AI data management: welcome to the future of data handling

Artificial Intelligence keeps astounding us in every way. As its adoption increases across all industries, so does its ability to challenge society as we know it. Now has come the time to talk about how this technology is revolutionizing the way businesses manage data. From collection to storage and analysis, AI data management is the new goal for data-driven organizations. 

The importance of effective data management for business growth is undeniable. As data turns into big data, an unprecedented complexity rises for data readability and integration. Companies need to create the proper environment to make sense of all the information that’s being created. New technologies appear to assure data quality, accessibility, and security. And among the most powerful, rest Artificial Intelligence. 

What is AI data management, and where does its power come from?

AI data management is the use of machine learning algorithms and other intelligence techniques to ingest, identify and organize large amounts of data. With AI, organizations can detect non-obvious patterns and uncover actionable insights. The result? Improved operations and faultless decision-making. 

What are the benefits of AI in data management?

The benefits of using AI for data management are related to increasing efficiency: reducing time and avoiding errors by automating tasks like:

  • Data capture
  • Deduplication
  • Anomaly detection
  • Data validation

Automation capabilities also make AI in data management a great ally for data governance. Not only because it reduces data breaches, increasing data security. But especially due to AI’s power to train models to comply with privacy data policies and data protection protocols.  

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How can AI help data management?

AI can help data management in several ways like Data analysis, cleansing, storage, integration, security, retrieval, etc.

There’s no doubt that businesses need to embed AI in their data management systems to achieve high-performance results. Prediction, automation, and optimization are getting sharper by the minute in data management with the use of artificial intelligence.

Here are three fields in which AI is changing the data management game for good. 

1. An achievable data fabric

Let’s start answering the question of what data fabric actually means. Data fabric is a distributed data management platform. Its purpose is to connect an organization’s data with all its data management tools and services. In a nutshell, data fabric is the ultimate data internal ecosystem. It unifies all data domains to transform data silos into flawless access and seamless data processing. 

Only artificial intelligence could have made this distributed environment possible. Before its time, creating such data networks would have been unthinkable. Not only due to budget limitations but mainly because of its technological complexity. Now, AI capabilities allow for the consolidation of all data sources and applications into one single platform. Among its main benefits are the following:

  • Large data storage for diverse data types
  • Simple integration
  • Centralized access to multi-source data
  • Superior risk management tools
  • 360º view of across-organization data

2. Upper-level data cleansing

Data cleansing is a very important step of data management. Poor-quality data is one of businesses’ biggest enemies. It leads to badly supported decisions, which consequently leads to substantial money loss. Get ready to be awe: the average financial impact of poor data quality on organizations rises to the breathtaking sum of 15 million dollars per year

Artificial intelligence in data management is leveraging machine learning capabilities to optimize the data cleansing process. Speed is accelerated by automating the data curation process. Some AI-powered data cleansing tools even offer sentiment analysis, language detection, and keyword extraction. 

3. Intelligent enterprise data catalogs

An EDC or Enterprise Data Catalog is a data and metadata management tool for inventory and organizing data. An enterprise data catalog is like a data guidebook for everyone across teams and departments within a company’s system. 

AI and machine learning algorithms are taking EDC to the next level. Datasets can be populated and updated without human intervention. Automation reduces manual data entry efforts and overall costs. AI data management enhanced enterprise data catalogs, optimizing data collection, curation, and discovery. With smarter data catalogs, even non-technical professionals can access high-quality data for their daily work.

ai data management 

A peek into the future of AI data management

The data management field is evolving deeply with the aid of artificial intelligence. But it’s also important to notice that there are some challenges that AI data management needs to overcome to reach its full potential:

  • Harder training on large and diverse datasets to improve data quality. AI algorithms still need practice to work effectively in real-business scenarios. 
  • Bigger investment for skilled AI experts. Despite automation’s power, humans are still in charge of designing, implementing, and overseeing AI data management systems. 
  • Artificial intelligence educational efforts. It’s key for all roles in an organization to understand how to work and collaborate with AI. 

Only time will tell, but bets are high on the victory of AI-driven technology in data management. It looks like we are not too far away from fully autonomous, self-optimized data management services. It will definitely be a must-have for companies’ multi-cloud environment of the future. Want to take a step forward in data management proficiency? Contact us for Artificial Intelligence Services.

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