6 Tips to Accelerate Adoption of Data Products for Business Growth

The rise of Generative AI is fundamentally altering how enterprises handle their data. Combining large volumes of data from disparate sources and managing data quality and integrity across the lifecycle is critical to delivering trusted data to AI models. The need to safeguard data against security, privacy and regulatory concerns while keeping it available and accessible for AI models is reshaping how data gets collected, stored, processed and utilized.

Findings from CDO Insights 2024: Charting a Course to AI Readiness suggests that delivering reliable and consistent data fit for generative AI (39%) is the main priority for organizations’ data strategies.¹ As a result, CDOs and data leaders are focused on bridging the ever-widening gap between data producers and data consumers.

Generative AI is redefining the rules of data engagement for both data producers and data consumers.

Data producers need greater access and visibility into more diverse data sources and need to ensure that the data used for AI is high-quality and trustworthy. They need to set up guardrails around the safe, secure and compliant data use while streamlining data operations. Hence, data producers are looking at ways to scale data delivery to data consumers and enforce data governance standards.

Data consumers require quick access to curated, fit-for-purpose and trustworthy data to train AI models. Utilizing trusted data products governed by robust data privacy and security measures gives data and AI consumers the confidence to explore unique insights. It also enables organizations to respond to changes faster.

Data products are fast emerging as a solution to navigate this shift in data dynamics and meaningfully connect data producers with data consumers to enable a data-driven enterprise.

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Explore our eBook, “Fuel Accelerated Business Growth with Data Products,” to understand how data leaders are leaning on data products to unleash the business value of data. The eBook outlines drivers for the shift towards data products, how to create a business case and a roadmap for executives to manage data products and maintain a competitive advantage effectively. Download your copy today.

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Data products are curated digital assets engineered to deliver data insights to data consumers in a user-centric and actionable format.

From increased operational efficiency to more informed strategic decisions, data products supported by a data marketplace or similar tools that drive the last mile of data delivery facilitate easy access to actionable, relevant, high-quality data.

Here are a few ways in which organizations benefit from adopting a data product strategy:

• Ready-to-use data: Quick access to diverse, high-quality, curated and governed data products available for immediate use helps teams scale AI applications and reduce deployment time.
• Deeper insights with high-quality data: Data products can be monitored to identify and remediate data quality issues efficiently allowing consumers to leverage reliable datasets for deeper insights confidently.
• Scalability and agility: Data products' modular design helps companies scale their data operations faster and with greater flexibility.
• Secure data democratization: A governed data marketplace and similar data delivery tools enable wider visibility and safer accessibility to curated data products while limiting risk exposure.

Data products offer an effective way to transform data from being a static repository of unused potential to becoming the lifeblood of innovation through the capabilities of generative AI. New business use cases can be delivered as much as 90 percent faster while the total cost of ownership declines by 30 percent.²

Widely adopted data products can deliver timely, accurate insights for smarter business decisions, increase the overall efficiency of data operations, promote innovation and foster a data-centric culture. However, driving adoption can be challenging for organizations due to complexities and shifts required in business strategy, technology infrastructure and organizational culture.

Lack of data product adoption can significantly undermine a company's data strategy leading to disjointed data operations and slower decision-making. Low usage impedes the full-value extraction from data assets, limits growth opportunities and compromises the company’s ability to respond to change faster.

Strategic focus on data product adoption ensures that data products are integrated efficiently across organizational functions, promoting consistency, accuracy and reliability in data processing and analysis. Below are six tips for data leaders that help reduce resistance to change and accelerate enterprise adoption of data products.

1)      Involve users early — Bringing in data stakeholders throughout the design and development process ensures data products are built with usability and utility in mind. It helps align the data product functionalities to intended and expected benefits.

2)      Focus on the experience (not just the product!) — Understanding the user journeys and how they interact with the product influences design choices and helps deliver delightful experiences. Designing the product with an experience that minimizes the user's need to change their behavior and status quo accelerates adoption.

3)      Build iteratively and test early —This approach allows companies to start small with data products, refine them to align with user expectations and market demands and then scale confidently. This prevents companies from falling into the trap of analysis paralysis and starts delivering value to stakeholders early. Early testing and validation with end users help identify potential issues or inefficiencies and reduce long-term costs associated with fixes and redevelopment.

4)      Garbage in, garbage out — Data products will only be as effective as the underlying data. Products fueled by inaccurate data can lead to unreliable outcomes and decision-makers may quickly lose confidence in the initiative.

5)      Data operation and process support — Data products require significant support from data processes to ensure effectiveness and reliability especially in the early stages. Integrating and curating data into data products in line with organizational standards may require a dedicated team. This presents a challenge for small data teams, often in service of many other teams as an enterprise function.

6)      Continuous relevance — Data products must be continuously assessed for significance. Companies often get caught up in creating non-relevant products and can very soon end up in a swamp of non-required data products that sap resources without delivering value, ultimately leading to program abandonment. Continuously evaluating the impact and improvements for data products can help deliver incremental returns and build credibility.

Deploying data products requires organizations to embrace a mindset change and fundamentally alter how data users interact with data. It presents an exciting opportunity for organizations to shape a culture of innovation and transformation in how data is utilized.

Each organization’s unique culture, complexity and data maturity brings distinct challenges and unique opportunities to unlock the full potential of data products. It encourages leaders to embrace data products as a journey that must evolve with the organization's needs.

AI-powered data management empowers organizations to accelerate value from data products by enabling a trusted data foundation for data products.

In the eBook, “Fuel Accelerated Business Growth with Data Products,” we outline the challenges data leaders can expect as they pivot to a data product approach and how they can overcome these roadblocks to accelerate business growth.

¹CDO Insights 2024: Charting a Course to AI Readiness

²https://www.mckinsey.com/capabilities/quantumblack/our-insights/how-to-unlock-the-full-value-of-data-manage-it-like-a-product