What is business logic in data automation?

If you’re a digital-first business, your priority is to make sure you’re managing your growing data as effectively as you can. When data is well managed, it helps you gain meaningful and actionable insights for the decisions that matter most to your business.

We’ve talked a lot about the need to automate the data ops process as it becomes unsustainable to unify data across tens, if not hundreds, of data pipelines.

But once your data is sitting in your cloud storage solution, ready to be actioned, there are two things you need to apply to make it useful to the business teams that need it. One is data governance, and the other is business logic.

In this four-part series, we’ll cover:

• The difference between business logic and business rules
• The importance of business logic in data automation
• The role of business logic in building an ETL pipeline
• A review of business logic platforms

What is business logic?

Business logic refers to the programming that defines how data connects between a database and the end-user interface (via a connector). In other words, it describes the process for how data in the database is used or viewed – through the application of specific business rules.

In simple terms, business logic is the sequence of operations that transforms data into useful information or business value. Retrieving data from a data warehouse isn’t necessarily business logic, but the process of filtering that data for a specific view uses business logic to do so.

Business logic vs business rules

While business logic describes the overall process of transforming data, business rules describe the individual steps, or tasks, that make it happen.

A business rule is essentially a statement that sets a threshold or constraint on a particular aspect of a database – or in other words – a piece of code that defines how the data is to be created, viewed or changed.

At Switchboard, we combine business rules to form what we call “data recipes”, which are human-readable scripts used to manipulate complex data pipelines according to custom business rules. This means that we can customize the steps needed for each business team to view just the right cut of the data to generate bespoke reports, or perform their daily activities. Rather like following the steps of a recipe.

Switchboard creates these bespoke recipes based on hyper-granular business needs, which means each individual team can tap into a company’s data to extract actionable insights. Using these data recipes is easy and you can change them at any time without having to burden the engineering team.

A business logic example in action

When a global publisher came to Switchboard, the revenue analytics team was dealing with a number of data-related challenges, including disparate data, inconsistency across partner metrics, data quality and reliability, and manual mappings.

After unifying and creating foundational data sets, they were able to normalize and map disparate partner data using Switchboard’s customizable data recipes according to their unique business rules. From there, they were able to consolidate business logic from previously disparate spreadsheets and tools.

Read case study: How Meredith unified 200 data streams to drive revenue growth

To summarize, business logic sits at the core of any data automation process. Building multiple data pipelines effectively is one thing – but without business logic, it’s impossible for business teams to gain any kind of meaningful insights from the process.

In our next post, we’ll take a closer look at why business logic is so important when it comes to data automation.