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 data.
Data models : Cloud-based data models make sense of and standardize how data points are related to each other. These are typically created with structured data types.
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.
Computing power : Companies need raw computing power at scale to ingest, structure, clean, analyze, and serve business data.
Analytic models : These mathematical models are closed functions used to predict outcomes and require strong computing power to create.
Sharing and/or storage of Data : Data warehouses as a service enable organizations to quickly implement a modern analytics architecture and easily scale.
Cloud analytics encompasses any implementation of these elements in the cloud.
What we offer:
Enterprise data consolidation
Large enterprises have many disparate data sources, and it’s difficult to see how all the moving parts of an organization are working together if they’re in different places. A cloud implementation can provide a data warehouse that’s accessible to those who need the data. Companies can easily ensure data governance so only those who need the data get it. Another advantage of consolidation is the ability to use online services to perform data mining and advanced analytics to create prediction models updated in real time.
Ease of access
Data in the cloud can be accessed by both employees and external stakeholders, and governance controls can be put in place to control access to the right people. Managing access from disparate data sources requires more resources to manage internally and slows down innovation and insights.
Sharing and collaboration
Increased ease of access and data consolidation lead to more sharing and collaboration between employees, which is why cloud analytics is a good fit for global companies. Employees can easily transfer files and collaborate in real time when they view analytics in the cloud from anywhere in the world. This is also conducive to the growing trend of a telecommuting work culture. Data discovery becomes an everyday part of the culture when cloud analytics is implemented within a BI system. Hence we offer Sharing and collaboration services.
Reduced operating costs
Setting up an in-house analytics solution can be extremely costly, especially for smaller organizations who may not have the internal skill sets to do so. With cloud analytics, organizations don’t need to purchase hardware and provide continuous support, which can be very demanding and creates vulnerability if not properly executed. There are also ongoing upgrades which need to occur and can create unnecessary downtime. A cloud solution will take this burden off your organization’s hands so you can focus on your core competency.
Scalability
It’s also easier to scale up capacity as the business grows, as the organization can simply increase its number of subscriptions as opposed to purchasing new hardware. It also ensures systems scale up accordingly if there is a sudden increase in demand for the analytics systems.