How can companies improve data insights? Remember these 4 steps to simplify complex problems
With the continuous penetration of digital management model, data to empower decision-making has become an immediate need for more and more enterprises. However...
With the continuous penetration of digital management model, data to empower decision-making has become an immediate need for more and more enterprises. However, how to improve data insight has become a problem for many enterprises.
By combining years of experience in data insight and customer experience management, Bechtel's customer experience management platform has compiled the following 4 steps, which are expected to help enterprises with data empowerment.
Step 1: Start from the scene
Essentially, data insight capability is actually "data + scenario", and data that is detached from specific business scenarios loses its value. Based on this, enterprises should first improve data insight capabilities with their actual business scenarios, understanding and analyzing the data situation.
Step 2: From extreme values to intermediate values
Usually, based on the understanding of data extremes, you can master the basic judgment criteria, and also accumulate analysis assumptions and analysis logic. And when it comes to situations that are not so extreme, we can also follow the analysis logic that has been accumulated to understand. If we really can't interpret it, we can also choose to continue to observe and see which extreme direction the data goes.
Step 3: From Static to Dynamic
Essentially, the interpretation of dynamic scenes is actually our insight into enough static scenes, and dynamic scenes are just an ensemble of a series of static scenes. It is worth mentioning that a business change often has regularity. Continuous regularity, in itself, has business implications. Therefore, accumulating the regularity of cycle patterns can improve the insight capability from point to line.
Step 4: From single indicators to multiple indicators
After the accumulation of insight on single indicator data, you can expand to multiple indicators, master the judgment of the results indicators, you can link the process indicators together. Note: Multi-indicators are not the accumulation of single indicators, and when put together, it is not better to have more of each indicator. When multiple indicators are combined, they will form a specific pattern in a specific business scenario, and the interpretation based on the pattern can make more accurate judgments.
After mastering the basic patterns, we can continue to observe the changes in patterns and accumulate more experience, so that we can slowly move from simple to complex, and accumulate more data insight rules and basis.