Select Page

5 Technical Reasons for a Cloud Analytics Migration

Author: Tom Hoblitzell | | July 7, 2022


 

The trends are clear: more and more companies are adopting cloud analytics to satisfy their increasing need for cutting-edge business insights.

 

For example, the global cloud analytics market size was $19.04 billion in 2020. The market is projected to grow from $22.84 billion in 2021 to $86.15 billion in 2028 at a growth rate of 20.9% during the 2021-2028 period.

Meanwhile, in an informal survey of attendees at a recent Datavail webinar, the majority (75 percent) of attendees said that their organization was pursuing a “hybrid” (partly on-premises and partly in the cloud) strategy for business intelligence and analytics.

There are many explanations for why businesses of all sizes and industries are shifting to cloud analytics. Some of the most convincing reasons, however, are technical in nature, as organizations labor under the weight of their legacy analytics infrastructure. In this article, we’ll explore 5 very good motivations for your company to do a technical refresh by moving to cloud analytics.

1. Agility and scalability

Traditional on-premises analytics infrastructure is limited in terms of the agility and scalability that you can achieve. With finite hardware restrictions on computational power and storage, companies often struggle to adapt when facing unprecedented demand for analytics workloads. As a result, you may need to purchase more on-premises resources than you use on a daily basis for these “just-in-case” scenarios.

Cloud analytics, on the other hand, is highly agile and flexible. Users can freely increase their resource consumption on-demand when under heavy load.

2. Cost of ownership

On-premises analytics solutions often have a high total cost of ownership. The associated expenses include the necessary hardware (often above your daily requirements, as discussed above), software licenses, and support and maintenance obligations (whether from in-house staff or a third-party IT managed services provider).

Like most cloud offerings, cloud analytics is generally less expensive than its on-premises equivalent. Users can leverage the cloud’s “pay as you go” business model, rather than making large capital expenditures on a recurring basis. In addition, the cloud provider is responsible for support and maintenance, removing a significant line item from your IT budget.

3. Unstructured data

Legacy analytics infrastructure was often designed for an era where the majority of enterprise data was structured (i.e. data that belongs in a row-column tabular database). Today, however, businesses are consuming and analyzing more unstructured data than ever before: information and files such as text, audio, and video that can’t be easily fit into this mold. Multiple studies estimate that unstructured data could make up as much as 80 to 90 percent of all information.

The best cloud analytics solutions offer cutting-edge methods of analyzing unstructured data and extracting the valuable insights it contains. For example, sentiment analysis software and other natural language processing (NLP) tools allow companies to understand the tone of social media posts that users are making about their business—from highly positive to highly negative.

4. High data volumes

Big data is often defined as the “four Vs”: volume, velocity, variety, and veracity. Working with unstructured data helps adequately address this data’s variety. But another of these challenges is of no less concern: volume.

Many aging legacy analytics solutions are not equipped to deal with 24/7 streams of data that need to be efficiently processed and mined for insights. Cloud analytics, on the other hand, can handle this data in real time or near-real time, allowing businesses to respond nearly instantaneously to emerging trends.

5. Artificial intelligence and machine learning

Last but not least, legacy analytics systems are often incompatible with the latest AI and machine learning techniques. Major public cloud providers such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and Oracle now offer a smorgasbord of artificial intelligence (AI) and machine learning (ML) services: speech recognition, image and video labeling, automated language translation, and more.

By moving to the cloud, businesses can start leveraging the latest ML and AI methods to gain a competitive advantage and better understand their customers.

How to Get Started with Cloud Analytics

There are many good reasons why companies want to switch to a cloud or hybrid analytics solution. To make this desire a reality, however, you’ll need a skilled, experienced cloud analytics partner like Datavail.

Thousands of clients have relied on Datavail as an IT managed services partner for help in a variety of domains, from analytics and business intelligence to databases and application development. We specialize in helping companies migrate to cloud analytics, with certifications including:

  • Microsoft Gold Partner (with 17+ years as a trusted Microsoft Partner)
  • AWS Advanced Tier Consulting Partner for Analytics
  • Oracle Specialized Partner for Business Intelligence

 

On average, our clients have partnered with us for more than 7 years, a testament to our high quality of work and excellent customer relations. The cloud data analytics services that we provide include:

  • Cloud readiness assessments of your IT hardware, software, and integrations
  • Roadmaps and strategic planning for minimal downtime
  • Total cost of ownership (TCO) analyses for the most cost-effective solution
  • Migrations and upgrades for your data warehouse or data lake
  • Integrations and connections for your on-premises and third-party data sources
  • Real-time dashboards and reporting for up-to-the-minute insights
  • Ongoing long-term support and maintenance

 

Thinking about upgrading your own legacy analytics infrastructure? Get in touch with our team of BI and data analytics experts today to discuss your goals and requirements. For more information about the benefits of cloud analytics for your business, check out our white paper “Journey to Cloud Analytics: Using the Cloud to Solve Your Analytics Challenges.”

Oracle BI Publisher (BIP) Tips: Functions, Calculations & More

Check out these BI Publisher tips including functions & calculations so you can understand more about the production and support of BI Publisher reports.

Sherry Milad | January 15, 2018

How to Index a Fact Table – A Best Practice

At the base of any good BI project is a solid data warehouse or data mart.

Christian Screen | March 16, 2010

Art of BI: How to Add Comments in Oracle BI (OBIEE)

Ultimately the goal of commentary in OBIEE is to have a system for persisting feedback, creating a call to action, and recognizing the prolific users.

Christian Screen | December 29, 2013

Subscribe to Our Blog

Never miss a post! Stay up to date with the latest database, application and analytics tips and news. Delivered in a handy bi-weekly update straight to your inbox. You can unsubscribe at any time.

Work with Us

Let’s have a conversation about what you need to succeed and how we can help get you there.

CONTACT US

Work for Us

Where do you want to take your career? Explore exciting opportunities to join our team.

EXPLORE JOBS