Data serves as a fundamental asset for businesses, providing
valuable insights for decision-making, market analysis, and monitoring of competitors.
However, managing and extracting meaningful information from vast amounts of
data can be challenging. Inefficient data abstraction processes often result in
storing irrelevant and redundant information, hampering the overall data
quality. To address this issue, businesses are turning to data abstraction
services, leveraging
AI and automation to streamline the process and enhance efficiency. In this
blog, we will explore the importance of data abstraction, the impact of AI on
the process flow, and the benefits it brings to businesses.
The Significance of Data Abstraction
Data abstraction involves simplifying and condensing
the database by eliminating
unnecessary or less important information.
It enables organizations to maintain
a clean and structured dataset, enhancing data-driven decision-making.
Currently, a significant portion
of stored data is considered irrelevant or of unknown value. Data abstraction
helps in resolving this issue and providing
a more meaningful representation of the entire dataset.
Building a Better Data Abstraction
Process
At an enterprise level, data cleaning and organization
are crucial for effective data-driven decisions. Businesses have introduced various
services like data mining, data
cleansing, data conversion, and data abstraction to make data
more accessible and user-friendly. AI technology has revolutionized data
abstraction services by automating and optimizing
the process, reducing human intervention, and ensuring accuracy.
The Three Levels of Data Abstraction
To understand data abstraction better, let's
delve into the three levels of abstraction:
a. Physical or Internal Level
This level represents
the actual storage location of the data. Database administrators determine
where the data is stored, how it is fragmented, and other physical aspects of
data management.
b. Logical or Conceptual Level
The logical level defines the data stored in the
database and its relationships. It caters to the organizational data needs and provides
a comprehensive overview of the data objects and their relationships.
c. View or External Level
The view level represents
how the data is presented to the users. It offers different perspectives or
views of the database, allowing users to interact with the system and access
relevant information.
Explanation with an Example
Let's consider a customer database. At the
physical level, the data is stored in memory blocks, which is hidden from
programmers. At the logical level, the data is organized into fields and
attributes, defining their relationships. Finally, at the view level, users
interact with the system through a user-friendly interface, unaware of the
underlying data storage details.
How AI Transforms the Data Abstraction Process?
The integration of AI and automation in the data
abstraction process brings about significant changes and benefits:
a. Identifying
Necessary Data Entities
AI enhances the accuracy and efficiency of identifying
relevant data entities by eliminating
invalid or irrelevant data. This process is crucial for data abstraction and AI
technology ensures improved precision, reducing the time required.
b. Identifying
Key Properties of Entities
With AI, the identification of key properties or
attributes of data entities becomes automated and accurate.
Machine learning algorithms efficiently assort the properties, eliminating
the need for manual intervention.
c. Connecting the Dots - Finding
Relations Among Entities
Manually connecting data and identifying
relationships among entities can be challenging and time-consuming. However,
with the implementation of AI, this process becomes seamless and reliable. AI
algorithms are capable of identifying patterns and relationships among data
entities with a high degree of accuracy. Through
iterative learning, these algorithms continuously improve and adapt, making subsequent
cycles of data abstraction faster and more precise. By leveraging
AI-powered data abstraction services from professionals, businesses can ensure
scalability and meet the growing demands of their data-driven operations.
d. Mapping the Properties to the
Entities
Another essential aspect of data abstraction is
creating a relational network among the data entities and their properties.
This allows for easy visualization of their interdependencies and how changes
in one property or entity can affect others. AI and automation significantly
accelerate this process, reducing the time required for data transformation and
onboarding. By leveraging
machine learning algorithms, AI can infer data mapping predictions from
existing libraries of tested and certified data maps. This reduces the effort
and time needed to build intelligent data mappings, improving efficiency and
data integrity.
e. Removing or Preventing Duplicate
Data Entities
Duplicate data entities pose a common challenge to
data quality. They can occur when records mistakenly share data with each
other, leading to inconsistencies and inaccuracies. Duplicate data negatively impacts
data quality and can result in significant costs for businesses. AI integration
in the data abstraction process enables the effortless identification and
removal of duplicate data entities. AI algorithms consistently prevent data
decay and duplication, ensuring a clean and efficient database.
f. Validation of the Outcome
The final step
in the data abstraction process involves validating
the abstracted data against the desired data. With the use of AI, this
validation becomes more efficient and time-saving.
As data volumes continue to grow, it is crucial for data-driven businesses to
employ proactive strategies to monitor
and maintain data quality regularly. AI-driven data validation processes ensure
the accuracy and reliability of the abstracted data, reducing the risk of
acting on faulty insights. Opting for data abstraction services provided by
professionals who leverage
AI algorithms guarantees accurate,
high-quality, and powerful databases.
Conclusion
The exponential growth of data poses complex
technological challenges for businesses. Data abstraction plays a vital role in
managing and understanding large datasets efficiently. When combined with AI
and automation, data abstraction becomes more streamlined, accurate,
and reliable. AI enables businesses to identify
necessary data entities, identify
key properties, establish
data relationships, map properties to entities, remove duplicate data, and validate
the outcome. By leveraging
AI-powered data abstraction services, businesses can unlock the full potential
of their data assets, make informed decisions, and transform their operations.
For AI-driven data abstraction services, you can choose Outsource BigData,
a trusted tech company. Visit the official website of Outsource
BigData and learn more about their service.
Original Blog- https://outsourcebigdata.com/blog/data-abstraction-services/how-ai-automation-can-change-data-abstraction-process-flow-in-2022-2/
About AIMLEAP - Outsource Bigdata
AIMLEAP - Outsource Bigdata is a division of AIMLEAP, AIMLEAP is an ISO
9001:2015 and ISO/IEC 27001:2013 certified global technology consulting and
service provider offering Digital IT, AI-augmented Data Solutions, Automation,
and Research & Analytics Services.
AIMLEAP has been recognized as ‘The Great Place to
Work®’. With focus on AI and an automation-first approach, our services include
end-to-end IT application management, Mobile App Development, Data Management,
Data Mining Services, Web Data Scraping, Self-serving BI reporting solutions,
Digital Marketing, and Analytics solutions.
We started in 2012 and successfully delivered
projects in IT & digital transformation, automation driven data solutions,
and digital marketing for more than 750 fast-growing companies in the USA,
Europe, New Zealand, Australia, Canada; and more.
⭐An ISO 9001:2015 and ISO/IEC 27001:2013 certified
⭐Served 750+ customers
⭐ 11+ Years of industry experience
⭐98% Client Retention
⭐Great Place to Work® Certified
⭐ Global Delivery Centers in the USA, Canada, India
& Australia
Email: sales@outsourcebigdata.com
USA: 1-30235 14656
Canada: +1 4378 370 063
India: +91 810 527 1615
Australia: +61 402 576 615
No comments: