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  /  Analytics   /  Updating Data and Analytics: Impact of Covid-19 Pandemic on Data Analytics
impact of COVID-19 on data analytics, data analytics, AI and advanced analytics

Updating Data and Analytics: Impact of Covid-19 Pandemic on Data Analytics

The Impact of Covid-19 on data analytics has reshaped the strategy of data and analytics

The effect of the COVID-19 pandemic has been abrupt, expansive and sensational. It has disturbed supply chains, moved immense numbers of employees to work remotely, changed consumer behavior significantly and driven sudden movements in demand. A report by Chicago-based counseling firm West Monroe shows that 69% of C-suite chiefs are putting resources into more technology during the current pandemic. The organization’s quarterly pulse survey of 150 executives additionally found that data analytics platforms are the most widely recognized technology to be embraced, with 57% of respondents attempting to adopt the tech in the past six months. A further 21% detailed trying machine learning and artificial intelligence.

 

The initiatives taken during the pandemic – wide lockdowns, limitations on movement and dining, the extensive adoption of remote working– combined with the changing behavior of customers have overturned the suppositions supporting business analytics. Accordingly, numerous organizations have been evaluating the effect that COVID-driven changes in business conditions have had on their analytics and their data management processes. These endeavors have given a significant beginning stage to see how analytics and data should be updated.

 

The year of 2020 has quickened a pattern toward utilizing external data, Davenport and Camm said in a webinar by MIT Sloan Management Review. With external elements causing critical interruption and internal information about past exercises are no longer a decent indicator of things to come, organizations are going outside to find out what’s happening, especially about customer behavior and demand. Davenport and Jeffrey Camm are a professor and associate dean of business analytics at Wake Forest University.

 

Organizations need agility to integrate external information, Camm added. An excessive amount of red tape or extensive cycles can defer utilizing data from different sources. However, organizations should likewise contemplate the data being brought into their models to evade predisposition, errors, or different issues. This restored interest in analytics isn’t restricted to a customary business intelligence approach. However, it is dependent on new methodologies that give real-time insights into operational processes and the fundamental structured and unstructured data that upholds them.

 

As many began utilizing data to drive real-time operational and strategic decisions around the supply chain of individuals, resources and material, company leaders acknowledged there was a need to make a robust platform for the long-term. This platform would serve to address progressing vulnerabilities and assist businesses with changing as they subside into a more proactive than reactive mode.

 

A study by Cognizant found that 83% of organizations have either finished such evaluations on their analytics (37%) or have them in the process (46%). The most well-known issues recognized were analytics being excessively weighted toward pre-COVID information (51%) and not being adaptable (46%)

 

Countless organizations have been adopting steps to reshape their strategy of analytics Generally 45% of the studied organizations said they had made major or critical changes to their data management and to their analytics. Looking forward, a large portion of the respondents anticipated that their organizations should make major or critical changes in every region throughout the following year.

 

Camm and Davenport said an organization’s probability of reinforcing its data analytics program, regardless of the recession, will probably rely upon whether an organization has just seen a return on investment in their analytics programs. AI and advanced analytics are quite crucial in amplifying investments in new tech and mining the most insights from data, at any stage of an organization’s digitalization journey.