Most modern-day enterprises depend on data to thrive. Enterprises accumulate multiple types of data from various business processes. If an enterprise needs to benefit from this data, it must have the ability to fuse a variety of acquired data first. Then, it can consume it under an ever-evolving, progressive and extensible data monetization strategy.
Business objectives can vary based on the business model and industry segment. To achieve these objectives, organizations need to leverage analytics for use cases such as data modelling or data science. Data analytics can help enterprises extract patterns that could be used for machine learning or to forecast/predict outcomes of any event/behaviour to stay ahead of the competition. An enterprise’s ability to produce insights from their sensitive data through analytics is critical and determines whether you can outpace the competition.
Previously, enterprises employed conventional data warehouses and big data platforms on-premises. These solutions haven’t met the expectations of enterprises for analytics which are to:
- Eliminate Data Silos – Enterprises wanted to store many data types and seamlessly operate beyond workload or application-specific context. On-premises solutions have not enabled enterprises to run analytics at scale.
- Enable rapid analytics
- Deliver reliable insights
- Address incremental security concerns associated with sensitive data handling
As mentioned above, on-premises deployments of data warehousing and big data platforms are producing lower than expected returns on investment. Therefore, enterprises started exploring alternative solutions that could deliver their desired business outcomes. Despite various data privacy and security risks, two significant developments attributed to this shift:
- Rapid evolution and adoption of cloud-based serverless services
- The emergence of cloud-based data warehousing platforms
Cloud data warehouses have seen enhanced adoption by enterprises across industry segments. This increased adoption is due to the accessibility of storage from anywhere, higher levels of computing services available on-demand, and better ROI with the pay-as-you-go consumption model.
Also, cloud data warehousing platforms let enterprise data teams quickly load, integrate, and analyze all types of structured and semi-structured data organized as a single unified repository. It allows seamless operation across regions while still supporting all kinds of workloads and applications. These benefits enable an enterprise to collaborate across all the business units of its organization. Enterprises can easily hyperscale on the cloud with cloud data warehousing (CDW) platforms and native analytics services offered by cloud services providers (CSP) to deliver value to the business.
Stay tuned for the following two blogs in this series, in which I will explore how:
- Enterprises can become resilient from the beginning of their cloud data analytics journey by adopting a data-centric approach to protection across their hybrid cloud environment.
- Voltage SecureData can enable secure cloud analytics with privacy by default and allow you to take advantage of low-cost data storage combined with elastic computation.
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