case study on data warehouse and mining

Practical Data Warehousing: Successful Cases

Table of contents:.

No matter how smooth the plan may be in theory, practice will certainly make adjustments. Because each real case has its own characteristics, which in the general case cannot be taken into account. Let's see how the world's leading brands have adapted to their needs a well-known way of storing information — data warehousing.

The Reason for Making Decisions

The need to make business decisions based on data analysis has long been beyond doubt. But to get this data, it needs to be collected, sorted and prepared for analytics .

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This is what data warehousing specialists do. To focus on the best performance, it makes sense to consider how high-quality custom assemblies came out of this constructor.

Data warehousing interacts with a huge amount of data

A data warehousing is a digital storage system that integrates and reconciles large amounts of data from different sources. It helps companies turn data into valuable information and make informed decisions based on it. Data warehousing combines current and historical data and acts as a single source of reliable information for business.

After raw data mining (extract, transform, load) info enters the warehouse from operating systems, such as an enterprise data resource planning system or a customer relationship management system. Sources also include databases, partner operational systems, IoT devices, weather apps, and social media. Infrastructure can be on-premises or cloud-based, with the latter option predominating in recent times.

Data warehousing is necessary not only for storing information, but also for processing structured and unstructured data: video, photos, sensor indicators. Some data warehousing options use built-in analytics and in-memory database data technology (info is stored in RAM rather than on a hard drive). This is necessary to access reliable data in real time.

After data is sorted, it is sent to data marts for further analysis by BI or data science .

Why consider data warehousing cases

Consideration of known options for data warehousing is necessary, first of all, in order not to keep making the same mistakes. Based on a working solution, you can improve your own performance.

• When using data warehouses, executives access data from different sources, they do not have to decide blindly.
• Data warehousing is needed for quick retrieval and analysis. When using warehouses, you can quickly request large amounts of data without involving personnel for this.
• Before uploading to the warehouse, the system creates data cleansing tasks and puts them for further processing, ensuring converting the data into a consistent format for subsequent analyst reports.
• The warehouse contains large amounts of historical data and allows you to study past trends and issues to predict events and improve the business structure.

Blindly repeating other people's decisions is also impossible. Your case is unique and probably requires a custom approach. At best, well-known storage solutions can be taken as a basis. You can do it yourself, or you can contact DATAFOREST specialists for professional services. We have a positive experience and positive customer stories of data warehousing creating and operating.

Case 1: How the Amazon Service Does Data Warehousing

Amazon is one of the world's largest and most successful companies with a diversified business: cloud computing, digital content, and more. As a company that generates vast amounts of data (including data warehousing services), Amazon needs to manage and analyze its data effectively.

Two main businesses

Amazon's data warehousing needs are driven by the company's vast and diverse data sources, which require sophisticated tools and technologies to manage and analyze effectively.

1. One of the main drivers of Amazon's business is its e-commerce platform , which allows customers to purchase a wide range of products through its website and mobile apps. Amazon's data warehousing needs in this area are focused on collecting, storing, and analyzing data related to customer behavior, purchase history, and other metrics. This data is used to optimize Amazon's product recommendations engine, personalize the shopping experience for individual customers, and identify growth strategies.

2. Amazon's other primary business unit is Amazon Web Services (AWS), which offers cloud computing managed services to businesses and individuals. AWS generates significant amounts of data from its cloud data infrastructure, including customer usage and performance data. To manage and analyze this modern data effectively, Amazon relies on data warehousing technologies like Amazon Redshift, which enables AWS to provide real-time analytics and insights to its customers.

3. Beyond these core businesses, Amazon also has significant data warehousing needs in digital content (e.g., video, music, and books). Amazon's advertising business relies on data analysis to identify key demographics and target ads more effectively to specific audiences.

By investing in data warehousing and analytics capabilities, Amazon through digital transformation can maintain its competitive edge and continue to grow and innovate in the years to come.

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Obstacles on the way to the goal.

Amazon faced several specific implementation details and challenges in its data warehousing efforts.

• The brand needed to integrate data from various sources into a centralized data warehouse. It required the development of custom data pipelines to collect and transform data into a standard format.

• Amazon's data warehousing needs are vast and constantly growing, requiring a scalable solution. The company distributed data warehouse architecture center using technologies like Amazon Redshift, allowing petabyte-scale data storage and analysis.

• As a company that generates big data, Amazon would like to ensure that its data warehousing solution could provide real-time data analytics and insights. Achieving high performance requires optimizing data storage, indexing, and querying processes.

