The BFSI enterprises run on the amount of trust customers put in them – something which data reliability engineering (DRE) keeps intact. Sitting on humungous volumes of data, the BFSI sector has an increasing need for DRE. It’s essential we decode the framework to study it.
A study by Accenture reveals the second biggest reason why consumers leave their bank or insurer—failure to safeguard data. Next to money, data is equally, and in fact, more personal.
Banking, financial services and insurance firms, or BFSIs, are in the customer-trust business, which necessitates a consistent message that financial data, personally identifiable information, and transactional data are secure and accessible at the customer’s discretion.
Since BFSIs frequently handle data, compliance and risk management must remain top priorities. The industry is also very tightly regulated. As a result, BFSI businesses must also abide by laws demanding adequate controls to segregate operations. Moreover, these ecosystems’ effectiveness depends primarily on the frictionless exchange of customer data, which generates feedback on consumer demands and enables service providers to build more pertinent and personalised offerings.
The situation demands a focus on data accuracy and reliability. Data Reliability Engineering (DRE) can assist with managing complex data streams with minimal error rates. However, given that several stakeholders interact with the data throughout its lifecycle, data might fail for various reasons, including unforeseen code changes and operational problems.
The what behind DRE
Similar to the principles of Site Reliability Engineering, DRE is the process of enhancing data quality, maintaining data flow on time, and ensuring that analytics and machine learning products are fed with a healthy set of inputs. According to Gartner’s report, poor data quality costs businesses $12.9 million yearly. In addition to having an immediate negative effect on revenue, insufficient data over time makes data ecosystems more complex and results in poor decision-making. Naturally, the importance DRE holds is sound.
The end users don’t care about data quality in the abstract, whether they are data scientists analysing A/B test results, customers viewing product recommendations, or executives viewing dashboards. Instead, they are concerned with whether the information they consume is pertinent to the current task. DRE focuses on identifying and satisfying those demands while allowing the business to expand and develop its data architecture without hindrance.
The approach to reach
A subset of DataOps, DRE refers to the more comprehensive set of all operational difficulties that owners of data platforms might experience. The core practices for DRE include:
Stop chasing perfection: Focus on the process and prepare plans to detect, mitigate or control the failures that occur, rather than trying to build an error-proof model.
Monitoring is the key: The problems that cannot be identified cannot be managed or addressed. Thanks to monitoring and alerting, teams have the visibility they need to know when something is wrong and how to repair it.
A standard needs to be set: Is the data quality good or bad? To arrive at any conclusion is impossible as it is subjective. First, it needs to be clarified, quantified, and accepted. It will be challenging to take action if there is uncertainty or lack of alignment in understanding what is good or bad.
Go for automation: The complexity of data platforms has increased dramatically, and administering them manually increases linearly with personnel. This is both costly and unsustainable. Data teams can scale reliability efforts by automating manual processes, which frees up time and brainpower for solving more complex issues.
Simplify: Complexity is the enemy of reliability. Although complexity cannot be entirely avoided because the pipeline still modifies the data, it can be diminished. One of the best ways to keep a pipeline job reliable is to isolate and reduce its complexity.
The implementation of these principles will benefit the BFSI sector at large. This will guarantee high-quality data and low downtime, significantly boosting the organisation’s productivity and decision-making abilities. Further, the maintenance of databases, data pipelines, deployments, and the availability of these systems are also the key areas of DRE’s focus. These data silos may be eliminated with an enterprise-wide unified data platform, which would also guarantee smooth access to and processing of the data across the organisation.
To sum up
The responsibility for assuring the reliability and integrity of the company’s data systems is falling more frequently on the shoulders of data engineers and analysts. In addition, companies will begin to embrace new-age technologies, processes, and cultures to keep up as stacks become more complicated and the need for data will continue to rise.