Predicting data trends is risky because there is always a chance the future will look vastly different than what we imagine.
However, during a recent podcast episode, Claravine CEO Verl Allen and Data Chats Host Chris Richardson discussed some of the trends that could shape data in the coming year.
Trend #1: Privacy Becomes the Paramount Concern
Apple, Amazon and Google are all promoting new initiatives around privacy. These will affect digital media and advertising, which historically have been highly dependent on third-party cookies.
Essentially, we’ve built this massively profitable, optimized and targeted ecosystem, and the stakeholders who were left out were always the end users.
Businesses that built their business on the backs of these third-party IDs are at risk of extinction. This is a tectonic shift.
Trend #2: A New Focus on First-Party Data
There are many people hoping the effects of privacy changes aren’t going to be as staggering as predicted, but regardless, there will be a shift in focus from a dependency on third-party data to first-party data.
Every industry will experience some shift in architecture that will be more about testing and experimentation. The future will be about summary-level data and less about user-level data. By next year, there could be an entirely new set of winners and losers based solely on this trend.
Trend #3: Growth of Cloud-Based Data Infrastructure and Platforms
There is a growth in both scale and sophistication in purpose-built cloud-based software that is designed to help with the processing side of the data equation and the infrastructure side.
In addition, companies are going to be making sure they are capturing the right data to power the infrastructure, so they can capture the right insights that will influence business decisions.
Trend #4: Data Becomes an Organization-Wide Initiative
Organizations have to think of data as an enterprise-wide imperative.
There is still a belief that data quality is someone else’s problem and no one actually owns it. This creates an environment where companies kick the can down the road without actually investing time and resources into a solution.
Businesses should be honest about their data quality and decide how they are going to measure their progress on their journey to improving it.
Trend #5: New Solutions to the Proliferation of SaaS Applications
The average enterprise has over 300 SaaS (software as a service) applications. Just in the marketing and digital departments of an organization, there are somewhere between 50 and 100 applications.
Subscription-based software made it easy to invest in and implement solutions, which is great, but this ease created new problems. Namely, there are hundreds of applications creating and building data sets in silos within the enterprise. Next year, there could be new solutions to help manage and reduce this proliferation.
Trend #6: Data is an Increasingly Valuable Resource
Companies have come to realize there’s a huge amount of value in the data. In fact, that data is gold. In response to the growing amount of data available, cloud-based data infrastructure solutions are helping organizations scale.
While the opportunity scales, so do the problems. In response, companies are trying to improve data quality so they can confidently turn it into insights that will influence decisions.
In addition to scale, specificity is also increasing. Now, businesses are also grappling with the question “How do we take data coming from a particular channel or silo and apply it broadly in the business?”
Trend #7: Creating, Managing and Enforcing Data Standards for Success
If you’re going to scale anything, you have to have standards. Where many companies fall short is there’s no sense of ownership of the standards.
Instead, they’re relying on third-party applications. As a result, their data model is simply the attributes and fields that are being supplied from third-party solutions. Instead, companies have to be more proactive at building their standardized data models.
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Learn more about data standards: “4 Strategies to Improve Data Quality“