• Amazon stores sensitive customer data in its warehouse, prioritizing data security. To protect against security threats, the brand implements various security measures, including encryption, access controls, and threat detection.

• Building and maintaining a data warehousing solution can be expensive. Amazon leverages cloud-based data warehousing solutions (Redshift) to minimize costs, which provide a cost-effective, pay-as-you-go pricing model.

Amazon's data warehousing implementation required careful planning, significant investment in technology and infrastructure, and ongoing optimization and maintenance to ensure high performance and reliability.

Change for the better

When Amazon considered all the needs, found the right tools, and implemented a successful data warehouse, the company got the following main business outcomes:

• Improved data driven decision

• Better customer enablement

• Cost effective decision

• Improved performance

• Competitive advantage

• Scalability

Amazon's data warehousing implementation has driven the company's growth and success. Not surprisingly, a data storage service provider must understand data storage. The cobbler's children don't need to have no shoes.

Case 2: Data Warehousing Adventure with UPS

United Parcel Services (UPS) is an American parcel delivery and supply chain management company founded in 1907 with an annual revenue of 71 billion dollars and logistics services in more than 175 countries. In addition, the brand distributes goods, customs brokerage, postal and consulting services. UPS processes approximately 300 million tracking requests daily. This effect was achieved, among others, thanks to intelligent data warehousing.

One mile for $50 million

In 2013, UPS stated that it hosted the world's largest DB2 relational database in two United States data centers for global operations. Over time, global operations began to increase, as did the amount of semi structured data. The goal was to use different forms of storage data to make better users business decisions.

One of the fundamental problems was route optimization. According to an interview with the UPS CTO, saving 1 mile a day per driver could save 1.5 million gallons of fuel per year or $50 million in total savings.

However, the data was distributed in DB2; some included repositories, some local, and some spreadsheets. UPS needed to solve the data infrastructure problem first and then optimize the route.

Four letters "V."

The big data ecosystem efficiently handles the four "Vs": volume, validity, velocity, and variety. UPS has experimented with Hadoop clusters and integrated its storage details and computing system into this ecosystem. They upgraded data warehousing and computing power to handle petabytes of data, one of UPS's most significant technological achievements.

The following Hadoop components were used:

• HDFS for storage

• Map Reduce for fast processing

• Kafka streaming

• Sqoop (SQL-to-Hadoop) for ingestion

• Hive & Pig for structured queries on unstructured data

• monitoring system for data nodes and names

But that's just speculation because, due to confidentiality, UPS didn't declassify the tools and technologies they used in their big data ecosystem.

Constellation of Orion

The result was a four-year ORION (On-Road Integrated Optimization and Navigation) route optimization project. Costs — about one billion dollars a year. ORION used the results to data stores and calculate big data and got analytics from more than 300 million data points to optimize thousands of routes per minute based on real-time information. In addition to the economic benefits, the Orion project shortened approximately 100 million shipping miles and a 100,000-ton reduction in carbon emissions.

Case 3: 42 ERP Into One Data Warehouse

In general, the topic of specific cases of data warehousing implementation is sufficiently secret. There may be cases of consent and legitimate interests in the contracts. There are open-source examples of work, but the vast majority are on paid libraries. The subject is so relevant that you can earn money from it. Therefore, sometimes there are "open" cases, but the brand name is not disclosed.

Brand X needs help

World leader in industrial pumps, valves, actuators, controls, etc., needed help extracting data from disparate ERP systems. They wanted it from 42 ERP instances, standardized flat files, and collected all the information in one data warehouse. The ERP systems were from different vendors (Oracle, SAP, BAAN, Microsoft, PRMS) to complicate future matters.

The client also wanted a core set of metrics and a central dashboard to combine all the information from different locations worldwide. The project resulted from a surge in demand for corporate data from database management. The company knew its data warehousing needed a central repository for all data from its locations worldwide. Requests often came from top to bottom, and when an administrator required access to the correct data, there were logistical extracting problems. And the project gets started.

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The foundation stone.

The hired third-party developer center has made a roadmap, according to which ERP data was taken from 8 major databases and placed in a corporate data warehouse. It entailed integrating 5 Oracle ERP instances with 3 SAP ERP. Rapid Marts have also been integrated into Oracle ERP systems to improve the project's progress.

One of the main challenges was the need for more standardization of fields or operational data definitions in ERP systems. To solve this problem, the contractor has developed a data service tool that allows access to the back end of the database and displays info suitably. Since then, the customer has known which fields to use and how to set them each time a new ERP instance is encountered. These data definition patterns were the project's foundation stone and completely changed how customer data is handled. It was a point to launch consent.

