At this phase of the 21st century we are often baffled by diverse terminologies starting from digitisation to artificial intelligence (AI) to the Internet of Things (IoT). Such terminologies raise critical questions. How are they related? Which one should we focus on first? Is there a way to make a transition from one to another? Or, is there a way to leapfrog?
The journey of benefiting from information technology has been progressing from computerisation to artificial intelligence, often creating confusion and raising the temptation of leapfrogging. It appears that to benefit from higher-level digitisation such as AI, our journey should progress through a series of 07 maturity levels. In the absence of adequate maturity at lower levels, investment made at a higher level fails to deliver the intended results. It's about creating economic benefits from innovations around digital technologies proceeding through maturity levels in a step-wise fashion. Here are a few maturity levels, through which we should manage our journey in leveraging digital economy.
COMPUTERISATION: During the latter part of the 20th century, we started experiencing the controlling of business or work process through computers. To benefit from computerisation, discipline was brought in work process through business process reengineering exercises. To enable technology in taking increasing roles, subjectivity was reduced. Among others, a set of policies was defined, standards of inputs and outputs were set, and often-redundant steps were reduced or eliminated (often giving the name of service simplification). As a result, transparency, clarity, and predictability increased, consequentially reducing time, cost, and physical interactions.
CONNECTIVITY: The first decade of the 21st century witnessed rapid expansion of connectivity, particularly due to the expansion of mobile internet penetration in the developing countries. Affordable coverage of mobile internet has also opened the opportunity of turning existing industrial products such as mobile phones or utility meters, and innovating sensors as well as actuators with internet connections. They are called the Internet of Things (IoT). According to Statista, the installed base of IoT devices has jumped from 15 billion in 2015 to 23 billion in 2018. These devices are producing an enormous amount of diverse data, starting from car positions to energy consumption. They are being also installed in farmlands for producing data related vital parameters starting from soil ingredients to moisture level.
EXTRACTING INFORMATION: Data produced by connected devices are to be processed to extract information, such as how much water or energy each household consumes. Similarly, data provided by moisture monitoring IoT devices are to be processed to extract information regarding the rate at which moisture level of cropland is changing.
DERIVING KNOWLEDGE: Information derived from the processing of data gathered from connected devices should be translated into knowledge, often by fusing with applicable science and complementary information contributing to our understanding about various situations. Such knowledge is a vital precursor to predict likely situations. For example, data provided by in-vehicle IoT devices could be processed to generate knowledge about the real-time diving practice of the drivers.
PREDICTIVE ANALYSIS: Knowledge about the past and current situation, derived from data provided by connected devices, could be interpreted within applicable model to predict likely situations. Predicting such situations is vital in taking a decision to maximise the leveraging of unfolding situation, as well as in minimising the likely risk.
AUTONOMOUS DECISION MAKING: Once we succeed in installing related base of IoTs, reliable connectivity, defined policies and procedures in guiding work processes, and adequate algorithms for extracting and fusing information, knowledge and predictive analysis from data offered by connected devices, we reach autonomous decision-making capability.
ARTIFICIALLY INTELLIGENT ACTION TAKING: Autonomous decision-making should lead to taking action in an intelligent manner for maximising the benefit from the possibilities of the digital economy. For example, once unmanned aerial pesticide spraying vehicles are empowered with controlling the nozzle precisely in response to the knowledge about the health of crop on the ground, and decision about variable requirement of pesticides in different parts of the filed, the benefit from precision agriculture in minimising inputs, and maximising outputs and food safety are realised from digital possibilities.
Here is an example of a step-wise progression of using digital technologies, through seven maturity levels, in the transportation sector. The computerisation in the 1990s started for reservation and ticketing. The rapid expansion of mobile internet offers the opportunity of installing IoT devices in buses and trucks for offering us real-time data about locations, speed, acceleration and so on. Data delivered by those devices could be processed to extract information such as average speed, so that speed limit compliance could be ensured. Further data analysis could lead to knowledge about the driving practices of different vehicles in different situations and locations. Additional analysis may lead to predicting likely undesirable outcomes such as the occurrence of accidents. Fusion of such knowledge with drivers' track records, and applicable policies and regulation will lead to autonomous decision-making. The next step is to have onboard vehicle capability of implementing such a decision leading to intelligent control of driving practices in reducing the accident rate and maximising the vehicle efficiency.
Digital economy offers us a significant opportunity in driving wealth creation. But to benefit from such possibilities, we need to keep making step-wise progression, starting from establishing discipline in our work processes through computerisation towards the installation of IoTs and taking artificially intelligent actions. In the absence of such step-wise progression, economic returns from digital possibilities will likely be sub-optimal. For example, in the absence of reliable as well as affordable connectivity, IoT devices cannot offer us dependable data. Similarly, in the absence of discipline in the form of clearly defined policies and standards, such data will not lead to autonomous decision-making leading to intelligent action. Most of the developing countries' journey of the digital economy is basically focusing on connectivity now. Unfortunately, adequate discipline in work processes is yet to be established, which was supposed to be done in the 1990s. It's time to have adequate maturity at each previous level in order to prepare for subsequent investments at higher levels.
M Rokonuzzaman Ph.D is academic and researcher: Technology, Innovation and Policy.
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