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Nurturing High Performance Teams

Titash Neogi

Artificial Intelligence can be defined as a stack comprising of the following technologies -

  • Natural Language Understanding - The ability of Machines to understand and respond to human language.
  • Knowledge graphs / Social Graphs / Graph databases - The ability of machines to understand complex relationships between people, objects, places and numbers at a global scale.
  • API driven economy - The global economy of APIs for all products and services around the world that helps drive data interoperability and information exchange.
  • Machine Learning algorithms, frameworks and techniques - These include the range of statistical algorithms like k-means, KNN, Bayesian theories, game theory etc. that allow machines to crunch large volumes of data and return predictive results.
  • Deep learning frameworks such as Neural Networks (RNNs, CNNs, GANs) that can leverage large amounts of data and GPU power to mimic how the human brain works, and build models for solving complex problems and help in making decisions.
  • Chat and voice platforms like Slack, Alexa, which are the final touchpoint for a human to interact with a machine in a natural speech driven manner.

When we speak of AI solutions or AI driven changes coming around us, we usually club all or most of these technologies that are put together to implement an AI solution. In terms of significance, AI solutions are changing the way we look and interact with computers - they are bringing about a paradigm shift in Human Computer Interaction. This shift is spurring further changes to the way industries work and the way software itself is being used or built.

Power 1

Data Visualisation is a pictorial or graphical way of presenting data. Data can be presented in an interactive manner that enables decision making.

The goal of any analytics / BI system is to make sense of the underlying data generated by a business and help humans make decisions about the business. BI today is driven by a lot of human operations, performed through the UI on various datasets. In light of the AI revolution, this will undergo a major change and the industry will move away from dashboards - the mainstay of data visualisation.

Behavioural shift in people due to technology changes

The future of human-computer interaction is going to be governed less by GUI, which again is synonymous with data visualisation and more by Character User Interfaces (CUI) and Voice User Interfaces (VUI). It is a clear tilt towards AI. There are multiple forces at work and in convergence which will cause this shift.

Behavioural forces like outsourcing decision making to machines and engineering advances, such as those in Neural Networks, Augmented reality etc. will mean underlying shift in the way people go about using information for decision making. A good example of this is how, we no longer rely on human map reading skills for finding directions, but are increasingly using Google Maps. Even though they are not always accurate.

Power 2

Need to harness Tribal knowledge / community knowledge to solve problems of cold-start and lack of expertise

AI powered systems can detect and build a global repository of domain specific KPIs or metrics to be tracked, based on other businesses tracking them, etc. There is already a lot of content (articles, best practices, blog posts) for every niche of analytics, created by the community. A global domain-specific repository can be used to harness this knowledge in an actionable way, and plug it back to first timers or people who are not aware of specific KPIs relevant to them.

This is the analytics equivalent of “People who bought this, also found this useful”.

Opportunity for personalisation down to the segment of one

AI powered systems are capable of adapting to users' needs and usage patterns making it possible to design for personalisation at the user level. This personalisation can range from mapping the users personal lingo to that of the company or the domain or understanding the role that user performs in the organisation and what his important metrics are, listening to those specific metrics and providing them.

Personalisation also refers to understanding the context in which a user is asking a specific question or demanding some metrics, by keeping track of previous usage or conversations over a period of time. For example: Is the user asking for revenue numbers before a presentation for all his products or before a competitor analysis meeting for a specific vertical.

In conclusion, The power of AI is providing decisions as opposed to Data Visualisation, which merely provides information for making decisions.  With AI in full bloom, data visualisation will have outlived its utility.

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