The term cloud has been widely used for over a decade, but whether everyone understands what it means is not clear. “The Cloud” is one of the most commonly misunderstood terms in history. Most people conflate the cloud with the internet. Though it has been 14 years since it became a big trend, even people in the tech industry still get confused. Users might need the internet to access the cloud, but the two are not the same in any way. Some private clouds have nothing to do with the internet.
Traditionally, if you forget to save a document you are working on, and your computer crashes, your document cannot be retrieved. This is where the cloud comes in. If you are working on a report in the cloud, several computers are working on it simultaneously. Therefore, even if one them crashes, your work would not get affected. The user would not even notice the crash because the rest of the computers will pick up the slack.
Big Data in the Cloud
The big data industry is growing faster than ever. Wikibon’s market study found that the big data analytics market grew at 24.5% in 2017 from the year before. The rate at which it is growing is faster than predicted rates. The entire big data industry hinges around three major players – Amazon Web Services, Microsoft Azure, and Google Cloud. These cloud providers offer infrastructure services to customers and partners. Infrastructure services provide virtualized computing resources over the internet. As a result, pure data platform vendors such as NoSQL are becoming marginalized in this big data environment. Such vendors are being taken over by diversified public cloud providers. What we know as the database is starting to become obsolete. The database storage engine is becoming a repository for machine data. This data is addressable through other structures such as key-value indices.
The top 5 Cloud Analytics available in the market are:
- Azure Stream Analytics
- AWS Analytics
- Zoho Analytics
- Domo
- IBM Cognos
What is Cloud Analytics?
Cloud Analytics is a service model in which one or more components of data analysis is being implemented in the cloud. Such services can be offered as part of a hybrid model or entirely in the cloud.
In the hybrid model, some components are physically present while others are on the cloud. The cloud model allows organizations to upgrade their analytics capabilities as they grow. With this model, businesses do not have the burden of management and installation of physical components. Any data analytics that is carried out in collaboration with a cloud service provider is Cloud Analytics. This service model is one of the fastest-growing aspects of modern business intelligence systems.
Fundamental Components of Cloud Analytics
Cloud Analytics consists of the implementation of these elements in the cloud:
- Data Sources – The data originates from these sources. It includes social media data, ERPs, CRMs, and website usage data.
- Processing Applications – These applications produce massive volumes of and integrates it into a data warehouse.
- Data Models – Cloud-based data models standardize how data points are related to each other.
- Analytic Models – They are mathematical models with substantial computing power that are capable of predicting outcomes.
- Computing Power – Scalable raw computing power is required to ingest, structure, clean, and analyze business data.
- Storage – Data warehouses that are offered as services enable organizations to implement a modern and scalable analytics architecture quickly.
Types of Cloud Analytics
- Public Cloud – They offer applications-as-a-service to organizations. Applications such are virtual machines, storage, and data processing are provided. In this structure, IT systems are shared, but the data is not. Public clouds allow companies to reduce cost and streamline IT management.
- Private Cloud – These are proprietary cloud services that are dedicated to a particular organization. It serves as an extension of an organization’s existing IT infrastructure. Data privacy and security are the top priorities in private clouds.
- Hybrid Cloud – They are a combination of public and private clouds. They allow companies to benefit from on-demand IT infrastructure services for non-sensitive data. Hybrid clouds also will enable them to store sensitive data in the cloud.
Advantages of Cloud Analytics
- Ease of Access – Both employees and external stakeholders, can access the data in the cloud. Governance controls can be put in place to give control access to the right people.
- Enterprise Data Consolidation – Implementing Cloud Analytics can provide a data warehouse that is accessible to all those who need the data. It also allows them to perform advanced analytics to create prediction models in real-time.
- Reduced Operating Costs – Organizations do not need to purchase hardware and provide support. They also do not need to upgrade equipment, which can be expensive and creates downtime. Cloud solutions take this burden off their hands.
- Sharing and Collaboration – Ease of access and data consolidation leads to more sharing and collaboration within the organization.
- Scalability – Cloud services make it easier to upscale hardware as the business grows. An organization can increase its number of subscriptions instead of purchasing new hardware.
Disadvantages of Cloud Analytics
- Performance – While cloud service providers are reliable, if their systems crash, the organizations dependent on them cannot function.
- Data Security – Organizations fear that there will be increased data leaks and vulnerabilities with the implementation of cloud solutions.
- Training and Hiring – Companies have a hard time finding the candidate with the right skills to build and manage a cloud analytics operation. It is challenging to train employees to keep up with the changing technology.
- Management Cost – Companies often underestimate how much they will use their cloud analytics capabilities, which can lead to unexpected costs. Hiring of cloud experts can also be expensive due to the shortage of these skills.
- Migration – The migration of data warehouses to the cloud can be expensive and time-consuming. If not done correctly, there is also the risk of data loss.
Key Trends Driving Cloud Analytics
- Dashboards and Advanced Visualizations – The need for real-time analytics has made data visualization a prime business requirement.
- Sales and Marketing Needs – Sales and marketing departments need to understand their initiatives from an ROI perspective. Cloud data analytics optimize their performance.
- Increased Adoption Rates – Organizations are increasingly recognizing the advantage of cloud analytics in mitigating challenges.
- Salesforce and NetSuite – These are the two most common CRM systems that organizations use. They create a need to automate data integrations with cloud visualizations.
- Google Analytics – Industries dependent on their websites seek Google Analytics integration. It provides them with feedback on what content is more popular.
Governance of Cloud Data
Despite all the advantages that cloud offers, companies still need to worry about maintaining privacy and preventing data loss. By taking the right measures, companies can easily protect their data. Big data can be masked using MD5, secure hash and randomization functions. Many services offer authentication, authorization, and encryption capabilities that help companies combat potential security issues. Proper governance enables analysts to work faster and more effectively.
Final Thoughts
The cloud era has brought numerous benefits to organizations. It also has its set of disadvantages, but the pros far outweigh the cons. Before the cloud, coordinating and sharing data was cumbersome. Transferring large amounts of data was extremely slow. Cloud services have drastically reduced these limitations. Many issues associated with cloud analytics become less of a concern as cloud adoption increases. With more organizations using them, service providers can vastly improve their offerings. Since the most innovative companies in the world depend on the cloud, it has emerged as a vital tool.
