The lifecycle of a "data product" is the same as the standard product development starting from opportunity identification to solve a core user need, building an initial version, and then evaluating its impact and iteration. To tackle the complex data walls, companies should emphasize cross-functional collaboration, evaluate and prioritize data product opportunities with an eye to the long-term, and start simple. Here are the step by step benchmarks to build great data products:

Opportunity Identification

Identifying the best data product opportunities involves integrating product-and-business perspective with the tech-and-data vision. User researchers, product managers, and business leaders traditionally have the strong intuition and domain expertise to identify key unsolved user and business needs. Data scientists and engineers are increasingly identifying feasible data-powered solutions based on strong intuitions on what can be scaled.

To get the right data product opportunities identified and prioritized, here are the norms that can help:

Education of the trends and business needs among data scientists: Data scientists have to work in tandem in close alignment with product managers, user researchers, and business leads, for a better understanding of data directly to understand users and clients.

The role of data evangelists: There are socializing data opportunities with the broader organization. This can range from a varied of tasks like providing the organization with easy access to raw data and model output samples in the early ideation stages, to building full prototypes in the later stages.

Develop the data-savvy of product and business groups: Individuals across a range of functions and industries have employers who can accelerate the data trend by investing their time and resources in learning programs. The higher the data literacy of the product and business functions, the better is the collaboration with the data science and tech teams.

Understanding the adaptability of Data Science: Data science can thrive in different places across centralized and decentralized organisations. Data scientists have to understand and develop logical thinking in spears of product and business strategy discussions to accelerate data product development.

Futuristic Vision

Data product applications accelerate data collection thereby improving data applications. With limited data, the initial cold stage may be uninspiring. Data products can be built out to power multiple applications which are not just about spending on costly R&D across different use cases; it is about building network effects through shared data. The data produced by each application feeds back to the underlying data foundations, which improves the applications, to turn drives for more utilization and making the virtuous cycle continue, focusing on near-term performance that can yield underinvestment in promising long-term opportunities. The criticality of high-quality data cannot be overstated more applications are collecting and storing data to be prioritized at every stage.

Product Development

Data products require validation from algorithm deployment to making them to the client's needs. Thus builders of data products have to logically invest in the R&D upfront and quickly get to the application to validate that data models solve a core need. Here is the key to developing data products that fit the bill:

•  Developing lightweight models which have the added benefit of being easier to debug, explain and build upon over time will go a long way towards building a good data product.

•  External data sources, whether open source or buy/partner solutions, have the potential to accelerate development.

•  Narrowing the work domain can reduce the scope of the algorithmic challenge; some applications can initially be launched and developed only for a subset of users or use-cases.

•  Hand curation where the human mind either does the work of review and tweaking the initial model's output can further accelerate development, which is ideally done with an eye to how the hand curation steps can be automated over time to scale up the product.

Evaluation and Iteration

Evaluating results after a data product launch is not as straightforward as for a simple UI tweak, as the data product may improve substantially if data is updated live and model is iterated to make way for changes. Data scientists are behind the few important questions that may arise due to change in data sets with the passage of time. As data products often need iteration on both the algorithms and the UI, the speed of iteration matters. The challenge is to determine the highest-value iterations, based on data and user feedback so that the teams know which functions are on the hook for driving improvements.

Fostering collaboration between business leaders, data scientists and product is the need of the hour. Prioritizing investments with an eye to the future is imperative for all companies of different shapes and sizes to accelerate the development of powerful data products to fuel the business create lasting competitive advantage and solve core user needs.