Modern business applications use machine learning(ML) and Deep Learning ( DL) models for analyzing real and large-scale data, predicting or reacting to events intelligently. Unlike research data analysis, the models deployed in production have to manage data on a scale, often in real-time, and produce reliable results and forecasts for end users.
Often these models must be agile enough in production to handle massive streams of real-time data on an ongoing basis. At times, however, such data streams change due to environmental factors that have changed, such as changes in consumer preferences, technological innovations, catastrophic events, etc.
These changes result in continuously shifting data trends — which eventually degrade the predictive capacity of designed, educated, and validated models based on data trends that are suddenly no longer important.
This change in the meaning of an incoming data stream is referred to as “concept drift” and what they predict is nothing new. Although idea drift has always been a matter for data science, its effect has rapidly escalated and reached unparalleled rates due to the COVID-19 pandemic. And this is likely to happen again as the world continues to plan for COVID rehabilitation and more changes in human behavior.
Concept drift exists because of the significant changes in human behavior and economic activity resulting from social distancing, self-isolation, lockdown, and other pandemic responses.
Nothing lasts forever — not even carefully built models trained with well-labeled mountains of data. Concept drift leads to limits of decision divergence for new data from those of models developed from earlier data. Its effect on predictive models developed across industries for different applications is becoming widespread, with far-reaching implications.
For example, in-store shopping has experienced a dramatic decline and an unparalleled rise in the number of items purchased online. Additionally, the type of things customers buy online has changed — from clothing to furniture, furniture, and other essential products.
ML models designed for retail companies now offer no longer the right predictions. Because companies no longer have precise predictions to guide operational decisions, they cannot optimize supply chain activities adequately.
Concept drift also impacts models designed to predict fraud across various industries. For example, models were previously trained to see buying one-way flight tickets as a reliable indicator of airline fraud. That is not the case anymore. A lot of fliers have bought one-way tickets with the advent and spread of the Coronavirus. It will possibly take some time to be a reliable predictor of fraud before this returns.
Insurance is not being left out. Until this pandemic period, predictive models were used to evaluate various factors to determine customers’ risk profiles and thus arrive at pricing for different insurance policies. As a result of self-isolation and movement limitation, along with a demographic-related shift in risk, many of these variables are no longer the predictors they used to be. Also, a previously unknown range of data is added, requiring new categories and labels.
Primarily, 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.
AI and ML models need to train raw data on mountains before implementing or operationalizing data science into real-world scenarios. There’s a catch, though — once these models are deployed, while they continue to learn and adapt, they ‘re always based on the same concept they were initially designed on. Development models don’t compensate for factors and don’t react to patterns emerging in the real world.
As a result, model predictions appear to deteriorate over time, and their purpose is no longer served. Models trained to predict human behavior are particularly vulnerable to such deterioration, especially in acute circumstances such as the current pandemic, which has changed the way people spend their time, what they buy, and how they spend their time altogether.
Drift detection and adaptation mechanisms are crucial under these changing conditions. The continuous method is to track models to detect drift and adapt accordingly.
Mechanisms must be in place to monitor errors on an ongoing basis and allow predictive models to be adjusted to rapidly evolving conditions while preserving accuracy. Otherwise, these models may become outdated and generate results that are no longer reliable or efficient for the organization.
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
With this, new data can be obtained to retrain and adjust models, even if the original data set is considerably smaller. Teams should build and construct new data, where possible, in a way that accounts for missing data.
Most notably, MLOps automation allows teams to implement these change management techniques in rapid iterations, as long-term implementation is no longer time-bound. The lifecycle of data science needs to be carried out in much shorter periods, and this can only be done by automation.
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.
Businesses can track and identify changes that affect their AI models using MLOps automation, make rapid improvements to their AI applications, and bring new technologies to market quicker and in a much more agile manner. Anyone who can quickly adjust, make appropriate changes to their models to ensure accuracy, and harness AI will come out top.
This investigation measures COVID-19 ‘s future impact on the global Big Data Analytics (BDA) market. The embedded environment has resulted in a hyper-connected world and the Stuff Internet (IoT). IoT has connected all kinds of endpoints, and unveiled a treasure trove of data, thanks to ubiquitous networks. This unprecedented volume of data is capable of empowering decision-makers like never before. Especially during the COVID-19 pandemic-including efforts to contain it’s spread and help companies remain alive, the need to collect, imagine, and execute this intelligence in near-real-time is becoming a mission-critical objective increasingly.
