What is Cloud Analytics?

Cloud analytics refers to a service and delivery model for the hosting that deals with the analysis or computation of business data using cloud technologies. These technologies handle the storage and or processing of data. Cloud analytics may include any type of data analytics or business intelligence that is performed in the cloud, perhaps one or several components of a company’s analytics. Some companies implement a hybrid cloud analytics model that reserves some functions to be performed in cloud-hosted environments while others utilize on-premises servers. Other companies are moving full to the cloud in order to scale their analytics program as their business grows. This also takes away the load of traditional performing analytics only on-premises, which can be costly to manage across an organization. Cloud analytics has become increasingly popular and a top concern for most modern businesses today. With promises of increased productivity and reduced operating costs, cloud migration is shooting to the top of many organization’s to-do lists. If you haven’t started your cloud journey, chances are you will soon.
Cloud Analytics process diagram
Big data analytics and business intelligence (BI) have been forever changed by the cloud revolution. With an ever-growing tide of data from countless digital sources, the cloud allows that data to be gathered and analyzed in one place to then be shared out across an entire organization. Now everyone can access the same trustworthy data from the many disparate data sources available today.

6 Key Elements of Cloud Analytics

    Cloud analytics may include combinations of any of the following elements in the cloud. Most successful cloud analytic strategies cover a majority of them which are all supported by today’s most popular cloud computing vendors, including Amazon Web Services (AWS), Google Cloud, and Microsoft Azure.

    • Computing Power: The first step of implementing cloud analytics is ensuring that you have the required raw computing power to ingest, structure, and analyze data at scale.
    • Data Sources: A powerful cloud analytics solution must be able to capture data from various data sources, including company websites, ERPs, CRMs, social media platforms, the Internet of Things (IoT), mobile apps, and more.
    • Data Models: Using data models to move data from on-premises to the Cloud can help minimize business disruption. Cloud-based data models standardize how elements of data relate to one another and determines the structure of the data.
    • Processing Applications: There must be applications in place that can process the large data sets coming in from different systems. Organizations need a data processing framework for their cloud-based environments. Apache Spark, Google BigQuery, and Hadoop are some options for developing such applications.
    • Analytic Models: You also need to develop models for predictive analytics and other advanced analytic functions to run in the cloud.
    • Data Sharing: Cloud analytics solutions should also support easy data sharing and storage through a modern cloud architecture.

    3 Types of Cloud Analytics

    There are three different options for cloud computing and cloud analytics, detailed below:

    Public Cloud: A public cloud is a type of cloud computing where services are available publicly through a third-party as virtual machines, storage capabilities, applications, and more. They are often offered for free though sometimes users pay for usage or consumption. On this public cloud, IT systems are shared and data is kept private, allowing for reduced costs and less upkeep for an organization.

    Private Cloud: Instead of being offered to the general public, a private cloud is reserved to select users of one organization. It offers the same advantages as a public cloud, scalability and democratized access, but is located at a data center owned by that single organization or a hosting service. While this provides greater privacy and data security, it can often be much more expensive.

    Hybrid Cloud: The third option is a combination of the two. Organizations that opt for a hybrid cloud structure use the public cloud for non-sensitive data while maintaining a smaller private cloud for data meant only for that company’s eyes.

    What are the Benefits of Cloud Analytics?

    Scalability: Analytics in the cloud allows companies to scale their analytics and avoid the expenses associated with on-premises data storage. With cloud computing, you can add data storage and data analysis capacity as needed to reflect changes in the business. An organization can easily increase its cloud storage when the business grows or decrease if the business slows down, which is a much simpler process than purchasing hardware. It allows the business to be responsive to new demands and quickly adjust capacity of its analytics to meet consumer needs and seize opportunities as they come.

    Streamlined Operations: Cloud analytics can also enable businesses to streamline their business intelligence processes and more efficiently uncover insights to make smarter business decisions. With more agile processes, users can more easily ask new questions of the data on demand. This can help key decision-makers explore the data, collaborate across teams, and more strategically leverage data to gain a competitive advantage.

    Easy Access and Collaboration: Without cloud analytics it can be difficult to gain a comprehensive view of a company’s data, limiting opportunities to work together from different locations. Cloud analytics can help consolidate a company’s data from disparate sources and models can be updated in real time. Cloud-based workflows and file sharing applications give different teams the ability to work together more easily and efficiently—perfect for global companies. With easier access and consolidated data, employees can share files and collaborate in real time from anywhere.

    Security and Governance: It’s also important to note the security and governance benefits of cloud analytics. Cloud analytics provides more granular control of data access, increased auditing capabilities, and a single source of truth when it comes to understanding a company’s data. Cloud storage of analytics can also prove useful in safeguarding information during natural disasters and other emergencies.

    Reduced Costs: Where in-house, on-premises analytics solutions can become costly fast, cloud analytics does not require hardware, equipment, data centers,or continuous upgrades. This can result in significant savings and allow for a more flexible budget with simple subscription models.

    Considerations for Deploying Cloud Analytics

    If you are thinking about deploying data analytics in the cloud, consider the following questions in determining the best approach:

    • What is your usage pattern? Your current usage pattern can be used to determine what you need to build your cloud environment for scalability. Keep in mind that you can scale both up and down as needed.
    • What are your plans for data privacy and security? Consider what security model you need for every layer, including the app layer, data storage, OS, and network.
    • Do you have a strategy for high-availability and disaster recovery? If so, what will it consist of?
    • Where do you want to host your solution?