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Top four trends in data and analytics

The Covid-19 pandemic in 2020 exposed many deficiencies in the current socio-political order and wrecked many of the developed and developing economies. Apart from this, the pandemic accelerated the adoption of cutting edge digital tools, related to data and analytics, for a variety of purposes. Data and analytics are also instrumental in 500+ clinical trials all over the world that are being taken as the search for effective medicine intensifies further. These are increasingly being used in addition with Artificial Intelligence (AI) in mapping probable effects on and measures for the economy worldwide in a synchronized manner. These yield valuable insight for effective countermeasures as well as determining the pathway of post-pandemic economic recovery in a sustainable way.

Gartner published top trends in data and analytics worldwide in an article published in October, 2020. The top four trends in Data and Analytics among them are

. Faster and more responsible AI– There is a widespread apprehension that pre-pandemic models based on historical data may seem obsolete in a post-pandemic world. AI techniques such as machine learning etc may provide a valuable foundation in restructuring of supply realigning the supply chain with the altered patterns of demand. In the current context, AI techniques are used to build more adaptable and flexible systems to handle complex business situations in an efficient manner. Significant investments in new chip architecture are enabling deployment of AI in various edge devices replacing reliance on centralized systems with a requirement of higher bandwidth. Gartner predicts that 75% of enterprises will shift from piloting to operationalizing various AI by the end of 2024. This will drive a five-fold increase in streaming data and analytics infrastructure. This will also prevent poor decision making in the organization building trust and understanding between humans and machines and alignment of various crucial policies within the enterprise.

. Decline of Dashboard- The visual, point-and-click data stories are increasingly being replaced by more automated and consumerized dynamic data stories. In this context, predefined dashboards are declining in user interaction. Dynamic, in-context data stories provide each user with the most relevant insights streamlined on the basis of their context, role or use. This decline of static dashboard will continue while new augmented and NLP-driven user experiences replace them in existing analytics and business intelligence tools within the leading data and analytics enterprises.

. Increase in Cloud Storage- Data and analytics leaders are moving to cloud storage increasingly and are still experiencing difficulties to align right services to right use cases. This has driven unnecessary governance and integration overhead. Gartner predicts that by 2022 90% of data and analytics innovation would use public cloud services. So even with the initial difficulties, data and analytics leaders continue to prioritize workloads that are in sync with cloud capabilities. This transition focuses on optimization of operational cost along with accelerating change and innovation in data and analytics.

. Augmented data management- Augmented data management uses machine learning (ML) and AI techniques to streamline and optimize operations. It also uses metadata to power dynamic systems. It can examine large samples of operational data like actual queries, performance data and schemas. An augmented engine is able to operationalize and to optimize configuration, security and performance using the existing usage and workload data. Data and Analytics leaders are increasingly relying on augmented data management increasing automation and optimization in their redundant data management tasks.

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HERBERT BAYARD SWOPE