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data analytic tools: In recent times, many new tools have been coming to market to convert this flood of raw data into insights and eventually into profit.

       Machine learning and artificial intelligence: These new generation algorithms are rapidly replacing the old method of calculations and emerge as new data analytics. Both data and computational power enable next‐generation machine learning methods, such as a deep learning neural network.

       Value creation: As a consequence, data has become the new oil of the chemical industry – and the best way for companies to generate and access is to digitize everything they do. Digitizing customer feedbacks provides a wealth of information for marketing, sales, and product development, while digitizing manufacturing processes generates data that can be used to optimize operations and improve productivity.

      The confluence of data, storage, algorithms, and computational power today has set the stage for a wave of creative disruption in the chemical industry.

      2.5.2 Different Links in the Value Chain

Data in its raw and most basic form is virtually worthless until we generate knowledge and business insights from it. The biggest challenge to confront the chemical industry today is how to generate business insights from these huge data banks sitting in their server and convert that knowledge to increase profit. Today every leading chemical industry talks about Big Data and Advanced Analytics and even machine learning and artificial intelligence (AI). Today's leading chemical industry is in a hurry to implement the advance analytics in their business and they focus too much on single technical components of the “insights value chain,” as we call it. However, the value creation of data consists of following five components and companies need to focus on all the components if they want to capture the full value (or any value at all) from relevant (smart) data (Figure 2.4):

(2.1)

It is important to understand Equation (2.1), which reveals that the insights value chain is multiplicative, meaning that if one single link in that chain is zero, your impact will be zero. In other words: the entire data ecosystem is only as good as its weakest component. The chemical industry needs to understand this critical concept and should give importance to developing all components and steps of the insights value chain – not focusing on only one piece and forgetting about the others.

The following sections briefly explain the function of each of the insights value chain's core components (see Figure 2.5) along with its upstream as well as its downstream steps and processes.

Figure 2.4 Different components of the insights value chain

Figure 2.5 Overview of the insights value chain upstream processes (A–B) and downstream activities (D–E)

      2.5.2.1 The Insights Value Chain – Definitions and Considerations (Holger Hürtgen, 2018)

      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).

2.6 From Dull Data to Critical Business Insights: The Upstream Processes

      The insights value chain's upstream processes comprise two steps (see Figure 2.5).

      2.6.1 Generating and Collecting Relevant Data

Today's big chemical complexes have at least 10–20 plants and each plant consists of approximately 4000–7000 transmitters or sensors that can collect data every second (Holger Hürtgen, 2018). For instance, a petrochemical complex having 10 individual chemical plants generates 3.127 trillion data in one year. It is very costly (and perhaps impossible) to capture and save every bit of the tera‐ and petabytes of data that will be generated every second and will create a data overload on the system. Not all the data are relevant to make an impact on the business in the chemical industry. Hence it is important to know which data should be collected and at which frequency so that it can be used to generate business insights and drive the profits up.

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