[big_title2]What is Operational Intelligence?[/big_title2]
Operational Intelligence (OI) is a term that describes the ability to obtain insights from your business operations. It can fall under the Business Intelligence umbrella as the idea is to generate value out of the organization’s raw data which can be gathered from multiple sources.
SAP has a great and simple way to explain Operational Intelligence. They call it a Fitness Tracker of your processes.
Multiple organizations are looking at Operational Intelligence to improve, not only the decision making process, but also to do it in a timely fashion.
One of the key characteristics of Operational Intelligence is the ability to do all the analysis in real time. This means that the information displayed is the latest and greatest available.
In today’s business world, having a proper set of tools to perform this kind of analysis is a “must” for many organizations.
[big_title2]Examples of Operational Intelligence[/big_title2]
- You can think about OI like a Formula 1 race. Each car generates millions of records automatically that are displayed to the team in real time and, as a result, they can make decisions that will be critical to their success. Should we change tires and what type should we use? Should we go for the pit stop now or wait? What should we do if the safety car comes out? Among others.
Now going back to the business world, imagine being able to monitor plants, sales, customer satisfaction, etc. as it happens and be able to make decisions based on that:
- Organizations can now keep track of what customers are saying on social networks and use that as a “focus group” to make changes or even study what the next move should be. This is called sentiment analysis.
- A customer visits your website and, based on his or her behavior browsing the products, you can offer the specific item(s) to will really get their attention.
[big_title2]Implementing Operational Intelligence[/big_title2]
Operational Intelligence is usually categorized as a Big Data project, therefore, you need to take the “V”s of Big Data into account. That is:
- Volume: analyze how much data you are dealing with, terabytes? Zettabytes?
- Variety: make sure you take in account all the different data sources you will have. Structure vs unstructured, local vs third party, etc.
- Velocity: If you plan for the volume and variety but the speed to process and make the data available is slow, you won’t get value out of the real time.
Once the data is available, one of the key elements of monitoring data live are dashboards. Dashboards are one of the best ways to visualize and monitor live data. Because there could be many data streams, designing your dashboards will also be important to get the most value out of your implementation. Some basic tips include:
- Go from summary data to detailed data to make better use of the real state.
- Enable filters and alerts to be able to identify key points (deviations, contributions, etc.)
- Use consistent colors and icons to avoid having discussions about format instead of content.
Also don’t forget, get your end users involved in the process so facilitate adoption and make sure you are delivering the right value.