30% reduction in data storage expenses while driving more informed decisions. For insurers, actuarial teams can cut data preparation time from 8-10 days to just one. Claims processing can be reduced from 35-40 days to about a week.

Selecting the right data distillery requires consideration. When evaluating options, prioritize platforms that facilitate data democratization through low-code or no-code architectures. These tools empower users with sector-specific expertise to manage data without extensive programming knowledge. Features such as synthetic data creation can further enhance your data strategy.

Effective data governance and quality controls are crucial for ensuring data ownership, reliability, and compliance across the organization. A robust data distillery should integrate governance, modeling, architecture, and warehousing capabilities while providing comprehensive oversight aligning with industry standards and regulations. This approach minimizes risks, maintains customer trust, and ensures the delivery of reliable insights.

From an implementation standpoint, choose a cloud-based distillery that integrates with your existing cloud infrastructure. The ideal solution should be scalable and flexible, capable of evolving alongside your organization’s needs. Opt for platforms that can be deployed within a few months, with easily integrated AI and machine learning capabilities. This ensures your organization effectively utilizes data, scales effortlessly, and stays agile and adaptable.

Visit EXL’s website for more information on transforming processes with data.

Ankur Jain, vice president of data, analytics & AI at EXL, a leading data analytics and digital operations and solutions company.