Data automation is the bridge between enterprise analysts and IT and engineering departments.
In a recent global survey of data analysts, participants identified data engineering and IT needs as a major pain point. In many enterprises, data analysts need to build insights for fast-moving, data-reliant business units, and depend upon IT data teams that control access to the data.
Today’s data analysts make decisions based on data housed in on-premise systems, web applications, APIs and more. But it’s tough for IT data teams to keep up with the challenges associated with gathering data from multiple sources: non-reliable data flows, stale data sets and schema changes that break pipelines altogether. Sometimes, data analysts have to rely on "shadow IT" networks to satisfy their data needs.
Luckily, there’s a better way. Businesses can use automated data integration to blend data sources from internal databases, web apps, services and APIs, and bridge the work of data analysts and those building the data systems they rely upon. Combined with a modern data stack, automated data pipelines bring many benefits. For a concrete example of an enterprise automated data use case, explore Autodesk's data stack.
With an automated data integration provider, setting up data pipelines is a no-code process, which involves logging in, selecting fields, and watching the ready-to-use data sets populate. All necessary data sets can be joined into data marts or reporting tables so analysts can provide meaningful analytical insights for their stakeholders. The data is continuously updated, eliminating "stale" data issues. Also, when data schema or API changes occur with such a managed solution, legacy processes such as ETL pipelines or custom solutions don’t need to be re-engineered.
Many Fivetran customers aggregate their data to improve their business intelligence by creating dashboards that show insights and statistics from multiple critical sources. However, this is just the beginning – we’ve seen companies build sales qualification data models that speed sales conversion, product teams monitor service and application health, and customer success teams predict issues before their support queues fill up.
Data teams also want the ability to store data in multiple destinations. For example, consider setting up multiple logical databases within one Snowflake warehouse, which can be done in a few lines of SQL. This sort of setup is common for companies that want to segregate their data by business units or have distinct areas for “raw” data to be manipulated further for reporting, or loaded data that is immediately ready to be loaded into a BI tool to apply the business logic there. This is all possible with data automation and smartly configured data pipelines.
When it comes to data pipelines and data flow, many issues can cause breakages or outages, including schema and API changes, connectivity issues, larger-than-expected data sets, and more. By using an automated solution, your team doesn’t have to be concerned with ETL or pipeline breakages in the event of these common changes. Tools like Fivetran incrementally update data in your destination using a merge operation that updates new, changed or deleted data for optimized and efficient data flow.
As an added bonus, your team can realize cost savings with Fivetran’s approach to efficient data routing, as our team continuously reviews each destination to ensure we’ve optimized load queries to have the smallest impact possible.
Lastly, for busy enterprise data engineering and IT teams, automated data integration enables teams to focus on core projects, such as developing machine learning models, cataloging and governance, rather than moving data from A to B.
Go forth and see what automated data integration can do for your IT and analytics teams! Let’s see what bridges can be built.