How to Build Great Data Products

May 27, 2024
product management
5 min read

By SV Varadarajan – Director of Analytics at indeed.com

The ability to transform raw data into actionable insights has become a cornerstone of organizational success. At the heart of this transformation lie data products – innovative solutions that leverage data to deliver value to users. From recommendation engines to predictive analytics platforms, data products empower businesses to make informed decisions, enhance customer experiences, and drive competitive advantage. However, building great data products requires more than just technical prowess. It demands a strategic approach that encompasses ideation, development, and deployment. In this blog, we’ll explore data product management and the essential steps and best practices for creating exceptional data products.

Key Takeaways:

Data product management is crucial because it enables companies to harness data to create valuable products and services. By analyzing data, product managers can understand user behavior and preferences, leading to personalized and engaging customer experiences that enhance satisfaction and loyalty.
The driving forces of data product management are data economy, data privacy and governance, data monetization, and data sovereignty.
Building a successful data product involves several crucial steps that ensure the product is well-conceived, effectively utilizes data, and meets the needs of its users.
Embracing product management principles, such as prioritizing user requirements, aligning features with market needs, and understanding the product lifecycle, is crucial for the success of data products.
A user-centric approach, coupled with strategic adaptation and iterative development, is paramount for overcoming challenges, managing regulatory complexity, and achieving sustained value creation in AI/ML integration.

In this article

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Why Data Product Management?

Data product management is crucial in today’s digital age because it enables companies to harness data to create valuable products and services. By analyzing data, product managers can understand user behavior and preferences, leading to personalized and engaging customer experiences that enhance satisfaction and loyalty. This competitive edge allows companies to quickly adapt to market changes and innovate faster than their rivals.

Additionally, data product management ensures responsible and compliant data use, protecting organizations from legal risks and building trust with customers. It also facilitates digital transformation by improving efficiency, reducing costs, and opening new revenue streams. In essence, data product management drives innovation, enhances customer experiences, maintains competitive advantage, and ensures compliance, making it essential for any modern organization.

The term “data economy” refers to how data drives substantial revenue for the top five organizations globally: Apple, Amazon, Alphabet (Google), Microsoft, and Meta (Facebook). These companies generate combined quarterly profits of $55 to $70 billion, far surpassing the next 15 Fortune 500 companies. Their primary revenue streams are not physical products but software, services, and data licenses. For instance, Apple, while making significant money from devices, also earns substantially from its services.

Driving Forces of Data Product Management

1. Data Economy

The term “data economy” describes an economic system where data is a critical resource for generating value and driving innovation. In this economy, data is collected, analyzed, and utilized to make informed decisions, enhance products and services, and create new business models.

Top Companies Leading the Data Economy:

- Apple, Amazon, Alphabet, Microsoft, and Meta: These companies are at the forefront of the data economy. They leverage vast amounts of user data to optimize their services, personalize user experiences, and drive significant revenue through data-driven strategies. For instance, Alphabet (Google) utilizes data from its search engine, advertising platforms, and other services to offer targeted advertising, which is a major revenue source.
- Quarterly Profits: The combined quarterly profits of these top companies range from $55 to $70 billion, underscoring the immense value generated from data-centric business models. In contrast, the next 15 Fortune 500 companies do not match these profits even when combined, highlighting the power of data.

Revenue Streams:

- Software and Services: Unlike traditional companies that generate revenue from physical products, these tech giants earn substantial income from software, services, and licenses. For example, Microsoft’s cloud services and software licenses contribute significantly to its profits.
- Data Utilization: These companies collect and analyze user data to improve their products, target advertising, and create new services. Apple, while also generating revenue from its devices, heavily relies on services like the App Store, iCloud, and Apple Music.

2. Data Privacy and Governance

Importance of Data Privacy:

- User Trust and Legal Compliance: Data privacy ensures that personal information is handled responsibly, maintaining user trust and complying with legal requirements. The misuse of data can lead to significant legal and reputational damage.
- Cambridge Analytica Scandal: This incident highlighted how data privacy violations can influence political outcomes and erode public trust. Facebook users’ data was used without consent to target voters in the US presidential election, leading to a massive public outcry and legal repercussions.

