One of the biggest problems of companies is how they handle their Data. Due to the variety of data sources, definitions, and standardization issues, most data must be modified in order to be useful for its intended use. Whether it’s being used as part of an Extract Transform Load process in data warehousing (or in some cases with modern cloud data stacks, ELT), for use in ML (Machine Learning) models, or simply ad hoc analysis, data transformation is a key part of nearly any project involving data. 

In basic terms, data transformation can be defined as the process of modifying data to make it usable for application in tools, processes, and analysis. Transforming data starts with an understanding and cleansing of data, followed by reviewing the changes with key stakeholders. However, the steps can vary depending on your project goals and data needs.

Step 1: Exploring and Validating Data

The goals of this stage are ultimately to understand key characteristics of your data and explore what changes might need to occur to data in order to meet your end purposes. 

You should ask questions like: 

• What is the ultimate goal for what I hope to achieve with this data? 

• What are the different variables and how are they related? 

• What does each variable mean and will it be useful? 

For example, in the case of transforming data for ML applications, text data might need to be transformed into numerical data, or ranges of values may need to be normalized.

Step 2: Mapping Data

Often you will need to join data from multiple sources into a single dataset. In this situation, you have to match data fields between datasets in preparation for joining them. 

Step 3: Code Generation and Data Transformation Execution

This is when you actually transform data to meet the previously identified goals. First, code is generated either by hand or visually with some tools. Then, the code is executed and the transformation occurs.     

There are many types of data transformation, including additive activities to enrich data with greater meaning such as deriving age from birthdate, subtractive activities that include deleting records or unnecessary fields, standardization or normalization to rescale data, and structural changes to the dataset itself. 

Step 4: Validation and Review

In the end, after data transformation has occurred, the person doing the transformation validates that it meets the ultimate goals set forth in the initial stages of exploration. Finally, those involved in the downstream use of the data validate it meets their needs, such as business SMEs that will use the data to enrich a new business application. Often at this point revisions will need to be made and so the cycle begins in some form again. 

How Cubik can help you transform Data More Efficiently and Effectively

This process can be incredibly time-consuming, especially when done manually. Data transformation can be even more of a hassle with several rounds of revision and can be expensive with large datasets. Here’s how Cubik can help at each stage of the process: 

1. Exploring and Validating Data
   Cubik will help you identify and set your key data sources and fields. Define what needs to be measured and its relevance to the business with the help of an AI assisted tool.

2. Mapping Data
   Relating datasets with no code required. We got rid of the data cleansing process, simply select the key, join type and your datasets will be joined. 

3. Code Generation and Data Transformation Execution
   Using automated scenarios to eliminate manual labor for common data transformations

4. Validation and Review
   The visual flow in Cubik makes it easy to track everything that’s occurred to data in order to backtrack your work as well as explain data transformations to stakeholders.

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.

Some challenges of data transformation are:

• 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 Cubik which requires little to no code to begin using.

Summary

To be able to utilize 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 Cubik can help you accomplish your business goals.