Top 5 Data Transformation Best Practices

As the world generates dizzying amounts of information each day, your business needs to implement reliable data transformation best practices. Big data is no longer a buzzword, but the reality of the corporate world. Your business needs to have the proper tools in place to analyze vast amounts of data and use them to drive sales. This process may require data transformation.

What is data transformation?

Data transformation involves changing the structure, format, or values of data to convert it to a validated and ready-to-use form. Learning to make sense of the data your company collects is an important step in turning data into essential insights that can positively impact your business. Transformed data should also be consistent, accessible, and secure.

Data transformation best practices

You can take advantage of the ever-growing data available to achieve a new business value. But you won’t be able to harness this data without efficient data transformation strategies that can orient it around your company’s needs. These five practices can help you.

Start with the target

It could be tempting to jump right into the nuts and bolts of data transformation when faced with oceans of data. A better idea is to engage business users to understand the processes you want to analyze to design the target format. This process will reveal two types of target tables for transformed data:

  • Dimensional: They contextualize the data according to the who, what, where, why, and how. These tables provide entry points and descriptive labels that will allow you to leverage the business analysis data.
  • Fact: These tables display the outcome of the data you have measured, such as transaction records or a periodic snapshot which gives a summary of events over a regular interval of time

Data profiling

Before you can plunge into the business of transforming data, you need to identify its source. This process helps you understand the raw data state and the amount of work it requires before it is ready for data transformation. For instance, analyzing sales trends requires access to customer and product databases and pulling the sales results from the point of sale. Understanding the source helps you to determine the size of data and data type. You can then create appropriate tables.

Weeding out bad data

The insights from data profiling can also help you identify workable data and expose junk or bad data. This process ensures that unsuitable data does not get to end-users and damages your business’s reputation. If some fields have glaring gaps, then you know it is time to talk with your business stakeholders to see if you will estimate values or exclude the records altogether.

Aligning data with the target

It is also important to align your data with the target format by mapping source columns to target columns. Your data transformation team can use ETL tools to automate data flow between these columns on successive data loads. Conforming your data to the target format needs little pre-processing and frees up some analysts for more tasks.

Engage the user community

The ultimate measure of your data transformation success is the extent to which the user community accepts and continues to use the transformed data. To achieve this milestone, you must present the transformed data for rigorous acceptance testing while also addressing any defects noted from the process.

Helios can help you make sense of the data explosion in your company. We provide a clear picture of your business’s past, present, and future so you can prepare for any challenges ahead. Contact our team today to discuss our contracts on building better data services for your firm.



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