When you sit in the middle of massive data sets, what is the first thing that pops up in your mind−how to manage the data? That is probably one of the most common questions among businesses dealing with high volumes of data.
Considering the fact that we are living in this era of technology, it is but obvious to expect advancement in the data science space as well. This is exactly where Augmented Data Management (ADM) comes into picture.
True to its name, ADM augments the process of data management.
So what is Augmented Data Management?
Augmented data management (ADM) is basically a set of tools or processes, which allows regular users to carry out the tasks which were earlier undertaken only by Data Scientists. Emergence of this technology was necessary as more and more companies are collecting range of data and in larger amounts than ever before.
The kind of tasks data scientists do with data are − data collection, data cleanup; basically manipulating data. They can combine one data entity with another to use for reporting. Also, they can integrate one data type with another data type to find co-relations.
All of these exercises can be done by using ADM tools by the users who are not very proficient in programming languages like R and Python.
Why do you need it?
Data is undeniably a key asset to your organization and management of this key asset is integral to the overall performance. Hence, you must need a feasible yet an effective method to manage your data; and there could be nothing better than integration of artificial intelligence (AI) to automate and expedite data management tasks. This method is popularly known as Augmented Data Management.
The agony of sifting through large datasets, identifying data quality issues, and tracing the source of data often put data stewards down the rabbit hole. This results in poor management of data and finally you lose control of your data. AI not only helps to streamline the entire data management process, but also deals with large variety and volume of data. As a result, it can provide you complete data integration solution.
Benefits of Augmented Data Management
So, what are the key benefits of Augmented Data Management? Let’s find out from the list below.
Automates data management operations: One of the biggest advantages of implementing ADM is that it can reduce the manual efforts that are redundant and have no impact.
Data cleanup: It’s used to automate the process of cleaning up data so it can be easily manipulated and integrated with accuracy.
Meta Data: DA helps in adding attributes to data to better classify and organize them for ease of referencing.
Better decision management: ADM enables informed and advanced decision making. It helps in providing framework for composing, designing and executing decision models.
Better data security: ADM ensures data security by preserving meaningful data and catering to the organization’s dynamic security needs.
Now that you have a fair understanding of augmented data management (ADM), how about getting some insights into the latest AD tools? Excited to know? Watch out this space to learn more.