COVID-19 and Its Impact on Data Science

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The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

The Forecasting Problem

Data science relies on historical data to predict future outcomes. However, it cannot account for random events, such as the coronavirus pandemic. Key industries, such as retail, rely heavily on predictive analysis to meet demand. The sudden disruption because of the COVID-19 pandemic has severely affected their forecasting models. For the most part, this is only a short-term problem; however, there is a longer-term implication to it: How do data scientists deal with the anomalous data of 2020?

Any good forecasting model needs at least 12 months' worth of historical data to make accurate predictions. That means that for the foreseeable future, predictive data models are going to have 2020 data in their datasets. Do you reject the entire data from the pandemic as an aberration? You could, but that is a substantial data set to discard completely.

Ultimately, data scientists will have to deal with the situation on a case-to-case basis. For example, as the world approaches pre-pandemic functionality, it may be safe to discard energy-use data from 2020. However, things might be tricky with retail, where the effects of the pandemic are expected to be long-term.

Related Reading: 5 Big-Data Problems and How to Solve Them

The Demand and Budget Conundrum

The pandemic has brought the importance of data analytics sharply into focus. Businesses that were already on the path to digital transformation are expected to be more resilient to the effects of the pandemic. The need to extract and analyze data quickly, even in real-time, has only increased with the pandemic. Consequently, there is a growing interest from investors in the potential of big data analytics.

However, there is a catch. The severe slump in consumer demand across industries has left several businesses wanting. People are staring at an uncertain future, which has led them to curb their spending. This slump has affected the bottom line of enterprises, which means lower budgets.

With limited budgets, research expects enterprises to be conservative in their spending on big data solutions, despite the growth in interest. There is also the factor of longer sales cycles that will affect the actual demand for big data analytics over the next couple of years.

The Impact on Security Analytics

Security analytics helps enterprises proactively mitigate security threats to their digital infrastructure.

In recent years, the digitization of data has increased the number and severity of cyberattacks, such as the 2019 data breaches at Facebook and Capital One. The pandemic had further increased the amount of digitization of enterprise data. This, in turn, will likely cause a related increase in the frequency of cyberattacks. In response, experts expect that deepening concerns about data safety will grow the demand for improved security analytics in enterprises.

There is also the factor of work-from-home at play here. COVID-19 forced many organizations to adopt a telecommuting model, a trend that is likely to stay to some degree even after the pandemic is over. Employees working from home pose new security challenges for enterprises, which will require newer security analytics solutions. Together with the rise in the adoption of IoT devices, there is a new set of challenges emerging for data scientists on the cybersecurity front. In terms of demand, experts expect the security analytics market to grow to $28 billion by 2027.

The Impact on Data Analytics Professionals

The COVID-19 pandemic might accelerate digitization and have more enterprises moving towards data analytics solutions. However, the sudden slump in enterprise profits has had an adverse impact on the data analytics industry, too, despite the projected increase in demand. Some companies are laying off or furloughing data analytics professionals. More importantly, a majority of data professionals are concerned about their job security which can affect their productivity.

Furthermore, the nature of work itself is changing for professionals in the industry. Enterprises expect them to increase data sources and change existing data models substantially based on shifting consumer behavior. These changes need to happen quickly, given the immense amount of pressure enterprises are under. So, while data professionals might be getting more visibility, things have simultaneously become more stressful. Typically, changing data models and building requisite ETL pipelines is a labor-intensive process. Modern data transformation tools with plug-and-play solutions can expedite the process considerably and make it a lot easier.

Related Reading: What is an Analytics Engineer?

Minimizing the Impact of COVID-19 on Your Business

In the short term, enterprises will need to ensure that data teams remain operational. Even more importantly, they will need to scale their capacity to ingest and analyze data rapidly. The rapid proliferation of data and businesses’ hunger for critical insights to make decisions is driving this need. Solutions such as robotic process automation and data warehouses are helpful to help with these immediate issues.

In the medium to long term, here is how enterprises can prepare to minimize the impact of COVID-19 on data analytics:

1) Plan Ahead

COVID-19 is expected to have far-reaching effects on businesses and geopolitical events. In turn, data professionals can inform the kind of reports enterprises needs to tackle emerging challenges. Enterprises should keep a close watch on likely challenges that are to emerge from this crisis. These could be prolonged high unemployment rates, a sizable number of small businesses filing for bankruptcy, and important geopolitical events that impact trade, such as direct conflicts between nations.

2) Reprioritize Data Initiatives

Businesses are under immense pressure to cut costs as a result of the pandemic. However, history tells us that slashing budgets across entire data teams is going to leave businesses at a disadvantage. Repriotizing budgets, on the other hand, works much better. For example, digital is expected to be the preferred channel for sales across industries going forward. Adopting more flexible data architectures will help enterprises scale up and scale down quickly. For instance, Amazon Redshift Spectrum allows enterprises to keep their data costs low while still giving them access to the cold data they need.

3) Invest in Agile Data Solutions

Enterprises can use the crisis as an opportunity to bring more agility into their data architecture. Adopt solutions that allow quick integration of new data sources without burdening the data team. Similarly, look at solutions that enable data professionals to focus on data analysis without worrying too much about the operational side of things. For example, Integrate.io can handle the monitoring, maintenance, and security side of things, freeing the data scientists to focus on analytics.

How Integrate.io Can Help

With Integrate.io’s intuitive graphic interface, enterprises can build the most complex data pipelines in minutes, saving time and money. Plug-and-play integrations with all the popular data analytics tools further enhance enterprises’ flexibility when it comes to critical data tasks. Finally, our dedicated team of support staff is available to help walk your team through any issues that might arise.

Learn more about how Integrate.io can help your business navigate the challenges of a post-COVID world. Schedule an intro call to start your14-day trial and experience the full Integrate.io platform for yourself.

Big Data

How Has COVID-19 Impacted Data Science?

The Forecasting Problem

The Demand and Budget Conundrum

The Impact on Security Analytics

The Impact on Data Analytics Professionals