Data in the Cloud is Different. Analytics Should Be, Too.

The debate on whether or not companies are moving to the cloud is over. Pick your favorite analyst report, and you’ll see data growth in the cloud is far outstripping growth anywhere on premises. The trend has been dramatically accelerated by the pandemic, with organizations cutting ties with legacy on premises technology at unparalleled rates. As if that wasn’t evidence enough, Snowflake’s record shattering IPO shows just how powerful the cloud has become, both for customers and for Wall Street. 

Organizations are racing to the cloud for many reasons. They want more flexibility, the ability to be more agile, innovate services more quickly, improve customer experiences, and drive the bottom line. 

At the heart of all these efforts? Data. 

But data in the cloud is different from data on premises. While adoption of the cloud skyrockets, leaders chasing the benefits of the cloud need to understand the fundamental differences between these two. If they don’t, they stand to make mistakes that will be costlier than ever and will fail to tap into the cloud potential.

  1. Data Volumes are Larger<br>The last decade has seen a veritable explosion in the sheer amount of data we generate. With mobile, the internet of things, and the ever growing number of SaaS applications, this growth is only accelerating. IDC expects we will have 175 zettabytes of data by 2025. That’s right - zettabytes. <br><br>Cheap, efficient storage in the cloud has made this growth in data volumes possible. But it's introducing entirely new problems. Businesses are literally drowning in their data. The technology used in the past to crunch all this information cannot handle the scale. Even more troublesome, however, is the fact no human can go through all this data to find meaningful insights. There’s simply too much. We’ve gone from looking for a needle in a haystack to looking for a needle in an entire field. 

  2. The Shelf Life of Data is Shorter<br>Data in the cloud isn’t just orders of magnitude larger than when on premises. It also loses value more quickly. Data is being generated and brought to the cloud from all these various digital sources. This data continues to update as new interactions happen. What was new data at the beginning of the day is old by the end of the day. <br><br>Think about your website. As you do a major launch, for example, you want to know what’s happening in the last hour. The data from yesterday has less value to you as you look to capitalize on your web traffic, whereas the new data is priceless. It lets you shift and adjust quickly to make the most of your launch. 

  3. Data Governance is Harder <br>One of the greatest obstacles plaguing businesses when it came to their data in the past was fragmentation. Organizations had data living all over the place, with different departments and teams using data in their own way. This created a major hurdle when it came to governing and securing data. <br><br>In the world of the cloud, this is even more challenging. Enterprises have thousands of different applications, each generating their own data. This data may be stored in the SaaS apps themselves, in data lakes, in public clouds, private clouds, or even across multiple clouds. <br><br>Governing this data, ensuring each person has the right access to the right data is critical to get right. Most organizations are woefully behind, and technology not built for data governance at the most granular level makes it nearly impossible for organizations to get a handle on their data. 

New Analytic Capabilities are Required

Cloud data requires a new type of analytics. There’s simply no way to force fit the solutions built for designing dashboards on Windows desktops into the cloud without a fundamentally new architecture. 

For leaders ready to start leveraging their cloud data, look for platforms with the following three traits. 

  1. Search for Sifting through Data Quickly. With so much data in the cloud, and so much that’s rapidly changing, analytics solutions must provide a simple, fast way to access data at the most granular level for everyone in your organization. If business users are waiting days for a report or dashboard to be created, they will make the decision without the data. With search, business users can go through data quickly and get the insights they need when and wherever they make a decision. <br><br>What’s more, this search experience must be able to get to the most granular level of data, and return insights quickly. If it requires data to be aggregated or averaged to deliver performance, you’ll fail to uncover the nuanced insights that can result in massive value for your organization. 

  2. AI for Discovering Hidden Insights. If you don’t have a solution that helps your people make sense of all the data you have in the cloud, they will be overwhelmed. They need technologies that have AI and machine learning baked in to automatically help them find what’s important, what’s new, what’s changed. Otherwise, you’ll miss critical insights that will remain buried in the mountains of data in your cloud.

  3. Fine-Grained Security for Governance at Cloud Scale. With the data sprawl the cloud has created, security and governance have become 10 times more important. The only way to contend with this sprawl is to utilize analytics platforms that have a data governance model that can scale as your cloud data does the same. Anything built with a desktop architecture will break as your data grows, not only slowing your business down, but introducing serious risks along the way. 

Cloud data is the way of the future, but to unlock its value, organizations need new technologies to make sense of all this data. If they try to retrofit an old BI solution on a modern cloud data warehouse, they won’t see the results they are expecting.