Data as a Product: How to Unlock Value

Data has progressed from being merely a byproduct of company activities. It has evolved into a valuable asset that can provide tremendous corporate value and drive innovation when managed and used appropriately. The concept of treating data as a product is one method that is gaining favor. 

Let’s understand what a Data Product is vs Data as a Product. DJ Patil, the former United States Chief Data Scientist, aptly defined a data product as "a product that facilitates an end goal through the use of data" in his book Data Jujitsu. 

According to this definition, a "data product" is any digital product or service that uses data to achieve a certain aim. For example, the homepage of a digital newspaper dynamically curates news items based on a user's previous navigation data.

Seeing "data as a product" entails thinking about datasets in terms of products. This includes ensuring that they have essential features like discoverability, security, explorability, understandability, and trustworthiness, among others.

Consider the following example of "data as a product." A typical data product includes the code, its data, and metadata, as well as the infrastructure required to run it. It must meet the aforementioned capabilities.

In the parts that follow, we will go into the nuances of this notion, examine its benefits, and offer advice on how to approach your data like a product to maximize its worth. 

Data as a Product vs. Data as a Service

It's critical to distinguish between 'Data as a Product' (DaaP) and 'Data as a Service' (DaaS). The two notions may appear similar, but their approaches are fundamentally different.

DaaP suggests that data is packaged in such a way that it can stand alone as a product. It, like any other product, can be delivered to internal or external clients. This could include data sets, reports, dashboards, or analytics tools that are produced and managed in the same way as products are. They have a life cycle and are intended to serve the demands of a certain group of users.

DaaS, on the other hand, refers to the distribution of data to consumers on an as-needed basis. It's a service model that allows users to access data hosted in the cloud or on-premises without having to manage the underlying infrastructure. This model is more concerned with the delivery mechanism than with the data itself.

Treating Your Data as a Product

One of the fundamental concepts of approaching Data as a Product is to have a clear grasp of your "data customers." These are the individuals or systems that will be utilizing your data product. Organizations must understand their data and the potential insights it might bring in order to approach it as a product. 

You must understand the demands and requirements of your data clients, just as you would when developing a traditional product. What information are they looking for? What are their plans for it? What format do they require? All of these concerns must be addressed in order to create a data product that fits the needs of your customers.

Data product development can help with data consumption by offering consumers relevant and actionable information tailored to their specific needs. This can assist in overcoming data consumption challenges by enhancing data administration and making data more available and easy to consume.

Dashboards displaying key performance indicators, machine learning models offering automated predictions, and even algorithms like Google's search algorithm or Amazon's recommendation engine are examples of data products. These are successful instances of data products that leverage vast amounts of data to provide relevant results to users.

Once the needs have been defined, you may begin the process of acquiring, cleaning, and converting data to fulfill those objectives. This approach frequently necessitates strong data management and governance practices. It is also at this point that the usage of data profiling techniques becomes crucial.

Anomalies, missing values, and contradictory information can be identified using data profiling and transformation technologies. Tools like Enrich have the ability to create mass-customized data products that cater to specific business problems. By leveraging Enrich’s workflows, organizations can solve various data consumption challenges, such as detecting fraudulent transactions, improving conversion rates, and customer profiling. 

Unlocking the Full Value of Data

To fully realize the potential of data, businesses should treat it as a strategic asset and invest in tools and platforms that may assist them in creating data products. Enrich is one such platform that assists enterprises in swiftly and efficiently developing data products. This platform enables workflows such as anomaly detection to address various data consumption concerns, such as detecting fraudulent transactions, improving conversion rates, and consumer profiling.

For firms trying to maximize the value of their data assets, data products are a useful tool. They offer various advantages, including increased decision-making precision and efficiency, as well as the possibility of new revenue streams. McKinsey estimates that data-driven organizations that use data products can make decisions up to 30% faster than competitors and enhance decision-making accuracy by up to 15%. 

However, simply providing access to data does not unlock its full worth. It necessitates the creation of data products that are user-friendly, dependable, and relevant. This includes not only the data itself but also the tools and interfaces that are used to interact with it.

These data products include the wiring required for various business systems to ingest the data, such as digital apps or reporting systems. Each sort of business system has its own set of requirements for data storage, processing, and management, which are referred to as "consumption archetypes." Data products designed to satisfy these consumption paradigms can be easily applied to a wide range of business applications.

Teams that use data products save time searching for data, converting it into the appropriate format, and creating bespoke data sets and data pipelines—efforts that result in an architectural mess and governance difficulties. The benefits of employing data products can be substantial: new business use cases can be supplied up to 90% faster, the total cost of ownership can be cut by 30%, and risk and data-governance burdens can be decreased.

To successfully manage data as a product, however, an operating model that ensures dedicated management and finance develop standards and best practices, tracks performance, and maintains quality is required. Each data product should have a product manager and a team of data engineers, data architects, data modelers, data platform engineers, and site reliability engineers who are paid to build, upgrade, and enable new use cases.

Organizations may standardize their approach to data management, save time and money, and unleash the full potential of their data to drive company development and innovation by treating data as a product. 

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