New Dimensions for the Big Data Planning Cycle
With the advent of big data, some changes can impact the way you approach business planning. As more businesses begin to use the cloud as a way to deploy new and innovative services to customers, the role of data analysis will explode.
You might want to think about another part of your planning process. After you make your initial road map and strategy, you may want to add three more stages to your data cycle: monitoring, adjusting, and experimenting.
Monitor big data in real time
Big data analytics enables you to monitor data in near real time proactively. This can have a profound impact on your business. If you are a pharmaceutical company conducting a clinical trial, you may be able to cancel a trial to avoid a lawsuit. A manufacturing company may be able to monitor the results of equipment sensors to fix a flaw in the manufacturing process.
Adjust the impact for big data
When your company has the tools to monitor continuously, it is possible to adjust processes and strategy based on data analytics. Being able to monitor quickly means that a process can be changed earlier and result in better overall quality.
This type of adjustment is something new for most companies. In the past, analysts often were able to analyze the results of monitoring processes, but typically after a problem had already become apparent.
Therefore, this type of analysis was used to find out why a problem happened and why a product failed or why a service did not meet customer expectations. While understanding the cause of failure is important, it is always better to be able to avoid mistakes in the first place.
Enable big data experimentation
Being able to try out new product and service offerings is important in an increasingly real-time data world. But it is not without risk. Experimentation without the capability to understand the outcome quickly will only confuse customers and partners.
Therefore, combining experimentation with real-time monitoring and rapid adjustment can transform a business strategy. You have less risk with experimentation because you can change directions and outcomes more easily if you are armed with the right data.