By Upasana Hembram
Big data and analytics are at the helm of innovations that touch almost all parts of our everyday lives. Even with the most sophisticated tools and the most intelligent scientists on board, an organisations efforts could fail if necessary behavioural changes arent implemented to support the required decision-making and action. Be it the government, private sector or public sector, majority of processes are still governed and carried out by human resources. Hence, the human factor of advanced analytics cannot be undervalued.
One of the reasons we do not see initiatives driven by advanced analytics translating into desired outcomes is the lack of behaviour change among people. Though there is a widespread recognition of the power of analytics in governance and policymaking, there isnt enough investment towards human behaviour in order to make the process of governance truly data-driven.
Where is the impediment?
There are several reasons why people could prove to be a hindrance in producing sustainable analytics-driven results to fix critical problems. More often, people employed at the grassroots or field level arent involved at the start of the forays into analytics and hence, dont see the value of this data. This could be a result of lack of communication from officials in higher ranks on how these employees could make the most out of advanced analytics. There is usually also a communication between data science team and the execution team. Here is when marketing teams come into play that converts insights from data scientists into a language that is easy to understand, accompanied by imperative visuals.
Technology vs intuition
Sometimes people take the easy way to use analytics in providing solutions by providing opaque, generic, overly academic quick-fixes which do not take into account the ground realities of the situation. These solutions usually offer over-complicated observations that are difficult to understand and almost impossible to deploy and adopt. Employees may lack access to constant training and coaching required to learn how to use new technology and understand the implications of decision-making and judgements that follow such analysis. Process and tools employed to calculate decisions could be perceived as threatening to employees who would rather rely on their own experience and expertise in the field. This would, in turn, discourage employees from taking the intended actions.
The key to achieving near perfection
Even though there is a plethora of evidence to corroborate that data should be at the heart of decision and policy making, there is still a prevalent scepticism on whether analytics can fundamentally alter organizational processes and human behaviour. While analytics provides solutions in order to make decisions and judgements, it can also result in human beings taking actions different from the norm. In order for this to happen, there has to be a redesigning and restructuring of operations in order to establish at the heart of human behaviour. This can be achieved in a two-fold mannerrectifying the organizational flaws discussed and incentivizing individuals who are required to make these changes. Humans need to adapt and organizational structures need to evolve for transformational results to ensue.
Making situation conducive to policy making
While analytics usually involves machine-learning algorithms, big data and computer technology, behavioural nudges deal with human psychology. While there may appear to be a vast disconnect between two and viewed as two completely different responses, predictive analytics offers tools to correct psychological biases. Instead of endowing the people with rationality, designing choice environments that provide opportunities to comport with the psychology of the individual used for decision-making can be more effective. If the default setting was one where people had more time, information and mental energy, people would not tend to dislike making changes. Making use of comparisons among peers, simplifying language and user interface while designing forms, policies and programs could encourage people to participate in the policy-making and implementation process, rather than drive them away from it. The idea is to arrange options in a certain architecture that enables them to make day-to-day choices that are also consistent with their long-term goals that align with the desired behaviour change.
Refocusing attention from data capture to data delivery can help envision tools and products that can be applied to entire populations in a one-size-fits-all model instead of prompting behavioural change that aligns with the calculated judgement of the user that belongs to sub-segments identified via analytics. This behavioural science can help overcome the last-mile problem of advanced analytics. Behavioural science principles need to be part of the data scientists toolkit and vice versa in order to attain best results.
Ultimately, any organization is fundamentally a manufacturing plant that produces decision making and decisions. It is crucial not just to understand the technical aspects of analytics of the data but also how decisions are made and implemented.
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