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Data has become a mission-critical asset for organizations of all sizes. Yet, simply managing data is not enough. Companies must also optimize and effectively leverage data to drive business value and outperform competitors. Chief Data and Analytics Officers (CDAOs) face the critical challenge of using data to win in the marketplace, enhance stakeholder value, and ensure data quality, accessibility, and security for both structured and unstructured data, all while preparing their infrastructures for AI evolution.

The Data Dilemma: Bridging Gaps to Unlock Full Potential

Many business leaders recognize that transforming data into reusable products can provide their companies with a competitive edge. According to a 2024 survey by ÀÖÓ㣨Leyu£©ÌåÓý¹ÙÍø LLP, 92 percent of respondents consider data products essential for their organizations' success. However, only 35 percent reported achieving significant value from their data product initiatives so far.

The reasons for lackluster returns:

• Poor Data Accessibility: Difficulty accessing timely, relevant data hampers decision-making and innovation.
• Inconsistent data governance: Poor data quality and accessibility lowers trust and prevents consistent outcomes.
• Ambiguous Data Ownership: Undefined or unclear data ownership leads to inconsistencies and accountability issues.

These barriers, coupled with the historical gap between employees managing data and those using it for decision-making, hinder the realization of data's full potential.

The Path to Data-Driven Success

The solution lies in transforming how data is collected, delivered, and managed. Building high-utility, high-value data products is not easy, but taking a modern data approach is essential.

What is a Data Product? A data product is a collection of data assets that have been taken from raw to curated status for a specific business purpose. Built for reuse and actively managed from definition through retirement by the owner, data products power solutions and dashboards with high-fidelity, trusted insights. The more that a data product can be reused or monetized, the more it's worth. These products present findings in user-friendly formats like reports, dashboards, or recommendation engines.

Successful organizations not only use data products to increase efficiency but also to drive transformational changes. Data product leaders integrate these products into their strategic goals, utilizing them to achieve operational excellence, speed to market, competitive advantage, and regulatory compliance

Key Attributes:

• Accessibility & Usability: Ensure data products are easy to access and user-friendly.
• Reliability & Quality: Maintain high data quality through continuous validation and updates.
• Alignment to Business Outcomes: Focus on creating data products that directly support strategic goals.

Actionable Strategies to Optimize Data for Business Value

1 | Inventory your data assets and experts 

Start by gathering, consolidating, and analyzing your data assets at the portfolio level. This foundational step ensures alignment with strategic goals:

• Assess Existing Resources: Identify who is using the data, how it's being used, and applicable business rules.
• Identify Gaps: Highlight areas where information, skills, or knowledge are lacking.
• Prioritize Key Data Products: Focus on high-value data product candidates and identify champions for your data modernization initiative.

2 | Quantify the Data Value Chain

Understanding the value of your data is critical for strategic alignment. Organizations need to:

• Understand Their Data Library: Recognize valuable data assets and the resources required to develop them into high-value data products.
• Evaluate ROI: Estimate if a proposed data product will provide a sufficient return on investment.

3 | Confirm Data Portfolio Utility and Prune Where Necessary

Optimizing data involves confirming utility and addressing what is necessary:

• Funnel Data Consumers to High Fidelity Assets: Use AI-enabled catalogs to reconfirm data utility.
• Use Data Minimization Best Practices: Ensure you only collect, store, and maintain what is absolutely necessary.

4 |Shape Behavior, Self-Service, and a Marketplace Mindset

Encouraging a culture of self-service and collaboration can drive better data utilization:

• Create a Collaboration Hub: Allow users to 'shop' for the data products they need to assemble new solutions.
• Encourage Rating and Idea Sharing: Use AI to automate social data gathering and confirm the utility.

5 |Prioritize Value Measurement

Ensuring data products create ongoing value requires meticulous measurement:

• Evaluate Potential for Value Creation: Measure adoption over time using AI to surface relevant metadata for continuous feedback.
• Incremental Adoption: Track incremental adoption and measure wide impact to gauge the success of each data product.

Lead the Data-Driven Transformation

Optimizing data products for business value requires a strategic and collaborative approach. While many organizations recognize the importance of data products, actualizing significant value from these initiatives often remains challenging due to issues like poor data accessibility, slow acquisition times, and unclear ownership. By focusing on creating structured, reliable, and high-quality data products tailored to specific business outcomes, companies can leverage and optimize their data to create more value and enhance their competitive edge.

Drive Data Excellence Today

Time is of the essence. Act now to ensure your data is accessible, responsibly managed, and ready to drive innovation and strategic decision-making. ÀÖÓ㣨Leyu£©ÌåÓý¹ÙÍø’s expertise can guide you through this transformation, ensuring you harness the full power of your data assets. The time to elevate your data strategy is now.

Build data products that rely on agile management systems, elevated data quality, and solid operational foundations. We’ll help you establish federated data ownership practices and data models optimized for specific domains and lines of business.

Anticipate and adapt to the wide-ranging impacts AI can have on your data and organization, including budgets and data controls, secure data practices, and cloud-native architectures.

Harness the power of data ethically and responsibly with trusted data principles and governance models for managing risk.

Create a consumer lifecycle approach that incorporates self-service models, AI assistants and agents, and builds a foundation for enterprise insights.

Operate and manage your data infrastructure with integrated frameworks that support access to a broad range of data sources and make analytics faster with less friction.