Value

This is the most important vector in terms of big data, but is not particularly associated with big data, and it is equally true for small data as well. After addressing all the other Vs, volume, velocity, variety, variability, and veracity, which takes a lot of time, effort, and resources, now it's time to decide whether it's worth storing that data and investing in infrastructure, either on premises or in the cloud. One aspect of value is that you have to store a huge amount of data before you can utilize it in order to give valuable information in return. Previously, storing this volume of data lumbered you with huge costs, but now storage and retrieval technology is so much less expensive. You want to be sure that your organization gets value from the data. The analysis needs to be performed to meet ethical considerations.

Now that we have discussed and understand the six Vs of big data, it's time to broaden our scope of understanding and find out what to do with data having these characteristics. Companies may still think that their traditional systems are sufficient for data having these characteristics, but if they remain under this influence, they may lose in the long run. Now that we have understood the importance of data and its characteristics, the primary focus should be how to store it, how to process it, which data to store, how quickly an output is expected as a result of analysis and so on. Different solutions for handling this type of data, each with their own pros and cons, are available on the market, while new ones are continually being developed. As a big data architect, remember the following key points in your decision making that will eventually lead you to adopt one of them and leave the others.