With Big Data technologies having matured and become more accessible, many technology companies are looking to identify and introduce new data-driven products and services. In doing so, companies must understand the unique characteristics of these offerings and the context in which they will be sold, delivered, and consumed. This consideration can often be unfamiliar and underestimated, because it is often significantly different from traditional products and services deployments that companies are familiar with. These differences stem from a handful of characteristics that are unique to data-driven products. They include:
To establish the best possible footing to launch new data-driven products and services, companies should holistically examine their operating model and make the requisite adjustments to tune for this new context. The top five aspects of the operating model that require evaluation are:
Core to selling data-driven products is the ability to articulate the data’s value and the benefits of incorporating data usage and analysis into current business processes. As a result, a shift to a consultative-selling mindset is essential in order to be able to relate the data’s value to a prospective customer’s business objectives, pain points, and needs. The consultative-selling model must include detailed segmentation of the target market that reflects how each customer group will consume and derive value from the data. Sources of data—what is being produced, what is being stored—need to be identified across the value chain.
Customer business processes often need to be modified to take advantage of data-related products. This need for process change opens up an opportunity to redefine and expand the services portfolio beyond the product’s initial implementation and rollout to include change management and consultative services. These additional services offerings optimize business processes on an ongoing basis, simultaneously driving further value to customers and creating an ongoing revenue stream.
The complexity of customer support requests can increase for data-related products because the issue’s root cause can potentially span across hardware, software, and data realms. As a result, traditional Tier 1 support structures will need to be enhanced to be able to diagnose and triage across hardware, software, and data, and to escalate to appropriate Tier 2 groups as needed. New Tier 2 capabilities around data management (i.e., diagnosing and resolving data-related issues) will need to be added. For example, part of resolving data-related issues may require reloading of data sets, running queries to ensure data integrity and consistency, or conducting data quality checks.
While unit data storage costs continue to decline, housing and hosting massive data volumes can be a significant driver of cost of goods sold (COGS). Consequently, it is imperative to design an optimal data storage architecture that delivers on target service quality measures and is optimized for service delivery costs. Two such architectural elements we have seen are 1) ensuring the data replication strategy has been optimized to reduce the number of redundant copies of data stored in the hosted environment, and 2) leveraging alternate storage architectures (e.g., “cold storage” architectures) for sparingly used datasets where retrieval times may not be as immediate.
By definition, developing and innovating on new data-related products requires core capabilities around sophisticated data analysis and visualization of data sets. Some of the related skill domains include statistics, algorithms and machine learning, knowledge of open source tools and programming languages, and design of new ways for users to consume data (e.g., data visualization, external APIs, integration with other services, etc.). It is important that enterprises leverage their human resource and staffing teams to attract and retain these skillsets to support their data-driven product portfolio.
Many technology companies are sitting on untapped sources of revenue in the form of operational data. Substantial value can be created by tuning the innovation management system to develop and launch data-driven products and services. Companies must take care to fully embrace the requisite changes to their operating model in order to realize sustained, profitable growth through these offerings.