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Is Your Data Vulnerable: 5 Questions for Better Data Security

When you think of data security and data vulnerability, what kind of images instantly spring to your mind? Do you instantly think of some sitting in a darkened basement, hacking the newest data stack on the block? While data hacking is one side of the reality we know as data vulnerability, it is not the only possibility. There are a number of scenarios where your data can be compromised, lost, deleted, duplicated, and rendered downright inaccurate! Apart from this, you can also wind up with inconsistencies and data which is redundant due to a time bound need for a particular data set.    While there are a number of ways to deal with this, there is a trio of solutions that can make your life easier:  
  • Data Monitoring: Building observability and analytics into your functions for data transformation and better checks on freshness, volume, categories, formats and distributions as well. 
  • Data Reconciliation: With this tool on hand, the data quality can be reconciled while it moves from one source to another and makes it way to stack after stack. This will ensure that errors are spotted and reported before they catch the eye of the customer. Outliers are the main outcome here, and they help in keeping a check on the data health. 
  • Data Security: Many companies cannot afford to retain a team for data security and many others struggle since traditionally, data security needs to be scripted manually into every layer of the code. But, with a tool like 4D Alert’s automated data security, you get the full triage in terms of monitoring, reconciliation and security with a few clicks to implement the tool. 
  Now that we have covered those basics, let us look at the questions that you should ask to ascertain the security of your data and to assess the right tool for your data’s security:  
  1. Do I have to script the security manually? This is one of the foremost questions that you should ask. Here’s why – with companies like 4D Alert, you have the option of using a tool that can be easily installed so that you do not have to retain a data security team or script the function manually (and rather painstakingly as well!) to implement security for your precious data. 
  2. Which data needs protection? And how much visibility to accord to each set? This is a crucial question to ask. Let us look at a quick scenario. There are a number of different kinds of information that we seek from a customer. Now, this information would go from one area of operation to another in order for a solution to be processed – this is usually the final product or service that we are providing. But would you want each person to be able to access all the information? Wouldn’t it be better if the person in charge of a certain area sees the exact data that they need in order to process that function? For example, the bank statement does not have to be accessed by everyone. The address does not have to be seen by everyone either. These are ways in which you would have to build security into your data sets. 
  3. How to build responsibility and accountability? When data changes hands and jumps from one source to the other, or is loaded across a number of stacks – especially in very large data teams and enterprises, where this would be happening on a minute to minute, daily basis – it tends to become more visible, and hence more vulnerable. How do you build responsibility and accountability into the system? When you choose to give access only to relevant data to relevant business heads and process heads, you are ensuring that there is accountability for every piece of information since you would also know which information went to which person. This is a good way to ensure that you keep everyone accountable as well. 
  4. Data quality disruption? While data quality has been hailed as one of the greatest disruptors of our times, it has certainly changed the way we operate and do business. Hence, when you are gathering, extracting, merging and using data, you would have to be mindful of protecting it so that it appears on your platform and does not get compromised anywhere else. This is also a part of assessing and assuring data quality. 
  5. Data Reconciliation and Data Monitoring – part of the deal? Like we mentioned earlier, this would have to be a part of the overall data security tool since all three are mutually inclusive. If we are to look at data quality in general, we would realize that the reconciliation of the pipeline accuracy would only be perfected with monitoring. At the same time, this creates the need for enhanced security at every stage while the data moves from one source to another target. Therefore, it goes without saying that there should be proper between all three functions including data reconciliation, data monitoring and data security for a presence that is accurate, well formatted, and secure. 
  Checking for various factors including freshness, consistency, categories, formats and more would make it necessary to have data security built into each and every step while the data moves from one source to a target. Yet, this would not be possible with manual scripting. A tool that can be implemented with a few clicks could fortify the data and provide strength to your data quality measure as well. 

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