Tavant Logo

Why Cloud and Data Analytics go hand in hand?

Share to

As per Gartner, adoption of data and analytics will increase from 35% to 50% in 2023, driven by industry vertical and domain-specific augmented analytics solutions. By 2024, 75% of organizations will have deployed multiple data hubs to drive mission-critical data and analytics sharing and governance. The research also highlights that nearly 70% of enterprises will use cloud and cloud-based AI infrastructure to operationalize AI systems for their businesses over the next two years.

Cloud adoption has significantly accelerated post-pandemic, with enterprises increasingly focusing on the digital transformation across their business functions. One of the critical drivers in cloud adoption is the onset of data-driven strategy across industries. Cloud has helped in the paradigm shift to implementing data and analytics solutions and fast-tracked the time to market for data analytics solutions.

 

Why Cloud and Data Analytics go hand in hand?

 

recent survey by IDG Research and Tavant indicates that data analytics is a key focus area for organizations across industry verticals in the USA, where enterprises are looking at leveraging the cloud to implement data-driven systems. More than 80% of C-level survey respondents plan to leverage the cloud to drive enterprise data analytics. Thus, it is evident that cloud technology is pivotal in driving faster data analytics adoption, the emergence of next-gen SaaS products, and modern-day cCloud data platforms.

Role of Cloud in Data Analytics:

With the wide range of solutions focused on infrastructure and data analytics-specific services, the cloud has acted as a catalyst in driving the adoption of Data analytics. Today, Cloud Service Providers (CSPs) are accelerating the data analytics adoption with broadly two service offerings;

  1. Infrastructure services – The fundamental cloud Compute and Storage solutions help organizations implement custom solutions faster and address scalability challenges. The mere availability of computing and storage faster elasticity has enabled enterprises to adapt quickly. Modern data platforms also leveraged infrastructure services in providing cloud-agnostic services.
  2. Data Analytics services – CSPs are leading cloud providers to provide data-specific services to build Cloud-native data solutions. Examples are Hadoop on Cloud as PAAS – AWS EMR, Azure HDInsight, GCP Dataproc, and the related services to create a complete data solution. The CSPs will glue these cloud components together to build custom solutions for future enterprises.

 

Cloud-enabled Data Analytics solutions

As organizations embark on building complex data solutions, the cloud becomes an integral component of the data architecture. Understanding the various alternatives helps select the right technology based on business context.

Below is the broad category of cloud-driven solutions.

  1. Cloud infrastructure-focused data solution 
    • The solution leverages cloud infrastructure services to deploy data analytics solutions faster. These solutions are most suited for companies that need to rehost existing data solutions from an on-premises environment to the cloud or build a custom solution from scratch.
    • Examples include AWS S3/Azure ADLS/GCP cloud storage as the data lake and various computing services by AWS/Azure/GCP
  2. Cloud-specific data solutions 
    • These solutions leverage cloud-native data services to build data analytics solutions. The data services and pre-built integrations across different cloud services are helpful for enterprises and CSPs to co-create custom solutions faster with data privacy and security needs.
    • Examples include EMR, Kinesis, S3, from AWS, HDInsight, ADLS, NoSQL databases, Stream Analytics from Azure, and Dataproc, Storage, NoSQL DBs, Pub/Sub from GCP.
  3. Cloud Datawarehouse
    • Cloud-native Datawarehouse solutions by CSPs help to deploy enterprise-grade Datawarehouse faster.
    • Examples include AWS Redshift, GCP BigQuery, and Azure Synapse analytics, which have pParallel processing, pre-built integrations for ingesting data, and AI/ML capabilities.
  4. Modern data platform on cloud -Modern Cloud-native data platforms like Databricks and Snowflake focus on building a single platform addressing the needs of Data Analytics.

 

As cloud and data analytics drive the adoption of each other, it is imperative to understand the mutual dependence and leverage it while planning for cloud adoption or data analytics solutions within the organizations.

Tags :

Related insights

  • All Posts
  • Article
  • Awards & Recognition
  • Blog
  • Brochures
  • Case Studies
  • Fintech
  • Insights
  • News
  • Stories
  • Testimonials
  • Uncategorized
  • Whitepaper
  • All Posts
  • Article
  • Awards & Recognition
  • Blog
  • Brochures
  • Case Studies
  • Fintech
  • Insights
  • News
  • Stories
  • Testimonials
  • Uncategorized
  • Whitepaper

Let’s create new possibilities with technology