Your data is collected waiting to serve its purpose. Data processing is a crucial part of making sure that you will be able to use data vigorously. Verifying, organising and transforming, are some of the activities that have to be conducted within this stage. In addition, integrating and extracting the data in suitable output formats decides if the data can be used in practice or not.
Processing data possesses similar threats with that of the pre-processing stage regarding the potential lack in consistency and incompatibility. Other challenges when you reach this stage include:
- Raw data with unknown encoding that prolongs the time needed to refine the data
- Large amount of time is allocated to fixing poor pre-processing
- Inconsistent time allocated to processing after finalising pre-processing due to restricted time
[…] “the problem thus concerns how we’re storing data rather than where we’re doing it”, reveals IFP (Insights for Professionals)
Such challenges are very common when integrating data and despite their commonality they are not easy to overcome. Known approaches to integrating data that can ease the workflow are:
- Data Consolidation
- Data Federation
- Data Propagation
However, a great integration process should not only consolidate data but also standardise it, maximising quality and improving consistency. This will ensure that your data is always available for decision-making.
“Data integration comprises the practices, architectural techniques and tools for achieving the consistent access and delivery of data […] to meet the data consumption requirements of all applications and business processes” – Gartner
Integration can, to a limited extent, be done manually with 100% chance of it being very impractical. The use of Modern Data Platform can not only facilitate the processing of data but also “ address a wide range of use cases that rely on key data delivery capabilities “, according to Gartner.
While data pre-processing (preparation) and data processing are two separate steps in managing data, they can both be done in just one go. An efficient and effective data integration is a definite solution to problems such as lengthy pre-processing fixes and unknown encoding of raw data.
Gartner has put together 4 crucial capabilities to take into consideration when opting for a data integration tool:
- Data Engineering – helps build, manage and operate pipelines within the desired architectural plan
- Cloud Migration – architecture that allows cloud storage and on-premises to guarantee accessibility
- Operational Data Integration – data management, collection and sharing, supporting integration, consolidation and synchronisation
- Data Fabric – allows the use of ML aptitudes to enable rapid access of distributed data
As such, using a Modern Data Platform ensures a smooth transition of input to output while “allowing data engineers to build, manage, operationalize and even retire data pipelines in an agile and trusted manner”. Platforms that allow optimization of code and pipeline execution are highly preferred, says Gartner.
The need for rapid deployment of data pipelines is growing exponentially. Data and therefore, data processing requirements are becoming even more important when pointing toward a completely automated data access and sharing infrastructure.