Operational analytics is a more specific term for a type of business analytics which focuses on improving existing operations. This type of business analytics, like others, involves the use of various data mining and data aggregation tools to get more transparent information for business planning. Operational analytics is the process of using data analysis and business intelligence to improve efficiency and streamline everyday operations in real time.
A subset of business analytics, operational analytics is supported by data mining, artificial intelligence, and machine learning. It requires a robust team of business and data analysts. And it also requires the right tools.
Businesses can pursue operational analytics in many different ways. Different software packages will offer various models for showing what happens within a business, in real-time or over a specific time frame. Many of these tools will provide visual models. For example, businesses may be looking each day at how many customers look at or buy a particular product in an e-commerce store. Operational analytics tools may graph or chart these customer events in a visual way to allow human decision-makers to see what’s really going on.
In general, operational analytics and other business analytics support the idea of enterprise resource planning, where software systems aggregate information across a complex enterprise in order to enhance communications between stakeholders, streamline or optimize business processes, and give leaders a better idea of how to chart a course for the future. Again, with operational analytics, experts in the field will have specific guidance on how to perform quicker or more targeted analysis and use of the valuable information that’s provided by operational analytics software.
Thanks to operational analytics, businesses can see the inefficiencies that exist in their workflows. Accordingly, they can then change their processes to streamline operations.
For example, a company might run analytics and realize that the process for approving a purchase order is too cumbersome. In this case, it requires too many signatures from too many people who are moving around constantly.
Enhanced Customer Experiences
Businesses that react to situations in real time are able to provide better customer experiences. It’s that simple.
For example, imagine an e-commerce company runs operational analytics. After that, they find that a significant percentage of its users are adding items to their carts but not completing transactions. Armed with that information, they then investigate the issue. It quickly becomes apparent that their website is buggy and checking out is a nuisance.
Quite simply, businesses that can analyze and react to customer data in real time are able to make much faster decisions.
Traditionally, businesses would make adjustments to their operations based on a quarterly or annual data review. In this reactive manner, they might miss out on serious revenue or glaring issues. They’d only become aware after the fact.
On the other hand, companies that embrace an operational analytics platform can make adjustments to processes and workflows in real time. Or at least close to it. As such, they are in a better position to increase profitability and reduce waste. They can also detect problems and inefficiencies quickly and respond to them rapidly.
Traditional Analytics vs. Operational Analytics
Traditional analytics uses data to understand business operations, while operational analytics uses data to drive business operations. This difference may seem subtle at first glance, but it cuts deep into the role data plays within your company.
I’m defining traditional analytics here as the data presented via business intelligence (BI) dashboards and static reports created at a regular cadence (weekly/monthly/quarterly). Broadly speaking, the job of traditional analytics is to use data to provide an understanding of what’s going on in your business to inform strategic decisions over time.
The types of questions traditional analytics answers are along the lines of “Is this product line generating revenue?”
Operational analytics is a flow of data from your data warehouse into other tools, like Zendesk, Hubspot or Intercom. The job of operational analytics is to inform specific activities inside your company, whether that’s sales, marketing, support, or customer success.
The types of questions operational analytics answers are something like “Which support ticket should I tackle first?”
The difference is in what you use the data to achieve. Traditional analytics looks at data over time to inform long-term strategy; operational analytics looks at data in the moment to inform strategic action.