Just like you take raw ingredients and transform it into an edible pizza, raw data must be transformed to be usable, too. Data transformation tools take the complexity out of having to manually reformat and adjust data to be able to gain insights.
Here, we will take a look at how the combination of data aggregation, data cleansing, data mapping,and data transformation techniques are crucial for any business of any size. We’ll culminate by looking at the best data transformation tools worth considering.
1. What is Data Transformation?
2. What are Data Transformation Tools?
3. How do Data Transformation Tools Work?
4. What are the Benefits of a Data Transformation Tools?
5. What are the Types of Data Transformation Tools?
6. What are Data Transformation Trends?
7. Who uses Data Transformation?
8. How to Choose Data Transformation Tools?
9. What are the Best Data Transformation Tools?
10. How to Evaluate Data Transformation Tools?
Data transformation is the process of converting data from one format to another format so that it can be used for a variety of processes. Data transformation processes are either simple or complex. They can be done using an ETL tool, which stands for extract, transform, load or using Python scripts.
Data transformation helps to organise data for indexing in data warehouses. In many instances, data analysts, data scientists, and developers are responsible for performing data transformation. But, with the aid of data transformation tools and software, it’s been democratised for all.
Data transformation tools automate the data transformation techniques to save time, reduce errors, and streamline the work. Data transformation tools are capable of pulling raw data from different data storage bases and computer systems.
Since most companies use cloud-based software systems that connect with one another through integrations, data transformation tools can also connect and draw the necessary data for business processes.
Such data transformation tools extract data (E), process it (T) and then move it into a unified data warehouse (L).
Data transformation tools reformat data from its starting state to a new state for use. Then, the data can be utilised for analysis, aggregation, or to be loaded into operational tools, such as to train machine learning platforms.
There are three main ways in which data transformation tools achieve their goal, namely:
Raw data isn’t intended for analysis. If you want to analyse data, data transformation tools can apply business logic to the raw data to answer questions and better understand trends.
Oftentimes, raw data is extremely large and difficult to handle. Data transformation tools can alleviate the chaos by reducing datasets into more manageable sizes.
It’s no secret that raw data is often rife with inconsistencies, such as field values that mean the same thing but are written differently. To ensure consistency and remove duplicates, data transformation tools help to clean data effectively for application.
With the only growing amount of data available to businesses, data transformation tools are becoming increasingly sought after. Data affects every aspect of a business, from customer service to supply chain management to financial control, and everything in between.
Data transformation tools provide various benefits like:
When choosing between data transformation tools, you’ll come across a few different types.
The most common two types of tools are:
With the speed and volume of data, many companies desire real-time data transformation tools so that analysis can be done on the spot to provide the most relevant and timely insights.
Along your journey to find the best data transformation tools, it’s good to know what you can expect from data transformation trends going forward. This can help you during your decision-making as to what to look out for.
Across business departments, people have different needs when it comes to data. Data teams and data transformation tools have to be equipped to move data between applications and at the same time so that no one gets held up in their workflows.
To take it back to our pizza analogy from the get-go, raw data needs to be cleaned, just like you want to make sure your ingredients are fresh and clean before cooking with them. Data cleansing is the process that ensures datasets are free from duplicates, missing values, or incorrect data.
Small data is useful and easy to access. Wide data is what makes it possible to gain insights from small, large, structured, and unstructured sets of data. The combination of the two is providing businesses with broad and valuable information.
Of course, when it comes to data, security is always a top priority. This stands true when dealing with datasets and data transformation, which is why companies turn to modernised data infrastructure and software tools to help secure their data.
Data transformation was once reserved for data scientists, data analysts and developers, who still perform it today for businesses. However, data transformation tools make it possible for anyone to reap the benefits of data transformation without having to know how to code or analyse huge sets of data.
With access to a useful tool, executives, stakeholders, managers, and any relevant business decision maker will be able to gain insights from raw data to make informed choices.
Before we dive into the best data transformation tools, here’s a guideline to help you assess which tool is most fitting for your business’ needs. Keep in mind these considerations and features:
Data transformation tools, in theory, shouldn’t require heavy IT support or data analysts to set up. After all, the point is to democratise the functions. Low-code and no-code tools allow for instant deployment so you can be up-and-running in no time.
Data mapping matches fields between databases. Some data transformation tools offer this feature so that you can visualise all data together.
If you’d like to automate and schedule tasks, look out for job scheduling which allows you to review a project timeline or visual dashboard to check task statuses.
To help streamline the set-up process, you can choose a tool that has data transformation templates. They are typically set up by industry, so you’d select a template from your relevant industry like healthcare, finance, or marketing, and then use a template to run the process.
It’s time to talk about data transformation tools!
Here’s a quick review of the top-performing tools. While there’s more to learn about each of them, this guide is meant to give you a bird’s eye view of what you can expect so that you can research the tools that catch your eye more thoroughly before moving forward.
As a low-code/no-code cloud-based solution, SolveXia offers automation capabilities to help make the most out of your company’s data. SolveXia provides end-to-end automation for business processes, such as reconciliation, rebate management, regulatory reporting, and data analysis, to name a few.
The benefits include: increased accuracy, reduction of manual errors, saved time, access to audit trails, and the ability to improve processes while maximising productivity.
By doing so, SolveXia collects, combines, and cleanses data from multiple business sources so you can visualise data in dashboards and automated reports. This way, you can make informed business decisions with no down time.
Since SolveXia connects all systems, including legacy systems, you can free up your team’s time from having to conduct manual tasks and instead allocate that time to gaining valuable insights.
dbt is an SQL-first transformation workflow that provides tools with analytics. It helps software engineers and business teams to develop faster and validate assumptions. It also connects data between departments so that there’s a shared workspace across teams.
Alteryx is a Windows app solution that is geared towards text mining and analytics. It offers automated natural language processing and connects data from warehouses and cloud applications.
Apache Airflow allows users to run and manage the ETL process as soon as you build your codebase. Beyond data transformation, Airflow also can help to organise and oversee data workflows. Developers that use Airflow will be able to transform data in any workflow by using Python code.
EasyMorph removes the need for Excel, VBA, Python and SQL with its automation and transformation platform. The platform offers features like scheduling, web APIs, and a data catalogue for supervised data delivery.
Partnered with Google BigQuery, Dataform is a data transformation tool that is aimed to help analysts manage their data processes in BigQuery. The tool will write data transformation as code, deploy to BigQuery, and then create a unified and single source of truth for data.
Matillion ETL is a data transformation tool that’s best suited for simple data movement into cloud data warehouses. It provides pre-built connectors and data processing components.
Additionally, if a user so chooses, you can use your own custom scripting in Python or call on other data processing servers to handle complex tasks. The software supports reverse ETL so you can write transformed data back out to a data warehouse or data lake.
Datameer provides teams with the ability to explore and automate data insights in its SQL or no-code solution. It is built for varying expertise levels with an easy-to-use interface that provides the same results as transformations that are otherwise written directly in Snowflake.
With a fresh look at some of the most popular tools, you may be wondering how to choose the best one.
Before you make your final decision, review the following factors:
With these answers in mind, the decision could possibly become very clear! To make your decision even easier, we invite you to demo SolveXia for yourself so you can see how data automation can boost your business!
Having access to data transformation tools is like having access to an automatic pizza dough roller and wood-fire oven – you’ll get the optimal result every time!
With the fast pace of data, your business relies on the ability to perform analysis in a timely manner to prosper and function efficiently. Data transformation tools are a key piece of the puzzle, and automation software like SolveXia offers these capabilities and so much more!
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