Data Governance:

- Framework for Data Management: Data governance involves establishing policies, procedures, and standards for managing data responsibly. It ensures data quality, security, and compliance with regulations.
- Role of Organizations: Companies must implement robust data governance frameworks to prevent privacy violations and ensure data is used ethically. This includes defining data access controls, data handling procedures, and ensuring transparency with users.

3. Data Monetization

Concept:

- Turning Data into Revenue: Data monetization is the process of generating revenue from data. Companies analyze user data to identify trends, preferences, and behaviors, which can be monetized through targeted advertising, personalized services, and other strategies.

Examples:

- Facebook and User Profiles: Facebook creates detailed user profiles based on activities, interests, and social interactions. These profiles are valuable for advertisers who can target specific demographics with tailored ads, generating substantial advertising revenue for Facebook.
- Amazon’s Personalized Recommendations: Amazon uses purchase history, browsing behavior, and other data to recommend products to users. This personalized approach increases sales and customer satisfaction, directly contributing to Amazon’s revenue.

Ethical and Regulatory Considerations:

- User Consent and Transparency: Data monetization must be conducted ethically, with clear user consent and transparency about how data is used. Regulations like GDPR require companies to obtain explicit consent from users before collecting and using their data for monetization purposes.

- Data Sovereignty

- Data sovereignty refers to the principle that data is subject to the laws and governance structures within the nation where it is collected. It ensures that data generated within a country is stored, processed, and managed according to that country’s regulations.

Global Examples:

- India’s Data Privacy Bill: India has introduced legislation to regulate how data is collected, stored, and used within its borders, aiming to protect its citizens’ privacy and data security.
- China’s Data Regulations: China has strict regulations on data, requiring that data about Chinese citizens be stored on servers within the country. This is to prevent foreign access and ensure data security and sovereignty.
- Europe’s GDPR: The General Data Protection Regulation (GDPR) in Europe sets stringent standards for data protection and privacy, ensuring that data collected in Europe is handled according to European laws, even if processed outside the continent.

Impact on Businesses:

- Compliance Challenges: Companies operating globally must navigate different data sovereignty laws, which can complicate data management and transfer. They must ensure that their data practices comply with the regulations of each country they operate in.

Strategic Data Management: To address these challenges, companies often adopt a global standard that meets the most stringent regulations, ensuring compliance while enabling business operations across multiple jurisdictions.

4. Data Sovereignty

- Data sovereignty refers to the principle that data is subject to the laws and governance structures within the nation where it is collected. It ensures that data generated within a country is stored, processed, and managed according to that country’s regulations.

Global Examples:

- India’s Data Privacy Bill: India has introduced legislation to regulate how data is collected, stored, and used within its borders, aiming to protect its citizens’ privacy and data security.
- China’s Data Regulations: China has strict regulations on data, requiring that data about Chinese citizens be stored on servers within the country. This is to prevent foreign access and ensure data security and sovereignty.
- Europe’s GDPR: The General Data Protection Regulation (GDPR) in Europe sets stringent standards for data protection and privacy, ensuring that data collected in Europe is handled according to European laws, even if processed outside the continent.

Impact on Businesses:

- Compliance Challenges: Companies operating globally must navigate different data sovereignty laws, which can complicate data management and transfer. They must ensure that their data practices comply with the regulations of each country they operate in.

Data Product Lifecycle

Building a successful data product involves several crucial steps that ensure the product is well-conceived, effectively utilizes data, and meets the needs of its users. Here’s a comprehensive guide:

1. Conceptualize the Product

Before diving into data acquisition or technical details, it’s essential to have a clear understanding of what the product is and what it aims to achieve. This involves identifying your target customers, understanding their needs, and defining the impact and purpose of the product. This foundational step sets the direction for all subsequent activities.

2. Determine Data Requirements

Next, identify the data you need. This can be internal data, which is data already available within your organization, or external data, which can bring in additional market and competitive insights. Internal data tends to be more accurate for specific organizational needs, while external data can provide a broader perspective, enhancing the overall effectiveness of your data models.

3. Refine the Data

Data refinement is critical because raw data can be messy and unreliable. The principle of “garbage in, garbage out” highlights the importance of cleaning and preparing your data to ensure it is accurate and fit for purpose. This step involves data cleaning, transformation, and ensuring the quality of the data aligns with the needs of the product.

