Why automation must support data transformation.
Posted: Tue Jan 21, 2025 6:58 am
Today we have access to more data than ever before , and organizations rely on rapid insights to make smart decisions.
However, it is true that many companies still continue to face bottlenecks in their data flow that limit access to potentially valuable information.
Qlik conducted research with IDC that reveals that just over half (59%) of global organizations believe they have identified and captured the majority (70%+) of their potentially valuable data.
Most leaders know that there is a lot of raw data in dispersed locations, but it is difficult to bring it into a reliable state available for analysis. In fact, where the biggest investments are expected in the next 12 months is precisely in the pipeline of data.
How can companies improve their process for transforming raw data into analytics?
The first point is to understand that traditional data transformation ivory coast phone number lead methods, such as Extract, Transform, Load (ETL), are powerful and have been essential to prepare large amounts of data for analysis, but today they are heavy tools that do not adapt to the agile approach to data that modern companies demand.
Batch processes to move transactional data into data warehouses where it can be controlled, cleansed, and queried , for example, can take six to nine months. This means that highly skilled people end up spending a lot of time transforming data, when they could be better spending it on higher-value activities.
Furthermore, these tools were designed to be used by experts with a deep knowledge and understanding of the solutions. Today, nearly a third (31%) of companies globally report that a lack of qualified resources is one of the biggest challenges they face in data transformation.
Leveraging automation to relieve pressure
With new approaches to data transformation, companies can automate elements of the process and relieve pressure on highly skilled staff.
To achieve this, it is important to promote a significant change that involves moving from batch data loading to a continuous data ingestion model from multiple sources that takes advantage of Change Data Capture (CDC) technology.
This allows data from any source to be replicated and streamed in real-time for analysis. Some more advanced solutions also eliminate the manual coding process, automating and accelerating data ingestion and updates, data replication, and data loading to new locations.
This significantly increases the speed at which data is transformed. In addition, moving away from manual integration processes reduces both the risk of human error in the scripting process and the reliance on specialized profiles for its execution.
However, it is true that many companies still continue to face bottlenecks in their data flow that limit access to potentially valuable information.
Qlik conducted research with IDC that reveals that just over half (59%) of global organizations believe they have identified and captured the majority (70%+) of their potentially valuable data.
Most leaders know that there is a lot of raw data in dispersed locations, but it is difficult to bring it into a reliable state available for analysis. In fact, where the biggest investments are expected in the next 12 months is precisely in the pipeline of data.
How can companies improve their process for transforming raw data into analytics?
The first point is to understand that traditional data transformation ivory coast phone number lead methods, such as Extract, Transform, Load (ETL), are powerful and have been essential to prepare large amounts of data for analysis, but today they are heavy tools that do not adapt to the agile approach to data that modern companies demand.
Batch processes to move transactional data into data warehouses where it can be controlled, cleansed, and queried , for example, can take six to nine months. This means that highly skilled people end up spending a lot of time transforming data, when they could be better spending it on higher-value activities.
Furthermore, these tools were designed to be used by experts with a deep knowledge and understanding of the solutions. Today, nearly a third (31%) of companies globally report that a lack of qualified resources is one of the biggest challenges they face in data transformation.
Leveraging automation to relieve pressure
With new approaches to data transformation, companies can automate elements of the process and relieve pressure on highly skilled staff.
To achieve this, it is important to promote a significant change that involves moving from batch data loading to a continuous data ingestion model from multiple sources that takes advantage of Change Data Capture (CDC) technology.
This allows data from any source to be replicated and streamed in real-time for analysis. Some more advanced solutions also eliminate the manual coding process, automating and accelerating data ingestion and updates, data replication, and data loading to new locations.
This significantly increases the speed at which data is transformed. In addition, moving away from manual integration processes reduces both the risk of human error in the scripting process and the reliance on specialized profiles for its execution.