Automation in reporting: breaching the cognitive ceiling with natural language generation

What is the value of NLG?

Communication is assessed by its efficiency in transmitting necessary information or to induce and support action. At the intersection of data growth and picky information consumption patterns, successful internal and external communications are becoming increasingly hard due to:

• Information Overload: Data proliferation accelerates as monitored digital channels permeate everyday interactions and transactions. At the same time, the proportion of collected information used to support insights is decreasing. We are hoarding data but on average failing to get real value from it.
• Complex Organisational Structure: Modern organisational designs are seeing their user base of reporting broaden as decisions are becoming increasingly data-driven. Proportionally, diverse users’ backgrounds need to be serviced with communication that is appropriate for the user’s data literacy and contextual knowledge. Proliferating an organisation with insights and information in the form of natural language will be important in making data-driven decision making a nimble, accessible process, removing reliance on the consumer to understand underlying data or information in its raw form.
• Cognitive Bias: Seeking out pre-conceived insights from a set of data, as opposed to performing objective analysis – increases as humans strive to make sense of increasingly complicated datasets and data models. As data growth transcends volume and impacts variety and velocity of data, the risk of cognitive biases and its limitations in the production of valuable insights become more material.
• The rise of the Bots: With business processes automated using Robotic Process & Desktop Automation, and statistical learning methods applied on huge volumes of data, narrative descriptions and explanations of bot actions and Machine Learning (ML) insights will be important in enabling control and integration of robotics and AI in ‘human-in-the-loop’ process designs.

NLG’s key features are addressing the above challenges in designing a future-proof reporting architecture. The graph below illustrates the relevance of the key NLG features in addressing these challenges.

What reports do I automate, and when?

Most reports can be automated, but in some cases, this may not result in noticeable value gains for the organisation. Return On Investment (ROI) maximisation is dependent on the maturity of NLG integration in reporting processes.
Initially, ROI is maximised by automating high volume and effort stand-alone reports. As simple use cases are operationalised, advanced use cases become tenable by leveraging the already built NLG analytics models and other robotics and cognitive automation ecosystem initiatives (e.g. Analytics, Machine Learning, Natural Language Processing).

The chart below illustrates indicative (but not exhaustive) types of reports and their relative prioritisation across the three key horizons outlined above.

NLG is one of many next steps to advance your organisation towards Integrated Automation, breaching the ceiling of automating rules-based tasks and entering the world of processes that require cognitive capability.
At the centre of processes and decision making, data that lies in your systems has a story to tell, and there are increasing challenges in ensuring its value is surfaced to create impacting value for your organisation.
And as early adopters continue to leverage NLG as a solution and provide real examples of competitive advantage, adopting NLG key’s skills to meet these challenges has become a reality that is within the reach of organisation’s digitisation agendas, with reporting being the first hotspot to realise early transformation.