Diana is an esteemed senior executive with a vast experience in operating within intricate IT environments encompassing numerous terminals and integrated systems within the petrochemical sector. She has effectively synchronized the IT strategy with the business strategy, resulting in enhanced operational efficiencies and a competitive advantage.
Please tell us about your current key roles and responsibilities at Vopak.
I ensure the smooth operation of core applications and that any modifications are implemented effectively through change management. I oversee the proper training of personnel, particularly key users, to enhance their utilization of the systems. In terms of data analytics, our organization is endeavoring to instill a culture of education. Although we haven’t reached the stage of predictive analytics yet, we have been striving for the past three years to educate various departments within our company. The ultimate goal is to leverage the data from all our applications to predict outcomes, improve processes, and make informed decisions, fostering a data-driven culture within the organization.
What are some of the major challenges in the predictive analytics industry?
In the realm of oil and gas, there’s a notable absence of companies fully leveraging data for decision-making. Despite the abundant opportunities for improvement, cost savings, and organizational changes that data analytics can offer, the current challenge lies in the lack of priority given to data analytics by management. Consequently, securing funding and support for data analytics projects is challenging, as daily operations and growth initiatives often take precedence.
Another obstacle lies in the adoption of predictive analytics, which necessitates a cultural shift in how organizations perceive their day-to-day operations and available information. Currently, the focus tends to be on analyzing past events to identify areas for improvement in the coming months and make better decisions. This cultural resistance may be a prevalent gap in many organizations, hindering the adoption of data-driven decisionmaking practices.
How do you see the predictive analysis industry evolve over the next few years?
I believe that leveraging predictive analysis in AI can significantly propel us forward, enabling individuals to focus on more critical tasks while allowing machines to handle predictive tasks. In my industry, specifically in stack storage, maintaining assets to prevent breakdowns and planning for vessels and equipment is crucial. With the assistance of AI in predictive analysis, people can redirect their attention from daily operational tasks, such as counting and ad hoc repairs, to more impactful and strategic activities. This shift would enhance efficiency and overall productivity in managing assets.
Any specific piece of advice that you’d like to share with your fellow peers or other industry leaders?
It is important to ensure that the organization is prepared to comprehend the potential of data and recognize the significant benefits it can bring to various areas, ranging from customer insights to various departments within the company. It is essential for them to grasp the true power the data holds.
