Page 1 of 8
Transactions on Engineering and Computing Sciences - Vol. 13, No. 1
Publication Date: February 25, 2025
DOI:10.14738/tecs.131.18275.
Achanta, P. R. D. (2025). Transformative Effect: Rise of Intelligent Data Management in Integration with AI. Transactions on
Engineering and Computing Sciences, 13(1). 116-123.
Services for Science and Education – United Kingdom
Transformative Effect: Rise of Intelligent Data Management in
Integration with AI
Padma Rama Divya Achanta
Information Technology, Illinois, USA
ABSTRACT
Power of Artificial Intelligence (AI) is reshaping the way organizations collect,
process and leverage data. Traditional data management systems often struggle to
handle velocity, volume, and variety of modern data and types. This leads to
inefficiencies in the system and losing opportunities of growth. AI powered data
management systems handle these challenges very efficiently by automating tasks
such as data cleansing, classification, integration to analysis. This transformative
rise of intelligent data management, powered by Artificial Intelligence (AI) is
reshaping the way organizations collect, process, and leverage data and has enabled
organizations to extract actionable insights in real-time. This integration of AI
combined with intelligent data management helps businesses to enhance its
decision-making process, streamlines operations and unlocks new avenues of
growth and innovation across the industries. This paper explores the
transformative effects of intelligence data management systems powered by AI,
highlighting its capabilities, benefits and potential pitfalls. Furthermore, it outlines
its implications on privacy & ethical challenges which need to be addressed and
future role of AI in shaping the future of data-driven ecosystems.
Keywords: Intelligent Data Management, AI Integration, Adaptive Systems, Data-Driven
Insights, Automation.
INTRODUCTION
Artificial Intelligence (AI) has disrupted many industries across the world with rapid
transformation and is impossible to escape the fact that organizations of all sizes across every
industry are producing data at an unparalleled rate. The scale, speed, and complexity of data
generated across industries, today, is too immense for traditional data processing methods to
handle. The infusion of AI is changing data management from a reactive manual process into
an intelligent automated and adaptive system. With the use of AI-powered tools and techniques,
companies can observe workable insights, optimize workflows, and take more informed real- time decisions.
Intelligent data management using AI is more than just basic storage and retrieval. A singular
solution designed to cover a range of data integration, cleansing, governance, and advanced
analytics, allowing organizations to realize the maximum potential of their information.
Through identifying patterns, spotting anomalies and forecasting trends, AI-based solutions
enable organizations to pre-emptively tackle challenges and seize opportunities. AI-Driven
Data Management applying to all sectors ranging from healthcare, finance, and logistics, the
evolution of AI-driven data management is spurring innovation, driving efficiency and creating
competitive advantage for organizations in every sector.
Page 2 of 8
117
Achanta, P. R. D. (2025). Transformative Effect: Rise of Intelligent Data Management in Integration with AI. Transactions on Engineering and
Computing Sciences, 13(1). 116-123.
URL: http://dx.doi.org/10.14738/tecs.131.18275
The growing dependence of industries on data to drive decision making and the accelerating
effect of AI integrated with data management spells a revolution in the way companies function
and adapt to the impending dynamics of our landscape. As natural language processing,
machine learning, automation, etc., becomes more powerful, the opportunity for transforming
data management practices becomes significant. This promises to revolutionize its internal
processes and raise the standards of innovation and value creation.
WHAT IS INTELLIGENT DATA MANAGEMENT
Intelligent Data Management (IDM) is about automating and improving the processes used for
themanagement, processing, and analysis of data with the help of technologies such as
Artificial Intelligence (AI), Machine Learning (ML), and automation. Whereas traditional data
management systems are concerned solely with storing, securing and querying data, IDM
brings intelligence which is the ability to learn from data, to adapt as conditions change and to
act independently, according to the specification. With the help of AI-oriented tools, IDM helps
companies manage large amounts of data so that it is kept clean, accessible, and like good gold
for decision making and business activities.
