Companies on the digital transformation journey are often found adopting big data technologies to make sense of the ballooning volumes of data being generated every day. Big data helps organizations in overcoming operational bottlenecks and improving business efficiency and it also allows them to focus on improving their sales and growing their business. But relying on on-premises systems to carry out big data analytics is not going to take organizations far. Instead, using Cloud as a foundation to leverage the resources and services needed is a more efficient way of unearthing insights from data every day – thus making big data technologies accessible and affordable to every enterprise.

Big data and Cloud – The perfect union

The volume, velocity, and variety of data that needs to be analyzed daily are swelling with each passing day. To process and analyze this data and extract timely insights from it, you require massive amounts of storage – which on-premises systems, unfortunately, fail to offer.

Relying on on-premises systems will compel you to either constantly carry out infrastructure upgrades, add more capacity to your existing data warehouse, or power up additional servers to cater to your rapidly growing analytics requirements. Regardless of what course you choose, your infrastructure eventually will not be able to keep up.

This is where the Cloud comes in, enabling you to process and analyze your big data faster – without storage issues – and thus leading to insights that can boost business performance and transform your organization.

With Cloud, you can

• Have a scalable and flexible environment in place to deploy data-intensive applications and power your business analytics
• Eliminate the need to set up massive servers or data warehouses and bring down CAPEX down to zero
• Simplify communication and collaboration within your organization, giving more employees access to the analytics they need to do their jobs
• Manage large spikes in data analytics requirements via readily available infrastructure and increased processing power
• Allow your analytics experts to focus on high-value work instead of wasting time on facility management and daily system maintenance and upgrades
• Encourage a culture driven by agility and innovation to improve operational efficiency and gain competitive advantage
• Ensure the underlying analytics infrastructure is running 24×7 – restoring almost immediately post a cyberattack, power outage, or equipment failure

Defining the right Cloud strategy for your big data projects

Using the Cloud as the foundation for your big data analytics projects can ensure continued access to the infrastructure needed to unearth vital big data insights.

Here are some tips that can help you define the right Cloud strategy for your big data initiatives:

1. Don’t overlook security: Although Cloud opens doors to unlimited storage and computing capabilities, the large datasets that need to be analyzed often contain sensitive information. Ensuring this data is safeguarded against attacks or misuse requires you to carefully evaluate security protocols, so you can have the required control and visibility over your data in the Cloud. Remember, security is always a shared responsibility between you and your Cloud provider; so, make sure to be clear about the terms and your roles and responsibilities before you embrace Cloud for your big data projects.
2. Limit access via least privilege: The Cloud makes it extremely easy for employees to get access to the data they need and when they need it. But such seamless access can also result in unauthorized use. Therefore, to protect the data that is being analyzed and stored in the Cloud, it is important to employ full authentication and authorization. Make sure to embrace the least-privilege tactics to restrict while also implementing multifactor authentication mechanisms at the group or the individual level – as needed.
3. Ensure the right level of compliance: Compliance is another concern that needs to be addressed when you use Cloud for your big data initiatives. To ensure you comply with all the required regulations, you must have clarity on where your data is going to be stored, who has access to it, and who is going to manage it. Make sure you know exactly where your data is going to reside and accordingly create SLAs with your Cloud service provider for compliance.
4. Address latency issues: Although Cloud enables easy access to computing and analytics resources, it also brings with it the problem of bandwidth and latency. Since the availability of these resources is highly dependent on network connections – which are highly prone to service interruptions – you need to ensure there is sufficient bandwidth to analyze and process your big data. You can either choose dedicated network connections to overcome latency issues, or even opt for edge computing to enable analytics at the edge.
5. Be cognizant of the costs: Embracing Cloud for your big data projects, although extremely beneficial, can also prove to be a costly endeavor. With Cloud providers making it so easy for users to spin up resources as and when required, the chances of costs overshooting budgets are extremely high. Therefore, it is important to limit the number of Cloud instances to a bare minimum while also keeping an eye on who can furnish these new resources and for what purposes.

As companies accelerate their digital transformation efforts to keep up with disruptions from conventional and unconventional frontiers, they are quickly realizing that the only way to drive value is by leveraging massive volumes of data. Ramping up the ability to make better business decisions in real-time is the need of the hour, which is why the shift to big data in the Cloud isn’t surprising. Given the numerous benefits the powerful combination of big data analytics and Cloud computing can bring, it is time to embrace the two technologies and change the way your organization does business and achieves its objectives.