雲端分析技術

Cloud analytics is the application of analytics to big data sets within cloud environments. While cloud environment gives the term cloud analytics its name, cloud analytics is essentially data analytics scaled in the cloud. Data analytics aims at extracting meaningful operational and business insights by discovering patterns within internal and external data sources.

Data analytics has moved to the scalable cloud as data increasingly becomes more voluminous and therefore more valuable when properly analyzed. Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are associated with cloud analytics because they are leveraged to make rapid data-driven decisions against such large data sets.

In practice, cloud analytics applications are found in industry and manufacturing, scientific research, business, security, Internet of Things (IoT), and business sectors that rely on data analysis to gain competitive advantage.

Cloud analytics solutions provide enterprise the following capabilities:

• Aggregate multiple disparate data sources
• Create access to data for users and stakeholders
• Provide collaboration and insight sharing features
• Eliminate costs associated with managing and owning data analytics infrastructure

Scale data support to match business needs

Cloud analytics has several key benefits.

• Update business software — Businesses that move to cloud analytics from outdated legacy systems leapfrog those that continue to hold on to antiquated models. Once the decision is made to migrate to the cloud, organizations can build and own their own configurations, integrating cloud into their on-premise ERP and other data systems. Or, they can choose to perform a full cloud migration. Cloud analytics can be as versatile in updating legacy systems as they need to be.
• Improve productivity — Cloud analytics provide advanced tools, like automation, that improve productivity by eliminating the need for people in many mundane and error prone tasks. As well, many of these tools, like natural language search for data discovery, provide superior performance.
• Automations emancipate time — Automations help to emancipate time spent administering data operations, and return it to staff who now can spend it improving system performance.
• Reduced error — Many data analytics tools help to collect, clean and prepare data for processing. Tools like data mapping and deduplication perform tasks simply time consuming for humans.
• Consolidated data — Today, data is produced in staggering quantities, and organizations may expect to synthesize insights from hundreds, even thousands of data sources. Cloud data analytics platforms are specifically designed for this type of data ingestion.
• Improved workflows and processes — Analytics platforms standardize data workflows to ensure that data is accurate and consistent, but also allow significant customizability to fit the needs of users. Together, organizations are able to enforce improved workflows and rely on proven processes.