4. Storage and Retrieval

Consider both the technological and regulatory aspects of data storage and retrieval. Your data storage solutions should not only be efficient and scalable but also comply with relevant data protection regulations. This ensures that the data is accessible when needed and is handled securely and legally.

5. Distribution

Decide how the data product will be distributed to users. This could be through dashboards that present data visually or through APIs and microservices that allow other systems to interact with your data. The method of distribution should align with how users will best interact with and benefit from the data product.

6. Presentation

Presentation refers to how the data product is visually represented to users. This includes designing user interfaces, dashboards, and reports that effectively communicate insights derived from the data. A user-friendly and intuitive presentation ensures that users can easily interpret and act upon the information provided, enhancing the overall value of the data product.

7. Seek Feedback

Once the data product is deployed, gathering feedback is essential. This feedback can come from internal stakeholders, such as senior executives or data analysts, or from external customers. Understanding how the product performs and where improvements can be made is crucial for continuous enhancement.

Example: A/B Testing in User Recommendations

A practical example of feedback and testing is A/B testing. For instance, Amazon might deploy two versions of a user recommendation system to different segments of users—one to East Bangalore and another to South Bangalore. By analyzing how each group responds, Amazon can determine which version is more effective. This method allows for micro-level testing and refinement, ensuring the data product meets user needs more precisely.

AI and ML in Product Management

From a product management perspective, integrating AI and ML involves continuous learning and iteration. Products should be tested in real-world scenarios, refined based on performance data, and adjusted to improve accuracy and effectiveness.

To grasp the essence of AI/ML in product management, it’s essential to dissect its core components and understand its strategic significance in driving innovation and business growth.

The Foundation of AI/ML in Product Management:

At its core, AI/ML integration in product management revolves around four key elements, often depicted as intersecting circles in a Venn diagram. These elements include:

1. Data Science: This encompasses the technical expertise required to handle data at scale and leverage its capabilities effectively. It involves a blend of data analysis, statistics, and programming to extract meaningful insights.

2. User Experience (UX): Beyond visual aesthetics, UX focuses on understanding user needs and ensuring a seamless journey when interacting with the product or service. A user-centric approach is vital for driving engagement and satisfaction.

3. Feasibility: This aspect addresses the practical considerations of deploying and scaling AI/ML-powered products. It involves assessing infrastructure requirements, technology constraints, and scalability to ensure the product’s viability.

4. Product Management: Acting as the glue that binds the other components together, product management ensures the sustainability and success of AI/ML-driven products. It involves strategic planning, stakeholder management, and effective communication to drive adoption and maximize value.

The convergence of these four elements forms the nucleus of AI/ML integration in product management. It’s at this intersection that the magic happens, where data-driven insights meet user-centric design and strategic execution. By harmonizing data science, UX, feasibility, and product management, organizations can unlock the full potential of AI/ML technologies and deliver impactful solutions.

Challenges and Pitfalls:

Despite its immense potential, many AI/ML-driven product initiatives face challenges that hinder their success. One common pitfall is the tendency to prioritize technical efforts over marketing and advocacy. While technical prowess is crucial, effective communication and stakeholder engagement are equally vital for driving adoption and realizing value.

The Importance of a User-Centric Approach:

At the heart of AI/ML integration in product management lies a fundamental shift in perspective—from a focus on technical intricacies to a relentless pursuit of solving real business problems. Users care less about the underlying ML models or data science algorithms and more about tangible outcomes that address their needs. Thus, cultivating an AI product mindset that prioritizes user value over technical sophistication is paramount for success.

Understanding Regulatory Complexity:

In an era marked by heightened data privacy concerns and evolving regulatory landscapes, understanding the legal and compliance aspects of AI/ML integration in product management is critical. While internal projects may require less legal oversight, initiatives targeting external markets—especially across continents—demand careful consideration of legal implications and risk management strategies.

Achieving Success through Iteration and Adaptation:

Ultimately, success in AI/ML integration in product management hinges on continuous learning, iteration, and adaptation. By embracing a culture of experimentation, conducting thorough market testing, and soliciting user feedback, organizations can refine their AI/ML-driven products iteratively and drive sustained value creation.

