How to Build Data Products that Work

Revelate
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Table Of Contents

In 1854, Dr. John Snow changed the course of history by plotting cholera cases on a map of London. Without even knowing what bacteria were, he used this early “data product” to identify a contaminated water pump as the outbreak source. Fast forward to today. The tools have evolved, but the essence remains; data products extract clarity from chaos and point us toward actionable insights and innovative solutions. 

Data products have emerged as vital tools for enterprises seeking to gain insights and make data-driven decisions. They include datasets, APIs, code, models, and dashboards, all working together to provide actionable information to users. Treating data as a product and catering to data consumers is essential for achieving success. This approach requires close collaboration between data team members to ensure that data products meet the needs and expectations of their users.

Developing data products is a complex process that requires a deep understanding of the data sources, meticulous data preparation, and the ability to build scalable, privacy-conscious, user-friendly solutions.

Understanding the core components of data products

Data products have several components that work in tandem to deliver insights and enable data-driven decision-making. These components include:

  • Datasets
  • APIs
  • Code
  • Models
  • Dashboards

Product components provide the structure and functionality that users need to interact with data and derive meaningful insights. A cloud data warehouse is a key part of data product development. It stores and manages large volumes of data.

Data products are comprised of data assets and come in various forms, such as:

  • Raw data
  • Derived data
  • Algorithms
  • Decision support
  • Automated decision-making systems

Each type of data product plays a unique role in enabling enterprises to analyze data, gain insights, and make informed decisions. Knowing what makes up data products helps businesses create solutions that meet user needs and drive success.

People commonly confuse data products with data assets, but they are quite different. Data assets are typically raw data bounded by access. If you can’t access the data, you can’t access the asset. A data product is not necessarily bounded by access, especially when it comes to third-party data products. However, first- and second-party data assets are often bounded by access. Data products built with first- and second-party data may be packaged up in a manner that does not depend on security or access controls.

Steps to building effective data products

Building effective data products involves a systematic approach that includes:

  1. Identifying the problem or need
  2. Collecting relevant data
  3. Cleaning and preparing the data
  4. Developing the data model
  5. Designing the user interface
  6. Incorporating continuous feedback and iteration

in the 1850s, Dr. Snow applied many of the same principles that are fundamental to data products today: he gathered data (cholera cases), cleaned and prepared it (organized by location), developed a model (visual map), and presented it in a user-friendly manner (spatial representation of the outbreak). Let’s explore each step in more detail.

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Identifying the problem or need

Understanding the specific problem or need that the data product will address is a crucial first step in the development process. Recognizing this need ensures enterprises allocate resources efficiently and tailor the data product to meet the company’s requirements. By understanding the business objectives, enterprises can establish clear goals for their data product, which in turn guides their decision-making process and increases their chances of success.

Engaging with domain data teams early in the development process identifies critical problems and needs. Data teams often have a deep understanding of their business context and can ensure that a data product aligns with organizational objectives. By engaging with experts early, enterprises can better understand user needs and design data products that are more likely to be successful.

Collect relevant data

Once the organization identifies the problem or need, the next step in the data product development process is to collect relevant data. Gathering pertinent data identifies patterns, trends, and correlations, which often leads to valuable insights and actionable recommendations. Ensuring the data your organization uses for decision-making is accurate and applicable to the specific problem is crucial. Without relevant data, your organization may base your decisions on assumptions or incomplete information, leading to ineffective outcomes.

The initial step for gathering data is to create a list of datasets within your organization’s internal intranet. It is important to validate the data to ensure its accuracy, currency, and relevance to the issue or requirement. By collecting relevant data from various sources, your enterprise can build a solid foundation for your data products, enabling you to deliver value and drive business success.

Data cleaning and preparation

Data cleaning and preparation ensures the accuracy, reliability, and quality of the data, which analysts then analyze to make informed decisions and draw meaningful insights. Data cleaning and preparation involves identifying and removing errors, inconsistencies, and duplicates from the data. Additionally, organizations may need to transform data into a format that is more conducive to analysis and modeling.

