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In order to reap insights from data, it must be organised and accessible. The process of data transformation is mandatory to be able to make sense of raw data for operational processes and decision-making practices. Here, we will cover what you need to know about the process of data transformation, as well as the automation tools that can aid your organisation.

Coming Up

1. What is Data Transformation?

2. How Does Data Transformation Work?

3. What are the Benefits of Data Transformation?

4. What are the Challenges of Data Transformation?

5. Examples of Data Transformation

6. What are the Best Data Transformation Tools?

7. Wrap Up

What is Data Transformation?

Data transformation encompasses the process of extracting, cleansing and transforming raw data into analytical information. The process entails altering the data’s structure, format and potentially its values. Data transformation takes place to accomplish data management and integration tasks like data warehousing and data wrangling.

Data transformation can be:

  • Constructive: adding, replicating or copying data
  • Destructive: deleting records or fields
  • Aesthetic: standardising records in the same format
  • Structural: moving, combining or renaming columns

Depending on the raw data (initial source) and intended target (final) data, transformation can either be simple or complex. It’s possible to perform manually or automatically, or as a mixture of both. However, automated data transformation comes along with many benefits, including: accuracy, speed and volume (more on this soon).

How Does Data Transformation Work?

Data transformation works by extracting data from its source and then changing it into a format that is usable. Many organisations are utilising cloud-based data warehouses in this day and age because it cuts costs and its easily accessible–data is available quickly, no matter where you may physically be located.

With a cloud-based data solution like SolveXia, data transformation works by way of ETL, or extract, load and transform. Essentially, the cloud platform doesn’t require preload transformations. It loads raw data from various sources into a centralised source. Then, the data is transformed automatically by way of a query. This provides businesses with an automated and cost-effective method to perform data transformations.

Data transformation follows these steps:

  • Data discovery: Profiling tools help to understand the use for the data so it can understand how the data must be formatted for its intentions.
  • Data mapping: The transformation is planned. Manually, this would require someone with technical knowledge to code the process. With an automation tool like SolveXia, your team can remain hands-off as the system does the heavy lifting.
  • Code generation: To carry out the process of transformation, code must be generated so the tool can follow the steps.
  • Code execution: The code is put into action and the data is converted into the necessary and defined format.
  • Review: It’s time to check that the data output is meeting its requirements and fulfilling its purpose. The final end-user will be able to review data. If anything was amiss in the process, the automation tool will have alerted the user.

Data can also be transformed with on-premise ETL tools which also automate the process, but exist on-site. Because they require expertise to set up, they tend to be costly. Another method for data transformation is scripting. Scripting requires coding like SQL or Python, and as such, is a manual process (can end up being more timely and error-prone than automated alternatives).

What are the Benefits of Data Transformation?

Data transformation is at the heart of data analytics. Poor quality data is both costly and useless. With the ability to extract and transform data into usable bites of information, businesses can remain agile and adaptive.

Data exists in every aspect of a business environment, whether it be with concern to internal or external factors. From customer behaviors to processes, supply chains, weather and competitors, understanding data translates into having the power to cut costs, increase profits and outshine competition.

Some benefits of data transformation include:

  • Speedy queries: Data that is transformed is standardised and stored in a centralised location so it’s easy to retrieve it quickly.  
  • Better data quality: Bad data (incomplete or incorrect) costs businesses money and can increase compliance risk. Data transformation increases accuracy reducing any data quality issues and calls attention to any missing or incorrect values.
  • Added value: With the sheer amount of data that businesses collect, a majority of its volume is at risk of never being used because it’s too overwhelming to manage. Data transformation makes it possible to standardise data and utilise it in a timely manner.
  • Effective data management: Data sources are plenty, so having a single repository to collect, store, transform and manage data makes it easier to understand.

What are the Challenges of Data Transformation?

According to feedback from companies, there are several challenges they face when attempting data transformation, that is, without an automated solution designed to manage the task for them.

Here are a few pain points that have been shared:

  • Complex and time-consuming: Data scientists end up spending the bulk of their time simply organising, sourcing and cleaning data. This time could and should be better spent analysing data.
  • Overwhelming process: The process of data transformation can be daunting if not properly outlined. The roadmap starts with discovery and mapping, but without someone leading the charge, it may never be initiated.
  • Requires buy-in: Since data impacts every aspect of business, stakeholders must be on board with the tools and process. Receiving consensus may pose a challenge.
  • Lack of talent: On-premise infrastructure will likely require a highly-skilled team of professionals and substantial investment to come to fruition. However, to easily overcome this hurdle, you can consider deploying an automated cloud-based solution like SolveXia which requires little to no code to begin using.

Another challenge of data transformation is that it isn’t static. As data continues to grow and your business expands, there can be exponential increases in information. This is yet another reason why a scalable automation tool proves beneficial.

Examples of Data Transformation

Any type of business can apply data transformation. For a finance department, let’s consider how it can make account reconciliation 10x faster. The process relies on sourcing data from multiple different locations- a bank and internal ledgers, for example. Tools like SolveXia can pull required data in seconds and transform it to compare thousands of records quickly. When something is off, the user will be alerted to remedy the reconciliation. If all looks good, the process finishes automatically with a report.

What are the Best Data Transformation Tools?

If you’re in the market to choose the best data transformation tool for your organisation, consider these options:

Improvado:

Improvado is a web-based marketing data aggregation tool used to connect business intelligence tools. Users can track campaign performance and visualise data in customisable dashboards.

SAP:

SAP is a data transformation tool that is geared towards analytics and data management (MDM) initiatives. It’s designed for data scientists, business owners, IT Leaders and data analysts with special attention to the energy, automotive, financial services, consumer, public and service industries.

Alteryx:

Alteryx is a Windows app solution that automates natural language processing for text mining and analytics. It makes it easier to connect data straight from data warehouses and cloud applications.

SolveXia:

SolveXia is a low to no-code automation solution designed with finance professionals in mind. Our tool services various industry leaders in: travel, financial services, healthcare and retail with the ability to collect, store and transform data in minutes. SolveXia has bank-grade secure, helps to lower compliance risk, provide data analytics and connect you applications for end-to-end automation.

Wrap Up

To be able to utilise data to make key decisions, data transformation must take place. With automation solutions, this process becomes seamless and efficiently executed. Without automation, the process of data transformation can be costly, timely and stressful because it requires high-level expertise and very careful attention to detail.

The ability to transform raw data into usable insights can help your business better service its customers, implement new products or services and make data-backed decisions that boost the bottom line. Data tools like SolveXia can help you accomplish your business goals.

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