As the modern data stack has matured, it has begun to push aside the outdated methodologies and inefficient data pipelines that ruled data analytics for two decades. Think back to the chaos and frustration those tools and systems caused at the onset of the “big data” era — and then ask yourself why business users are still downloading CSV files into Excel spreadsheets to do data analysis on their desktops. 

In the early part of the data revolution, accessing information was almost always a challenge, the stack too often required time-consuming troubleshooting and managers frequently lacked confidence in the data’s accuracy. Those problems are now rapidly disappearing.

Locating the source of the chaos is simple: The tools and systems supporting the stack were not up to the task. Thanks to far more modern, cloud-based tools and improved best practices, the road ahead looks much brighter.

Here’s a quick preview of our 2022 predictions with business intelligence platform Sigma Computing — but don’t miss the full ebook on data analytics and BI trends.‍

ELT is evolving to become fully managed

Companies will benefit from increased automation and performance with the newest Extract-Load-Transform (ELT) pipelines. The days are gone when companies worried about overloading data warehouses with too much information for fear of slow query times or crashes. The shift from batch-based and brittle ETL processes to a more continuous, cloud-based ELT approach means companies can now expect access to all their data all the time and in real time. 

You can’t be data-driven without DataOps 

There is more competition all the time in DataOps, the processes, technology and infrastructure that supports analytics. This trend is underscored by rising companies that are building DataOps features directly into their (non-DataOps-specific) product. One example is dbt Labs, a command-line tool that enables data analysts and engineers to transform data in their warehouses by writing SQL statements, building in data lineage tracking and version control. As the entire DataOps ecosystem continues to mature, companies that follow DataOps best practices will have a marked advantage.

Companies will unlock operational analytics at scale 

Operational analytics, the use of sending analytics data directly to operational tools, will continue to boost the superpowers of frontline business managers. It will enable them to seize opportunities or preempt trouble earlier than ever, radically improving internal processes and customer experiences. Operational analytics will become a larger factor in powering day-to-day operations in 2022, simply by making data more accessible throughout an organization. 

BI tools will increase collaboration between data teams and business units

Expect significant progress in how companies exchange data. Communicating about analytics in today’s business environment too often resembles the Tower of Babel. Analytics teams and their counterparts on the business side often speak different languages and use different terms. Some groups converse using Slack while others choose ad-hoc emails. But this year, collaborative analytics will finally mature into an experience that mirrors live edits in GSuite, enabling data teams to be on the same page. 

The continued rise of automation will translate into competitive advantages

One of the more exciting developments we’ll see next year is the increased use of coding frameworks that enable distributed data processing at scale, such as Apache Spark, Beam, Flink, and PySpark. As data engineers continue to extend these software development tools into the data processing and analytics arena (as is already happening with Databricks), we’ll see huge gains. Companies that are able to leverage these frameworks in 2022 will reap the competitive advantages that come with being on top of even bigger mountains of data.

To learn more about these data analytics and BI trends — and how they can benefit you — register for Data Forward: Analytics & Business Intelligence Trends in 2022.