Through automated data pipelines, analysts can access the data they need and engineering teams can complete higher-value projects.
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 corporate IT teams, who hold the keys to data streams and sources, are on one side while data analysts, who want to provide analytics and mission-critical insights based on that data, are on another.
Data development teams at large companies are saddled with balancing the development and maintenance of systems, such as legacy on-premises solutions and cloud-based applications, while also also serving the business/IT needs for all aspects of the organization.
Yet data analysts are closer to business decision-makers and are tasked with creating actionable insights off dozens or even hundreds of data streams. As 2020 advances, businesses are becoming more data savvy, and, as a result, data analysts are under pressure to move faster and faster. Analysts make requests to corporate IT development teams and often have to wait through approval processes. Sometimes the process takes so long that by the time a solution is achieved, the business needs have already changed.
In a recent survey by Fivetran, 62% of respondents reported waiting on centralized development teams to provide access to much-needed data "several times per month." The added step of going through a middleman to access mission-critical data delays analysis and ultimately business decisions.
We believe there’s no room in an "us vs. them" relationship when it comes to data access and insights. Let’s take a step back and realize we’re all on the same team, trying to keep up with a fast-moving business environment that’s constantly in flux.
It’s a fact that your company’s development teams are spending far too many precious cycles building data connections, while wishing they didn't have to write ETL data pipelines.
Analysts — we know you have profit-generating ideas that you wish you had time to get to. The automated modern data stack is here to help bring both sides together around a common goal.
At the heart of such a data stack are data pipelines, and pipeline automation is where all the magic happens.
Data pipeline orchestration must be augmented in three ways to fully automate the process and relieve developers of the headaches resulting from unexpected data source changes:
Prebuilt connectors: The data pipeline automation tool must 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: The data pipeline automation tool must be able to automatically detect data updates in the data sources. For example, when new records have been inserted into a source database, the tool should automatically detect the updates and forward those updates 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, or even when a new table/object is added or an existing one is removed.
What does the business stand to gain by deploying an automated data stack?
Data development teams are happy because they can build out the core data infrastructure and not work on ETL pipelines that change with time and occupy too many development cycles. With automated data, pertinent data shows up where analysts can action on it and make recommendations to their business stakeholders.
Update your browser to view this website correctly. Update my browser now