3 Reasons Why Data Products Fail to Succeed

In the realm of data-driven initiatives, a staggering 80 percent of professionals have witnessed the demise of data products or technology projects. Delving into the core of this lack of success unveils three pivotal factors that serve as the missing link.

1. Lack of Focus:

The first crucial element contributing to the downfall of data products lies in their perception as projects rather than products. Unlike projects or solutions, products demand a distinct mindset—one that prioritizes user requirements and aligns features with market needs. While product managers meticulously curate features to cater to user demands, solution architects often overlook this critical aspect, leading to a misalignment between product capabilities and user expectations.

2. Ignoring the Product Lifecycle:

Similar to tangible products, technology products, and data products undergo a lifecycle evolution. Failure to acknowledge and steer through this evolution renders the product stagnant and obsolete. Just as a product manager shepherds a product through its lifecycle stages, the absence of this stewardship leaves data products vulnerable to stagnation and irrelevance.

3. Lack of Measurement Metrics:

The third cornerstone of product success lies in the ability to measure its impact. Whether launching a physical product or a digital counterpart, success metrics serve as the compass guiding product viability. In the digital realm, where the cost of maintaining products is relatively lower, the temptation to prolong a failing product’s lifespan often prevails. However, this myopic approach risks tarnishing the organization’s reputation and eroding consumer trust, thereby impacting the adoption of future data or technology products.

Embracing a Strategic Approach:

To avert the pitfalls that plague data product initiatives, organizations must adopt a strategic approach rooted in product management principles. By embracing a product-centric mindset, acknowledging the product lifecycle, and establishing clear success metrics, organizations can nurture a culture of innovation and resilience in the face of evolving market dynamics.

How To Build Great Data Products- 3 Main Ways

In data product management, success often hinges on a strategic blend of innovation, iteration, and relentless commitment to user-centricity. While the journey to launching and scaling data products may seem daunting, organizations can glean valuable insights from the tried-and-tested practices of industry titans like Google and Facebook. Let’s delve into the core tenets that underpin their success:

1. Identifying Opportunities:

At the heart of every successful data product lies a keen ability to identify market opportunities and unearth unmet user needs. Organizations must leverage data analytics and market research to pinpoint areas ripe for innovation and product development.

2. Building with Precision:

Armed with insights gleaned from opportunity identification, organizations embark on the journey of product development. This phase entails meticulous planning, agile development methodologies, and a relentless focus on delivering value to end-users.

3. Evaluating and Iterating:

The hallmark of successful data product management lies in its iterative nature. Organizations must continuously evaluate product performance, gather user feedback, and iterate based on evolving market dynamics. This iterative approach fosters agility, adaptability, and responsiveness to changing user preferences.

For industry giants like Google and Facebook, success in data product management is not a stroke of luck but a testament to their steadfast adherence to best practices. By prioritizing small iterations, continuous improvement, and iterative development, these organizations have set the standard for excellence in the field.

How to Bridge the Talent Gap and Empower Employees for Data Product Success

The ability to view data as a product is a coveted skill that holds the key to organizational success. However, despite its paramount importance, this skill remains notably scarce in today’s industry. Recognizing the critical need to bridge this talent gap, organizations are increasingly investing in upskilling their workforce through a myriad of internal training initiatives.

According to a recent survey conducted by McKinsey in 2021, over 50% of senior managers expressed a strong inclination towards building the skills of their existing employees to address the talent deficit. This proactive approach underscores the pivotal role that upskilling plays in nurturing a workforce equipped to understand and manage the complexities of data product management.

In the pursuit of skill augmentation, organizations are leveraging a multifaceted approach. Internal training programs serve as the cornerstone of this strategy, offering employees the opportunity to delve into the intricacies of data product management within the familiar confines of their organizational ecosystem. Through hands-on training, workshops, and mentorship programs, employees gain invaluable insights into the nuances of product-centric data management, thereby bolstering their efficacy as data product champions.

Moreover, organizations are exploring innovative avenues to foster skill development, including cross-functional training initiatives and talent redeployment programs. By facilitating seamless transitions between domains and leveraging existing skill sets, organizations empower employees to embark on transformative journeys toward proficiency in data product management.

