What You Should Know About Cloud Solutions For Real-Time Analytics

CEO of Cleveroad. Evgeniy is a specialist in software development, technological entrepreneurship and emerging technologies.

Experts from all industries admit the need to use and analyze data, especially generated in real-time. Therefore, business decision-makers need to know how these processes occur, under whose control they are and how to optimize them. But what technologies can help companies better cope with masses of data?

Let's look at the trends: The results of an IDC study predict that IT investments will grow at a CAGR of 15.5% between 2020 and 2023, and a significant share of digital transformation will be in the cloud. In fact, cloud technology has been referred to as the new "center of gravity."

Cloud analytics showcase the remarkable flexibility, functionality and speed essential for today's businesses. These advantages are preserved in processing historical and (especially) real-time data, allowing companies to react to changes almost instantly and improve decision-making quality. Let's take a closer look at cloud real-time data analytics.

Trends In Cloud Data Analytics

I've seen the cloud being increasingly used in industry solutions for real-time data analytics. It used to be complex and inaccessible due to the procurement process and high infrastructure costs. Today, cloud solution vendors provide their capacity, technical support and equipment, making real-time data analytics available to most businesses.

The distribution of cloud services to various physical locations is a service that increasingly more vendors provide. According to Gartner's analysts, by 2025, most cloud platforms will offer distributed cloud services to customers while being responsible for operations, management and development. Their services enable businesses to simplify and improve real-time analytics by:

• Increasing network bandwidth.

• Speeding up data analysis to just minutes in some cases.

• Providing access to large amounts of memory, high performance and other benefits.

Why The Cloud Is A Good Choice For Real-Time Data Analytics

A real-time data processing solution can be created in your own data center on physical servers. But this approach has some drawbacks. For one, businesses need significant IT infrastructure capabilities, but it's difficult to predict how much power you'll need. At the same time, the storage capacity must be able to grow. Finally, maintaining a data center can become very expensive.

The cloud allows businesses to solve these problems. Its key benefits for real-time data analytics are:

• Cost savings: Cloud data analysis is relatively inexpensive, especially if the workload is unpredictable and the company proliferates. Clouds are economically beneficial for the initial stage of working with big data, conducting experiments and testing technologies. Companies can launch solutions more cost-effectively, even if the requirements are strict.

• High speed: The cloud is well suited for accelerating time-to-market, fast hypothesis testing and ad hoc analytics. You can prototype a solution quickly, develop it iteratively and gradually increase resources. The high throughput allows companies to respond to changes rapidly, make informed business decisions and remain competitive.

• Scalability: With the cloud, you can reach as many data analysis resources as you need. It also provides flexibility in settings and storage options. Companies can consolidate data at any scale with a corporate data warehouse. Clouds have a higher capacity than a physical server with almost no data storage limit, so you'll always have enough storage space.

• Security: Cloud providers have done a lot to ensure that their solutions’ security level is not inferior to on-premise systems. The separate storage of data, code and business intelligence in the cloud allows businesses to reduce the risks of data loss and exclude corporate network access while sharing analytics with partners, helping ensure their information stays secure and controlled.

Cloud Analytics In Practice: Things To Consider

Cloud analytics provides companies with systems that offer a competitive advantage. However, implementation is often more complicated than it initially seems. To make the transition as painless as possible, you need to answer some questions in advance:

• Who will supply data storage and processing subsystems? If a single vendor offers all the solutions, the analytical platform will likely be more stable, predictable and efficient. It's also good if the provider has integration partners to help with implementation and the initial configuration.

• Should I choose a private or public cloud? For example, if a company has a predictable amount of data processing, the hardware, infrastructure and personnel costs to deploy a private cloud will pay off. Public clouds use a pay-as-you-go model (for the used leased capacity). Their low entry threshold and maintenance costs will suit most small businesses and new projects.

• Who will have access to the analytics? If the list of users includes employees without special skills, it's necessary to provide them with understandable reports and visualizations. To grant individual access rights to data for different employees, you’ll need a solution with role-based access control (RBAC).

You also need to determine the possible workload and latency. The workload depends on the applications used, but some of them may not be prepared for the cloud. Exceeding the acceptable latency threshold negatively impacts apps' performance, analytics timeliness and, as a result, the customer experience.

Cloud Real-Time Data Analytics: Lessons For Businesses

Despite the attractive prospects and efficiency, real-time data analytics in the cloud still have limitations and peculiarities. Sometimes, it's more appropriate to use both local and cloud solutions; other cases require an entire transition from local analytical tools to cloud ones.

Businesses need to take a sober approach to cloud data analytics, not expect miracles and clearly see all the pros and cons. You have to ask yourself questions in advance — about data security, the cost-effectiveness of new solutions and a plan to move to them. And the answer to these and other questions lies in the cooperation between business leaders and the IT team in making technological and management decisions.

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