As organizations move to the cloud, they should ensure their analytics solution lives up to cloud realities. This is the only way to exploit the potential of the cloud entirely. The debate over whether or not companies are moving to the cloud is over. No matter what analyst reports you to look at, they all indicate that data growth in the cloud is far outpacing on-premises growth.
The pandemic accelerated this trend, as companies are phasing out legacy on-premises technologies in record time. As if that wasn’t proof enough, Snowflake’s impressive IPO shows just how powerful the cloud has become — for both customers and Wall Street.
Businesses go to the cloud for many reasons. They want more flexibility, agile, deliver innovative services faster, improve customer experience and increase profits. And what is at the heart of all this effort? The data.
But data in the cloud is different from data on-premises. As cloud adoption accelerates, organizations need to understand the differences between the two systems. Only in this way can they exploit the potential of the cloud and avoid costly mistakes. Here are three main differences:
Larger Amounts Of Data
Over the past decade, the amount of data we generate has exploded. This growth is only accelerating with cellular, the Internet of Things, and the ever-growing number of SaaS applications. IDC projects that by 2025 we will have around 175 zettabytes of data. That’s right – zettabytes, which is 10 21 bytes.
Cheap, efficient storage in the cloud has made this growth in data volumes possible. But that also brings with it entirely new problems. Businesses are drowning in their data. The technologies that have historically been used to process all this information can hardly handle this scale. However, even more, problematic is that no human can quickly sift through all of this data to uncover meaningful insights. Given the amount of data, this is not feasible. We leapt from a needle in a haystack to looking for a hand in a wheat field.
The Half-Life Of The Data Is Shorter
Data in the cloud is not only larger by many dimensions than on-premises. They also lose value faster. Data is generated from many different digital sources and stored in the cloud. This data is updated as new interactions take place. Data that was brand new in the morning is already out of date by the end of the day.
Let’s take the example of a company website. Anyone conducting a significant product launch wants to know what has happened in the last hour. To take full advantage of website traffic and get the most out of the launch, the company needs to react quickly and possibly reallocate and adjust resources. The data from the previous day is useless, while the current data is priceless.
Data Governance Is More Difficult
One of the biggest obstacles for companies has been the fragmentation of their data. Company data was distributed everywhere and used differently by different departments and teams. That was already a major hurdle in managing and backing up the data.
In the cloud world, this challenge is even more significant. Businesses have thousands of different applications, each generating data. This data can be stored in SaaS applications, data lakes, public clouds, private clouds, or even across multiple clouds.
Managing this data requires ensuring that each person has access to the correct data. Most companies are lagging miserably here. Technologies not designed to collect data at the most granular level make it almost impossible for them to get a handle on their data.
New Analytical Skills Are Required
Data in the cloud requires a new kind of analysis. Without a fundamentally new architecture, there is no point in forcibly moving the solutions developed for designing dashboards on desktops to the cloud.
Businesses looking to get more from their cloud data should look for analytics platforms that have three capabilities:
- Search for quick data scrutiny: With so much data in the cloud and so much change happening so quickly, analytics solutions must provide an easy and fast way to access data at the most granular level for everyone within the organization. If business users wait days for a report or dashboard to be generated, they will make decisions without data. Business users can quickly search through data and get the insights they need to make decisions with a search function. In addition, the search function should be able to drill down into detail. It doesn’t provide the valuable nuanced insights needed for decision-making when aggregated or averaged data.
- AI to uncover hidden insights: Companies need analytics solutions that make it possible to use all the data in the cloud in a meaningful way. Otherwise, employees will quickly be overwhelmed by the sheer volume of data. Technologies that integrate AI and machine learning automatically show employees what’s important, new, and changed. They ensure that essential insights do not remain buried in the mountains of data in the cloud.
- Fine-grained security for cloud-level governance: With the growth of cloud data, security and government have become tenfold more critical. The only way to counter this growth is to use analytics platforms whose governance model is as scalable as the data in the cloud. Anything built with a desktop architecture can’t handle data growth, slowing down business and introducing severe risks.
Data in the cloud is, without question, the future. But to unlock its value and make meaningful use of the data, companies need new technologies that can cope with the new requirements. If companies try to retrofit an old BI solution for a modern cloud data warehouse, they will probably not see the expected results. They will not be able to exploit the potential of the cloud entirely.
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