In today’s business environment, data transformation is a key priority. Data-driven strategies are becoming essential for making informed decisions and acting swiftly in response to anomalies or rapid changes, so organisations must be equipped to use data when it is needed.
Data transformation is a crucial process for businesses, as it enables companies to monetise their vast data resources effectively through techniques such as cleansing, aggregation and enrichment.
An increasing number of organisations are moving this process to the cloud.
However, there are still questions about why this is being done and how data transformation can be effectively implemented in the cloud.
Data transformation: why do it in the cloud
The advantages of data transformation in the cloud may seem trivial at first glance, but they are significant. By leveraging the cloud’s benefits, organisations can unlock a world of potential.
Some examples:
- Scalability. Cloud solutions allow companies to scale up or down their resources as needed, eliminating the need for costly physical infrastructure investments.
- Optimised costs. The cloud allows companies to only pay for the resources they use, which can be a much more cost-effective solution than maintaining and purchasing on-premise servers.
- Improved accessibility. Cloud services allow users to access data from any location at any time, greatly facilitating collaboration between geographically distributed teams.
- Enhanced security. Cloud service providers invest significantly in security and regulatory compliance, often offering levels of protection that many companies would be unable to implement independently.
- Ease of Integration. In addition, cloud platforms offer a full range of integrated services, including advanced analytics tools, machine learning, and storage, which can be integrated into the data transformation process with minimal effort.
How to do data transformation in the cloud
Once the advantages of data transformation in the cloud are understood, the next step is to determine the best way to implement it that aligns with the specific needs of one’s own company.
Various techniques fall under the broader umbrella of data transformation. One common technique is data cleaning, which removes errors in the original data. For instance, a value may be missing, or there may be a duplicate.
Additional techniques include:
- data normalisation ensures that all data is enclosed within a standardised range of values. This allows for the correlation of different data sets;
- data aggregation is combining different data sets into a single summary data;
- data imputation is a process through which gaps in specific data can be filled using advanced database analysis. In other words, the system completes the data set by interpreting all the other data.
A cloud-based platform allows these techniques to be automated within an ETL (or ELT, as it is increasingly known) context. Cloud platforms facilitate more sophisticated data transformation by integrating data transformation within a contemporary pipeline, from data collection to visualisation.
Data transformation in the cloud to stay competitive
Cloud data transformation offers numerous advantages in scalability, cost efficiency, accessibility, security and integration, making it an ideal solution for many businesses. It is important to note that organisations must plan carefully and use appropriate tools to ensure a smooth and secure process.
In other words, adopting cloud-based data transformation can be an important step towards modernising business operations and increasing competitiveness in today’s market.
Transforming data into the cloud is not just a passing fad; it is a vital necessity for businesses that wish to remain at the cutting edge of technological advancement and derive the most significant possible benefit from their data.
