A Not-to-Miss Opportunity: How to Build Data Products

Becoming data-driven has been a long-standing ambition for companies since lavish amounts of data came into sight. But the transition to a data-centric company is not that simple. Today, top performers are setting the bar high — thinking of data as a stand-alone product rather than a side activity. 

A data product strategy is hailed as the fastest way to realize near-time value from data and pave the way for new revenue streams. Around 84% of the market value of S&P 500 companies stems from intangible assets, including data and software. 

Any company can build a data product, so why not grasp this opportunity and get the most out of the insights you already have? Let’s dive deep into data product development and its benefits.

Why does a project mindset no longer work?

Over the past few years, companies have changed their perspective on how teams operate to better position them for adopting new technologies. A transition from a project-driven to a product-based mindset takes center stage in this transformation. Why? Because a project approach has been rendered ineffective in managing data assets.

Takes more time than it could

Whenever a business function stumbles, the team kicks off a project to solve the problem through data technologies. The project initiation sets in motion the entire lifecycle of data collecting, cleansing, and aligning it with a specific use case. In this context, each business case requires a separate data preparation cycle.

Focuses on completing tasks rather than delivering value to customers

A project is born and planned to deliver a clearly defined scope, within budget and on time. Rather than focusing on evolving customer needs, it is rooted in a specific business case, task, and process. There is no place for personas or recipients of value, while data products are wired to solve a core user need.

Fails to connect the dots across the company’s data sources

A project mindset supports a silo mentality where data exists in isolated applications and is taken as a necessary step for project needs. Therefore, a project framework neither channels data opportunities to the entire organization nor provides employees from different units with easy access to data.

Conversely, a product-first vision allows teams to set up their data estate as an interoperable organism, with each asset contributing to the whole picture.

Makes it impossible to reuse the outputs to address other use cases

As a project originates as a response to a specific task, its outcomes can hardly be repurposed for other projects. In this case, both data sets and technologies are stored as use case-specific assets instead of becoming a unified hub. This fragmented structure doesn’t accommodate evolving requirements or support multiple outcomes.

Leads to a heavy reliance on the data team and an array of bottlenecks

Within a project framework, data is a luxury kept in the hands of data science teams. Individual teams have to collate the data they need, which results in effort duplication and a raft of dependencies. Most of the time, the input isn’t aligned with business use cases and doesn’t scale to end-user needs.

What is a data product?

A data product is a self-adaptive and versatile power plant built at the intersection of data, industry insights, software engineering, and analytics. The input it produces can fuel the development of brand-new data products designed for other business contexts. Data products are built on top of data management systems such as warehouses or lakes.

Spotting opportunities for new value through different types of data products

Data products come in different forms and shapes. Based on their granularity, organizations can make them available within a specific business unit, hand out to the entire company, or market beyond the company’s settings. Let’s go over these top three data products examples:

Used within a single business unit

This group of products provides data as it is, with some level of processing. The main goal of this product cohort is to improve internal efficiency and augment decision making within a given area of business activity.

Most examples of data products in this category revolve around an analytical record or dataset, such as customer data. For instance, this can be a customer 360 dataset added to your CRM application to support the decision-making process of the sales department.

Cross-organizational

This group of products isn’t limited to a single business domain. With innovation as their core value, such data products make data available to users across the entire organization, enabling them to come up with ideas for other breakthrough products and services. As a result, this product category supports a business unit’s decision making and accelerates time-to-insight for other units.

Data governance plays an important role here as all business units must agree on the right levels of service level objectives, data quality, and lineage. The usability of enterprise-wide analytics solutions can be enhanced through experiment sandboxes that allow for exploring new data use cases.

Used outside the organization

At this level, an organization gives access to the data product to external or third-party providers to drive new revenue streams. A central data team makes datasets compliant with relevant regulations and governance rules. Authenticated and easy data access is an important prerequisite for an external data product.

For example, a medical device manufacturer can develop a new monetization opportunity by providing device data to healthcare providers via compliant APIs.

Anatomy of a data product: four core components

When building data products, organizations can present them to end users through different types of interfaces and interactions.

Datasets

A dataset is the most basic and well-known type of data product presentation. In this case, it should be a reusable dataset, stream, feed, or API that backs up various business objectives.

Code

Data as a product may come in the form of a transformation code that changes an existing solution or creates the data product on top of the existing basis. You can also pack it into out-of-the-box data models that can be combined to construct flexible, scalable data models for different business domains.

Algorithms and analytics models

Reusable machine learning models are another way to pack data products. Instead of locating a suitable model from an external resource and training it with the data, organizations can apply reusable machine algorithms to solve a variety of different problems. The algorithm feeds on the underlying data foundations while improving the applications.

