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The insights value chain has two foundations, namely a technical foundation and a business foundation. The technical component of the technical foundation consists of data, analytics (algorithms and technical talent), and an IT infrastructure (Hürtgen, 2018). This essentially means that the value creation from data is possible when efficient data scientists and domain experts use smart algorithms to extract meaningful information from high‐quality data. In today's world of Big Data, companies also need an IT infrastructure capable of capturing, storing, and processing large amounts of data fast. Second, the business foundations of the insights value chain consist of the components of people (non‐technical talent) and the company's adaptive processes, both of which are required to turn the knowledge gain from data into (business) action.

Here are some key considerations concerning the components of the insights value chain:

 Data: The basic building block of this value chain is data and data must be thought of as the entire process of collecting, linking, cleaning, enriching, and augmenting internal information (potentially with additional external data sources). In addition, the security and privacy of the data throughout the process are fundamental.

 Analytics: The second component of the insights value chain is the data analytics, which can be considered as an IC engine that will utilize the data (new oil) to generate business insights. Analytics describes the set of digital algorithms (e.g. software) deployed to extract knowledge from data as well as the talent (e.g. data engineers and data scientists, domain experts in the chemical industry) capable of deploying the right tools and methods.

 IT: IT infrastructure is the technical layer enabling the capturing, storing, and processing of data, e.g. data lakes, two‐speed IT architecture.

 People: People are the ultimate drivers who will implement those insights in business actions. People from the front lines of manufacturing and sales are needed to guide and run an advance analytics course that converts data into insights and successfully implements those insights in the business. Today's chemical companies need to change the old mindset and should develop this critical capability to “translate” analytics‐ and data‐driven insights into business implications and actions.

 Process: Another crucial challenge in the digital journey is to develop adaptive processes and systems within the company that can deliver these business actions at scale. To develop the ability of seamless implementation, some old operating procedures might need to be adapted, some might need to be fully automated, and others might need to be made more agile.

In addition, there is an overarching frame and an underlying governance in which the insights value chain is operating:

 Strategy and vision are the overarching frames in which the insights value chain is meant to operate. Data analytics should not be “done” for the sake of a data analytics but in fulfillment of the organization's vision and in support of its overall business strategy. “Think business backwards, not data forward” (Holger Hürtgen, 2018).

 The operating model is the underlying governance in which the insights value chain lives. Core matters to be addressed include deciding where the analytics unit will sit within the organization and how it will function and interact with BUs (e.g. centralized, decentralized, hybrid).