In this article, we will talk about why data transformation is so important for your business, the different types of data transformation, and how you can readily apply these principles to transform your business.
Why is data transformation important?
Data needs to be in a consistent format in order to be useful for a business. Say, for example, your point of sale system uses numbers for different product SKUs. When transactions are being processed through the system, you would expect each SKU to be scanned and identified by a unique number. If the system forgets to input a number or adds additional letters to the SKU, this could damage the system and make it unreliable.
Data transformation helps solve common data issues that occur in business processes. Since businesses are constantly evolving and changing, their processes are often imperfect, causing their data to become imperfect. Data transformation is an intermediary step to help ensure clean and accurate data.
Different types of data transformation
Data transformation comes in a variety of forms. It’s important to understand each type of transformation, when it should be used, and why it is important. The main forms of data transformation are:
Data cleansing is a transformation type used to ensure the integrity of the data. Validation rules can be put in place to ensure data is in a consistent format that is human-readable and ready for processing.
Oftentimes, data from source systems can be extremely messy. For example, a date field may be stored as a floating decimal computer format, making it difficult to interpret. Data cleaning can help remedy this by performing an operation to convert the field to a standard date format.
Aggregation is an extremely common data transformation technique that involves summarizing the data to create a new view of it. Many data sources can have hundreds of millions of rows and columns, which can make it quite confusing for the end-user. Aggregating the data allows you to reduce its size and complexity of it.
Common aggregations include finding the sum, average, minimum, maximum, or total count of the data. An example of using an aggregation could be counting the number of open opportunities in the pipeline for your sales team. This provides a high-level overview of the sales organization. Aggregations can be extremely valuable in creating summary numbers for dashboards and reports.
Joining data involves combining information from different data sources or data tables together. This creates a unified view of the data that can be used for further analysis. Data that is stored in databases usually require joins in order to present the data in a format that will be useful to the business.
Data joins are critical for any organization that wants to make better use of its data. Since data lives in so many different parts of the business, joins can be used to combine data and extract insights that otherwise would not have been possible. An example of this is joining sales CRM data to billing data from an accounting system. This creates a more unified view of the customer that can be distributed and used by different departments within the organization.
Appending data is another process used to combine data that is stored in similar formats. When new data occurs in a system, it can be added — or appended — at the bottom of the dataset. That data then becomes part of the table, as it shares similar characteristics to the data above it.
Data appending is an important data transformation process to maintain the stability and data integrity of source systems. It can also be used to add data from different sources together for analysis. An example of this is a corporation that operates in different countries and uses different systems of record for inventory. Since data is not natively stored together, it can be appended afterward as a data transformation.
How can I automate data transformation?
Data transformation is critical to maintaining the data infrastructure of your business. It’s also a continuous process that can require large amounts of manual effort if not handled properly. Luckily, modern business intelligence (BI) tools help with the automation of data transformation.
BI tools can create automated data pipelines that are constantly performing data transformation steps. Using pre-built data connectors, they are able to hook into software and business systems that you are already using and perform data transformation steps such as the ones we’ve outlined above. Automation is a powerful capability that modern BI tools possess, saving your company time and resources.
Using a BI tool for data transformation
Now that we understand the importance of data transformation in modern business, it’s important to partner with a BI vendor that possesses the capabilities and expertise when it comes to data transformation. Not all BI vendors are alike in this aspect, as many focus solely on end visualizations and dashboards.
BI tools are very easy to get started with because they are built with the end-user in mind. Business users of all types can get up and running with a BI tool in a matter of minutes—not days. They also do not require the expertise of IT teams to run, which saves you time and money.
The best BI tools also offer a wide range of data transformation steps that are pre-built and ready for use. If you need to customize according to the unique needs of your business, there are also tools that can guide you through transforming your data.
Data transformation is a critical step to extracting the most insights as possible out of your business data. Companies that effectively use their data are more competitive in today’s marketplace and continue to innovate and delight their customers. By adopting a BI tool to automate the data transformation steps within your organization, you can continue to focus on the most critical aspects of your business.
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