Profit Maximization Techniques for Operating Chemical Plants. Sandip K. Lahiri

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Figure 2.6 Data science is an iterative process that leverages both human domain expertise and advanced AI‐based machine learning techniques

2.7 From Valuable Data Analytics Results to Achieving Business Impact: The Downstream Activities

      The downstream part of the insights value chain is comprised of non‐technical components. It involves people, processes, and business understanding that – through a systematic approach – these new data‐driven insights can be operationalized via an overall strategy and operating model (Holger Hürtgen, 2018).

      2.7.1 Turning Insights into Action

      Once we have extracted important insights from the models, the next crucial step starts: turning these insights into action in order to generate a business impact. An example would be when a predictive maintenance model gives you warning as to when a compressor or some asset might break down, but maintenance is still required. It is very crucial to understand that just knowing the probability of a breakdown is not sufficient; prevention, not prediction, is the key to business impact. Turning insights into action thus requires two things: first, to understand the insights coming from the data analytics and to know what to do. Second, even once it becomes clear what action needs to be taken, success will depend on when and how that action is taken. This step is very crucial. Knowledge generation by data analytics software is not sufficient; taking corrective and preventive actions from these insights is the key driver for business impact.

      2.7.2 Developing Data Culture

      The long‐term success of digital transformation requires a company to develop a data culture. This essentially means developing a culture so that all of the business decisions of the company would be based on data analytics and the regular employees of the company would be equipped to implement the data analytics insights into their day‐to‐day actions. A company's internal structure and reward systems should be adopted in such a fashion that promotes the data culture.

      2.7.3 Mastering Tasks Concerning Technology and Infrastructure as Well as Organization and Governance

      In this step, the organization work process, culture, responsibility hierarchy, governance structure, etc., need to be changed in such a way that will facilitate organizations to take action on the insights from advanced analytics and create an impact. An organization needs the right set of easy‐to‐use tools – e.g. dashboards or recommendation engines – to enable personnel to easily generate business insights and a working environment that facilitates the integration of those insights, e.g. governance that enables and manages the necessary changes within the organization.

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