The Importance of Automation to the Enterprise Data Stack

Today’s enterprises and medium-sized companies are looking to ensure that critical business decisions are guided by rigorous data analysis. They have scaled up their analytics teams (composed of data engineers, data scientists and data analysts), and their IT departments have tried to meet the needs of those teams.

Despite these efforts, however, analytics programs — and data analysts themselves — have struggled. When Dimensional Research surveyed over 500 data professionals earlier this year, respondents reported that they’ve had to wait on IT teams to provide access to the data they need to do their jobs.

IT and data integration teams face significant engineering challenges as they try to integrate legacy on-premise systems, remote data stores, and an ever-changing, ever-growing list of cloud-based APIs and SaaS tools. As a result, data is often stale and unreliable, and the business suffers from unmet needs and missed opportunities.

Even if your company uses a modern data stack, which consists of a data pipeline, cloud-based data warehouse and business intelligence tool, it’s possible that the stack isn’t being managed effectively.

Automated data integration to the rescue

Modern data teams face daunting integration challenges, including:

Added data complexity. An ever-increasing number of data sources complicates IT development teams’ ability to keep up with demand.
Ever-changing schemas. API and schema changes break ETL pipelines and custom solutions developed in-house.
Multiple destinations. If more than one team needs to analyze available data, destinations with varying technical needs add time to development cycles.
Increase in data latency. This occurs when data replication can’t keep up with an organization’s changing needs.

The answer to these problems is automated ELT (extract, load, transform), the modern successor to traditional ETL (extract, transform, load). Automated ELT provides nearly instant, self-service access to data, and allows data development teams to allocate engineering resources to higher-level activities.

Fivetran automated data connectors are prebuilt and preconfigured, and support 150+ data sources, including databases, cloud services, applications and APIs. Fivetran connectors automatically adapt as vendors make changes to schemas by adding or removing columns, changing a data element’s types, or adding new tables. Lastly, our pipelines manage normalization and create analysis-ready data assets for your enterprise that are fault-tolerant and auto-recovering in case of failure.

Explore automated data integration with Fivetran and David Loshin

We’ve teamed up with industry analyst David Loshin to explore the problem points of modern enterprise data management and describe how automation reduces operational risk, ensures high performance, and simplifies development and ongoing maintenance and management of data integration.

Join Fivetran and David Loshin for a 30-minute interactive workshop in which we discuss the challenges enterprise data teams face and explain how a modern data stack can help.