How AI is revolutionizing Data Management

How AI is revolutionizing Data Management

The world is expected to produce around a whopping 120 zettabytes of data in 2023, and it will further increase by 150% in 2025. The numbers speak for themselves; from IoT devices to sensors and social media, businesses are generating data at an unprecedented rate.

However, this data is worthless unless it can be processed at speed. One way to do that is through effective end-to-end data management, but not the kind that relies on traditional methods and technologies. The world today is all about AI and so data management needs to amalgamate with AI as well.

In this blog we will explore how data management has changed over the years and the limitless potential of AI in data management.

The Evolution of Data Management

If you look at end-to-end data management, it starts from data extraction, then moves on to data cleansing, data integration and then finally data analysis. Ideally, effective data management should generate insights as soon as the data is collected.

In the past, this was possible even manually as there were limited data sources, mostly structured and the velocity of data was also not that high. However, the world is different today. With the rapid transition to the cloud and the introduction of unconventional data sources such as streaming data from IoT devices, data management has become quite complex.

It is not like that data management hasn’t evolved. From complex coding that was limited to IT professionals, it has become more code-free and open to non-technical users as well. Today, the market is flooded with tools that are not only easy to use but are also automated. But is that sufficient? Probably not!

Most businesses still struggle with utilizing their data effectively. According to a survey by Gartner, 87.5% of the companies still identify their data and analytics maturity as low. Not surprising, since now data management is not about the ease of use but rather the speed. It all depends on how quickly a company can process data and even code-free tools don’t suffice unless they are embedded with AI.

The intersection of AI and data management

In 2022, the global corporate investment in AI stood at a whopping $92 billion. Seeing this trend, it is clear that businesses that are still not considering investing in AI-based products are likely to stand still in the near future.

So, what is it about AI and why it has become imperative for data management? To understand the role of AI in data management, it is important to understand two aspects of AI: machine learning and Natural language processing.

Machine learning feeds of data and then produces algorithms that can imitate human intelligence and do complex tasks without being trained for it. Natural language processing, on the other hand, can interpret human language. Both techniques can used in each stage of the data management process, especially when it comes to data extraction and interpretation.

Data Extraction and AI

Traditional data extraction methods are typically designed for structured, tabular data. However, 90% of data is unstructured, which makes it harder to extract and analyze. In the recent past, data extraction tools became template based, which worked for one kind of templates. It was an upgrade from all the manual coding. However, template-based tools cannot catchup with the amount of unstructured that is generated today.

NLP can make data extraction much easier. By using name entity recognition, for example, it can easily extract the data fields regardless of the template. NLP also utilizes sentiment analysis, which is quite useful for social media data analysis.

Data Mapping with AI

AI is perhaps the most useful in data mapping. It not only accelerates data mapping, but it is also accurate as AI can easily detect any missing mappings, or discrepancies.  The best part about using AI for data mapping is perhaps that machine learning can adapt itself based on the existing mappings and feedback from users. The use of AI makes data mapping future proof. So, in the case when new data sources are introduced, data mapping can adapt itself without significant modification.

Data Quality and AI

One of the biggest hurdles in data management is weeding out inconsistencies and errors in data. Data quality is the backbone of data management, and it is one of the toughest stages of data integration as well. However, AI can easily resolve data quality issues.

AI algorithms can easily validate data against predefined rules or constraints. They can also learn normal data patterns and then automatically detect any anomalies. AI can assist in data profiling, which involves analyzing, and summarizing the characteristics of data, such as data types, distributions, and patterns. AI algorithms can automatically generate metadata, which provides insights into the data quality, lineage, and usage.

The most impressive part about AI in data quality management is that it can be trained to predict data quality issues based on historical data.

Data Analysis and AI

Data analysis is the most important stage of any data management and AI has a lot to contribute to it. AI algorithms excel at recognizing patterns and relationships in large data sets. AI also enables predictive analytics by using historical data to make predictions and forecasts about future events or outcomes. One of the biggest advantages of using AI for data analysis is that it can analyze unstructured data at speed. Businesses can easily leverage NLP techniques for text classification, entity recognition, topic modelling, and summarization.


Only the future will tell what new sources and data structures there will be.  However, AI somewhat future proofs data management. Its ability to learn and adapt itself will ensure that businesses can quickly adapt to any future.

Data management will probably see the involvement of new technology. Especially, with the introduction of big data, technologies like distributed computing, cloud storage, and processing frameworks will play a vital role in managing and analyzing big data effectively.

So, businesses that want to stay ahead of competition must evolve rapidly with time and adopt new technologies as they come in.

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Within the bustling realm of data science, our editorial team stands as a collective force of learning and exploration. Meet the dynamic minds behind the scenes—Sukesh, Abhishek, and other Authors. As passionate data science learners, they collectively weave a tapestry of insights, discoveries, and shared learning experiences.
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