If you’ve seen Dilbert, you’ve witnessed the divide between corporate groups such as sales and marketing. In the data world, some might say that data development teams, who hold the keys to data streams and sources, are on one side. Data analysts, who are closer to business decision-makers and need to provide actionable insights based on that data, are on the other.
As businesses become more data-savvy, data analysts are under pressure to move faster and faster. Yet they rely on corporate development teams for analysis-ready data, and often have to go through lengthy approval processes. Sometimes approval takes so long that by the time a solution is achieved, business needs have already changed.
In a recent survey of data analysts by Dimensional Research, 62% of respondents reported waiting on centralized development teams to provide access to much-needed data “several times per month.” Having to go through a middleman to access critical data delays analysis and ultimately business decisions. Many analysts say they have profit-generating ideas that they simply don’t have time to act on.
Lifting the engineering burden from dev teams
A key problem is that development teams themselves have to spend far too many cycles building and maintaining data pipelines, while wishing they could work on improving products and other strategic initiatives. Automated, zero-configuration data pipelines offer three key features that take this burden off data engineers:
- Prebuilt connectors: Automated data pipeline tools contain a wide variety of prebuilt connectors to a wide array of files and file types, databases (both proprietary and cloud-native), applications and SaaS services, and event streams.
- Automatic data updates: Automated tools can automatically detect updates in data sources. For example, when new records have been inserted into a source database, the tool automatically detects the updates and forwards them to the target centralized analytical data store.
- Automatic schema migration: The data pipeline automation tool must be able to determine when there are changes to a data source’s schema: added columns, removed columns, modifications to a data element’s type, even new or deleted tables/objects.
Uniting analysts and dev teams
With automated data integration, there’s no need for “us vs. them” relationships between development and analytics teams. Analysts get the data they need, when they need it, without having to go through cumbersome approval processes. They can rapidly deliver insights to decision-makers based on real-time data. Development teams are happy because they can focus on building out core data infrastructure instead of spending development cycles fixing brittle ETL pipelines.
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