Marketing data is an important asset of every business. Analyzing these statistics requires well-organized and structured insights for the best possible result. This is where data transformation comes in handy. It helps businesses to transform and format their data in a way that is more appealing to them. Simply put, it is a type of process that changes the form, structure, and value of data-driven insights.
There are two types of warehouses that organizations use to transform data: on-premises and cloud-based. This process can be constructive, destructive, aesthetic, or structural. People involved in this tend to use specific languages, like Python or SQL to finish the task.
Benefits and Challenges of data transformation
This process is very important for business for various reasons, like consolidating records, deleting duplicates, changing formatting, and a lot more. Marketing data and its transformation have various benefits. It helps companies stay better organized and is helpful for both sides, humans, and computers. It also improves the quality of the information. And most importantly, it makes it easier for applications, systems, and types of insights to be more compatible between them.
Like in every process, besides the benefits, there are also some challenges. This may include the fact that this process is very expensive. It all depends on the software and tools used for the data-driven insights. It can also be resource-intensive, and some businesses can use it for things that they do not need. Also, if it is done by analysts that do not have much experience it can bring problems for the company in the future.
How to Transform Marketing Data?
The first step in this process should always begin with extraction and parsing. Then it should follow with translation, mapping, filtering, and summarization. When there are different sources the insights can be merged to create enriched information. That should be split into multiple columns. The next step in the process is indexing and ordering, and then obviously encryption, which is a must. Finally, the process ends with formatting and renaming everything that needs to be done, to ensure clarity.
The Bottom Line Here
Data-driven insights are very important for every company. The transformation of these insights is what makes the analytics sector to be accurate, and that results in improving the business. So, it is very important to use the transformation tools correctly, and by people who know their way around them. Eventually, the success of the company depends on that.