One of the greatest challenges that face businesses is figuring out effective ways to move data into the hands of teams so they can make powerful, data-driven decisions in daily operations. This is a challenge simply based on the nature of what data analytics is. Traditionally, data analytics is something that is used retroactively. This means that a company will acquire data, and this data could be used as a tool to help that company make decisions, but rarely does it work out that the data affects daily, operational, or real-time decision making.
The solution to this is a category of analytics called operational analytics, and it can boost the four types of business analytics that will move your company forward.
What is Operational Analytics?
In a nutshell, operational analytics is data that is pushed out to source points and departments where it can be used in real-time to help guide and navigate operational decision-making. This is a powerful solution for companies that are looking to better empower their departments to make more effective, customer-centered decisions.
How is Operational Analytics Possible?
The concept of operational analytics is easy enough to grasp, however, the execution is another thing altogether. Businesses would of course want their various departments to have access to the kind of information that would help their teams make data-driven daily decisions, but how do you get that data?
That’s where the new innovation of reverse ETL comes into play. Reverse ETL interacts with data warehouse, much in the same way that ETL does with silos. While ETL was used to create your data warehouse and transformed the data silo into a central aggregate of truth for a company, reverse ETL makes that data accessible.
Ironically, data warehouses became plagued with the same problems as data silos, and so companies struggled to access the data they have. Reverse ETL takes this transformed data and pushes it back out to data sources where it can be turned into operational analytics. This puts data in the hands of the departments that need it.
But what kinds of analytics does this improve and how does this move a business forward?
What Four Types of Analytics Are Improved?
Operational analytics focuses on creating opportunities for departments to make real-time, data-driven decisions. Unlike traditional analytics, operational analytics leverages data to be active, making it data that actually does something. Here is how it is used effectively in business.
Sales Analytics
Operational analytics can aggregate dispersed data on customers to create a robust, well-rounded customer profile. The data that a sales team needs on a customer is typically already in existence in the data warehouse. When this data is pushed back out to the data source, operational analytics can help to aggregate that data into one report. This brings all relevant information on a customer from the warehouse to a department where it can be used.
Not only that but as this happens, the existing data can be appended and enriched by the new customer experiences. This creates a scenario where sales teams can leverage their time and talent to make data-driven decisions regarding customer interactions and improve customer experiences.
Marketing Analytics
Marketing departments can be empowered by operational analytics to improve their knowledge of customer behavior and interaction. This can most effectively be used to distinguish high-value customers. This is all possible because operational analytics gives these departments the tools they need, so they don’t have to work through a data engineer to acquire the data necessary.
Product analytics
Operational analytics helps product analysis distinguish patterns and usage, as well as customer profiles, that can help improve high-value decision making. This can be effective for things like product pricing, and usage. By allowing teams to see real-time customer profiles through operational analytics, they can access the data that they need that’s relevant to product interaction.
Enriched customer profiles help teams use product analytics to make the best decisions regarding how their products are being used and what the customer experience is like.
Automation
The last area that operational analytics impacts heavily is automation. Operational analytics is analytics that actually does something. That means that it doesn’t just acquire and examine data, it actually moves that data out to where it needs to be, packages it to different departments, and works in real-time.
One of the ways that this is valuable to companies is in its automation. Operational analytics can be automated to improve messaging and notification tools like Mattermost and Slack. Operational analytics makes sharing important information easy, automated, and effective.
Conclusion
Analytics is one of the most important tools that a company can use. Retroactively it can be a valuable tool to assess new and developing campaigns or decisions, but it can also be a powerful way to help create data-driven, daily operational decisions. Investing in operational analytics gives your various teams the tools they need to make real-time decisions that can have a big impact on your customer experience.