One of the most important characteristics of IDM is the automation of the data Integration,
aggregation, and processing. With AI, these systems can easily aggregate data within a single
facility from different sources, both structured and unstructured, or semi-structured, which can
then be automatically integrated in a single format. This minimizes, if not eliminates, the time- consuming effort to cleanse and categorize data, thereby facilitating rapid, precise data
workflows. Moreover, these systems monitor the quality of data every minute, identifying
duplication, missing values, inconsistencies, and the automated processes are in place to rectify
these anomalies to ensure that you have quality data always available for decision making
analysis and reporting.
Apart from enhancing data quality, IDM uses AI to glean insights from data by using advanced
analytics. AI algorithms can mine extensive data sets for patterns, anomalies, and trends that
may not be apparent to humans. These insights are utilized for predictive analytics, real-time
decision making, and trend analysis. AI can also provide personalized experiences by studying
user behavior and providing tailored recommendations across a range of channels, for
customers, employees, and stakeholders, thus increasing engagement and satisfaction.
Furthermore, Intelligent Data Management also puts its focus on securing and governing data.
AI tools track access patterns, identify anomalies, meet compliance requirements around data
protection. It enables organizations to better protect sensitive data and to implement
governance policies. IDM systems automate security and compliance verifications, thus
decreasing the chances of data breaches and also bring transparency and accountability to how
data is used between two enterprises which is essential in gaining trust between them and
maintaining compliance with various industries. Simply put, Intelligent Data Management
fundamentally changes how organizations treat and harness data optimizing it from every
angle, keeping it secured, and making it consumable, intelligent & actionable. By providing
organizations with automation, AI-based analytics and real-time adaptiveness on stored data,
IDM enables organizations to be more data-centric, competitive and agile in the digital world.
Page 3 of 8
118
Transactions on Engineering and Computing Sciences (TECS) Vol 13, Issue 1, February - 2025
Services for Science and Education – United Kingdom
TRANSFORMATIVE EFFECTS OF IDM USING AI
With industries producing unprecedented levels of data, existing solutions for managing,
integrating and using this data have become untenable. Artificial Intelligence (AI) has now
made its way into data management, changing the way companies think about data in different
areas. The application of AI in data management processes is paving the way for new
automation, efficiency, and intelligence, from enhanced data quality and security to real-time
analytics and experiences tailored to the individual.
Here are the few areas of IDM in Integration with AI
Architecture Integration and Aggregation
AI speeds up the integration and aggregation of data from various, and usually, heterogeneous
sources. Artificial intelligence can automatically identify, extract, and transform data into
common formats using Natural Language Processing (NLP) and machine learning algorithms.
Not only this, it reduces the manual time and effort but also decreases human errors and
expedites the hands-on time in the process of data integration helping organizations create
updated datasets to quickly decide at the top of the organization. The ability of AI to integrate
information in real-time allows businesses to utilize data-driven decision-making when it
matters most often in fields such as finance and healthcare.
Data Quality Management
The emergence of AI tools has a significant influence on data quality management, carrying out
the identification and outmoding of any identification discrepancies and errors and removing
duplicates. They help discover anomalies and patterns that are often missed, flagging wrong
entries, missing values, or conflicting data. This minimizes the hassle of having to clean the
data manually and enables the organization to keep a sound and trustworthy database which
is necessary to make accurate predictions and insights. When AI training is done on clean,
reliable data, the output from AI models is much more accurate and actionable, which
translates to far more effective business results.
Improved Governance, Security & Compliance
With AI-driven data governance, policy enforcement automatically watches how data is
accessed and used and ensures regulatory compliance. Artificial Intelligence (AI) can help in
tracking data lineage, which enables organizations to understand where their sources of data
are, how it is being used, etc. AI has a particularly important role in security, recognizing
deviations from usage patterns and potential security threats, which opens the gate to detecting
and mitigating risk early on. With the help of AI in data governance and security practices,
businesses can protect sensitive information and privacy-ready, protected against fraud or
breaches and compliance-ready.
Analytics and Insights
AI has transformed organizations' approach to data analytics enabling companies to gather
insights from their data. Artificial intelligence algorithms have the power to sift through
petabytes of data and discern underlying patterns, relationships, and trends that would be
nearly impossible to identify using traditional approaches. AI enables organizations to forecast