Businesses are increasingly harnessing data for various analyses—exploratory, predictive, inferential, and causal. As the role of data increases, problems related to data flow get magnified in scale and impact. Data flow can be unreliable and in transit, bottlenecks can interrupt the flow. That is why a defined data pipeline architecture becomes critical. Data pipeline architecture ensures smooth data flow and enables real-time analytics for faster and more effective data-driven decision-making.
A review of the existing literature help understands how data is powering modern enterprises, but at the same time, how a lack of a robust architecture or frameworks stands in the way of utilising the same to its full potential. For instance, a Capgemini study has found that while organisations accelerate their data-driven decision-making, only 22% of them can quantify the value of data in their accounting process, and only 43% can monetise the data through their products and services.
To monetise data effectively, organisations need to have a mature approach to designing products and processes that capture new data. Identifying the right data sources forms the base of an effective data pipeline. This makes investment in the data and analytics pipeline of utmost importance. According to an IDC study, 87% of CXOs are of the opinion that in the next five years, their objective is to make their organisations more intelligent, and that is not possible without harnessing quality data and spending on solutions to effectively manage it.
