Data scientists can no longer rely on historical data alone to train models in real-world scenarios and then deploy them. The pandemic’s ripple effect tells us that we need to be more agile, flexible, and use better approaches to keep deployed models responsive and ensure they provide the value they were designed to provide. Here is How Have ML Models Shifted During COVID-19. https://www.datatobiz.com/blog/impact-of-covid-19-data-analytics/

1. Impact Of COVID-19
On Data Science
Industry You Should
Be Aware Of!

2. How Have ML Models
Shifted During COVID-19?

3. There is more to projects in data science than creating and deploying ML models.
Monitoring and preserving model output is an ongoing process that’s made simpler
with MLOps being embraced. While you can re-label data and retrain models on an
ongoing basis, this is an extremely expensive, cumbersome, and time-consuming
approach.
To identify, understand, and reduce the effect of design drift on production models
and automate as much of the process as possible, data scientists need to exploit
MLOps automation. Given DevOps’ track record of enabling the fast design and
delivery of high-visibility and quality applications, it makes sense for data science
teams to leverage MLOps to manage the development, deployment, and
management of ML models.
MLOps allows data science teams to either leverage change management
strategies continuously update models upon receiving new data instances or
update models upon detection of a concept or data drift
FEASIBLE AND FAST NEW SITUATIONS

4. Data science needs to respond rapidly to the rapid changes taking place across the
globe. Many companies are currently in a tight spot. Getting the right kinds of data,
knowledge, and information to respond rapidly to the unforeseen changes brought on
by the pandemic may be the making or breaking of individual companies in the
current situation is where MLOps automation can provide tremendous value — by
allowing data scientists to track and control the effect of their AI applications, and to
be able to respond rapidly to new production situations.
ML teams need to design and store recipes to generate models on demand, rather
than creating solutions based on stored models that were trained using static data. It
helps them build new models based on new data quickly and easily and deploy them
rapidly.
Data science teams need to track and identify design drift on an ongoing basis to
ensure the validity of their AI models and the value they bring to the company. MLOps
speeds up model development, implementation, and management, allowing for the
creation of AI applications that can quickly adapt to changes in the environment.
THOSE WHO ADAPT WILL SURVIVE

5. Liquidity and liquidity risk
Morale, inspiration, and efficiency for workers
Reduced Distribution Pipelines
Supply chains are broken
Optimization of the costs and processes
Review of established business models
Change / Engagement Models for Customers
Growth Pipeline Rebuilding-Preparing for a Rebound
The projected base year is 2019, and a projection period from 2020-2025. Revenue
on the market is calculated in US Dollars.
COVID-19 has become a vehicle for change in all sectors. The following top of the
mind problems have been discovered in this research:
The report highlights key developments impacting the BDA industry and discusses
consequences for the future.
HIGHLIGHTS OF THE RESEARCH

6. Readers who are benefiting from this research are advanced analytics vendors,
vendors of data exploration as well as analysis, companies seeking to understand
the BDA better, vendors across banking, government, retail, telecommunications,
health, and life sciences, and any business finding market opportunities.
BENEFITS OF THE RESEARCH
READ THE FULL ARTICLE: https://www.datatobiz.com/blog/impact-of-covid-19-data-
analytics/

7. Advanced visualization has helped policymakers, and researchers keep a close eye
on regular COVID-19 trends and make informed decisions. Not just concise
statistics, but the study of the correlation between various factors allows decision-
makers to assess and appreciate the pandemic ‘s effects. Various organizations
process a vast quantity of data to provide analysis to demonstrate how the virus
came out.
After months, however, such visualization came out, and by then, it was too late for
the world to take steps to contain the virus effectively. Nevertheless, those
visualizations were helpful further to reduce the impact of COVID-19 in the decision-
making process. If we had such information earlier, it could have helped
international institutions like WHO to declare an emergency in the very early stages.
ADVANCEMENT IN STREAMING ANALYTICS

8. AI quickly took center stage in various sectors, such as finance and media, but was
slow to penetrate the healthcare sector due to concerns about misuse of data from
patient health records. Furthermore, another reason to hold AI at bay in healthcare
data is that any incorrect forecast could cause a doctor to prescribe false
treatments that could directly affect the patient.
Although such concerns are not unfounded, data science ‘s exposure to the current
pandemic has demonstrated how useful it may be to exploit patient data. It can
help experts and decision-makers to create frameworks through different policies
that enable data science to develop medicines and other healthcare.
DEMOCRATIZATION OF MEDICAL HEALTH
RECORDS

9. Cloud has driven companies to scale while reducing operating costs swiftly. Yet, for
privacy reasons, several businesses have been wary of putting their business
processes on the cloud. And most critically, for their mission-critical tasks, data
science teams typically stick to on-premise infrastructure. Due to the lockdown of
cities, such projects have now taken on a hit. It has led organizations to shift all
successful projects from remote locations to the cloud to add versatility in working
collaboratively.
CLOUD ADOPTION
READ THE FULL ARTICLE: https://www.datatobiz.com/blog/impact-of-covid-19-data-
analytics/

10. Fake news and conspiracy theories about COVID-19 have created a lot of
uncertainty among people and hindered governments in ensuring people are
compliant with their lockdown initiatives. It isn’t fresh because there was an influx of
fake news on social media and popular chat applications.
Companies such as WhatsApp have taken numerous steps to restrict users’
freedom to communicate on-the-go, and Youtube has also limited the suggestion
of conspiracy theories. Yet because of the strenuous complexity of natural language
processing, these have not removed fake news from social media sites. Researchers
could focus extensively on developing solutions to identify false reports accurately.
NATURAL LANGUAGE PROCESSING

11. Data scientists are forecasting COVID-19 spread along with details on how many
lives it is likely to impact. However, in these challenging times predicting with
incomplete data can further confuse people. “It is highly biased to provide current
datasets (on COVID-19). For example, developers usually look at deaths per
reported case when measuring the mortality rate. But, the presumption is that all of
the reported cases were identified, which is not valid.
BEYOND PREDICTION

12. Why Data Analytics Is Used
In The Covid-19 Period?

13. Data analytics is commonly used to tackle the market problems emerging
from the epidemic. According to a recent survey conducted by Burtch Works
and the International Institute for Analytics (IIA) of 300 analytics
professionals around the US, 43 percent of respondents said analytics is at
the forefront of their operations, helping their organizations make big
decisions in response to the COVID-19 crisis.
Communications providers rely on large operations of call centers to
perform such vital functions as sales and customer service. Most retail
stores are now closing, putting immense pressure on call centers. Using
analytics to predict consumers who are most likely to be impacted and
establish constructive contact strategies to keep them aware of policy
changes and service delays to address the issue and reduce call center
capacity. Consider using the chatbots as well as automating the call
centers.

14. Check the data assets of your company, need for additional acquisition, data
from third parties, data management to track the situation and make details
available
Tackle leading issues such as low data consistency, interdepartmental
cooperation, siloed tools/data, and data compatibility with company KPIs with
analytics dashboards.
Assess the accuracy of the data and preparation for use in the analysis,
planning, and decision-making. Note that the data preparation and cleaning
takes up 80 percent of all Data Analytics processes to make the data suitable
for a specific even

15. Read How Data Analytics and AI Are
Playing Their Part in the Mitigation of
COVID-19
https://www.datatobiz.com/blog/impact-of-covid-19-data-
analytics/