From the
very start or the initial stage of human civilization until 2003, we humans
made about 5 exabytes of data. But now, we make that much data in just TWO
DAYS! Eric Schmidt from Google said this in 2011. Every day, we make a huge
amount of data - like 329 million terabytes! That's a lot! Everything we do
online, like clicking or swiping, can affect businesses. However, the major
issue is that dealing with so much data so fast. It's like a really hard
puzzle!
The
evolution of data management has been commensurate with the acceleration of
data generation. Simple relational databases and ETL were our starting point,
but big data and unstructured data soon followed, opening the door for
automated data pipelines and data lakes. Nevertheless, it appears that this
data avalanche will never stop. Contemporary data is too complex, largely
unstructured, and originates from multiple sources to be handled by traditional
technology. Thank goodness AI has arrived to ease our problems with data
management.
AI has
been a popular term for some time now. With the advent of generative AI in
particular, technology is consuming every part of our lives at a rapid pace.
Consequently, using it for data management also makes sense.
But how
is data management being changed by AI? In this article, we examine in more
detail the effects of artificial intelligence on data extraction, mapping,
quality, and analysis.
Integrating AI with Data Management
A third
of the total people surveyed by McKinsey said they're using generative AI in at
least one part of their business. When generative AI launched in 2023, it made
more businesses start using AI. Also, 40% of companies that already use AI want
to spend even more money on it.
Today,
the use of AI in data management has brought about a major shift in what's
needed from data. In addition, nowadays data sharing has started to quickly
spread throughout society. Companies want to decentralize their data and offer
it as a product to their clients, both internal and external. In addition,
because of the increasing demand for data fabric, the market is searching for
solutions that enable improved and automated data integration.
In fact, AI is perfect for handling changing data demands.
It simplifies and speeds up data management, from collecting data to analyzing
it.
Amazon is a great example of how AI in data management can
lead to huge revenue growth. They use AI to predict customer needs before they
even know them! They look at things like shopping history, spending habits,
wish lists, and location.
But what happens behind the scenes? Amazon uses AI
technologies like machine learning to automate tasks like data organization,
cleaning, and pattern detection. They also use advanced AI techniques to
analyze text, emotions, images, and more.
To see
how AI affects data management, let's break down each step.
Data Extraction and AI
In any
data management cycle, data extraction is the first step. With unstructured
data sources like Text, PDFs, images, and more, it has grown more difficult for
conventional tools to handle. When template-based tools were first developed,
you could automatically extract data from documents based on a template.
However, AI has eliminated the need for templates to be consistent. Natural
language processing is used by AI-powered data extraction tools to comprehend
the fields that businesses need to extract. For example, a company can simply
specify the fields, and the tool will retrieve customer data from purchase
orders or invoices, regardless of their format.
Data Mapping and AI
Data
extraction involves mapping the data from the source to the desired
destination. Previously, code was written by IT specialists as part of a manual
process. Data professionals can easily perform and visualize data mapping using
a simple drag-and-drop method, thanks to the rapid rise of code-free data
mapping tools. Now, AI has completely transformed the landscape of data
mapping.
Data
sources, attributes, and relationships can now be automatically found thanks to
artificial intelligence. Time and effort are saved because machine learning
algorithms examine already-existing data to find patterns and connections.
Additionally, schema mapping is made easier by AI, as algorithms find
similarities between dissimilar schemas using pattern recognition and semantic
analysis.
Data Analysis and AI
In the
final stage of any data management process, data analysis, and artificial
intelligence may have the biggest impact. Since th introduction of chatGPT,
lightweight NLP integrations in the data analytics have increased
significantly. Also, textual data from sources like social media, customer
reviews, and documents is analyzed using NLP techniques. Clustering algorithms
are another tool AI can use to group together similar data. Regression analysis
and decision trees are two fundamental techniques used in data analysis.
AI-driven machine learning models can easily generate complex decision trees,
even with multidimensional datasets.
Regression
analysis and decision trees are two essential techniques in data analysis.
AI-driven machine learning models can easily generate complex decision trees,
even with multidimensional datasets.
In Summary
It is
impossible to dispute AI's importance in data management. It is not merely an
elegant method of conducting analysis; rather, it is an urgent necessity. AI
can provide the real-time insights that businesses require today. The use of AI
will only grow in importance over time.
Edge AI,
which would do computation and data analysis at the point of data collection,
may get closer to reality. Many manual tasks will be eliminated by this
technology, which will also simplify data management.
Author Bio:
Vishnu Narayan is a dedicated content writer and a skilled copywriter
working at ThinkPalm Technologies. More than a passionate writer, he is a tech
enthusiast and an avid reader who seamlessly blends creativity with technical
expertise. A wanderer at heart, he tries to roam the world with a heart that
longs to watch more sunsets than Netflix!
