Data transformation defined
Data transformation is an IT process where data scientists analyze, review and convert data from one type of format to another. It is a process that is essential to a business, especially when there is a need to integrate data from different databases, integrate data more efficiently or change it to be able to store it securely. To complete a data transformation process securely and quickly, there is usually a range of tasks involved. These services might include the following:
Converting Large Amounts of Data and data types
Removal of unnecessary nodes and duplicate data
Enrichment of the data
The different tasks needed will depend on the data integration level the transformation needs. As a general rule, it is a two-stage process.
Discover the data and identify data types and sources.
Understand the structure and the transformations that need to happen.
Create data mapping that defines how each of the fields is mapped, joined, modified, filtered and aggregated.
At this point, data specialists will extract data from the original source. The different sources will vary depending on the structure, the database and even the streaming services such as log files from web portals. Your data scientist team will then perform transformations and changed data such as aggregate sales data, customer service data, etc into text strings or joining rows and columns. This information is then sent to the Target place which could be a database or data warehouse that is capable of handling structured and unstructured data
Why Transform Data?
Your organization may need to transform your data to make it compatible with other data, move it to another location, join it with other data, are added to other data parts.
Here is an example of how this works:
Let’s say your company purchases another smaller company. You need to combine the information for the different departments. however, the purchased company uses a different database. In this case, you need to make the data compatible and transform one set to join the other. You need to change the formatting of the data and remove any duplicate rows when combining both databases. These are critical functions make all the data usable.
Other reasons to transform data include:
Moving the data to a new store or cloud data warehouse
Joining unstructured data with structured data
Adding additional data fields and information to enrich existing data
Perform aggregations. This is when you want to do data analysis and comparisons of data or additions of sales, etc.
The Data Transformation Process
There are several ways to transform data. This can include stripping using SQL or Python, on-premise or cloud-based ETL tools which take much of the small detail out of data transformation.
Data transformation can be difficult for your IT team and it may take time. Some of the most common challenges include time and money. Data transformation can be a slow process and is probably the largest complaint of data scientists and management teams. Because of the time constraint, it can also be expensive. The total price will depend on your database, the infrastructure and the number of data scientists needed to complete the task. It can be a process that slows the system as well and may mean you have to wait 24 hours for each batch to process.
Data Transformation Practices to Keep in Mind
Design the End
When faced with mountains of data, it can be tempting to just jump in and start the transformation. However, you need to have a plan and help the business team understand the process. You need to have an end in mind and explain the “Who, what, where, why and how” of it all to the business organizational team. In the end, this helps by engaging the management team and giving them a sense of ownership. It also scopes the compete process and identifies the data needed to be transformed and it offers a target for the effort.
Find The Data Source
Knowing what you want to analyze and transform points the team to the appropriate data source. So if need to analyze sales trend you need to access and transform the customer and product databases and then pul the sales results from the point of sale system. This data profiling helps you understand the amount of work you will need to perform the transformation.
Clean the Data
Once you’ve done the data profiling, you understand the type of data work you need to do to make it usable. This will require you to clean the data and determine what the junk data is and what the organization wants to do with these records. Cleaning the data helps ensure that bad data will not influence the final merging of data and the data analysis.
Cleansing data early in the data transformation process helps ensure the obviously bad data will not make it to the end-users and will help improve business user confidence in the data.
Track and Audit the Data Quality
Audit tracking gives you the number of records you load in each transformation process. It also tells you of the time you use in each of the steps. Getting the data quality test results helps prove the validity of the metrics calculated.
Find an in-house or cloud-based ETL solution to help you manage the process. The good thing about a cloud-based service is that the provider has a team of experts to help expedite the process. You can plan your data, execute and transform it to get the result you need in the quickest time possible. This leads to a more cost-effective process and avoids the need to hire a team of experts to transform the infrastructure.
A cloud-based ETL solution offers a faster turnaround time as well. It can extract, transform after uploading the data in real-time. This type of cloud-based application allows you to perform transformations quickly and attacked problems that could arise.
It also offers more security and removes sensitive information before transforming at parent cloud-based ETL encrypts data in all forms. This gives you greater peace of mind. Contact us today to find out how we can help.