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How is AI Transforming Data Management?

Explore how AI is transforming data management, from enhancing data extraction and mapping to improving data quality and analysis.
Sep 2023  · 7 min read

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“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days." This quote from Eric Schmidt, Executive Chairman at Google, was made in 2011. Today, this number stands at about 0.33 zettabytes per day (328.77 million terabytes). In the modern world, everything is about data; every mouse click, swipe, tap, and view has the power to influence business decisions. However, the real problem is managing the volume and velocity at which data is being generated.

Data management has been evolving proportionally with the increase in the speed of data generation. We started off with simple relational databases and ETL, but then came along big data and unstructured data, paving the way for automated data pipelines and data lakes. However, there seems to be no end to this avalanche of data. Modern data is complex, highly unstructured, comes from a variety of sources, and thus beyond the capacity of conventional technology. Thankfully, we now have AI to solve our data management woes.

AI has been a buzzword for a while now. Especially after the introduction of generative AI, the technology is rapidly penetrating every aspect of our lives. So, it only makes sense to leverage it for managing data as well.

But how is AI transforming data management? In this article, we take a closer look at how artificial intelligence impacts data extraction, mapping, quality, and analysis.

The Amalgamation of AI and Data Management

2023 catalyzed AI adoption with the introduction of generative AI. According to the latest survey by McKinsey, one-third of all the respondents said that generative AI is being used in at least one business function. 40% of the organizations said they have adopted AI, and their companies expect to invest more in AI.

When it comes to AI adoption in data management, it is important to understand that data needs have evolved as well. Data sharing is rapidly becoming a common phenomenon. Organizations are looking to decentralize data and serve it as a product to their internal and external customers. Moreover, with the increasing demand for data fabric, the market wants solutions that enable automated and augmented data integration.

AI is very well equipped to keep up with these changes in data needs. Right from ingestion of data to analysis, AI has the capability to abstract the complexities of the data management process and thus accelerate it.

Amazon is the prime example of how AI adoption in data management can help skyrocket revenue. The giant retailer looks at data points such as prior shopping activity, the amount spent on site, wish lists, and geographic location, and utilizes AI and predictive analytics to predict what customers need even before they need it.

So, what goes on behind the scenes? How does AI work? AI technologies, such as machine learning algorithms, can accelerate routine tasks such as data cleansing, classification, clustering, and anomaly detection. Other than that, there is natural language processing and deep learning that simplify text analysis, sentiment analysis, and image analysis, and the list goes on.

Let’s break down each step of data management to see the impact of AI.

AI and Data Extraction

The first step in any data management cycle is data extraction. Given the unstructured data sources such as Text, PDF, images, etc., it has become challenging for traditional tools. Initially, the tools used were template-based, where you could automatically extract data from documents that followed the same template. However, AI has eliminated the need for uniformity in templates. AI-powered data extraction tools use natural language processing to understand the fields a business needs to extract. For example, if a business wants to extract customer information from invoices or purchase orders, it will just have to specify the fields, and the tool will extract it regardless of the format.

AI and Data Mapping

Once the data is extracted, it is mapped from the source to the target destination. In the past, this used to be a manual process that included IT professionals writing code. Soon, code-free data mapping tools emerged that allow data professionals to visualize and conduct data mapping with a drag and drop. Now, AI has completely transformed data mapping.

Artificial intelligence has enabled the automatic discovery of data sources, attributes, and relationships. Machine learning algorithms analyze existing data to identify patterns and connections, and consequently reduce time and effort. Moreover, AI simplifies schema mapping as algorithms use pattern recognition and semantic analysis to identify similarities between disparate schemas.

AI and Data Quality

While businesses have become experts at generating high volumes of data, they still struggle with data quality issues. According to IBM, the yearly cost of poor data quality is $3.1 Trillion in the US alone, which shows that the evolution of data management software has still not helped much. However, AI can prove to be different.

AI algorithms can scan datasets for errors, inconsistencies, and anomalies and immediately rectify them. The best part about AI algorithms is how well they can handle missing data. AI algorithms can detect missing values in the data and then can fill them with estimated values without compromising accuracy.

AI and Data Analysis

AI can perhaps contribute the most to data analysis, the last step in any data management process. With the introduction of GPT, there has been a rise of light-weight integrations of NLP in data analytics. NLP techniques analyze textual data from sources like social media, customer feedback, and documents. AI can also group together similar data using clustering algorithms.

Decision trees and regression analysis are fundamental techniques in data analysis. AI-powered machine learning models can easily create complex decision trees, even in multidimensional datasets.

Conclusion

The role of AI in data management cannot be denied. It's not just a fancy way of conducting analysis; rather, it is the need of the hour. Businesses today need real-time insights, and AI can deliver those. The role of AI is only going to become more prominent with time.

Progress could be made towards Edge AI, meaning that data analysis and computing will be done at the source of data collection. This technology will eliminate many manual tasks and make data management easier.

Ready to dive deeper into the world of AI and Data Management? Explore DataCamp’s AI Fundamentals track and Understanding Artificial Intelligence course to uncover the challenges and societal implications of AI.

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