GCP – A revolution is coming for data and the cloud: 6 predictions for 2021

Offering predictions can be a challenge, because specific predictions depend on specific timeframes. But looking at the trends that we’re seeing in cloud adoption, there are a few things I’ve seen in 2020 that imply changes we will be seeing in 2021. 

As someone who was a network engineer when the internet revolution happened, I can see the signs of another revolution—this time built around the cloud and data—and acting on the signs of change will likely tell the difference between the disruptors and the disrupted. 

Here’s what I see coming down the road, and what’s important to keep in mind as we head into a new year.

1. The next phase of cloud computing is about the benefits of transformation (not just cost). 

In 2021, cloud models will start to include a governed data architecture, with accelerated adoption of analytics and AI throughout an organization. In the past, we’ve seen notable developments that have driven massive cloud adoption movements. The first wave of cloud migration was driven by applications as a service, which gave businesses the tools to develop more quickly and securely for specific applications, e.g. CRM. Then, the second generation saw a lot of companies modernizing infrastructure to move on from physical data center maintenance.  

That’s all been useful for businesses, but with all that’s happened in 2020, the third phase—digital transformation—will arrive in earnest. As this happens, we’ll start to see the benefits that come from truly transforming your business. Positive outcomes include the infusion of data analytics and AI/ML into everyday business processes, leading to profound impacts across every industry and society at large.

2. Compliance can’t just be an add-on item.

The modern cloud model has to be one that can withstand the scrutiny around data sovereignty and accessibility questions. It’ll change how companies do business and how much of society is run. Even large, traditional enterprises are moving to the cloud to handle urgent needs, like increased regulations. The stakes are too high now for enterprises to ignore the critical components of security and privacy. 

One of the big reasons the cloud—and Google Cloud specifically—is so vital to better data analytics revolves around these questions of compliance and governance. Around the world, for businesses of every size, there’s an increased focus on security, privacy, and data sovereignty. So much of the digital transformation that we’ll see in 2021 will happen out of necessity, but today’s cloud is what makes it possible. Google Cloud is a platform built ground-up based on these foundational requirements, so enterprises can make the transition to the cloud with the assurance that data is protected.  

3. Open infrastructure will reign supreme. 

By 2021, we’ll see 80% or more of enterprises adopt a multicloud or hybrid IT strategy. Cloud customers want options for their workloads. Open infrastructure and open APIs are the way forward, and the open philosophy is one you should embrace. No business can afford to have its valuable data locked into a particular provider or service. 

This emerging open standard means you’ll start to see multi-cloud and on-premises data sources coming together rapidly. With the right tools, organizations can use multiple cloud services together, letting them gain the specific benefits they need from each cloud as if it was all one infrastructure. The massive shift we’re seeing toward both openness and cloud also brings a shift toward stronger data assets and better data analytics. If you’ve been surprised over the past year about how many data sources exist for your company, or how much of it is gathered, you’re not alone. An open infrastructure will let you choose the cloud path that works best for your business. 

Data solutions like Looker and BigQuery Omni are specifically designed to work in an open API environment on our open platform to stay ahead of continually changing data sources.

4. Harnessing the power of AI/ML will no longer require a degree in data science. 

Data science, with all of the expertise and specialized tools that have typically been involved, can no longer be the purview of just the privileged few. Teams throughout an organization need to have access to the power of data science, with capabilities like ML modeling and AI, without having to learn an entirely new discipline. For many of these team members, it’ll bring new life into their jobs and the decisions they need to make. If they haven’t been consuming data, they’ll start. 

With this capacity to give the whole team the power of analytics, businesses will be able to gather, analyze, and act on data far quicker than those who are still using the traditional detached data science model. This improves productivity and informed decision making by giving employees the tools to gather, sort, and share data on demand. It also frees up teams with data science experience that would normally be assembling, analyzing, and creating presentations to concentrate on tasks that are more suited to their abilities and training.  

With Google Cloud’s infrastructure and our data and AI/ML solutions, it’s easy to move data to the cloud easily and start analyzing it. Tools like Connected Sheets, Data QnA, and Looker make data analytics something that all employees can do, regardless of whether they are certified data analysts or scientists. 

5. More and more of the world’s enterprise data will need to be processed in real time. 

We’re quickly getting to the point where data residing in the cloud outpaces data residing in data centers. That’s happening as worldwide data is expected to grow 61% by 2025, to 175 zettabytes. That’s a lot of data, which offers a trove of opportunity for businesses to explore. The challenge is capturing data usefulness in the moment. Following past stored data can be informative, but more and more use cases require immediate information, especially when it comes to reacting to unexpected events. For example, identifying and stopping a network security breach in the moment, with real-time data and a real-time reaction, has enormous consequences for a business. That one moment can save untold hours and costs spent on mitigation.

This is the same method that we use to help our customers overcome DDOS attacks, and if 2020 has taught us anything, it’s that businesses will need this ability to instantly respond to unexpected problems more than ever moving forward.

While real-time data revolutionizes how quickly we gather data, perhaps the most unexpected yet incredibly useful source of data we’ve seen is predictive analytics. Traditionally, data is gathered only from the physical world, meaning the only way to plan for what will happen was to look at what could physically be tested. But with predictive models and AI/ML tools like BigQuery ML, organizations can run simulations based on real-life scenarios and information, giving them data on circumstances that would be difficult, costly, or even impossible to test for in physical environments.

6. More than 50% of data lakes will span multiple clouds and on-premises. 

We know that aligning the right services to the right use cases can be complicated. And while the cloud opens up a ton of opportunities for better data options, the fact that so many businesses are moving to these cloud solutions means that organizations will need a strong digital strategy to stay competitive, and this extends down to their data storage. Lots of businesses are choosing multicloud for flexibility, especially with so many options available. In the cloud, data storage has taken the shape of either a data warehouse—which stores primarily structured data so that everything is easily searchable—or data lakes—which bring together all of a business’ data together, regardless of structure. 

We’ll see more of the trend we’ve already seen, starting with the line between lake and warehouse getting blurrier. Google Cloud has a variety of data lake modernization solutions that give organizations the ability to integrate unstructured data as well as use AI/ML solutions to make data lakes easier to navigate, driving insights and collaboration.

What’s next for your business?

Change is happening fast, and while it can be overwhelming, all these technology changes are really exciting. At the end of it, you’ll be able to respond in real-time to problems, help your business users get their data without delay, and know for sure the entire lifecycle of any of your data. Let’s get started.

Check out our guide to building a modern data warehouse or see how data-to-value leaders succeed in driving results from their enterprise data strategy in the report by Harvard Business Review Analytic Services: Turning data into unmatched business value.