“Data is the New Oil,” But That Also Means it Can be Risky

British mathematician Clive Humby famously said in 2006 that “Data is the new oil.” In the 16 years since, companies of all sizes have drilled for and stored more and more data about their customers and business operations to drive performance and growth to reach their goals.

In conjunction with the increased use of the consumer internet, data creation has exploded in recent years—considering the majority of the world’s data collected over the course of human history has occurred in just the last two years alone. As a result, entire businesses and industries have been built solely on having access to unique and useful data.

To illustrate that fact, the location data plat­form Foursquare got its start as a social networking service; that is until the company realized its most valuable asset was actually the data it owned about where people were going.

The once-hyped social media company has built itself into one of the world’s largest data empires. Similarly, an entire industry of credit card transaction companies have cropped up to provide hedge funds and other companies with unique analysis of con­sumer purchases to better understand spending patterns.

Businesses of all sizes now realize the power of data to better serve their customers, identify new business opportunities, grow sales, and improve processes. Data enables better and smarter business deci­sions, and for that reason, a company’s data is considered one of its greatest assets. But, due to a number of growing risks to businesses, data can also be one of an organization’s greatest liabilities.

With shifting consumer privacy and data regulations both in North America and around the world, companies need to be increasingly careful about how they store, secure, and use their data. What started with Europe’s General Data Protection Regu­lation (GDPR)—which set the stage for how companies manage data about their customers—has continued with the California Consumer Privacy Act and the pending American Data Privacy Protection Act that will present new regulatory requirements for how companies store and use data about their customers.

When it comes to managing and leveraging data, it’s not just regulatory issues that companies need to consider, but also business issues. For example, as more com­panies embrace digital transformation initiatives to improve operations, many organizations are now turning to artificial intelligence (AI) and machine learning (ML) models. But now, more than ever, the old adage about the data sources used in AI and ML models rings true: garbage in continues to equal garbage out.

If the underlying data used in models is low-quality or flawed in any way, the resulting output will also be low-quality and flawed. Bad AI models resulting from bad data can have severely negative impacts on a business. Accurate, useful, and ideally real-world data is key for businesses, especially as AI becomes increasingly used for business recommendations about new revenue opportunities, ways to expand business lines, and improve customer experiences and retention.

Data issues can also cause headaches with a number of busi­ness-critical applications and can have serious, real-world conse­quences. Because information powers various applications across a company’s sales, marketing, and customer experience tools, among others, even simple issues related to accessing and efficiently process­ing data can lead to decreases in productivity and performance, and ultimately lost revenue.

For example, if a company database experiences slow-running queries or blocking, the applications that need data to operate will experience slowed performance. Imagine trying to stream your favorite song and having to wait one minute for it to buffer! Even slight disruptions in accessing and processing data can have a sig­nificant impact.

Just like oil, when data is collected, processed, and used safely and efficiently, it can be supremely valuable. However, also like oil, if data is mishandled, stored, or processed incorrectly, it can potentially lead to negative outcomes.

As digital transformation initiatives accelerate, data will con­tinue to grow exponentially for every company. By embracing modern data solutions, such as using AI to help ensure databases are operating smoothly, companies can better manage and lever­age the glut of data for improved productivity, performance, and ultimately, customer satisfaction.