Analytics is the process of turning raw data into valuable business insights through quantitative and statistical methods. There are three ways of classifying business analytics methods according to their use case:
- Descriptive methods examine historical data to identify meaningful trends and patterns.
- Predictive methods use historical and current data to make forecasts and predictions about the future.
- Prescriptive methods run simulations and create models in order to hypothesize the best path forward in a given scenario.
The use of business analytics is a critical component of organizations’ digital transformation initiatives. As big data continues to grow in size and complexity year after year, organizations need to efficiently cut through the massive data volumes they have on hand to find the hidden insights within. When implemented correctly, business analytics enables smarter decision-making, helping you apply your conclusions to help solve complex business issues.
Enterprise IT has moved to the cloud in recent years, and business analytics is no exception. In one report by Gartner, 97 percent of the analytics and BI platforms studied offered a cloud version of the software. The most common platforms are AWS, Azure/Power BI, and Oracle/OACDo.
So what’s all the fuss about? There are several very good reasons that organizations migrate their analytics workloads to the cloud, including:
- Lower costs: Cloud analytics saves users from having to purchase their own hardware and provide their own support and maintenance. The switch from one-time capital expenses to monthly operating expenses is also more convenient for many companies, especially small and medium-sized businesses.
- Greater flexibility: Moving analytics to the cloud lets users do their work at the time and place that’s most convenient for them—whether in the office, at home, commuting to work, or on the road.
- Increased scalability: Cloud analytics uses a subscription-based model rather than a hardware-based model, which makes it easier to scale as your business grows: just purchase more subscriptions for more users. You can also easily ramp up your compute and storage resources during times of peak activity, which is something you can’t easily do with in-house hardware.
- Better data governance: Uniting your enterprise data in a single centralized data warehouse in the cloud helps you make better use of the data sources at your fingertips. Consolidating your data in the cloud also facilitates sharing and collaboration with the people who can most benefit from this information.
- Maintenance and disaster recovery: The cloud provider, not you, is responsible for general support and maintenance, which frees you from spending valuable time and money. Storing your data in the cloud, and backing it up in multiple locations, also protects it in the event of a disaster that damages or destroys your on-premises IT infrastructure.
But cloud analytics isn’t just advantageous in and of itself: it acts as a corrective force for the analytics delivery challenges that have been impeding your productivity and holding your business back. To learn more about cloud analytics and how it can build efficiency and flexibility into your data management strategy, download my white paper, “7 Analytics Delivery Barriers That Cloud Analytics Can Solve.”
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