What is cloud computing?
First, to examine how cloud analytics work, you have to start with a cloud computing model. Cloud computing is the delivery of computer services via the internet. The “cloud” is a metaphor for the many clusters of computers that make up internet infrastructure.
In the cloud computing model, organizations can rent the IT infrastructure and services they need on-demand from a cloud service provider instead of purchasing and operating their own data centers. These services include everything from essential infrastructures like networking, servers, storage, databases, and software to advanced tools such as artificial intelligence (AI) and machine learning systems. Cloud computing allows organizations to reduce costs and increase productivity, as they aren’t required to purchase and maintain as much of their own equipment. It also makes it easier to scale resources according to variable business needs. And because these services are centralized remotely, they are accessible from any web-enabled device.
As the name suggests, cloud analytics systems must be hosted on an internet platform. In most cases, they are run on state-of-the-art data centers that can provide the processing power and storage space needed for analyzing massive amounts of data.
In cloud analytics systems, all generated data is collected and securely stored in the cloud, where it can be accessed from any internet-connected device. The cloud analytics system can then clean, organize, process, and analyze the data using proprietary algorithms. These insights are presented to the user through different data visualizations and other intuitive formats.
Each cloud analytics solution comes with its own particular set of features, but all solutions have several common components. According to Gartner, referenced in this article, these include the following:
- Data sources: These are the various sources from which your business data originates. Common examples include web usage and social media data, as well as data from CRM and ERP systems.
- Data models: A data model structure retrieves data and standardizes how data points relate to each other for analysis. Models can be simple — using data from a single column of a spreadsheet, for example — or complex, involving several triggers and parameters, in multiple dimensions.
- Processing applications: Cloud analytics uses special applications to process huge volumes of information stored in a data warehouse and reduce time to insight (more on this below).
- Computing power: Cloud analytics requires sufficient computing power to intake, clean, structure and analyze large volumes of data.
- Analytic models: These are mathematical models that can be used to analyze complex data sets and predict outcomes.
- Data sharing and storage: Cloud analytics solutions offer data warehousing as a service so that the business can scale quickly and easily.
In addition to these features, AI is becoming a more integral part of cloud analytics. Machine learning algorithms, in particular, enable cloud analytics systems to learn on their own and more accurately predict future outcomes.
What are the benefits of cloud analytics?
Cloud analytics comes with many advantages for the enterprise. Here are a few benefits with the biggest impact on your business.
Big data produced from numerous, disparate sources across the organization makes it nearly impossible to get a unified view. Cloud analytics brings all of a company’s data sources together to produce a more complete picture. All stakeholders, regardless of their physical location (or the data’s location), can easily access this data in one place, to gain more accurate insights and make better business decisions in real time.
Big data siloed in individual departments such as Finance or Human Resources affect the whole business. A cloud analytics solution can better integrate the data from different parts of the organization — subject to configurable role-based access controls — leading to better communication and decision making.
When workloads and data volumes grow rapidly, administrators running on-premise platforms have to purchase and install new hardware to accommodate the rise in demand —a service model that often leads to over provisioning and expenses that can seem unnecessary if demand falls in the future. With cloud analytics services, organizations can scale up to accommodate spikes in demand by bringing more instances online (or reducing them when demand dips) and paying only for what they use.
In addition to the costs of the various hardware requirements, on-premise platforms need frequent upgrades and migrations, invariably leading to system downtime affecting business continuity. On-premise analytics also necessitate specialized skill sets that some organizations don’t or can’t afford to have in-house. With cloud analytics, organizations aren’t required to purchase and support additional hardware, and can also avail the in-house expertise of service providers.
Security monitoring is usually just one of the many areas that an organization’s IT staff is responsible for, but it’s a full-time focus for cloud hosts. Cloud analytics providers also use robust encryption to secure data as it is transmitted over networks. But the biggest security advantage they offer may be simply that the data is stored offsite: A recent report found that 34 percent of all breaches happened as a result of insider threat actors, including current and former employees who take classified or proprietary information with them when they leave the company.
What is a data warehouse?
A data warehouse is an electronic system for storing aggregated data from many different sources within an enterprise for analysis and reporting. Data warehousing is usually offered as part of a cloud analytics platform.
To create a data warehouse, data is compiled from an organization’s various sources and “cleaned” — a process through which corrupt, inaccurate, incomplete, improperly formatted, and duplicate records are corrected or removed. After the data is cleaned, it’s converted from a database format, which is designed for transaction processing, to a data warehouse format, which is designed for query and analysis. Once in the warehouse, the data is sorted, consolidated, correlated, and otherwise processed so it can be compared and analyzed. Data is continually added as the various data sources are updated so the data store remains current.
What is business intelligence (BI)?
