Data transformations are essential to connecting the dots between and making the most of your data.
Fivetran pipelines reliably load your data to your chosen destination, but then what? Without joining, filtering and aggregating your data, your business can’t produce data models to answer critical business decisions. This is why data transformations are essential to every business looking to maximize value from the data they collect from disparate sources.
In this blog, we will walk you through some use cases, data characteristics and cultural indicators that suggest your business would benefit from a modern approach to data transformation.
You will need data transformations if your business is pursuing the following types of analytics use cases.
The most common applications of data are internal reporting, business intelligence, and visualizations. Regular reporting lets us track business performance over time, while one-off reports help us answer critical but occasional business questions. In both cases, transformations are essential because they help us manipulate data into models that represent important metrics. Systematizing this stage of analytics requires a transformation solution that saves code and lets you schedule runs and ensures your dashboards are always up-to-date.
The term ‘big data’ is ambiguous at best, but some businesses truly have BIG data. An average company uses 88 applications that generate hundreds of terabytes of data. Transformations can be used to sample and subset data sets as necessary, allowing queries to become faster, more performant, and more cost-efficient.
A single source of data can be helpful but won’t paint a full picture. If we want to measure the effectiveness of an ad campaign, we need to join data today from a variety of sources such as LinkedIn, Facebook, Google, Twitter, etc. By combining data in this way, we can measure overall campaign performance, as well as compare and contrast how well each platform performed.
The adage “garbage in, garbage out” could not be more true for training machine learning (ML) models, which can be used for downstream artificial intelligence. In a machine learning context, transformation can be used to clean and enrich data, engineer features, exclude unusable records, and divide data into training and testing sets.
Data sometimes just needs to be transformed, regardless of use case.
Regional business units that feed into a global enterprise will collect localized data in a local timezone and currency. For this data to be used in global reporting, such as global annual reporting, the data must be represented in a universal unit.
Many businesses choose to load all their raw data into their data warehouse. But what if you are loading PII into your warehouse, where anonymized data should be stored? Run transformations to remove columns of PII to ensure that your data is compliant.
Data that has a lot of duplication, inconsistencies, missing or null values can mislead analytics teams. By transforming data, such as de-duplicating it or removing null records, the data teams can gain confidence in the reliability of that data.
Some data is just better together. To enrich business data with other third-party data sets, businesses need to join those sets up. By doing so they can build out 360-customer views.
Data transformation is increasingly important and growing in value within the modern data stack capabilities. But legacy transformation methods fall short in the world of cloud computing and data complexity. There are some cultural and team indicators that suggest it is time to adopt a transformation method for the modern enterprise.
Data engineers who have to manually code ELT pipelines can quickly become inundated with pipeline maintenance work. A modern transformations solution is SQL-based to allow analysts to transform data, sharing the workload. Leveraging a platform like dbt (data build tool), by dbt Labs, means that engineers can maintain the flexibility of writing SQL scripts while following development best practices - collaborative environments, version control, documentation.
Transformation tools featured within GUI-based orchestration tools are hard and time-consuming to learn. It can take data teams months to fully wrap their heads around a component-based drag and drop layout. By keeping transformations in SQL, analysts can easily jump into a model and transform data. Beyond that, the market is seeing the rise of the citizen data integrator. These data specialists can benefit from the pre-built logic offered by Fivetran dbt packages. Think back to our advanced analytics use case above. By using the Fivetran dbt package for ad reporting, minimal coding is required to combine data from multiple popular ad sources.
You know how valuable your data is. You invest in source systems that produce it, cloud infrastructure to store and secure it, and tools to move it to the cloud. If you are facing challenges with data latency, poor consistency or reliability, or inaccessible data, it’s time to consider a modern data transformation solution for your business.
Fivetran Transformations is a modern ELT approach that ensures that data is modeled for analytics to maximize its impact across your business.