Key Takeaways for Data Management from the Pandemic
Regardless of how COVID-19 finally plays out, the changes brought in response to the pandemic, and the increased importance of data, are here to stay.
The COVID-19 pandemic has dramatically impacted how organizations operate, from heavy reliance on remote work and online interactions to changes in business strategies. Data has become the lifeblood of practically every enterprise in the hybrid reality at the core of those responses. Antti Nivala, founder and CEO of M-Files, shares key takeaways and learning points for data managers.
As the pandemic grinds on, it’s a good time to look at the lessons it has taught us about data and what organizations need to do to ensure its quality and reliability. Regardless of how COVID-19 finally plays out, the changes brought in response to the pandemic, and the increased importance of data, are here to stay.
Here are the key takeaways about data from the pandemic that organizations should be sure to carry forward.
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Data Needs a Solid Infrastructure
For data to be reliable, it must first be accessible. One of the biggest takeaways from the pandemic is the critical importance of having a solid, reliable and scalable infrastructure, which for most organizations means a cloud-based solution.
Public health organizations felt the impact immediately. Traffic to the Centers for Disease Control and Prevention’s website, as you might expect, exploded in 2020, recording more than a billion page views in March that year alone, compared with just under 100 million during March 2019. And traffic for 2021 was still three times greater than in 2019, with COVID-related traffic accounting for 56% of the CDC’s total page views.
Businesses also saw a significant surge, with online retail sales increasing 32.4% year over year in 2020 and continuing to grow in 2021. Overall, peak internet traffic increased by about the same amount—32%—in 2020.
The importance of having accurate, trustworthy data during the most severe spikes in traffic underscores the need for infrastructures with high availability and easy failover. And that points to cloud-based solutions, which can offer nearly limitless scalability and the ability to adjust to sudden spikes in traffic within minutes.
Poor Data Quality Can Affect an Organization’s Bottom Line
It’s always been important never to take the quality of your data for granted, but that became especially true during the pandemic. Organizations that perform emergency response, distribute medical data or help with recovery efforts saw that importance up close, but every organization needs to trust its data if it is to be effective.
The costs of insufficient data are felt in a number of ways, including the amount of time employees spend each day looking for data instead of using it. Knowledge workers can spend half their time hunting for data, finding and correcting errors, and trying to confirm data they don’t trust. The most noticeable impact is on the bottom line. A Gartner study has found that poor data quality costs organizations an average of $15 million annually.
To trust their data, organizations need to know where and how often it’s being collected. An information management solution can automatically add metadata to information to give it this critical context, providing an organization confidence in all of its data.
Data Requires Context to be Valuable
Data without context is just random information, incapable of providing valuable insights. In the early days of the pandemic, the internet was rife with posts comparing the infection rates of different countries—the United States and Italy, for instance—in terms of raw numbers, without context such as the age of the population, the availability of health care or for how long the disease had been spreading. Without context, the numbers didn’t have any real value at all.
Data context is essential and goes far beyond simple dashboard visualizations. It’s also important to know what’s behind the data. For example, a business proposal might rely primarily on existing agreements and recent correspondence, but past agreements or correspondence related to the proposal, even if they involved another company, could also inform decision-making.
Context cannot only add relevant information but also filter out extraneous, unimportant noise that might otherwise influence a decision. For instance, an information management platform that uses advanced analytics powered by artificial intelligence can quickly locate relevant information to put data in clear context, whether the issue at hand involves contracts, compliance, records management, data governance or lifecycle management. Data-driven decisions can’t be effective without context.
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Data-Driven Enterprises are set up to Thrive in Crisis
By any measure, businesses and other organizations run on their data. But for many, developing a truly data-driven culture has proved to be an elusive goal. A data-oriented culture involves mutual trust and data-centric processes focused on collaboration, agile software development and deployment, and the quality and security of data.
An organization with such a culture already has the components it needs to run smoothly. For others, there are concrete steps they can take to create a data-oriented culture, such as implementing data governance, which can manage the availability, integrity and security of an organization’s data, as well as controlling access to data.
A data-oriented organization will make data a part of all decisions, which requires that data be accurate, up-to-date and available to everyone who needs it. It can help organizations respond quickly to crises as they arise and handle any new data challenges that come with them.
The shift toward digital, data-based processes was already underway in many organizations before the appearance of COVID-19, but the pandemic accelerated those transformations and, in the process, made data even more valuable. Going forward, organizations need to implement information management practices that ensure their data’s availability, quality, integrity, security, and shareability. Their future will depend on it.
What data management learnings from the pandemic are you retaining for the future? Share with us on LinkedIn, Twitter, or Facebook. We’d love to hear all about it!
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