Productizing Big Data assets requires an operator’s Big Data team to combine marketing, product design, analytics and IT implementation skills. By thinking about the market in a structured manner, and driving product design from clearly-identified customer needs, operators can launch Big Data products more quickly and with a significantly improved chance of market success.
The Big Data product categories
Based on an extensive analysis of potential Big Data customers in numerous markets around the world, Redwing has identified five product categories where mobile operators can provide high-value data to customers:
Launch focus: the quick wins
The mobile industry is still in the early days of Big Data. It is critical that early product launches are successful, both to maintain support from the C-suite and also to give momentum to the Big Data team as it expands its skill set.
For this reason, Redwing always seeks to identify early quick wins for Big Data products, while putting these within the context of a longer-term platform-based Big Data commercialization strategy.
To identify quick wins, the best approach is to overlay product types with market segments to create a product launch market map, as illustrated below.
(click on graphic to view a larger version)
We look at the process of identifying customer segments and customer needs in more detail in our Monetizing Big Data page.
Delivering Big Data products and services
A further critical element of product and service design is the means of delivery. Customers vary widely in their level of IT sophistication and their desire for ease of use and customization.
A smart Big Data commercialization strategy stratifies the market based on customer type, giving customers the option of:
- Pre-packaged Big Data services: simple and standardized data packages.
- Selectable Big Data services: access to various aspects of the raw data, customizable by the customer.
- Managed Big Data services: hosted systems and data environments, taking the technology burden off the customer.
See our Organizing for Big Data page for a detailed examination of the issues around creating an effective organization structure to deliver successful Big Data products and services.