Data Automation

Why is business logic critical for data automation?

Switchboard Jan 19

Business Logic part 2
Table of Contents

    As we explained in our last post, unifying and normalizing a growing number of disparate data sources manually, without a solid data automation process in place, quickly becomes unsustainable.

    Without automation, it’s impossible to derive meaningful insights, or even have the right data accessible to business teams when they need it.

    One of the critical processes involved in creating and maintaining data automation systems is business logic. As a quick recap, business logic refers to the programming that occurs to help drive a number of different business rules, or what we call ‘data recipes’.

    Read more: What is business logic in data automation?

    So why is business logic so important for data automation?

    Put simply, business logic is the brains behind data automation. You might have business rules in place, but without business logic to action those rules, nothing happens.

    Data automation relies on business logic to determine how data goes from point A to point B (i.e. raw data from a disparate source to be transformed into normalized data and stored in a data warehouse).

    Business logic is also important because business rules can change, and data sources can evolve, each bringing their own nuances. Therefore, your data automation system (and by extension, your business logic) needs to be adjustable over time, which requires robust domain expertise.

    The challenge nearly always comes down to resources. Continually adjusting business logic can be a full-time task and it can be difficult to track errors. You can’t simply “set and forget” business logic. You’re continually relying on an in-house team, using an internal data engineering solution.

    Leverage a trusted data automation solution

    One solution to the challenge of building and maintaining business logic and data automation systems is to use an enterprise solution, which can help you in three ways:

    • Build your data infrastructure. With automated ETL data pipelines to unify your disparate data, you can build foundational data as the building blocks for your data infrastructure. From there, you can use custom ‘data recipes’ and scalable workflows to manipulate your data pipeline according to your business’ unique rules.
    • Onboarding, support, and training. Instead of hiring extra staff to manage growing data operations, you can rely on your chosen enterprise solution to onboard, support, and train existing staff to use turnkey tools that can save you months of custom development.
    • Remain a trusted data partner. Continual support also means you can rely on your trusted data partner to help you make any changes to business rules or data recipes, without needing to write any new code.

    Read more: Financial Times Leverages Data to Drive Product Success

    As your business grows, you may end up relying on hundreds of business rules, which in turn rely on automated, adaptable business logic to drive them. While it is possible to hire a small army of developers to oversee this, it’s far more time- and cost-effective to plug straight into a trusted data automation solution that’s ready to go.

    If you need help unifying your first or second-party data, we can help. Contact us to learn how.

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