Companies are evolving their data infrastructure for benefitting from the latest technological developments, as artificial intelligence (AI) and machine learning (ML) continue with their upward spiral. The cloud-based data analytics infrastructure has been witnessed widespread acceptance and implementation, providing excellent scalability, simplifying data management, and reducing cost.
Cloud-based data analytics has not only facilitated enterprises in tracking social media traffic & engagement, but also tap potential opportunities to better connect with their customers. The cloud allows data analysis to gain accessibility and enables employees in an organization to remotely access company information. This frees them from being chained to the local network and makes data more accessible.
Persistent Challenges in Cloud-based Data Analytics
The trend of moving data analytics to a cloud-based platform continues among enterprises. Cloud environments for data analytics are easier to use. The degree of infrastructure management needed is relatively lower and the cloud enables to automatically scale up and scale down required resources, which further reduces upfront costs. However, significant challenges exist in the successful implementation of data analytics infrastructure in the cloud.
The Need for Manual Coding: Enterprises need the manually-written code for implementing data analytics in the cloud. Various problems are involved with the coding. It is error-prone and difficult to troubleshoot. Such coding lacks lineage and is not portable across fabrics of big data. The coding becomes more challenging to support and enterprises end up spending greater time fixing old data pipelines.
The Gap Between Production-ready and Ad Hoc Data Pipelines: Operationalizing data pipelines to run repeatedly with fault tolerance and ceaseless monitoring is as tedious in the cloud as on-premises. The need for either purchasing or building capabilities to manage the data pipeline production at scale exists, which is a key challenge to overcome.
The Portability of Data: According to Gartner, nearly 80% of companies bank on multi-cloud environments along with the on-premise data infrastructure. The synchronization and migration of data from on-premises to the cloud are complicated, with complex issues such as compute platform differences, format inconsistency, poor backward compatibility of cloud compute platforms. As none of these problems are trivial, measures must be sought and taken effectively.
Management of Hybrid and Cross-Cloud Deployments: Enterprises have to deal with issues related to data replication across cloud environments. There is the need for a two-way incremental replication for keeping multiple cloud clusters in sync and maintain the operational state.
The Opportune Potential of Cloud-based Data Analytics
Enterprises across geographies have expressed their interest in shifting their data analytics infrastructure to the cloud. The primary objective here is to leverage ML and AI. Studies have shown that several organizations use services from all 3 major cloud providers GCP, Microsoft Azure, and AWS, for some portion of their data analytics infrastructure.
It’s not a surprise that effective data collection & processing at scale has become an essential necessity, given the importance of access to data in real-time for developing, training, and provisioning business models to modern enterprises. According to an IDG Cloud Computing Survey, data analytics/data warehousing is considered to be the second-largest cloud computing adoption enabler.
The reasons why data analytics is the fastest-growing opportunity for the cloud are the changing approach of SaaS companies in improving their customer experience, differentiating their products & services, and optimizing marketing spend. It has now become of paramount importance for enterprises to include machine learning and analytics in their roadmap. New growth hacks such as leveraging cloud-based data analytics will give enterprises innovative ways to boost revenue while scaling faster.