Three Key Benefits to Keeping Your Analytics On-Premise

Three Key Benefits to Keeping Your Analytics On-Premise

Data availability is growing as companies tap new sources of information and take advantage of technology tools that make it easier to aggregate that data. But the ability to generate meaningful business insight lags behind the ability to capture raw data.

Increasing velocity, variety, and volume now drives the need for analytics solutions capable of identifying critical relationships, distilling key trends, and delivering effective strategies. As noted by research firm McKinsey, however "converting knowledge into action is easier said than done."

To bridge this insight gap and to empower data-driven decision making, organizations have two choices: Deploy on-premises analytics or shift these solutions into the cloud.

For many companies, the cloud seems the obvious choice: Adoption remains strong as companies recognize the value in shifting complex infrastructure support and on-demand service access away from local stacks. According to a recent report from Deloitte, 93 percent of organizations have already implemented or are considering cloud services.

Despite the generalized gains of cloud computing, however, there are advantages to on-premise data analytics. Here are three key benefits to consider for leveraging local solutions to boost your analytics firepower.

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1. The Cost of Doing Business

Cost reduction is often cited as a key advantage of the cloud. But, as noted by recent ZDNet report, 64 percent of businesses pointed to cloud cost control as their top priority. Moreover, according to Rackspace CEO Joe Eazor: "People have been somewhat surprised as they expand services that the cost of cloud computing is not as cheap as they were expecting."

Part of the problem is scaling. New cloud instances are easily spun-up, and just as easily forgotten, increasing total spend. Supply and demand is also potentially problematic. As the total compute power needed to handle intelligent analytics increases and cloud providers look to maximize revenue, costs can become unpredictable.

On-premise solutions, meanwhile, are bound by local stacks. Companies own their hardware and IT teams know exactly how much it costs to run. Of course, deploying powerful analytics tools capable of auto-generating insights and simulating specific actions require in-house resources. These include in-depth understanding of their Intranet, server stack and software interactions on-site allows companies to align compute use with budget expectations.

2. The Certainty of Security

Data security is essential for your company's analytical success. Failure to meet legislative and industry requirements for data handling, storage, and strategy development can result in both monetary fines and reputation damage.

This focus on security is especially critical for companies in highly regulated industries such as finance, law or healthcare. As noted by The 2019 State of Healthcare Report, healthcare cyberthreats are on the rise. Trojan malware attacks, for example, increased 82 percent in Q3 2019 compared to the previous quarter.

Measure-for-measure, cloud-based resources can often outperform local servers, but there's a disconnect: Trust. According to Forbes, despite these advantages, business intelligence (BI) adoption "continues to linger at approximately 30 percent" as cloud security concerns outpace performance gains.

While the overall state of cloud defense continues to improve, specific data protections are often lacking as IT teams trade granular control for general resource gains. On-site, meanwhile, it's possible for organizations to deploy VPNs, advanced firewalls and per-application access controls. If necessary, teams can also isolate data on servers with no access to public-facing Internet connections, or completely segregate information on machines without Intranet access.

3. The Role of Reliability

Cloud providers promise reliability — and in general, they deliver. But the nature of this reliability isn't governed by hardware. Instead, service-level agreements are the foundation of service uptime. As a Computer Weekly report noted, the frequency and severity of datacenter outages is on the rise as cloud-based workloads become more complex.

This leaves companies in a difficult position. While SLAs detail downtime remediation and recovery time objectives (RTOs), unplanned service interruptions can disrupt analytics in progress. In the best-case scenario, your company would lose time and have to pick up where it left off. Worst-case, you would be forced to start from scratch.

Keeping analytics on-premise solves the biggest challenge of reliable service: predictability. In the cloud, sudden bandwidth limitations as resource balances shift or unexpected power outages frustrate the continuity of data analysis. In-house, IT teams can deploy backup generators and redundant power sources to reduce the risk of failure. And by avoiding connected services in favor of Intranet-based solutions, you can also sidestep the issue of ISP disruption.

Finding Your Best Fit

Despite the rush to adopt cloud services for everything from software to storage, data backups and analytics, there's a case for keeping analysis in-house to help control costs, improve security, and enhance reliability.

When it comes to maximizing analytics impact, however, integration provides better outcomes than isolation. While all-cloud deployments are subject to unexpected outages and cost fluctuations, entirely on-premise solutions are at risk of sudden hardware failure or internal network disruption. Best-fit analytics tools will provide you with both cloud-native support and on-premises performance.

By combining the data capacity of the cloud with the direct control of local stacks, companies can increase their analytical ability and empower actionable insight

To learn more about how an AI powered automated analytics software can keep your data safely on-premise and supercharge your strategies, request a one-on-one demo!

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