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What is Advanced Analytics?

It refers to the study or analysis of data using more sophisticated methods and tools rather than traditional BI tools in order to get advanced insights into massive data volumes for improved decision making. These techniques are majorly used to generate deeper data-driven insights, predict user behaviour and enable better decisions across the enterprise.

Types of Advanced Analytics

With growing data and rapidly changing business landscape, the need of analytics has increased tremendously. These techniques enable enterprises to analyze their user behaviour and suggest products that adhere to their preferred tastes. Such insights are not only useful for businesses but also the end users as they can easily find the product of their choice. Some examples of such techniques are:

Predictive Analytics
Predictive analytics is a technique that uses hidden connections and relationships between different values and creates a forecast based on that. This technique collects and analyzes past data to come up with outcomes in the future.

Prescriptive analytics
Prescriptive analytics analyzes raw data about different possible scenarios including past and current performance. It gives out an outcome or course of action based on this information. It takes that given available data and goes even deeper into the potential results.

Data mining
Also known as knowledge discovery of data, data mining refers to analyzing patterns of data from one large data source through the help of one or multiple tools. It is a process used to figure out anomalies, patterns and correlations within massive data sets for predicting outcomes.

Advanced Analytics Solutions and Benefits

In today’s time when data is growing by the second, enterprises need advanced BI acceleration and analytics platform like Kyvos to achieve instant insights on massive volumes of data. Kyvos’ Smart OLAP technology and advanced analytics architecture enables business users to get a single, unified view of all their data for faster and actionable BI. Some of the benefits are:

  1. Business Growth- These analytics can be used to generate insights that can help businesses analyze their scope of growth and implement recommendations that provide significant business value for business transformation.
  2. Deriving insights from IoT systems- Using Internet of Things, you can collect data from multiple connected sources and derive meaningful insights from them to improve their performance and maintenance.
  3. Comprehensive Overview- Data analytics with advanced techniques enable you to drill down to lowest levels of granularities from data across enterprise. This way you’ll get insights that can enable you to create new more effective business models.
  4. Maintenance Automation- You can use data coming from different business assets to predict maintenance cycles, avoid cost overheads and streamline automation processes. It’ll help you understand maintenance cycles and apply them in advance to avoid any unnecessary costs.
  5. Empowered Innovation- To grow any business, you need to constantly evolve keeping in mind the interests of your end users. You can also analyze your data and create predictions and trend cycles. These insights will not only keep your business thriving but will also enable innovation around your business.
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