Augmented Data Management

    

Today we live in a world where Artificial Intelligence has harnessed our daily chores and made quality of living like never before .The next breakthrough in the world of Automation is Augmented Data Management which promises to reduce the manual task of decision making in the enterprise by 45 % .Augmented data Management uses Machine learning and Artificial Intelligence  algorithms to self configure and self tune the data.

The Need Of Augmented Data Management

Every time a Data Scientist team starts working on a new project, they need to clean, understand, process the data, and build a business model from scratch. Here is where Augmented Data Management comes to the rescue. All the time wasted on Data Wrangling could be invested on Data Analytics and Insights. With Augmented Data Management in the picture, the roles of data professionals are going to alter.

Augmented Data Management can automate many processes like data discovery ,employing outlier detection ,handling missing values ,error detection and others. Hence making data accessibility quicker and efficient for data professionals . It enhances the data quality ,detects relationships between data ,recommends  best action to be taken for cleaning  and makes better intelligent business decisions.

Areas affected by Augmented Data Management

Data Integration :

More than structured data , we are approached with unstructured data from various sources. Augmented Data Management. Automation allows accurate suggestions to eradicate anomalies in the dataset and optimize the performance 

Data Quality :

With the increased volume of data, data cleansing and wrangling need to be automated such that any type of data can be scanned in real-time to be available for analytics. Machine learning tools give us the leverage to detect outliers and remove the inconsistencies in the data . Secondly, Predictive categorization and time series forecasting is being applied to handle missing values to skyrocket the efficiency of business decision-making.

Metadata Management

Metadata management allows the user to capture a vivid picture of the lineage of data from source to destination . Allowing Metadata to be automated , gives the organization the boon to easily perform data preprocessing, label matching data cleansing, and classification such that data can be traced and back to its original source and  store this documentation to support downstream processes.

Database Management :

Augmented Data Management has the potential to overpower the job of DataBase Administrator by taking care of self-tuning parameters and indexes in the database.

AI bots perform routine tasks of the database using  ML algorithms, creating databases which boost up and automate the security service  and up-gradation facilities of the system.

One of the most common illustrations of Augmented Data Management can be of AI-oriented over speeding cameras,we spot on roads and highways where the humongous dataset is filtered to accumulate overspeeding cars as target values .The Algorithm result is fast ,trusted and lastly, it uncovers the hidden possibility.

We can conclude with the fact that the job of the data science team is going to be trouble-free with the entry of Augmented Database Management in the realms of AI .

The raw data will be extracted and converted into usable data sets in a jiffy . We are entering a new era where the possibility of errors will be trivial and automation is going to dominate the data world.