Dashboards

Most data visualizations and dashboards available through interactive visual interfaces do not cover domain-specific needs, such as a healthcare context. Reusable data visualization components and dashboards, on the contrary, can be customized and extended to meet end users’ specific needs.

Uncork the success of the product-led vision

According to Harvard Business Review, companies with a data-led product vision reduce the time it takes to adopt existing data heritage in new business cases by 90%. But along with accelerated innovation, creating data products allows companies to tap into other, equally essential business benefits.

Drive customer value

The product mindset places customer feedback at the heart of all processes and bases your application on real-time insights, rather than static data sets. Moreover, it enables you to serve data in ways that your customers can leverage, be it a warehouse for a data analyst or dashboards for business users.

Open up new revenue streams

Core data products are based on foundational data aggregated by your company. You can generate incremental revenue for and from every department or derive it from external customers by repurposing this data for other business use cases. For example, Apple shares users’ Health app data with healthcare organizations participating in the Health app.

Speed up time-to-insight

Data products enable more speed and efficiency as they are essentially pre-built. Teams don’t have to spend much time collecting and preparing raw data as it’s available on data platforms in a ready-to-use format.

Leverage real-time data

Data-as-a-product approach reinforces real-time business intelligence, allowing you to analyze data as soon as it’s available. As fresh-baked input is stored in a structured format in an integrated database, companies can both provide a real-time response for operational use cases and get updated about shifting consumer behaviors.

Make data available for everyone in the business who needs it

Product-led is a part of the data mesh architecture that empowers domain teams to perform cross-domain data analysis on their own. This approach democratizes data accessibility, making relevant enterprise data readily accessible for all teams.

Consume data easily

Treating data as a product involves understanding how to make the input easily digestible for the end user. Unlike raw datasets, data products are wrapped into a well-defined interface (e.g. visuals, apps, and others), which improves comprehensibility of insights for both internal teams and external customers.

Crafting a data product

A data-as-a-product mindset organizes the data life cycle along the lines of a production process. Just like any other commercial offering, a data-based product follows an iterative path to make sure it delivers the desired business outcome.

Ideation

The ideation stage in data product development is the first point of entry into the design process. It is where business objectives and requirements are defined, documented, and analyzed. At this stage, a product owner is assigned.

Design

Following the defined requirements, the team decides on whether they are developing a from-scratch foundational product or a derived one based on other core data models. The product’s granularity also plays a central role in defining the high-level design. The more granular it is, the more effort end users have to make to navigate it. This stage also finalizes the creation of a conceptual data model.

Engineering

At this stage, a cross-functional team drives the product from the idea phase to the actual building. This stage results in an MVP, an early product version that collects the feedback of a select group of lighthouse users.

Release

As the team iterates on the users’ feedback, the product gets new features and updates. The new versions, such as MMP, are then rolled out to the customer.

Maintenance

Once the product is out into the wild, a support team is assigned for troubleshooting and ongoing maintenance and support. Center of excellence teams disseminates best practices for easier product adoption, while the support team monitors the data pipelines associated with the product.

The bedrock of effective data products: expertise as the foundation

To create data products, companies need to develop a wide range of data management capabilities supported by data-first technologies, hands-on technological expertise, and profound domain knowledge.

Technology and data governance backbone

An efficient data strategy allows organizations to derive sustainable value from enterprise data and make it easily available for creating data products.

• Data gathering — systems of records that collect quality and valuable data from internal and external resources.
• Data storage and integration — domain-agnostic, shared data platforms for both long- and short-term storage, including data warehouses, lakes, and operational data stores.
• Data product management — a new organizational function to manage data quality, service level objectives, architectural improvements, and more.
• Data visualization — visuals and dashboards that display a specific data product.
• Cloud computing — scalable and flexible architectures that distribute data pipeline workloads.
• Artificial intelligence — ML models that consume data products to assist decision-making.

Cross-functional skills

To create successful and compliant products, an organization also needs a data utility group. The latter includes product managers and stream-aligned domain data teams that provide operational and process assistance to data product teams. Business analysts, DevOps, and product managers, in turn, enable rapid iteration and product alignment with business needs.

Domain knowledge

To execute a high-performing data monetization strategy, you also need profound expertise in the area you’re developing for. This way, you can accurately assess the feasibility and value of use cases in the domain.

The key to sustainable value creation

Data has become on par with financial capital that helps businesses thrive in the digital age. Data products come with greater ROI, fit into multiple use cases, and bring multiple objectives over the goal line — with lower cost-per-use.

But to unleash the potential value, you need to lead with architecture that brings your enterprise data together, makes it traceable and easily accessible. Coupled with the right expertise and operating model, it helps generate reusable data products with future-proof value.