Read the following case study, and briefly describe:

1. A potential mission and vision statement for each company. (4 marks)

2. A potential and meaningful business problem to be solved along with potential OKRs. (5 marks)

3. Whether you would choose a centralized, decentralized or COE model for the organizations structure and why. (4 marks)

Case Study: GE Power: Big Data, Machine Learning And 'The Internet Of Energy' Case study: GE Power: Big Data, Machine learning And 'The Internet of Energy' The global energy industry is facing disruption as it transitions from fossils to renewables (and occasionally back again). Its challenges include balancing growing demand in developing nations with the need for sustainability and predicting the effect of extreme weather conditions on supply and demand. How GE Power uses Big Data in practice GE Power - whose turbines and generators supply 30 percent of the world's electricity - has been working on applying Biq Data, machine learning. and Internet of Things (loT) technology to build an "intemet of energy" to replace the traditional, linear, oneway model of energy delivery. Ganesh Bell, first and current chief data officer at GE Power, tells me, "If you think about it, the electricity industry is still following a one-hundred-year-old model which our founder, Edison, helped to proliferate. It's the qeneration of electrons in one source which are then transmitted in a one-way linear model ... That whole infrastructure is now being tested and pushed every day because of the challenges we're talking about." The answer to these challenges, Bell believes, lies in taking advantage of the networked, grid-based generation and delivery infrastructures, while augmenting it with the flow of data. "We think of a world where every electron will have a data bit associated with it, and we associate and track that data and optimize it, and suddenly. from a linear model, we have moved to a networked model," says Bell. It certainly makes sense in a world where everything is increasingly becoming networked and connected-from the devices in our homes to transport networks. Functions enabled by advanced analytics and machine learning. such as predictive maintenance and power optimization, can then be applied to critical infrastructure machinery. As Bell tells me, "We have seen results like reducing unplanned downtime by 5 percent, reducing false positives by 75 percent, reducing operations and maintenance costs by 25 percent - and these start adding up to meaningful value." As well as asset performance management, GE categorizes its data-powered applications into two other groups - one is operations optimization, which focuses on insights that can be applied across a whole plant, or enterprise. And the other is business optimization - applications designed to improve the profitability of customers, "So they can use weather data, energy market pricing data, lots of internal and external data to make sure they are capturing every opportunity for optimizing their business and being more profitable." Put together, these three categories of the application make up the foundations of GE Power's vision for the "digital power plant" - the first step towards making the internet of energy a possibility. As an example of GE Power's need to innovate, it seems increasingly likely that tomorrow's cars will need a robust and reliable network of energy transmission and charging stations far beyond what is available now. If society is ever going to transition away from petroleum-powered vehicles in meaningful numbers, "smart" energy distribution is needed to make sure power is available to charge our vehicles where and when it's needed. The technical details GE's transition to data-driven energy distribution is being powered (excuse the pun) by its own Predix platform, billed as its "operating system for the industrial internet." The platform is behind every part of the analytical process, from the cloud repositories to "edge" analytics - algorithms running on the raw sensor or machine data as close as possible to the point it is collected. Data feeds directly into applications such as GE Power's own asset performance management software, which enables equipment to be monitored even if it's from a third-party manufacturer, meaning it covers every machine in a power plant, whether or not it's manufactured by GE. Ideas and insights you can steal Datafication is, along with decentralization (the move towards generating power close to where it will be used) and decarbonization (the move away from fossil fuels), one of the three "Ds" disrupting the energy industry. And with that data comes great value. A recent World Economic Forum report concluded that the power industry will create $1.3 trillion in value over the next 10 years by rolling out loT ideas such as those put into practice at GE Power. As Bell puts it, "When we start monitoring all these assets and collecting all the data, you unlock huge value - and that's what we're truly focused on." Case Study: GE Power: Big Data, Machine Learning And 'The Internet Of Energy' Case study: GE Power: Big Data, Machine learning And 'The Internet of Energy' The global energy industry is facing disruption as it transitions from fossils to renewables (and occasionally back again). Its challenges include balancing growing demand in developing nations with the need for sustainability and predicting the effect of extreme weather conditions on supply and demand. How GE Power uses Big Data in practice GE Power - whose turbines and generators supply 30 percent of the world's electricity - has been working on applying Biq Data, machine learning. and Internet of Things (loT) technology to build an "intemet of energy" to replace the traditional, linear, oneway model of energy delivery. Ganesh Bell, first and current chief data officer at GE Power, tells me, "If you think about it, the electricity industry is still following a one-hundred-year-old model which our founder, Edison, helped to proliferate. It's the qeneration of electrons in one source which are then transmitted in a one-way linear model ... That whole infrastructure is now being tested and pushed every day because of the challenges we're talking about." The answer to these challenges, Bell believes, lies in taking advantage of the networked, grid-based generation and delivery infrastructures, while augmenting it with the flow of data. "We think of a world where every electron will have a data bit associated with it, and we associate and track that data and optimize it, and suddenly. from a linear model, we have moved to a networked model," says Bell. It certainly makes sense in a world where everything is increasingly becoming networked and connected-from the devices in our homes to transport networks. Functions enabled by advanced analytics and machine learning. such as predictive maintenance and power optimization, can then be applied to critical infrastructure machinery. As Bell tells me, "We have seen results like reducing unplanned downtime by 5 percent, reducing false positives by 75 percent, reducing operations and maintenance costs by 25 percent - and these start adding up to meaningful value." As well as asset performance management, GE categorizes its data-powered applications into two other groups - one is operations optimization, which focuses on insights that can be applied across a whole plant, or enterprise. And the other is business optimization - applications designed to improve the profitability of customers, "So they can use weather data, energy market pricing data, lots of internal and external data to make sure they are capturing every opportunity for optimizing their business and being more profitable." Put together, these three categories of the application make up the foundations of GE Power's vision for the "digital power plant" - the first step towards making the internet of energy a possibility. As an example of GE Power's need to innovate, it seems increasingly likely that tomorrow's cars will need a robust and reliable network of energy transmission and charging stations far beyond what is available now. If society is ever going to transition away from petroleum-powered vehicles in meaningful numbers, "smart" energy distribution is needed to make sure power is available to charge our vehicles where and when it's needed. The technical details GE's transition to data-driven energy distribution is being powered (excuse the pun) by its own Predix platform, billed as its "operating system for the industrial internet." The platform is behind every part of the analytical process, from the cloud repositories to "edge" analytics - algorithms running on the raw sensor or machine data as close as possible to the point it is collected. Data feeds directly into applications such as GE Power's own asset performance management software, which enables equipment to be monitored even if it's from a third-party manufacturer, meaning it covers every machine in a power plant, whether or not it's manufactured by GE. Ideas and insights you can steal Datafication is, along with decentralization (the move towards generating power close to where it will be used) and decarbonization (the move away from fossil fuels), one of the three "Ds" disrupting the energy industry. And with that data comes great value. A recent World Economic Forum report concluded that the power industry will create $1.3 trillion in value over the next 10 years by rolling out loT ideas such as those put into practice at GE Power. As Bell puts it, "When we start monitoring all these assets and collecting all the data, you unlock huge value - and that's what we're truly focused on