Understanding Data Productization: Data History in the Making

Data products and data productization seem to be all the buzz! But a company’s storing and utilizing data isn’t new, and many corporate beginners and seasoned veterans, alike, are trying to understand – what are data products and what productization means. So let’s dive in…

From Fossils to Fuel

As I said, across industry boundaries, data has been at the core for quite some time. However, as we move into the generation of advanced analytics and artificial intelligence, organizations, both big and small, old and new, are realizing the increasing importance of recognizing the value of their data assets.

The goal of a data product is to derive meaningful insights, facilitate decision-making, or provide a solution to a specific, and sometimes very prevalent, problem. Data products add value to an organization by transforming data into actionable insights, predictions, or tools that can be utilized to achieve specific goals. These products play a crucial role in fuelling today's data-driven world, facilitating informed decision-making and driving innovation. Data productization is the process of transforming raw data into a valuable and marketable product or service. It involves taking data and converting it into a product that can be used by customers, stakeholders, or other parts of the business. Data productization requires a combination of data engineering, data science, and domain expertise to effectively transform raw data into products that drive business value.

Digging into Data Product Details

A data product is designed to be user-friendly, allowing individuals, teams, or systems to easily interact with and derive insights from the data. Data products are often designed to scale, accommodating increased data volumes or user demand without compromising performance. As data volumes grow, the infrastructure and architecture should be able to handle increased loads efficiently. The process of transforming raw data into a product is often automated, involving data processing, analysis, and potentially machine learning algorithms. Data products must be interoperable and integrated into existing workflows, systems, or applications, making them a seamless part of the overall business environment, compatibility with other tools and technologies is crucial for user adoption.

Fossilizing Data Products

Data product development is often an agile and iterative process, with continuous improvement based on feedback, changing requirements, and advancements in technology. I like to think of 4 key components:

1. Objectives: First, clearly defined objectives that understand the problem are required to ensure that value can be delivered.

2. Audience: Next, it is important to identify the data product’s target audience, their needs, preferences, and pain points.

3. Governance: Ultimately, data is the foundation of any data product so mastering and maintenance is key. Establishing robust data sources, optimized storage, and a clean, flexible, and secure model is key. To ensure that data is accurate, relevant, and high-quality, metadata and preprocessing are crucial in preparing for productization. Robust data governance practices are needed to establish clear guidelines for data usage and access. Documentation and monitoring tools must be implemented to track data product performance.

4. Analytics: Finally, a collaboration between data scientists, engineers, business analysts, and domain experts is essential to ensure modern advanced analytic technologies, such as AI and machine learning are optimally, ethically, and securely employed.

Creating a Footprint in Healthcare Data History

In healthcare, data products refer to applications, tools, or services that leverage health-related data to support decision-making, improve patient outcomes, and enhance overall healthcare delivery. These products are designed to make use and insight into the vast amount of data generated within the healthcare ecosystem, including electronic health records (EHRs), medical imaging, patient-generated data, clinical trials, and more. The goal is to extract meaningful information from this data to streamline operations and advance modern medical care. Some examples include:

Clinical Decision Support Systems use patient data, medical literature, and best practices to assist healthcare providers in making clinical decisions. It may offer recommendations for diagnosis, treatment plans, and drug interactions. Health Information Exchange (HIE) platforms facilitate the sharing of patient information across different healthcare organizations and systems. These products help ensure that relevant and up-to-date patient data is accessible to authorized healthcare providers for them to deliver evidence-based care.

Population Health Management Tools analyze data from a population of patients to identify trends, risk factors, and opportunities for preventive care. They help healthcare organizations proactively manage the health of their patient populations. For one, data products that leverage predictive analytics can help forecast patient outcomes, identify high-risk patients, and optimize resource allocation within healthcare facilities. In addition, data products for managing clinical trials streamline the process of collecting, analyzing, and reporting data from clinical research studies. They help ensure compliance with regulatory requirements and facilitate collaboration among researchers. Insights can be used for performance improvement, cost optimization, and more.

Data products in healthcare are making history for their abilities to drive innovation, improve patient care, and optimize healthcare processes. Now, it is our opportunity to enable data product solutions together.