Data cleaning and preparation is a challenging, time-consuming process requiring specialized skills. However, enterprises can use several tools and techniques to automate the process and improve efficiency. By using these tools, they can detect and rectify inconsistencies, errors, and duplicates in the data. They can also document the cleaning and preparation process, which provides a record of the steps taken and identifies potential problems, to ensure that the data is accurate and reliable.

Develop the data model

Data modeling expedites application development and realizes data’s value. A well-designed data model reduces the risk of data redundancy, enhances data quality, and allows the identification of errors or necessary changes before writing any code.

The process of developing the data model involves:

  • Understanding the data sources
  • Identifying the data elements
  • Establishing the relationships between the data elements
  • Validating the data model and confirming that it satisfies the requirements of the application.

Enterprises that build accurate data models can create better data products that deliver value.

Design the user interface

A well-designed user interface allows users to interact with the data and gain insights easily, which is crucial for achieving the product’s goals. To ensure the interface satisfies user needs, it is important to understand their requirements and pain points. The interface should be intuitive and user-friendly and visually appealing and engaging.

When designing a data product UI, it’s crucial to ensure the product has:

  • A user-centric interface
  • Consistent Interface
  • Intuitive Navigation
  • Feedback mechanisms
  • Error handling

Continuous feedback and iteration

Continuously gathering feedback from users and stakeholders is vital to the long-term success of a data product. By incorporating this feedback, enterprises can refine and improve their data products over time. Feedback ensures data products stay relevant and continue to deliver value. Methods for collecting feedback include:

  • Surveys
  • Interviews
  • Focus groups
  • Other forms of user research

Challenges in data product development

Creating data products comes with inherent hurdles. Enterprises must grapple with scalability, navigate ethics and privacy, and ensure smooth integration with existing systems. History offers parallels: Dr. Snow encountered skepticism from both peers and the public. Through incisive data visualization, he tackled a pressing health crisis and pioneered modern epidemiology. 

Similarly, enterprises that identify and overcome data productization hurdles create tools that resonate with lasting impact and genuine value. The most common challenges that enterprises face in the data productization process involve:

  • Scalability concerns
  • Ethical and privacy considerations
  • Integration with existing systems

Scalability concerns

Scalability concerns refer to the challenges and issues when a system or application must accommodate an increasing workload or user base. To ensure the success of a data product, enterprises must plan for scalability to ensure data product success. Creating such a system involves addressing issues with:

  • Application code
  • Hardware resources
  • Database limitations
  • Capacity to accommodate more users or process more data

Enterprises should proactively address scalability concerns by anticipating future needs and designing data products with scalability in mind. By considering scalability from the outset, enterprises create data products that are able to handle growing demands, ensuring they continue to deliver value as the organization grows.

Ethical and privacy implications

Enterprises should consider the ethical and privacy implications of data product development to ensure the trust and protection of their consumers. They must ensure they collect, store, and use data in compliance with applicable laws and regulations. 

Safeguarding user rights often entails implementing data encryption, anonymization, and other security measures to protect user data. Enterprises should also consider employing data analytics and machine learning techniques to identify patterns and trends in data without compromising user privacy. These techniques can provide valuable insights while adhering to ethical and privacy guidelines.

Enterprises that prioritize ethical guidelines and user privacy create data products that:

  • Deliver valuable insights
  • Build trust and confidence among users
  • Foster a positive relationship between the organization and its users
  • Ensure the long-term success of the data product

Integration with existing systems

Integrating data products with existing systems and processes maximizes their value and effectiveness. However, enterprises must first consider the compatibility of data products with their existing systems to ensure quick adoption and use. Enterprises may achieve integration using existing APIs, custom integrations, or data fulfillment platforms.

Successful integration with existing systems offers numerous advantages, including:

  • Enhanced efficiency
  • Increased productivity
  • Improved decision-making
  • Cost reduction
  • Enhanced customer experience

By ensuring that data products easily integrate with existing infrastructure, enterprises create solutions that deliver lasting value.

