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Rockset 2022 Predictions: Four Predictions for Data Analytics in 2022

vmblog predictions 2022 

Industry executives and experts share their predictions for 2022.  Read them in this 14th annual VMblog.com series exclusive.

Four Predictions for Data Analytics in 2022

By Dhruba Borthakur, Co-Founder and CTO, Rockset

The upheaval caused by the global pandemic in 2021 accelerated many changes in society, business, and technology. This was incredibly true in the sphere I work in, the cutting edge of data analytics. The uptick in remote working and business conducted via Zoom spurred companies to expedite their digital transformations. Suddenly, personalizing digital customer experiences in real time as well as enabling data-driven instant decisions moved from aspirational, back burner projects to top priorities.

This "real-time revolution," as per the recent cover story by the Economist magazine, has only just begun. 2022 will see more companies deploying cloud-native modern real-time data stacks as well as adopting the four practices and trends I outline below.

1) Democratization of Real-Time Data

The democratization of real-time data follows upon a more general democratization of data that has been happening for a while. Companies have been bringing data-driven decision making out of the hands of a select few and enabling more employees to access and analyze data for themselves.

As access to data becomes commodified, data itself becomes differentiated. The fresher the data, the more valuable it is. Data-driven companies such as Doordash and Uber proved this by building industry-disrupting businesses on the backs of real-time analytics.

Every other business is now feeling the pressure to take advantage of real-time data to provide instant, personalized customer service, automate operational decision making, or feed ML models with the freshest data. Businesses that provide their developers unfettered access to real-time data in 2022, without requiring them to be data engineering heroes, will leap ahead of laggards and reap the benefits.

2) Move from Dashboards to Data-Driven Apps

Analytical dashboards have been around for more than a decade. There are several reasons they are becoming outmoded. First off, most are built with batch-based tools and data pipelines. By real-time standards, the freshest data is already stale. Of course, dashboards and the services and pipelines underpinning them can be made more real time, minimizing the data and query latency.

The problem is that there is still latency - human latency. Yes, humans may be the smartest animal on the planet, but we are painfully slow at many tasks compared to a computer. Chess grandmaster Garry Kasparov discovered that more than two decades ago against Deep Blue, while businesses are discovering that today.

If humans, even augmented by real-time dashboards, are the bottleneck, then what is the solution? Data-driven apps that can provide personalized digital customer service and automate many operational processes when armed with real-time data.

In 2022, look to many companies to rebuild their processes for speed and agility supported by data-driven apps.

3) Data Teams and Developers Align Together

As developers rise to the occasion and start building data applications, they are quickly discovering two things: 1) they are not experts in managing or utilizing data; 2) they need the help of those who are, namely data engineers and data scientists.

Engineering and data teams have long worked independently. It's one reason why ML-driven applications requiring cooperation between data scientists and developers have taken so long to emerge. But necessity is the mother of invention. Businesses are begging for all manner of applications to operationalize their data. That will require new teamwork and new processes that make it easier for developers to take advantage of data.

It will take work, but less than you may imagine. After all, the drive for more agile application development led to the successful marriage of developers and (IT) operations in the form of DevOps.

In 2022, expect many companies to restructure to closely align their data and developer teams in order to accelerate the successful development of data applications.

4) Enterprises Hasten Move from Open Source Software to SaaS

While many individuals love open-source software for its ideals and communal culture, companies have always been clear-eyed about why they chose open-source: cost and convenience.

Today, SaaS and cloud-native services trump open-source software on all of these factors. SaaS vendors handle all infrastructure, updates, maintenance, security, and more. This low ops serverless model sidesteps the high human cost of managing software, while enabling engineering teams to easily build high-performing and scalable data-driven applications that satisfy their external and internal customers.

2022 will be an exciting year for data analytics. Not all of the changes will be immediately obvious. Many of the changes are subtle, albeit pervasive cultural shifts. But the outcomes will be transformative, and the business value generated will be huge.

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ABOUT THE AUTHOR

Dhruba Borthakur 

Dhruba Borthakur is Co-Founder and CTO of Rockset, a venture-backed provider of a real-time indexing database enabling companies to build data applications at cloud scale. Borthakur was an engineer on Facebook's database team, where he was the founding engineer of the RocksDB data store. Earlier at Yahoo, he was one of the founding engineers of the Hadoop Distributed File System and also a contributor to the open source Apache HBase project. Borthakur has also worked at Veritas and IBM and founded an e-commerce startup.

Published Friday, November 26, 2021 7:32 AM by David Marshall
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