Most companies often invest so much insecurity and data application that the profitable data application is diminished. But there must be a few ways by which both can be maintained. In this write-up, let us see how you can unlock the benefits by applying a few tips.
Data Agility – Unlock the Powers with These Tips
Before we understand the best measures you can take to derive maximum benefit from the same, we must understand in brief data agility meaning.
According to TDWI, the speed with which you can derive value from the bulk of data and how rapidly you can translate the same information into action is what is data agility. For business entities, this is crucial because the data must reach the hands of your analysts, and business stakeholders so that they can get an insight into your business at the earliest.
According to Statista, around 79 zettabytes of data were developed, captured, consumed, and copied in 2021 alone.
This is an incredible figure to deal with and opens several avenues for businesses. However, a few difficulties make it challenging to derive optimum benefits from the data at your disposal. These are-
- Most companies keep the data locked up for security reasons. As business entities add more security layers, it becomes harder to decipher the data and make it usable.
- Agreements related to data sharing are becoming more convoluted
- Risk assessments are costlier and time-consuming
- Due diligence is getting complex
- You need more resources for audit
So here are a few ways by which you can overcome these problems of data sharing and data agility.
1. You need a platform that will shield you from the complexity of processor partners.
Newer processing and controlling surveillance technologies can help with digital contracts and control the data that can be used and prevent misuse in real-time.
2. Be sure what analytics are being used for
Your data compliance team must be able to review and know with precision the analytics that is being used. It is essential to understand how the process occurs instead of just knowing the manufacturing analogy.
3. Validation of a trusted algorithm
You must be aware that the right people access the correct data at the right time, and the data platform must be capable enough to investigate fingerprint coding before the data is used. Most importantly, everything must have the so-called Zero Trust authentication backing it up.
4. Set limits on data sharing
Avoid sharing the entire database. Allow access only to that set of data that you will require for that moment. You must keep processing data safe by using the “confidential compute” which is trust-enabling technology. In other words, you are satisfied that you have allowed access only to that set of data that you wanted to or your partner needed, and nothing more than that.
5. Overview of the value you create
Pay heed to this aspect if you want that your data must also enhance revenue for your business. Also, know what the results mean for your partners. Only will you know what the data is worth, and it becomes easier for you to price the data.
Last but not the least, safeguarding the data is your utmost priority, even if that means you need to invest a lump sum for the same.
Also Read: Is A Masters In Data Science Worth It?
