Top Data Management Problems
The modern world functions on information. Millions of files are being created, stored, processed, and distributed.
A primary aspect of data management is digitizing large amounts of documents, books, and reports that have been collected for hundreds of years.
The Great Volume of Data
The more that data is digitized and managed on digital platforms the easier it is for some of it to be misplaced. So there are volumes upon volumes of data that needs to be processed in a well-organized and accessible way.
The Lack of Clear Processes to Manage Data
With the digitization of data being done on an organizational level, there is no uniform standard for how it should be handled. That being said, going from one platform to another makes it hard to find valuable information.
This allows for files to be misplaced – making them difficult to find, in addition to losing files in the mess. Without the right organizational system, finding one specific file in the massive stack is like looking for a needle in a haystack.
Data Access and Integration
Depending on the software development project, various types of information need to be accessed as a part of the research and development process. It also needs to be integrated efficiently with the systems used. The key information needs to be easily accessible for the sake of having an efficient process. On the other hand, it needs to be able to be integrated with the platform. If it is not in the right format, simply finding a way to integrate it is challenging in its own right.
How AI and Machine Learning Could Solve These Challenges
Aggregate Data
Aggregating data relates to collecting vast amounts of information that is then summarized, analyzed, packed, and presented as a report with statistical analysis. The purpose of this is to provide all essential information in a short form that allows for the quick decision-making process and assuring reliable results based on sufficient data.
Big Data is defined as: “Processing extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.“
Artificial Intelligence allows for all of the data to be analyzed and processed computationally with limited risks for mistakes that would otherwise be prominent if carried out by hand. Not to mention that the process takes years of manual work that could be directed elsewhere.
On the other hand, Machine Learning takes all of the analyzed information and then has the ability to make decisions based on it. While this is still being developed and has a long way to go before it can be allowed to work independently without supervision, it is already applied in certain areas of production and development.
Identify Unnecessary Data
Aside from analyzing, summarizing, and categorizing data, AI can also be used to eliminate unnecessary data. Such as repetitive files with the same contents. Identifying what information is no longer needed based on input directions. For example, if files are out of date or unneeded they can either be archived separately or erased to limit the chances of confusion in large amounts of files.
Sort and Store Data More Efficiently
The biggest and most reliable way that AI can manipulate data for the sake of easy access and efficiency when used and maximizing the value of data can be seen in the way it approaches data sorting.
To sort, categorize, distribute, and sort data takes years upon years of manual work. This can be a process that is on an organizational level, personal level, national, and international level.
Another thing that can be established with the use of AI and ML is a neat organizational system that can be carried out mostly automatically with a clear and reachable structure – unbiased to personal preferences. It can also be applied as a standard practice that allows easy access regardless of the type of data that is being managed.
To Sum It Up
The use of AI and ML when it comes to fixing issues with key data management is invaluable. The technology and its use are still being developed but its potential to better both personal and professional performance is vast, if even a little unpredictable.
The benefits of it will be more than influential on all level of society from administration to large production companies and international organizations. And easily one of the biggest differences that could be accomplished is allowing for employees to direct their attention more towards other interpersonal skills and work that is more dependent on the human aspect of the job. All the while all the documentation practices are followed strictly and in the most efficient way imaginable.
Do you believe in the potential of AI and ML to change the future of data management? Do you believe that this approach will be beneficial in the long run?
