What is Operational Analytics?
Whether you’re trying to improve the customer experience or better manage your inventory, data helps your business make well-informed decisions. The more nimble you are when making those decisions, the better your business operates. Operational analytics, commonly referred to as operational intelligence, is the practice of utilizing data in real time to make instant decisions in business operations.
Why is operational analytics important?
Traditionally, businesses have collected data to analyze and help inform decisions after the fact. From Uber to Shell to Amazon, the use of operational analytics has become widespread among companies because it focuses on the “right now.” This refers to data that are collected and aggregated from existing business operations is then analyzed and fed back into operations instantly to make intelligent decisions on the spot rather than later on.
There are many business operations that require intelligent decisions to be made immediately. Supply chain management, inventory management, customer service, and marketing are just a few examples of where operational analytics can make a substantial impact.
What are the benefits of operational analytics?
Traditional analytical systems have many benefits, but their weakness is the speed at which the insights gathered from crunching the data can be implemented back into the business. To more effectively streamline operations, modern businesses need real-time data that can be processed and put into action instantaneously. Moving past the limitations of traditional data collection and analysis through continuous intelligence means that:
- Instead of only relying on weekly, quarterly, or annual reports to make improvements to your business, you’re operationalizing your data to take immediate actions day-to-day.
- You’re able to react to customer behavior in real time.
- You can identify and improve inefficiencies as they’re happening.
What using operational analytics looks like for different business roles
What does this look like in practice? Here are a few examples:
- Marketing: Businesses can optimize sales in real time by using operational analytics to make personalized recommendations for products or deals while a customer is shopping. For example, a customer’s IP address could be used to identify their location and set prices dynamically based on the average purchasing power in that area.
- Development: Developers can use real-time data to look at how customers are using their products and make changes on the fly. For example, an online game developer may adjust the difficulty level of a section of a game if players are currently struggling to get through it or provide tools in-game to help players improve their likelihood of progressing to the next stage.
- Management: Businesses can better manage their operations through continuous intelligence, such as applying preventive maintenance for machinery before it breaks down or restocking products during popular sales.
Operational analytics means making sound business decisions during--not after--the fact
Data is what drives expert decision-making, and in many business operations, speed is as critical of a component to success as quality data. Operational analytics has become increasingly popular among major companies for good reason. Continuous intelligence means businesses can optimize productivity and profitability by reacting in real time.
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