Business intelligence — or cloud BI — is a solution that cloud analytics providers commonly offer through a software as a service (SaaS) model. In this context, BI refers to the tools and technologies that are used for the collection and parsing of business data. BI encompasses a number of processes including online analytical reporting (OLAP), data and text mining, predictive and descriptive analytics, and performance benchmarking. BI software gathers and analyzes relevant data from a data warehouse and produces easy-to-understand reports and data visualizations. BI works together with data analytics to help businesses optimize performance and make better business decisions.
While both traditional and modern business intelligence solutions arm decision makers with complete and accurate information for better decision making, their methods greatly differ. Traditional business intelligence, which is still used by many companies, is IT-driven and relies on specially trained data scientists or data analysts. A department submits a query to a report queue, and the data specialist creates a static report, which could take days or weeks to build based on historical data and past results. As a result, reporting cycles can be slow while also lacking current data.
Modern BI is far more nimble. Users from any level of the organization can access the real-time data they need and quickly generate sophisticated reports with little technical expertise. Web-based dashboards allow users to explore data more freely and take different approaches to a business question, freeing IT to focus on other core business concerns.
BI and cloud BI can be used to inform virtually any business decision. Some of the most common uses include:
- Analyzing customer behavior. BI can help organizations determine why a particular customer strategy did or didn’t work, identify behavior patterns, and more.
- Developing revenue strategies. BI can help organizations develop revenue growth strategies by identifying ideal customers, providing insight into purchasing decisions, and helping determine what will drive conversions.
- Uncovering business problems. BI software often integrates with financial software, allowing businesses to view data from a variety of angles and detect problems that might otherwise be missed.
- Optimizing performance. BI allows organizations to track goals such as sales targets and project deadlines, identify reachable goals, keep teams on course, and alert managers when goals are near completion.
How does cloud analytics differ from event analytics?
The most fundamental difference between cloud analytics and event analytics is that cloud analytics run exclusively in the cloud whereas event analytics can refer to software run anywhere, either in the cloud or on-premise. Further, cloud analytics is an umbrella term that refers to analytics applied to any number of business operations including sales, marketing and IT. Event analytics can come under this umbrella but refers specifically to a computing process that addresses the resolution of IT events and incidences.
Event analytics is the latest generation of event management, consolidating multiple event management systems into a single, centralized platform and automating much of the triage process. Anomalous events are easier to ferret out and resolve, requiring less human interaction.
A cloud-based approach to event analytics offers many advantages over an on-premise one. Because much of the infrastructure is supplied by the service provider, an organization’s costs are significantly reduced. The software is also easier to install and manage and is automatically updated, freeing IT staff to devote more attention to identifying and resolving network issues.
How do you choose the best cloud analytics platform?
Not all cloud analytics platforms are created equally, so it will pay off in the long run if you take time to identify your organization’s particular needs before making a purchasing decision. Some key factors to consider include:
- Scalability. A good cloud analytics platform will be able to accommodate your growing business and its data needs. Flexible pricing plans will allow you to pay for only the resources you use.
- Security. While nearly all cloud providers encrypt data as it’s moving over a network, many do not secure data when it is just sitting in storage. Look for a platform that encrypts data both “in transit” and “at rest.”
- Real-time integration. Your cloud analytics platform should integrate in real time with your organization’s other systems so your company can stay up to date without any extra efforts.
- Analytics features. Every company is guided by unique business metrics. Look for a cloud analytics platform that is capable of calculating yours.
- Responsive interface. Data analysis is increasingly run on mobile devices. Make sure any cloud analytics platform you’re considering isn’t going to bog down on users’ smartphones and tablets.
What are some challenges when leveraging multiple cloud analytics platforms?
When it comes to the cloud, businesses are increasingly deciding that more is better. Trends around multi-cloud (the use of multiple cloud providers), and hybrid cloud (the combination of private and public cloud infrastructures) are both growing. Businesses opt for hybrid cloud as a means of balancing workloads — using the public cloud for computing or storage spikes, for example. They choose multi-cloud when different providers meet different business needs. Both trends, however, come with their own sets of challenges.
One of the most significant challenges for both environments is security. A private cloud consolidates data security within the organization, but that changes as soon as you move some or all of that data to a public cloud and the organization has to manage two security platforms. Security issues are more starkly obvious in a multi-cloud environment because the organization has multiple security platforms to manage without any control of their security processes or policies.
Data governance and compliance also become more challenging in these cloud environments. It becomes more difficult to know where data is located, particularly in a multi-cloud environment, and can easily lead to regulatory compliance violations that put your business at risk. It’s critical that IT has the proper tools available to monitor these environments and meet the organization’s specific regulatory requirements.
The Bottom Line
Your business is churning out an extraordinary amount of data every day. Cloud analytics presents an irresistible opportunity to consolidate that data and turn it into actionable intelligence while simultaneously reducing procurement and maintenance costs. The key is to determine your business needs ahead of time so you can maximize the cloud analytics platform of your choice. The insights you need to give you a competitive edge and move your business forward are in front of you. Cloud analytics gives you a way to put them at your fingertips.
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