With each innovation and transformation in economics, geopolitics, and demographics, supported by technological advancements, product life cycles are constantly decreasing.
From my perspective, two Organizational Roles will be essential in all organizations, regardless of their industry. These are the Data Scientist and the Behavioral Economist/Psychologist. Allow me to elaborate on the reasons behind this.
Data Scientists/Data Analysts:
To explain the importance of data, let me explain about the Aviation industry, based on a Forbes magazine report published a few years ago.
Big data in the aviation industry refers to a significant amount of data. As stated by the U.S. National Air Traffic Controllers Association, 87,000 flights are operating in the United States daily. The sensors on an aircraft collect data on over 300,000 parameters, with engine data being a crucial data point. For instance, a typical commercial aircraft like the Boeing 737 generates 20 terabytes of engine data per hour. When considering a six-hour flight from New York to Los Angeles, this amounts to 240 terabytes of data per engine hour. The data generated from intercontinental flights from Asia or Europe to the USA or Australia is even more substantial due to longer travel times. (1 terabyte equals 1024 gigabytes)
How can a commercial aircraft manufacturer leverage the vast amount of data they gather and store? What would be the consequences of not utilizing this data? According to the International Air Transport Association (IATA), the primary cause of flight delays (42%) is related to airline-controlled processes such as maintenance. Each hour an aircraft is grounded costs the airline operator an average of $10,000. Therefore, utilizing sensor data can optimize operational efficiency to ensure a smooth fleet operation. Predictive analysis can transform extensive maintenance-related data, whether from machine sensors or ERP systems, into actionable insights. This aids in ensuring that maintenance technicians perform the necessary tasks at the right time and with the appropriate tools. With the integration of in-memory technology, the time and cost of analyzing large data sets have significantly decreased, enabling real-time predictive analysis against vast data volumes. Every modern aircraft is equipped with a health monitoring system to detect unforeseen events, reduce unscheduled maintenance, and minimize operational disruptions. According to a recent study by Technavio, the global market for these monitoring systems is projected to expand significantly in the coming years, leading to a competitive race among manufacturers. The frontrunners will enjoy a competitive advantage by being the first to market, capturing both revenue and market share.
Now, let's consider the various devices in our surroundings, such as TVs, water dispensers, fridges, cars, bikes, mobile phones, and more. These devices are constantly collecting data as we use them, and manufacturers monitor this data for preventive maintenance or to gain insights into customer/user behaviour. With the widespread integration of IoT into our equipment, these devices will continue to produce vast amounts of data. To interpret this data effectively, data scientists and analysts are essential. They analyze the collected data and develop models to help organizations make informed decisions regarding their products, operations, or customers. The demand for data scientists and analysts is on the rise in organizations dealing with large volumes of data that cannot be adequately analyzed using traditional tools like Excel. It is safe to assume that nearly all medium to large organizations will require these professionals if they do not already have them.
Behavioural Economists and Psychologists:
Even though companies will keep making rational decisions using historical performance data, much of which is generated by the IoT, they will also need to focus on the human aspect that goes beyond data. Understanding how customers and stakeholders behave is crucial, as human behaviour can often be counterintuitive compared to conventional expectations. Many innovations in the world are not solely driven by logic or rationale but are rooted in human behaviour and psychology.
Here are some ideas that emerged before becoming popular and societal norms:
1. Wikipedia was established as a free platform created by contributors worldwide.
2. Jeans were originally worn by labourers in the USA before evolving into a global fashion staple.
3. During its early days, McDonald's limited customers to choosing from a selection of three or four items.
4. The Red Bull success story involves challenging Coca-Cola's dominance by positioning itself as a higher-priced energy drink in smaller cans with a distinct taste compared to traditional cola beverages.
5. Individuals willingly pay $5 for a coffee at Starbucks, a price significantly higher than what they could easily make at home for just a few cents.
Numerous global business and technological innovations have emerged and become widespread, even though they may not have seemed rational initially. Therefore, organizations ought to invest in these divergent areas to remain at the forefront of their growth and ongoing transformation.
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