As a business decision-maker in the post-pandemic world, you might wonder about how scalable and cost-efficient your cloud migration strategy is, and whether you have data accuracy and accessibility. These are important concerns, particularly when Gartner forecasts that 95% of data sharing programs will fail in 2022 due to lack of trusted quality data.
Transitioning to cloud is a challenging process, one that can be streamlined with a well-defined roadmap and an implementation partner with the right cloud expertise and experience.
A Cloud-based Data Approach in the Modern Era
Moving traditional legacy systems and their data to the cloud requires the adoption of artificial intelligence (AI), cognitive data, and micro-services. This creates symbiotic enterprises governed by real-time analytics, digital bots with cognitive capabilities, and 360-degree customer views where privacy-preserved data is constantly enriched yet secure. Continuous data validation is necessary for industries that must adhere to evolving compliance standards and laws. Enabling such validation through experts leveraging the latest technological solutions will infuse efficiency, timeliness, and accuracy into the whole process.
Benefits of Cloud-Migration and Validation
Migrating to cloud and performing consistent data validation are some of the simplest and most effective ways to improve business performance, ensure data security, and improve the company’s quantitative decision-making capability. Enterprises ought to consider transitioning to cloud for the following reasons:
- Flexibility: No organization experiences similar demand levels by similar users every time. Cloud helps businesses stay prepared for unpredictable demand fluctuations in any given period.
- Scalability: Cloud has the ability to enhance the existing infrastructure of an organization rather than increase the workload on it, thus providing the ability to scale up or down as required.
- Security: Through centralized data storage, cloud offers stronger security compared to traditional data centers. It provides built-in capabilities such as cross-enterprise visibility, periodic updates, and security analytics.
- Profitability: Typically, cloud services follow pay-per-use models. There is no obligation to pay additional or high upfront fees or even to make ongoing investments in space creation, maintenance, and training for different physical servers.
Organizational success relies upon thoughtful assessment of critical considerations such as databases and platforms, data pipelines and streams, data consumption, operations, governance, and data privacy.
Infosys Data Quality Engineering Platform is an open source-based low/no-code platform powered by cognitive capabilities. Infosys has partnered with leading cloud hyperscalers to host the platform, which has been certified as one of the preferred solutions by Azure and AWS. The reusability and extensibility of the platform provides a ‘Buy, not Build’ option for enterprises. A dedicated SME team for support and building new data validation pipelines in the niche areas makes the platform future ready.
The Future of Cloud-based Data Approach
Moving systems to the cloud significantly improves connectivity among key players in the quality ecosystem and enables all stakeholders to collaborate effectively. Embedding automation can transform the validation process and significantly shorten the project duration from several months to just a few days.