The BDA industry is divided into two major segments: data discovery and visualization and advanced analytics (AA), respectively. Before the pandemic, both of these categories had growing interest from investors worldwide and had attracted millions of dollars in financing. Our research shows use cases across verticals of the industry and forming alliances across geographies to meet the increasing demand for such solutions.
In this scenario, the important factors which drive the BDA market include organizations that understand the organizational advantages of using BDA to make more informed decisions, as well as increased government and intelligence (G&I) and healthcare sector funding to handle the pandemic.
Overall market growth, however, is expected to be curbed by a decline in consumer spending on BDA solutions as budgets are frozen or redirected to meet immediate operational needs, as layoffs decline the resources available to BDA practitioners, and as sales cycles extend. Furthermore, government funding would be missing to support SMBs bounce back from the downturn in demand in the industry.
In the face of the COVID-19 pandemic, two scenarios are likely to be considered by the publisher. The research assumes that containing the virus will take over 18 months in the first, more conservative situation, and businesses will resume operations at full capacity within two years. Under the second, more ambitious example, the research predicts the COVID-19 virus will be suppressed by August, and companies will be able to restart full-capacity operations by the end of 2020.
This study also presents key market trends, a market outlook for the future, vendor analysis, and market growth opportunities.
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.
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.
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.
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.
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.
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.
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.
Policymakers, public agencies, and other institutions across the globe have used AI systems. Big Data analytics, data analysis software forecast where the virus may go next, monitor the virus spreading in real-time, recognize drugs that could be helpful against COVID-19, and more.
People who work at such epidemic outbreak locations usually collect important COVID-19 related data like transmissibility, risk factors, incubation period, as well as mortality rate. This data is used for visualization, mathematical model development, and neural network training.
The Canadian company, Blue Dot, used AI and was widely reported as the first company to disclose late December news of the outbreak.
At first, Johns Hopkins University released an interactive dashboard on January 22, 2020, which is used to monitor real-time data on infections, deaths, and recoveries of Coronavirus.
Insilico Medicine, a company based in Hong Kong, announced that its AI models had designed six new molecules capable of stopping the virus’s replication.
A Chicago-based nonprofit organization, the Medical Home Network (MHN), has introduced an AI program to identify Medicaid patients most at risk from COVID-19.
Google’s DeepMind published research on March 5, 2020, discussing how they used deep learning to predict the protein structure associated with SARS-CoV-2, the virus that causes COVID-19.
COVID-Net, an AI-based solution, was developed to diagnose COVID-19 in patients with different lung conditions, including COVID-19, using chest x-rays.
A computer vision camera system based on AI scanning public areas has been used to monitor whether people in the UK city of Oxford are adhering to the government’s social distancing measures.
IBM has aggregated and integrated COVID-19 data with The Weather Channel app, which will meld weather and local novel coronavirus incidents. IBM ‘s subsidiary can bring relevant COVID-19 data to 300 million active monthly users through its The Weather Channel App.
Esri is using its mapping and geolocation skills to monitor COVID-19. As well, Esri has located COVID-19 case data and combined it with Definitive Healthcare bed availability data. The dashboard, which uses ArcGIS Business Analyst tools from Esri, offers a snapshot of county-level preparedness.
As we can see, different agencies are using Data Analytics and AI to seek to combat the epidemic. And there are lots of tools and open sources of data that provide updated information.
In terms of the most effective approaches, most models used to monitor and forecast the epidemic don’t use AI.
Instead, most scientists prefer established epidemiological models, so-called SIR models, which use ordinary differential equations (ODEs) to track the numbers of susceptible, infected, and recovered people.
What concerns the use of AI to forecast and diagnose COVID-19? It has not been beneficial against COVID-19 because its efficacy is blocked due to a lack of historical training data. By using machine vision and image recognition, robotics (e.g., for disinfecting hospitals) are more likely to see it used for social control and other tasks.
Now let’s look at how your company can use Data Analytics to plan itself, respond efficiently, and build both short-term and long-term strategies.
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.
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 event.