It’s quite a common question: What should be considered by all the business executives when determining the best approach for adding cloud to an analytic system?
Planning for such a “large” framework within the cloud is immensely different than what would be needed for a “small” or greenfield systems.
What is Cloud Analytics?
Cloud analytics is a service model when one or additional elements of analytics are performed in the cloud. These services can be a part of a hybrid model, where some elements are on-premise, or absolutely within the cloud. The cloud model permits organizations to scale analytics capabilities as their company grows. It even removes the burden of on-premise management and implementation. This service model could be a growing side of contemporary business intelligence systems today.
A piece of advice: when considering migration to the cloud, or constructing a hybrid (combination of on-premises and cloud) design, one shouldn’t begin with technology and assess if it applies to their necessities. However, the reverse is the best approach: start with understanding your business requirements to conclude what are the tradeoffs, architectures, tools, and mitigation plans required.
Failing to begin with business necessities usually ends up in an expensive, short-lived “project” instead of an efficient, long term solution. Don’t be “that guy” who assumes. Trust, but verify.
Components of Cloud Analytics
Gartner defined six elements of analytics as:
1. Data sources
These are the original sources of data which could include ERPs, CRMs, social media data, or website usage data. An example of a cloud-based data source would be Twitter sentiment analysis data.
2. Data models
Cloud-based data models make sense of how data points are related to each other. These are typically created with structured data types.
3. Processing applications
These applications process large volumes of big data, as it’s ingested into a data warehouse. Hadoop is a popular application for data processing.
4. Computing power
Companies need raw computing power at scale to ingest, structure, clean, analyze, and serve business data.
5. Analytic models
These mathematical models are closed functions used to predict outcomes and require strong computing power to create.
6. Sharing and/or storage
Data warehouses-as-a-service enables organizations to implement modern analytics architecture quickly and easily scale.
Benefits of Cloud Analytics
Enterprise information consolidation
Large enterprises have several disparate data sources, and it’s troublesome to see how all the moving elements of a company are working together if they’re in different places. A cloud implementation will offer information about the warehouse to those who can access and need that data, and a company can easily ensure data governance. Another advantage of data consolidation is the ability to use online services to perform data processing and advanced analytics to form prediction models updated in real-time.
Ease of access
Each staff and external stakeholders can access data within the cloud. However, with Cloud Analytics governance controls can be put in place to control access to the right people. Managing access to disparate data sources needs a lot of resources to manage internally and slows down innovation and insights.
Sharing and collaboration
Increased easy access and information consolidation lead to more sharing and collaboration between employees; that is why cloud analytics may be a smart fit for global corporations. Employees can simply transfer files and collaborate in real-time when they access the analytics in the cloud from anywhere in the world. This can additionally contribute to the growing trend of telecommuting work culture. Data-discovery becomes part of the daily tasks once cloud analytics is implemented in a Business Intelligence system.
Reduced operational prices
With cloud analytics, companies don’t need to purchase hardware and provide continuous support, which can become very demanding and creates vulnerability if not executed properly. Some ongoing upgrades need to occur can create unnecessary downtime. A cloud solution can take this burden off from the organization’s hands in order so that they can focus on their core competence.
It’s additionally easier to scale up capacity as the business grows because the organization can simply increase its range of subscriptions as opposed to purchasing new hardware. It also ensures systems to scale up if there’s an explosion in demand for the analytics systems.
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