Designing A Data Product

As data scientists build increasingly sophisticated products, they need a systematic design approach.the idea is to start a dialog within the data science and business communities to advance our collective vision.

What is a Data Product?

A data product constitutes a production output resulting from a statistical analysis. Data products are used for stimulating complicated analytical tasks and use technology to expand the usage of inference, data informed utility or algorithm. Data products can be created through basics like interactive graphics, R packages and Shiny. Various statistical fundamentals contribute in creating a data product which can be utilized for storytelling regarding data to a large audience.

Uses of Data Products

Simply put data products help in facilitating an end goal by using data. Building a great data product is very challenging, with the data problem becoming more intractable and complicated as you begin solving it. The pre-requisite for creating a data product is to know its demand and target audience.

Trending

For the purpose of extracting and managing insights from the data driven environments of today, organizations are creating new data products. In this regard, capabilities are being added to the leading Multivalued and relational data management systems. Moreover, cutting-edge techniques and technologies are arising from NewSQL and NoSQL data management systems, cloud solutions, data visualization, SaaS, real time Business Intelligence and a lot more. The data products created today range from widely accepted offerings which continue to evolve for the purpose of meeting the requirements of the loyal following of organizations to breakthrough technologies which have been just released and are in the beginning of adoption. But, all of them seek to provide organizations with tools to meet the rapidly changing requirements of the market and represent a commitment to innovation.

Fast initiators

Amazon is said to be the leader in online data storage. It offers simple storage and database appliances which appeal to a large audience. It shows each purchase made through a giant sparse matrix, having products as columns and customers as rows. With the data in format, data scientists can apply collaborative filtering for filling in the matrix.

Google is regarded as one of the leaders in the market of data product. It has been selling analytic guts as online service to compete with competitors like Amazon in enterprise cloud computing. The recently launched data product, Big Query has the ability to scan terabytes of data in seconds, as service for corporate customers. 

In summary. Prediction technology can be mathematically elegant and interesting, but there is a need to take the next step. We have the technology to build such data products that can revolutionize entire industrial setup. For future, we hope that business schools teach optimization in statistical departments as well. We hope to see such products from data scientists that produce desirable business outcomes. We are still at the dawn of data science. The data science community needs to coalesce around a shared product design and vocabulary procedure which can be used for educating others in deriving value from their predictive models