Prescriptive analytics is the highest echelon in analytics hierarchy. It is where the best alternative among many – that are usually created/identified by predictive analytics and/or descriptive analytics – courses of action is determined using sophisticated mathematical models. Most of the optimisation and simulation models that constituted prescriptive analytics were developed during and right after World War II in 1940s, when there was a dire need to do the best/most with limited resources. Since then, they have been used by some businesses for some very specific problem types, including yield/revenue management, transportation modelling, scheduling, etc. The new taxonomy of analytics made them popular again, opening their use to a wide array of business problems and situation.*
With humans as immediate subject of analysis, people analytics aims to influence and optimise their behaviour through quantitatively analysing their conduct and their digital traces. However, these assumptions can be problematic because they contribute to a very positive perception of people analytics without attention to potential risks, which might entail serious consequences for organisations and employees. For instance, to treat employees similarly to quantifiable data objects, rather than as genuine fully-fleshed human beings could entail a conceptual category error.
People analytics based on recent technological advances in the field of AI could potentially underestimate human complexity, be more invasive, and have more serious consequences for employees than other forms of business analytics, as in analysing goods, money flows, key financial figures, etc. Consequently, the underlying assumptions of people analytics’ role, capabilities and promises among researchers and practitioners hold particular ethical and moral challenges. This can guide organisations and managers towards a strong or even unbalanced reliance on people analytics and result in severe perils.*
The future of web development looks to be low-code and no-code development and Gartner suggests that “by 2024, low-code application development will be responsible for more than 65% of application development activity”. Software applications are being created to serve those without a technical coding background, such as citizen developers/integrators. According to TechRepublic, “nearly 60% of all custom apps are now built outside the IT department. Of those, 30% are built by employees with either limited or no technical development skills”. This is to help organisations reduce backlog, decrease costs, improve agility, and create low-code automation.*