All roads lead to data warehousing

The company has one common and consistent way to obtain critical indicators. The long-term effect of the project is the ease of obtaining information. What was once a long and inconsistent process of getting relevant information at an aggregate level is now streamlined to store data in one central repository with one team controlling it.

Data Warehousing: Different Cases — General Conclusions

Each data warehouse organization has unique methods and tools because business needs differ. In this case, data warehousing can be compared with a mosaic and a children's constructor. You can make different figures from the same parts, arranging the elements especially. And if one part is lost or broken, you need to make a new one or find another one and "process it with a rasp."

Generalities between different cases of data warehousing

There are several common themes and practices among successful data warehousing implementations, including:

• Successful data warehousing implementations start with clearly understanding the business objectives and how the warehouse (or data lake) can support those objectives.

• The data modeling process is critical to the success of data warehousing.

• The data warehouse is only as good as the data it contains.

• Successful data warehousing requires efficient data integration processes that can operate large volumes of data and ensure consistency and accuracy.

• Data warehousing needs ongoing performance tuning to optimize query performance.

• A critical factor in data warehousing is a user-friendly interface that makes it easy for end users to access the data and perform complex queries and analyses.

• Continuous improvement is essential to ensure the data warehouse remains relevant and valuable to the business.

Competent data warehousing implementations combine technical expertise and a deep understanding of business details and user needs.

Your case is not mentioned anywhere

When solving the problem of organizing data warehousing , one would like to find a description of the same case and do everything according to plan. But the probability of this event is negligible — you will have to adapt to the specifics of the customer's business and consider your knowledge and capabilities, as well as the technical and financial conditions of the project. Then it would help if you took a piece of the puzzle or parts of the constructor and built your data warehouse. Minus — you have to work. Plus — it will be your decision on data storage and only your implementation.

Data Warehousing Is Like a Trampoline

Changes in data warehousing , like any technological and methodological changes, are carried out to improve the data collection, storage, and analysis level. It takes the customer to a new level in his activity and the contractor — to his own. Like a jumper and a trampoline: separately, it is just a gymnast and just equipment, and in combination, they give a certain third quality — the possibility of a sharp rise.

If you are faced with the problem of organizing a new data warehousing system, or you are simply interested in what you read, let's exchange views with DATAFOREST.

What is the benefit of data warehousing for business?

A data warehouse is a centralized repository that contains integrated data from various sources and systems. Data warehousing provides several benefits for businesses: improved decision-making, increased efficiency, better customer insights, operational efficiency, and competitive advantage.

What is the definition of a successful data warehousing implementation?

The specific definition of a successful data warehouse implementation will vary depending on the goals of the organization and the particular use case for data warehousing. Some common characteristics are: meeting business requirements, high data quality, scalability, user adoption, and positive ROI.

What are the general considerations for implementing data warehousing?

Implementing data warehousing involves some general considerations: business objectives, data sources, quality and modeling, technology selection, performance tuning, user adoption, ongoing maintenance, and support.

What are the most famous examples of the implementation of data warehousing?

There are many famous examples of the implementation of data warehousing across industries:

• Walmart has one of the largest data warehousing implementations in the world

• Amazon's data warehousing solution is known as Amazon Redshift

• Netflix uses a data warehouse to store and analyze data from its streaming platform

• Coca-Cola has a warehouse to consolidate data from business units and analyze it

• Bank of America analyzes customer data by data warehousing to improve customer experience

What are the challenges while implementing data warehousing, and how to overcome them?

Based on the experiences of organizations that have implemented data warehousing, some common challenges and solutions are:

• Ensuring the quality of the data that is being stored and analyzed. You must establish data quality standards and implement data validation and cleansing by data types.

• Integrating from disparate data sources. Establishing a clear data integration strategy that considers the different data sources, formats, and protocols involved is vital.

• As the amount of data stored in a data warehouse grows, performance issues may arise. A brand should regularly monitor query performance and optimize the data warehouse to ensure that it remains efficient and effective.

• To ensure that sensitive data stored in the data warehouse is secure. It involves implementing appropriate measures such as access controls, encryption, and regular security audits. They are details of privacy security.

• Significant changes to existing processes and workflows. Solved by establishing a transparent change management process that involves decision-makers and users at all levels.

What is an example of how successful data warehousing has affected a business?

An example of how successful data warehousing has affected Amazon is its recommendation engine. It suggests products to customers based on their browsing and purchasing history. By using artificial intelligence and machine learning algorithms to analyze customer data, Amazon has improved the fully managed accuracy of its recommendations, resulting in increased sales and customer satisfaction.

