6 Ways Cloud Analytics Is Better, Faster, Cheaper

Lance Weaver
6 Ways Cloud Analytics Is Better, Faster, Cheaper

Analytics is one of those business functions that is a perfect fit for public or hybrid clouds. Companies are turning to cloud-based analytics for easier access to increasing amounts of data, greater data sharing and collaboration, faster insights and time to value, and to reduce operational costs. IDC’s report, “FutureScape: Worldwide Big Data and Analytics 2016 Predictions,” estimates that through 2020, spending on cloud-based big data and analytics technology will grow 4.5 times faster than spending for on-premises solutions.

The components of the data analytics process that can be found in the cloud include the following:

  • Usage-based compute and storage resources
  • Structured and unstructured data sources, such as hosted data warehouses and repositories
  • Data models and complex event processing applications
  • Analytic models and business intelligence (BI) tools
  • Collaboration applications for sharing results
  • Enterprise information and performance management
  • Governance risk and compliance solutions

Here are just some of the reasons why cloud analytics is so appealing to today’s enterprises:

  1. The Cloud is Where the Data Is – If you’re looking to merge and analyze internal and external data from social media, third-party data subscription services and other sources in real or near-real time, then the cloud is where you’ll find it. The Cisco® “Global Cloud Index (2015-2020)” projects cloud traffic to rise 3.7-fold, up from 3.9 zettabytes (ZB) per year in 2015 to 14.1 ZB per year by 2020.

Squeezing high volumes of structured and unstructured data into an on-premises data store for processing and analysis is not the most agile or cost-effective solution for the enterprise, given increasing cycle time for new deployments, as well as increasing storage, power, cooling and management costs. More flexible cloud storage and processing resources can help alleviate the enterprise data overload dilemma and make it infinitely easier to analyze and gain insights from that data faster.

  1. Performance and Scalability – Many in-house analytics platforms suffer from serious performance and scalability issues, either due to a lack of an efficient data warehouse, ineffective, high-latency connections to data (typically over the public internet), or a lack of expertise and tools to quickly handle the huge, pent-up demand for real-time BI and answers to ad hoc queries. Out of all of these challenges, latency can have the most significant, long-term impact on analytics performance and scalability. By deploying direct interconnections between data and analytics in the cloud, you can reduce latency to single-digit milliseconds. And when you suddenly need more scale and processing power, it is there (in the cloud) for the taking, and you only pay for what you need, when you need it.
  1. Time to Value – There’s no question that putting together a workable analytics platform is a significant, time-consuming undertaking, even for a large IT department. In the cloud, all the data processing and analytics setup (and redundancy) is done for you, and you can start gaining more timely and reliable data-driven insights and making more informed operational and product decisions right away. And when you need analytics for a mission-critical application, such as resolving a sudden network or data security breach, you can get that up and running quickly.
  1. Collaboration – The Harvard Business Review “2015 Analytic Services” report found that 72% of IT executives see collaboration as a top driver of cloud adoption. Analytics, an extremely collaborative function, tends to work better in the cloud due to its ready access to data and processing and BI applications. It can also include capabilities such as shared data and visualization and cross-organization analysis, which make the raw data and resulting information more accessible to a broader, more distributed user base.
  1. Security – That’s right, security. You may be reluctant to start putting data in the cloud, but the reality is that most big data cyber breaches impact organizations’ on-premises systems, which often do not have the same security robustness as cloud services. Reported data breaches against cloud-based analytics services have been much lower than on-premises systems and they become even less likely if the data access between users, applications, analytics and clouds is going over direct and secure interconnection, bypassing the public internet.
  1. Low Maintenance and Costs – Analytics platforms tend to require frequent upgrades, migrations, redesigns and other ongoing maintenance. Placing your data analytics resources and capabilities in the cloud can ensure that everything is and will continue to be up-to-date. Costs can also be more easily managed if the data and analytics resources are being accessed on a cloud usage pricing model, rather than over-provisioning IT infrastructure for peak consumption.

Most enterprises are more comfortable with hybrid cloud data and analytics infrastructures where they can leverage the scalable resources of the public cloud, while still ensuring privacy and control for more sensitive workloads on a private cloud or on-premises infrastructure. Leveraging a global colocation and interconnection platform and harnessing solutions such as the Equinix Data Hub and Cloud Exchange that provide direct and secure connectivity among on-premises and cloud infrastructures accelerates access to distributed data and cloud analytics resources at the edge, close to where the data is created.

Read the Equinix Data Hub Solution Brief to learn how your organization can harness the many ways that cloud analytics can be a better, faster and cheaper solution for gaining valuable insights into your business operations and customers.

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Lance Weaver Former Vice President, Platform Strategy and Emerging Services at Equinix.
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