Introduction
Data is now the key strategic business asset. Every device, every customer, every activity that’s happening in the world today produces rich data that can help people create new experiences, new efficiencies, new business models, and even new inventions. Using this data can be the differentiator for your business' success.
Your organization has likely used reports. Moreover, users want reports and might ask for more reporting. Traditionally, these reports were referred to as static reports. These reports were indispensable and were often the first item that users checked when arriving to the office. Some reports were routine and more intuitive, while others were slightly challenging, requiring more thought and research before the user could take an action.
Interactive reports and dashboards are the next step in providing more reports to users. Having an interactive dashboard where you can explore the data is better than having to read through several static reports that show the same data being pivoted in different ways. Because the tool enables your users to explore the data in different shapes and forms, you can identify trends and patterns interactively and arrive at decisions faster. Additionally, reducing the hours that you spend on poring over reports will help you make decisions quicker.
With Microsoft Power BI, you can detect patterns in data with the quick insights feature. While interactive dashboards are beneficial in enabling users to discover patterns, the tools will help users find patterns and exceptions.
Machines have improved on detecting patterns and predicting outcome. The next step in the evolution would be to introduce predictions and forecasts. You could use predictions from machines and incorporate them into your decision-making process. For example, a machine can forecast the demand for your supplies, and you can incorporate the machine-generated forecast into your production schedule. Demand forecasting in Dynamics 365 Supply Chain Management is a great example of machine-generated predictions being used within a business process.
After your users have improved on their ability to spot trends and patterns, when you help them with predictions, they can make decisions quicker. Soon, you will notice that they have learned the patterns and the associated decisions and actions.
Machine learning algorithms in Microsoft Azure can operate with large volumes of data, detect patterns, and make recommendations based on millions of buying patterns. Machines can offer customized recommendations in milliseconds, enabling scenarios where machine-generated recommendations can be taken automatically. In this case, routine decisions (backed by machine recommendations) are taken by the machine while complex cases are sent for human review.