Why Is Data Management Important?

2 points

3 years ago

Hi, I am Eugene Rudenko, an AI Solutions Consultant at NIX Unite with more than fifteen years of experience in business consulting for the software development industry.

I make an effort to keep up with technological developments and implement the most effective ones in the software development process. When I found myself in the area of Data Science, I discovered that this is where cutting-edge AI solutions are being adopted and improved for business problems. My expertise mainly involves the development of business automation and software products and business research and advising. As a result, I'd like to discuss the relevance of data management for businesses and how to manage the entire data lifecycle efficiently.

Data visibility, reliability, security, and scalability

Businesses spend a lot of time and money acquiring data, which is transferred back and forth between storage systems. Therefore, it's critical to get the most profit and value from the collected data. Data management ensures data meets specific criteria.

Let's start with visibility, which refers to the capacity to quickly and easily find needed data. It's also referred to as accessibility. Data labeling is one of the most straightforward ways to make your data more accessible. Whenever you have data labeled, it's easier to find the data, process it, or filter it. Visualization is also a key to the possibility of sharing data insights whenever you need to present your findings to someone.

The next point to consider is reliability. Being involved in data management, you are the one to decide where your data comes from, how it's processed, and how it's integrated with other available data. So, ensuring that your data is consistent, comes from a trustworthy source, and is appropriately formatted is what reliability is all about.

Next is security, which is one of the most crucial parts. Data loss is something that your company and its employees must be protected from. Many businesses are concerned about data breaches, particularly those that deal with personal, financial, and health information.

The final factor required for data is scalability. This allows you to implement and run repetitive processes without duplicate data and do the same data processing on multiple ends of your data pipelines by different departments or employees. Avoiding data duplications improves data consistency by reducing the risk of errors and misinformation during data processing.