Six best practices for integrating data products in your organization

1. Have a clearly defined purpose and value

Why are we doing this?
Begin by clearly articulating the purpose of the data product. What specific problem or business question is it designed to address? Can you define measurable goals to gauge success? It’s important to be able to define your business reasons and frame each goal in terms of potential benefit to your organization:

Competitive advantage: How will developing this data product (or data products) create new opportunities?
Improved decision-making: How can providing accurate and timely information to stakeholders enable them to make informed decisions and improve business outcomes?
Cost reduction and avoiding duplication: How do reusable data products save time and resources by eliminating the need to recreate datasets for different uses and maximizing the return on data investments?

2. Focus on usability

Who is our target audience? How will they use the data product?
Identify who will use the data product and how they will interact with it. Design with the end-user in mind to ensure the product is intuitive and meets their needs. For example:

Customized executive dashboards for executives and senior management can provide near real-time visualizations of critical business metrics. These dashboards are often built using cloud-based business intelligence (BI) tools to ensure accessibility from any device, including mobile. The dashboards (which use advanced security protocols to safeguard sensitive company data) are typically role-based to ensure executives can see insights tailored to their scope supporting strategic decision-making.

Marketing teams can use web-based campaign performance dashboards to track the success of their digital and offline campaigns. These dashboards, which can be shared across teams using adjustable access privileges to protect sensitive marketing budget data, monitor critical metrics such as click-through rates (CTR), conversion rates, cost per acquisition (CPA) and return on investment (ROI). With API-enabled automated data feeds and consolidated reporting from marketing platforms (e.g., Google Analytics, social media ad managers), the dashboards offer near real-time updates, enabling quick adjustments to optimize ongoing campaigns.

Financial analysts can leverage risk management models that apply advanced predictive analytics and scenario modeling to evaluate financial risks. These tools, accessed through secure platforms that integrate with enterprise financial systems, provide insights into potential market fluctuations and credit and operational risks, which help analysts build risk mitigation strategies. The models incorporate external data sources (which may contain attributes that change periodically), such as economic indicators and industry trends, to enhance the accuracy of forecasts. Analysts can access reports via dashboards or download data for further analysis in a spreadsheet or analytics software, ensuring flexibility in how they use the data.

3. Optimize discoverability and accessibility

How will end-users discover this data product? How can you drive adoption?
To ensure users can easily find and utilize your data product, focus on discoverability and accessibility. Hosting the data product in a well-organized repository for curated data assets — like a data marketplace — is a best practice that simplifies access for users of all levels. Make the data product easily searchable by incorporating relevant metadata and tags to enhance visibility during browsing or searches within the platform.

An intuitive user interface (UI) is crucial for driving adoption. The design should be user-friendly, featuring clear descriptions, usage examples and well-labeled methods for accessing the data product to improve the overall experience.

Incorporating user feedback mechanisms like ratings and reviews helps build trust and encourages engagement. Additionally, ensuring that your data product complies with governance and privacy standards is vital—users are more likely to adopt solutions prioritizing security and compliance.

How do you access data products?

A data marketplace is a centralized hub where users can discover, assess and access trusted data products. It facilitates better organization and searchability of data assets, making it easier for data consumers to find the data they need.

4. Execute continuous improvement

What’s the best way to assure end-users about the quality of the data product and its underlying data?

Demonstrating the reliability and trustworthiness of data products is essential for building end-user confidence. Here are some practical ways to achieve that:

Provide comprehensive documentation that details the source data, and trace and communicate the data’s lineage, explaining where it came from and any transformations applied. This enhances transparency and allows users to assess the data’s origin and journey, building trust and helping users understand the data’s context
Display data quality metrics such as accuracy, completeness, consistency, timeliness and validity. Presenting these metrics can help users evaluate the data’s reliability
Certify data products to endorse their reliability and readiness for use. Organizations can tailor the certification process to meet their requirements, enabling data consumers to easily identify certified assets through visible labels. This endorsement indicates that the asset meets established standards and instills confidence in users, ensuring they can trust the data for informed decision-making
Enable crowdsourced feedback where users can leave reviews and ratings for the data product. Positive testimonials and user experiences can significantly enhance perceived quality and trustworthiness

What is a data contract?

A data contract is a vital element of a data product, representing the promise made by the data owner to data consumers. It outlines expectations, including service-level objectives (SLOs) related to system uptime and latency for a data product. Additionally, it details relevant information about pipelines or data delivery mechanisms and can also provide information about the data itself, such as schema and expected quality metrics.

This distinction highlights the difference between a data contract – which ensures the data is suitable for the consumer’s needs – and a data sharing agreement, which defines allowed and prohibited uses of the data. Together, they create a balance between maximizing the value derived from data and protecting data from potential misuse.

Watch this video to learn the critical
role of data contract in data products

5. Ensure that the data
product meets integration
and interoperability standards

Does the product meet organizational standards for interoperability?

The data product must meet integration and interoperability standards to optimize data flow
and operational efficiency across different systems and departments. To achieve this, well-known data standards such as JSON should be used to establish consistency standards for internal data. Forming a governance committee, appointing data stewards and employing data integration platforms and APIs are critical to ensuring seamless integration and interoperability.

Data product
framework

6. Consider scalability and maintainability

What mechanisms exist to collect feedback? How will maintenance affect end-users?

A robust data governance framework is essential for ensuring that data products maintain compliance and consistency as they scale. This framework helps organizations define clear policies and standards, which not only enhance data quality but also build trust among users. Also, best practices include defining lifecycle stages like 1) defining a business use case, 2) identifying and understanding data that will be used, 3) documenting the data product, and 4) verifying and monitoring the data product.

Additionally, seamless integration capabilities allow for easy connectivity with a wide range of data sources and tools, enabling organizations to build data products more efficiently. Coupled with automated workflows, which streamline data management processes, organizations can minimize the manual effort needed to maintain and scale data products. This combination of governance, integration and automation empowers organizations to adapt to changing data needs efficiently while ensuring high standards of quality and compliance.

Engaging users early and using an iterative development approach is vital for the successful maintenance of data products. This can include collecting feedback continuously through ratings, reviews and product analytics, for example.