While internal upskilling initiatives take precedence, organizations also recognize the complementary role of external hiring in augmenting their talent pool. By strategically leveraging external talent, organizations infuse fresh perspectives, diverse skill sets, and specialized expertise into their workforce, thereby enriching their data product capabilities.

Data Product Team Roles

Within the framework of a data product team, several key roles play pivotal roles in steering the product from ideation to fruition.

1. Data Product Manager:

At the helm of the data product team stands the Product Manager, tasked with overseeing the holistic function of the product. From conceptualization to delivery, the Product Manager orchestrates the strategic direction of the product and maintains a steadfast focus on customer engagement and satisfaction.

2. Data Product Owner:

Working in tandem with the Product Manager, the Data Product Owner assumes responsibility for engaging customers and advocating for their needs within the product development process. By harnessing customer feedback and insights, the Data Product Owner ensures that the product aligns seamlessly with user requirements and preferences.

3. UX Designers and Data Visualization Experts:

Central to the user experience (UX) design, UX Designers and Data Visualization Experts are entrusted with crafting intuitive user interfaces and seamless user journeys. By designing visually engaging and user-friendly interfaces, these professionals enhance user satisfaction and drive adoption rates.

4. Data Scientists:

Armed with a deep understanding of statistics and data analytics, Data Scientists delve into the intricacies of data trends and patterns. Leveraging advanced analytical techniques, Data Scientists unearth actionable insights that underpin the product’s value proposition and drive informed decision-making.

5. Data Engineers and ML Engineers:

Data Engineers and ML Engineers form the backbone of technical feasibility, translating conceptual designs into tangible solutions. From data deployment to machine learning model development, these experts navigate the complexities of data infrastructure and algorithmic intricacies with finesse.

6. DevOps Engineers:

Rounding out the team are DevOps Engineers, charged with seamlessly integrating development efforts into the broader business framework. From testing to deployment, DevOps Engineers facilitate the smooth transition of the product from prototype to production, ensuring alignment with organizational processes and architecture.

Building great data products requires a strategic blend of technical expertise, domain knowledge, and user-centric design. By following the steps outlined in this guide – from ideation to deployment and beyond – organizations can unlock the full potential of their data assets and deliver impactful solutions that drive business value. Embrace innovation, foster collaboration, and empower your teams to create data products that resonate with users, inspire action, and fuel growth in the digital age.

About the Author:

SV Varadarajan – Director of Analytics at indeed.com

Frequently Asked Questions

1. How do you create a good data product?

Creating a good data product involves several key steps: understanding user needs and goals, collecting relevant and high-quality data, employing effective data analysis techniques, and designing a user-friendly interface. It’s essential to iterate and refine based on user feedback to ensure the product meets expectations and delivers value reliably.

2. What is a great example of a data product?

One exemplary data product is Google Maps, which seamlessly integrates vast amounts of location-based data to provide real-time navigation, traffic updates, and location recommendations. It harnesses data from various sources to offer users a comprehensive and user-friendly experience, showcasing the power of data-driven technology in everyday life.

3. What is the data product life cycle?

The data product life cycle typically consists of several stages: inception, data collection, data processing and analysis, product development, deployment, and maintenance. Inception involves identifying the need for a data product, while data collection gathers relevant information. Processing and analysis refine the data for actionable insights, leading to product development. Deployment involves launching the product, followed by ongoing maintenance and updates to ensure its effectiveness and relevance over time.

4. What is the data product management?

Data product management involves overseeing the development, implementation, and maintenance of data-driven products to meet user needs and business objectives. It includes defining product strategy, prioritizing features, and collaborating with cross-functional teams such as data scientists, engineers, and designers. Data product managers also focus on optimizing product performance, ensuring data accuracy, and iterating based on user feedback to drive continuous improvement.

5. What is the difference between a data manager and a data product manager?

A data manager typically focuses on overseeing data governance, storage, and quality assurance processes within an organization. In contrast, a data product manager is responsible for the development, implementation, and optimization of data-driven products or features to meet user needs and business goals. While both roles involve handling data, a data product manager’s focus is on leveraging data to create value-added products or features for users.