What role does data integration play in data warehousing?

Data integration is critical to data warehousing, enabling businesses to consolidate and standardize data from multiple sources, ensure data quality, and establish effective data governance practices.

How are data quality and governance tracked in data warehousing?

Data quality and governance are tracked in data warehousing through a combination of data profiling, monitoring, and management processes and establishing data governance frameworks that define policies and procedures for managing data quality and governance. So, businesses can ensure that their data is accurate, consistent, and compliant with regulations, enabling effective decision-making and driving business applications' success.

Are there any measures to the benefits of data warehousing?

The benefits of business data warehousing can be measured through improvements in data quality, efficiency, decision-making, revenue and profitability, and customer satisfaction. By tracking these metrics, businesses can assess the effectiveness of their data warehousing initiatives and make informed decisions about future investments in data management and analytics with cloud services.

How to avoid blunders when warehousing data?

By following the best practices, businesses can avoid common mistakes, minimize the risk of blunders when warehousing data, and ensure their data warehousing initiatives are successful and practical to be analyzed with business intelligence.

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Data Warehousing and Analytics

Fueling the Data Engine

• © 2021
• David Taniar 0 ,
• Wenny Rahayu 1

Faculty of Information Technology, Monash University, Clayton, Australia

You can also search for this author in PubMed   Google Scholar

School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, Australia

• Covers all of data warehousing & analytics, incl. transformation, preparation, integration, aggregation, and analysis
• Explains concepts in a very practical way based on numerous case studies and exercises
• Datasets, sample codes, solutions to exercises and teaching slides are available on a dedicated web page

Part of the book series: Data-Centric Systems and Applications (DCSA)

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About this book

This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.

The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).

This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.

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From Star Schemas to Big Data: 20 $$+$$ Years of Data Warehouse Research

Data Preparation: A Technological Perspective and Review

Data Glitches: Monsters in Your Data

• Data Warehousing
• Big Data Analytics
• Business Intelligence
• Online Analytical Processing

Star Schema

• Database Design
• Data Dimensions
• Data Granularity

Table of contents (21 chapters)

Front matter, introduction.

• David Taniar, Wenny Rahayu

Simple Star Schemas

Creating facts and dimensions: more complex processes, snowflake and bridge tables, hierarchies, bridge tables, temporal data warehousing, advanced dimension, determinant dimensions, junk dimensions, dimension keys, one-attribute dimensions, multi-fact and multi-input, multi-fact star schemas, slicing a fact, multi-input operational databases, data warehousing granularity and evolution, data warehousing granularity and levels of aggregation, authors and affiliations.

David Taniar

Wenny Rahayu

About the authors

Bibliographic information.

Book Title : Data Warehousing and Analytics

Book Subtitle : Fueling the Data Engine

Authors : David Taniar, Wenny Rahayu

Series Title : Data-Centric Systems and Applications

DOI : https://doi.org/10.1007/978-3-030-81979-8

Publisher : Springer Cham

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Softcover ISBN : 978-3-030-81978-1 Published: 05 February 2022

eBook ISBN : 978-3-030-81979-8 Published: 04 February 2022

Series ISSN : 2197-9723

Series E-ISSN : 2197-974X

Edition Number : 1

Number of Pages : XVIII, 635

Number of Illustrations : 119 b/w illustrations, 262 illustrations in colour

Topics : Database Management , Big Data , Statistics, general , Information Storage and Retrieval

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Data Mining Case Studies & Benefits

• Key Takeaways

Data mining has improved the decision-making process for over 80% of companies. (Source: Gartner).

Statista reports that global spending on robotic process automation (RPA) is projected to reach $98 billion by 2024, indicating a significant investment in automation technologies.

According to Grand View Research, the global data mining market will reach $16,9 billion in 2027.

Ethical Data Mining preserves individual rights and fosters trust.

A successful implementation requires defining clear goals, choosing data wisely, and constant adaptation.

Data mining case studies help businesses explore data for smart decision-making. It’s about finding valuable insights from big datasets. This is crucial for businesses in all industries as data guides strategic planning. By spotting patterns in data, businesses gain intelligence to innovate and stay competitive. Real examples show how data mining improves marketing and healthcare. Data mining isn’t just about analyzing data; it’s about using it wisely for meaningful changes.

The Importance of Data Mining for Modern Business:

Data mining has taken on a central role in the modern world of business. Data is a major issue for businesses today. Making informed decisions with this data can be crucial to staying competitive. This article explores the many aspects of data mining and its impact on decisions.

