Summary
Abstract:
The abstract of the article "A Comparative Study of Business Intelligence and Artificial
Intelligence with Big Data Analytics" emphasizes on the importance of Business intelligence (BI)
systems and how they provide essential information to business planners and decision-makers by
combining operational and historical data with required tools. The main goal of these systems is
to increase the quality of data, allowing managers to better understand their company's position
with rivals. Data mining is the essential extraction of what’s understood, previously unknown, and
the beneficial information from data. Technological techniques such as clustering, data
summarization, learning classification rules, discovering dependency networks, analyzing
changes, and detecting anomalies are examples of technological techniques used in business
intelligence.
Introduction:
The introduction highlights the increasing importance of BI and AI in today's society, explaining
their use globally and their potential to reduce hazards safely. It also underscores the growing
deployment of machine learning techniques, particularly deep learning, for forecasting analytics,
and emphasizes the importance of data quality and timeliness in a highly competitive market.
Furthermore, it suggests that BI, big data, and AI technologies are necessary for businesses as they
aid in decision-making, forecasting, and the corporate economy, especially when supported by
strong leadership and training.
BI also encompasses tools and strategies for data collection, analysis, and visualization to aid
important decision-making processes. Statistical and machine learning approaches are used in data
mining to create decision-making models from raw data. Examples include decision trees,
regression, artificial neural networks, cluster analysis, and text mining. It is also said to be a broad
category of information technology (IT) solutions that include tools for acquiring, analyzing, and
reporting data about an organization's performance and its surroundings. These IT solutions are
among the highest-priority investment opportunities. For example, a retail chain can use data
mining to identify hot-selling goods, regional hot-sellers, seasonal hot-sellers, and fast-growing
customer categories. The article also mentions the Statistical Analysis System (SAS) which was
the cornerstone of a North Carolina State University's (NCSU) agriculture department project in
the mid-1900s, which kicked off the digital era of data science.
Literature review:
Business Intelligence (BI) is a crucial tool for companies, helping them collect, analyze, and use
data to make better decisions. It goes beyond just Decision Support Systems (DSS) and involves
gathering high-quality information to aid in understanding and analyzing data. BI is seen as a
strategy to enhance corporate performance by providing actionable information to executives. It
utilizes tools and technologies to make data processing more effective, evolving from older
systems like Executive Information Systems (EIS) and Decision Support Systems (DSS).
Timeliness, accuracy, and user acceptance are key for data consistency across different
applications. In a fast-changing business environment, BI is essential for decision making and
staying competitive, using technologies like data warehousing, online analytical processing, and
data mining to tackle complex business problems. It's essentially about using technology to gather
and analyze data for smart business decisions in a dynamic world.
Machine learning technology has been around since the 1990s, shifting from data-driven methods
to a focus on natural language search and information retrieval. Neural networks, first tested in
1957, made a comeback in 2005. Machine learning has seen successes and some failures, but it
might become widespread in the next 2 to 5 years. To support its growth, infrastructure and
technical capabilities must also improve.
Deep Learning, created in 1965, uses complex models for data analysis. Over the years,
improvements like long short-term memory and powerful GPUs have made it more efficient. Now,
it's widely used to handle large amounts of data, contributing to the advancement of AI.
As businesses face rapid changes, they use Business Intelligence (BI) to understand and improve
their processes. BI helps analyze market trends, consumer behavior, and other factors for better
decision-making. Artificial Intelligence (AI) began in the 1950s, facing hardware limitations. In
the 1980s, there was a surge in AI development with significant funding. From 2000 to 2010, AI
flourished, aided by advanced computer hardware. Machine learning, part of AI, became effective
in solving various challenges in industry and society.
Artificial Intelligence (AI) and data analytics are widely studied globally. Some countries invest
heavily, but few focus on unclassified AI research. AI benefits various businesses, especially in
data analytics. The data acquisition part involves connecting with operational systems to input data
into the data warehousing system's backend. Data goes through several processes before being
stored in the warehouse from operational databases and external sources.
Extraction and cleanse:
Data is collected from various sources and working systems that data is then combined and filtered
for any inaccuracy and inconsistence and are removed.
Transform:
The data is transformed into standard format and business rules are applied to align them with its
warehouse schema .We use attributes and aggregates to enhance quality of data.
Load:
The processed data is loaded in the data warehouse.
Machine learning methods:
The machine learning methods such as Decision trees (divide data based on features, forming a
tree structure where branches signify feature-driven decisions.), Support Vector Machine (SVM is
a learning model for classification and regression analysis that examines data .it can handle
nonlinear data as well), Naive Bayes classifier (it is a probabilistic machine learning algorithm that
makes predictions based on the application of Bayes' theorem.)
Deep learning Methods:
In deep learning back propagation is used to adjust the weights in the network, two learning modes
in this are stochastic and batch. During training, random units (neurons) in the network are
temporarily dropped or turned off which is called dropout. Skip gram is a technique that determines
relationship between words in a sentence.
Data marts:
Data marts are small repositories that unlike data warehouse stores the data of a specific
department such finance ,management ,marketing etc.
Metadata:
metadata is data about data.
Data Access and Analysis:
In data access and analysis data is access is not complex ,user can interact with GUI (graphical
user interface) to retrieve data ,in the background data is being analyzed using complex algorithms
through BI tools.(business intelligence).
Human Resources:
When we teach computers to learn, we use special parts made by companies like NVIDIA and
INTEL. These parts are like the brains of the computer and help it learn fast. NVIDIA's Jetson
Xavier is like a super-smart computer that doesn't use a lot of power.
Software Resources:
Many computer programs help with artificial intelligence and learning. These programs have funny
names like Pylearn2, Torch, and Tensor Flow. They make it easy for people to teach computers
without having to be computer experts themselves.
Trends and Market Analysis:
More and more companies are using artificial intelligence in different areas like cars, money,
health, and electronics. Big companies like Google and Microsoft are spending a lot of money on
this. It's like a big wave of change, and experts think it will grow a lot in the next few years.
Research challenges:
A long time ago, people had trouble making computers solve big problems. They were too slow.
So, smart people came up with new ideas using probability and economics to make computers
smarter. It's like teaching the computer how to deal with not knowing everything. Security:
Making computers understand things in a moving or changing world is hard. It's like teaching a
computer to recognize things when they're moving or being touched. And sometimes, it's tough
for the bosses to pick the right tools to keep things safe.
Conclusion:
Artificial intelligence is becoming a big part of our lives. It helps us make better choices and stay
safe. It's like having a brilliant helper. As we keep improving this technology, it gets more complex,
but it also helps businesses a lot. It's like having a crystal ball that shows us what's coming, and
businesses that use these tools well can do great things.