Heading into the holidays, we can’t help but look back at the whirlwind last two years. The COVID-19 pandemic caused a disruption unlike any we’ve seen in the last few decades. Employees across the world began working remotely like never before. Because data has become such a critical part of this work, it needed to follow the employees, quickly escalating digital transformation and the move of data to the Cloud. Snowflake’s record setting IPO in September 2020 demonstrates the value of this opportunity. But the shift also put an abrupt burden on IT teams to protect that remote work and the data required from new threats and escalating old ones. Data thieves and hackers took advantage of the disruption to step up attacks, like the recent Robinhood leak that exposed data of 7 million customers. As data exfiltration and PII leaks continued, regulatory attention around protecting personal information in the US increased.
All in all, it’s a challenging yet thrilling time to be part of the data ecosystem. And data governance and security are more critical than ever.
As we look toward 2022, it’s a given that data will continue moving to and consolidating in the Cloud. But this will lead to other shifts in the data governance landscape, uncovering surprising new possibilities and challenges for companies who want to stay ahead of the competition by making the most of their data. Over the next few weeks, we’ll share some predictions from ALTR leaders Dave Sikora, James Beecham, Doug Wick, and Pete Martin to help companies know what they might expect in 2022.
Prediction #1: Companies Will Dare to Data Share, Safely
Centralizing data in the cloud enables increased flexibility, availability and sharing of data – within the enterprise and without. In the past, if you wanted to share data that lived in an on-prem datacenter with another group or with an external partner, you would have to extract and then email or FTP the file – a cumbersome, manual process. Increasingly, companies will take steps to make data more easily available via the cloud – to connect applications, to monetize it or even utilize it to create a more effective AI.
For example, earlier this year, NBC Universal announced a new solution to monetize the audience data it gathers by making it available to partners, on a cross-cloud data clean room environment powered by Snowflake. Advertisers will be able to safely and securely join their own data, without exposing any viewer personally identifiable information (PII). The Snowflake platform lets NBC Universal govern what data is housed in the clean room, how data can be joined, what types of analyses can be performed on the data, and what data can leave. Disney is doing something similar by making Snowflake its single source of data to share securely with its internal teams and partners. Snowflake is enabling the monetization of sharing further with their Data Marketplace which allows companies to offer up unique, proprietary data to Snowflake customers to utilize along with their own data.
Secure cloud-based sharing will also help overcome one of the main ML/AI roadblocks: training data. 91.9% of firms report that the pace of investment in Big Data and AI projects is accelerating, but AIs don’t come fully formed out the box – they require massive amounts of data to learn on to be effective. Companies may contract with an ML or AI provider, but the model needs to be trained on relevant data for that specific company’s use case before deployment. Data privacy concerns have limited the ability to provide real data to AI vendors, with companies in some cases relying on synthetic data, but implementing secure data sharing allows for use of real data instead.
Essentially, the ability to securely share sensitive data easily from the cloud will enable increased data sharing and increased insights.
Watch our blog for more predictions to come around new risks to data, the crucial role of data in the business, and the regulatory environment ahead…
