3 Ways AI is Changing Data Management

Advanced Analytics and AI are Pushing Traditional Data Management Practices to their Limits. Here are 3 Examples of How AI is Revolutionizing Data Management

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Artificial intelligence (AI) and data management have a symbiotic relationship. When embedded into data management tools, AI can simplify, optimize, and automate processes related to data quality, governance, metadata management, main data management and enterprise data analytics. On the flipside, effective data management is mission-critical to enterprise AI adoption. Without a strong data management infrastructure and strategy in place, AI development efforts will likely crash and burn. 

With this in mind, we look at 3 ways AI is changing data management from the inside out.


From Data Management to Data Fabric

The race towards applied AI is on. However, establishing enterprise AI capabilities requires expansive, high performance data architecture. For many organizations, creating a data ecosystem akin to what we see at large tech companies like Facebook or Google is nothing more than a pipe dream given the reality of budget limitations and legacy systems complexity.  This is where the concept of data fabric comes.

Data fabric is essentially a distributed data management platform that connects all data with all data management tools and services. In other words, it serves as a unifying layer that enables data to be seamlessly accessed and processed in an otherwise siloed storage environment.

According to Dataversity, some of the benefits of data fabric are “large data storage for diverse types of data, easy integration, and centralized access to multi-sourced data, single view of data across an organization, and superior tools for risk management.” In addition, by consolidating all data sources and applications into one, unified distributed network environment, data fabrics accelerate AI adoption. 

Though the term “data fabric” is more of a design concept, there are a number of data fabric tools out there including Talend and NetApp.


AI-Powered Data Cleansing

Poor data quality costs businesses money. Not only are data cleansing processes time consuming and labor intensive, bad data also leads to bad decisions. In fact, according to Gartner research, “the average financial impact of poor data quality on organizations is $9.7 million per year.” Similarly, IBM found that in the US alone, businesses lose a combined $3.1 trillion annually due to poor data quality.

Increasingly, data scientists are leveraging artificial intelligence (AI) and its subset machine learning (ML) can automate and accelerate the data cleansing process. For example, tools such as Zoho’s DataPrep use ML to enrich data with sentiment analysis, language detection and keyword extraction. IBM also offers a variety of ML-powered data preparation tools to automate the data curation process to optimize AI development and training. 


Intelligent Enterprise Data Catalogs

An enterprise data catalog (EDC) is a data and metadata management tool companies use to inventory and organize the data within their systems. Sort of like a data and analytics menu or guidebook. 

By automating the discovery process, AI makes EDC much easier for non-technical professionals to use. AI and ML algorithms can also populate and update data sets without human intervention, eliminating the need for laborious manual data entry. By optimizing data collection, curation and discovery processes, intelligent EDC platforms such as Informatica and Microsoft Azure also enable additional AI/ML projects by ensuring data is high quality and prime to be consumed by machine algorithms. 


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