Why and how do you transform your data?

4 years later, let's stop seeing GDPR as a constraint

Without combining, filtering, and aggregating your data, your organization cannot create data models that meet critical business needs.

Because of this, data transformations are essential for any business looking to maximize the value of data collected from disparate sources.

You need data transformations if your business pursues the following analytical applications.

Reporting, BI and visualizations

The most common uses of data are internal reporting, business intelligence, and visualizations.

Regular reports help us track business performance over time, while one-off reports help us answer critical but occasional questions.

In both cases, transformations are essential as they help manipulate data into models that represent key indicators.

Systematizing this analysis phase requires a transformational solution that saves code, allows you to plan for execution, and ensures your dashboards are always up-to-date.

Process Big Data

The term “big data” is ambiguous to say the least, but some companies have big data.

The average company uses 88 applications that generate hundreds of terabytes of data.

Transformations can be used to sample and sort datasets as needed, making queries faster, more efficient, and less expensive.

Advanced analytics

A single data source can be useful, but doesn’t paint the full picture.

Today, to measure the effectiveness of an advertising campaign, we need to combine data from different sources like LinkedIn, Facebook, Google, Twitter, etc.

By combining the data in this way, the overall performance of the campaign is measured and the performance of each platform can be compared and contrasted.

Some data needs to be transformed.

Global data

Regional business units driving a global business collect localized data in a local time zone and currency.

In order for this data to be used in a global report such as the Global Annual Report, it must be presented in a universal unit.

Deletion of personally identifiable information (PII)

Many companies choose to load all of their raw data into their data warehouse.

But what if you upload PII to your data warehouse where you want anonymous data to be stored?

Run transformations to remove DPI columns to ensure your data is compliant.

Unqualified data

Data with lots of duplicates, inconsistencies, missing or null values ​​can mislead analytics teams.

By transforming the data, e.g. For example, by duplicating or deleting null records, data teams can gain confidence in the reliability of that data.

data enrichment

Some data is just better together.

In order to enrich corporate data with other third-party datasets, companies must combine these datasets.

They can create 360° views of the customer.

Data-driven cultures and business needs

Data transformation is becoming increasingly important and its value is growing within the capabilities of the modern data stack.

But traditional transformation methods fall short in the world of cloud computing and data complexity.

Certain culture and team indicators suggest it’s time to adopt a transformative approach to the modern enterprise.

You know how valuable your data is. They invest in the source systems they produce, cloud infrastructure to store and back them up, and tools to move them to the cloud.

If you’re struggling with data latency, poor consistency or reliability, or inaccessible data, it’s time to consider a modern data transformation solution for your business…

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