Watch the video to learn how to begin thinking about data assets that can be productized. Notes from the presentation are below:

Data is valuable

Data is intrinsically valuable. Firms that have data assets can capture that value, but it first must be packaged as a product.

A Data Product is a “product that facilitates an end goal through the use of data.”

Data value increases with exclusivity

House Data

Data that is acquired by the firm through its daily business practices and/or partnerships. It is exclusive to the firm, but not the user.

BYOD, Bring Your Own Data

Data is supplied by the user of the product. It is exclusive to the user, but not the firm.

Hybrid

Users bring their own data, which is combined with house data yielding insights they can’t anywhere else. The combination of this data is exclusive to each user of the product, and therefore presents the highest potential value.

Data value increases with refinement

The more data is refined, the more valuable it is. As data is further refined, the primary deliverable of a data product begins to shift away from the data itself to insights discovered using the data.

Raw Data
Products like contact databases simply sell data directly.

Hydrated Data

Sometimes referred to as "Data Enrichment," hydrated data products combine user data with the firm's data. The end deliverable is still the data itself.

Annotated Data

Annotated data products begin to shift toward insight as a deliverable. Tools like Google analytics take your website traffic data and slice it into categories and segments to derive actionable insights about what website content performs best for specific traffic segments.

Decision Support / BI

Business Intelligence tools, like Tableau, are typically used for decision support. Statistical analysis and visualization of user data enables decision support based on real data. These tools are often very powerful but geared towards exploration of data and require deep domain knowledge to use.

Decision Automation

The most advanced types of data products are designed around decision automation rather than decision support. These products include advanced applications of AI and Machine Learning. As an example, self-driving cars are at the core a data product that automates decisions. This decision engine is an extremely refined application of a data asset--vast amounts of telemetry and imagery of roadways, conditions, and hazards.

Different interfaces provide value in different ways

Integrations and APIs

API products provide data to customers directly into their internal systems, tools, and products. Customers use these integrations to connect data and insights to their own products or internal workflows.

Reporting Dashboards

Reporting dashboards provide customer reports and/or interactive dashboards to explore data, insights, or processing results. Customers use the data product to explore information or point a spotlight at important signals.

Verticalized Applications

Verticalized applications are specific software products that solve known problems, rather than exploratory tools. Customers get outcomes directly from the product.

Data Product Matrix

The matrix above shows 21 different data product archetypes, each providing different levels of value.

Raw house data products are lowest value providers. Decision automation combining house and user data are highest value providers.

Want help on your journey?

Ignition is Lofty’s discovery and strategy service.

Our workshops guide firms through the process of building product strategies around data assets.

‍