Most leaders know that there is a great wealth of raw data across disparate locations, but getting it into an analytics-ready, trusted and available state is difficult. In fact, it’s the greatest focus area for investments in organizations’ data pipelines over the coming 12 months.
But how can businesses improve their process for transforming raw data to analytics-ready?
The Challenges To Traditional Tools
Traditional methods of data transformation, such as Extract, Transform, Load (ETL), are powerful and have been instrumental in making huge quantities of data ready for analysis. However, such heavy tools are no longer suited to the agile approach to data that modern business demands. Batch processes for moving transactional data into data warehouses where it can be governed, cleansed and queried, for example, can take between six to nine months. This means highly skilled individuals end up spending a huge amount of time transforming data, when their resource could be better invested in higher value activity.
Furthermore, these ‘expert friendly’ tools are just that – made to be used by experts with a deep knowledge and understanding of the solution. And with nearly a third (31%) of companies globally reporting that a lack of skilled resources is one of the greatest challenges they face in transforming data, it is perhaps unsurprisingly that organizations too often rely on just one or two specialists to manage the process. This presents a massive risk, as their specialized knowledge of the bespoke process leaves the business when they do. In such an event, the data transformation process becomes brittle and risk prone – e.g. when it eventually breaks, significant delays can be experienced trying to find a candidate with the right skillset, not to mention the time it might take for that new hire to learn the process to unlock this valuable data.
Leveraging Automation To Alleviate The Pressure
To remove the burden from a small number of individuals, organizations should explore how new approaches to data transformation can automate elements of the process and alleviate the pressure on highly skilled staff.
To do this, a significant shift is needed away from batch uploads of data and toward a continuous model leveraging technology like Change Data Capture (CDC). This enables data from any source to be replicated and streamed in near real-time for analysis. Some more advanced solutions also eliminate the manual-coding process, automating and accelerating data ingestion, data replication and the loading of data into new locations.
This significantly increases the speed at which data is transformed and simplifies scripting to lower the technical barrier, relieving programmers of the burden of writing thousands of lines of code when integrating new sources into data warehouses. Furthermore, moving away from manual integration processes both reduces the risk of human error in the scripting process and of this specialized expertise leaving the business.
Transform Data At The Speed of Business
Too many organizations are sitting on a potential goldmine of invaluable insights hidden in raw data. However, the time-intensive, manual processes to transform it into analytics-ready data for analysis is holding too many companies back from realizing its true value. To help organizations transform and use their data at the speed that modern business demands, it will be critical to exploit technologies that help them automate the data integration process and relieve their highly skilled workers to focus on more complex, creative and value-add tasks.
To gain a better understanding of the performance of your data pipeline, take our Data-2-Insights assessment with IDC and receive a personalized report on how your organization can increase its data agility to achieve a competitive edge.