The importance of external collaboration and diverse sources

Just as Dr. Snow’s findings were bolstered by interviews with residents and data from various sources, business users have the capability to pool diverse data sources to gain more accurate and insightful results. Collaborating with external partners and leveraging diverse data sources enhances the effectiveness of data products. 

Enterprises may work with external collaborators and access a wide range of data sources to:

  • Gain improved insights 
  • Foster innovation and creativity 
  • Obtain otherwise unavailable specialized data
  • Enhance the accuracy of their data products

External collaboration and diverse data sources often take various forms, such as open data initiatives, public-private partnerships, and data-sharing agreements. By capitalizing on external collaboration and diverse data sources, enterprises are able to improve the overall quality and value of the data product.

Leveraging data fulfillment platforms for data productization

Data fulfillment platforms streamline the development process, improve the quality of data products, and increase their overall effectiveness.

What is a data fulfillment platform?

A data fulfillment platform is a software or system that facilitates automated data distribution and delivery from multiple sources to users or applications. It enables the efficient transfer of data between different systems, delivering the appropriate data to the relevant destination in a timely manner. Data fulfillment platforms offer a range of advantages, such as enhanced data accuracy, accelerated data delivery, and augmented scalability.

By providing the necessary tools and infrastructure to facilitate the exchange and monetization of data, data fulfillment platforms empower businesses to:

  • Create data products tailored to their requirements
  • Streamline the data product development process
  • Ensure data accuracy and consistency
  • Deliver data products to end-users in an expedited manner

Leveraging these platforms helps your organization achieve these goals.

Advantages of using data fulfillment platforms?

Data fulfillment platforms offer numerous benefits that enhance the data product development process and facilitate:

  • Faster data collection, processing, and analysis, thus improving efficiency
  • Data sharing between teams, resulting in improved collaboration
  • Data democratization for easier access to data and more informed decision-making

Using data fulfillment platforms, enterprises streamline development, create more effective data products, and ultimately drive business success. These platforms improve efficiency and collaboration and empower enterprises to realize the full potential of their data, delivering valuable insights to users and driving innovation.

How data fulfillment platforms complement data product development

Data fulfillment platforms support and enhance product development by providing the necessary infrastructure and tools to:

  • Manage and deliver data products  
  • Create solutions that are tailored to specific requirements
  • Access data from various sources
  • Develop more comprehensive data products

Why Revelate excels in data productization

Revelate helps businesses manage data from collection to distribution. Its standout features include rapid metadata definition, which can help your business quickly and easily define the metadata for your data products, and robust governance mechanisms, which can help you ensure your data products are compliant with regulations and that you use them in a responsible way. By leveraging Revelate’s mastery in data productization, businesses aren’t just accessing tools—they’re stepping into the lineage of thinkers like Dr. Snow, turning data into actionable insights to sculpt their success. 

Frequently Asked Questions

What is a data product?

Data products analyze and provide results based on data. Data products may range from platforms or tools to any other element that uses data as its primary facilitator.

What is an example of product data?

Product data refers to any data about an offered product or service such as a pair of shoes, a concert ticket, the rental of a car, a haircut, or an episode of a TV show streamed online.

Why do you need data products?

Data products provide enterprises with a way to effectively analyze large amounts of data, allowing them to make more informed decisions.

This feature is invaluable for any organization that wants to get the most out of its data.

Why do data products work?

Data products work by locating and collecting source data, processing it as necessary, creating data services to provide access to consumers, and delivering data through data pipelines to authorized analytical users. They are designed to make data more accessible and easier to use. They are often used to create insights, inform decisions, and drive business outcomes. By leveraging data pipelines, data products can be quickly deployed to provide access to data in a secure environment.

What are the core components of data products?

Data products consist of datasets, APIs, code, models, dashboards, and a data warehouse, all of which provide users with the ability to make informed decisions.

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