• Unraveling Data Landscape

Businesses generate a staggering amount of data, including customer interactions, market patterns, and internal operations. Decision-makers face an information overload without effective tools for sorting through all this data.

Data mining is a process which not only organizes, structures and extracts patterns and insights from this vast amount of data. It acts as a compass to guide decision makers through the complex landscape of data.

• Empowering Strategic Decision Making

Data mining is a powerful tool for strategic decision making. Businesses can predict future trends and market behavior by analyzing historical data. This insight allows businesses to better align their strategies with predicted shifts.

Data mining can provide the strategic insights required for successful decision making, whether it is launching a product, optimizing supply chain, or adjusting pricing strategies.

• Customer-Centric Determining

Understanding and meeting the needs of customers is paramount in an era where customer-centricity reigns. Data mining is crucial in determining customer preferences, behaviors, and feedback.

This information allows businesses to customize products and services in order to meet the expectations of customers, increase satisfaction and build lasting relationships. With customer-centric insights, decision-makers can make choices that resonate with their target audiences and foster loyalty and brand advocacy.

Data Mining: Applications across industries

Data mining is transforming the way companies operate and make business decisions. This article explores the various applications of data-mining, highlighting case studies that illuminate its impact in the healthcare, retail, and finance sectors.

• Healthcare Case Studies:

Data mining is a powerful tool in the healthcare industry. It can improve patient outcomes and treatment plans. Discover compelling case studies in which data mining played a crucial role in predicting patterns of disease, optimizing treatment and improving patient care. These examples, which range from early detection of health risks to personalized medicines, show the impact that data mining has had on the healthcare industry.

State of Technology 2024

Humanity's Quantum Leap Forward

Explore 'State of Technology 2024' for strategic insights into 7 emerging technologies reshaping 10 critical industries. Dive into sector-wide transformations and global tech dynamics, offering critical analysis for tech leaders and enthusiasts alike, on how to navigate the future's technology landscape.

• Retail Success stories:

Retail is at the forefront of leveraging data mining to enhance customer experiences and streamline operations. Discover success stories of how data mining empowered businesses to better understand consumer behavior, optimize their inventory management and create personalized marketing strategies.

These case studies, which range from e-commerce giants and brick-and-mortar shops, show how data mining can boost sales, improve customer satisfaction, transform the retail landscape, etc.

• Financial Sector Examples:

Data mining is a valuable tool in the finance industry, where precision and risk assessment are key. Explore case studies that demonstrate how data mining can be used for fraud detection and risk assessment. These examples demonstrate how financial institutions use data mining to make better decisions, protect against fraud, and customize services to their clients’ needs.

• Data Mining and Education:

Data mining has been used in the education sector to enhance learning beyond healthcare, retail and finance. Learn how educational institutions use data mining to optimize learning outcomes, analyze student performance and personalize materials. These examples, ranging from adaptive learning platforms and predictive analytics to predictive modeling, demonstrate the potential for data mining to revolutionize how we approach education.

• Manufacturing efficiency:

Data mining is a powerful tool for streamlining manufacturing processes. Examine case studies that demonstrate how data mining can be used to improve supply chain management, predict maintenance requirements, and increase overall operational efficiency. These examples show how data-driven insights can lead to cost savings, increased productivity, and a competitive advantage in manufacturing.

Data mining is a key component in each of these applications. It unlocks insights, streamlines operations, and shapes the future of decisions. Data mining is transforming the landscapes of many industries, including healthcare, retail, education, finance, and manufacturing.

Data Mining Techniques

Data mining techniques help businesses gain an edge by extracting valuable insights and information from large datasets. This exploration will provide an overview of the most popular data mining methods, and back each one with insightful case studies.

• Popular Data Mining Techniques

Clustering Analysis

The clustering technique involves grouping data points based on a set of criteria. This method is useful for detecting patterns in data sets and can be used to segment customers, detect anomalies, or recognize patterns. The case studies will show how clustering can be used to improve marketing strategies, streamline products, and increase overall operational efficiency.

Association Rule Mining

Association rule mining reveals relationships between variables within large datasets. Market basket analysis is a common application of association rule mining, which identifies patterns in co-occurring products in transactions. Real-world examples of how association rule mining is used in retail to improve product placements, increase sales, and enhance the customer experience.

Decision Tree Analysis

The decision tree is a visual representation of the process of making decisions. This technique is a powerful tool for classification tasks. It helps businesses make decisions using a set of criteria. Through case studies, you will learn how decision tree analyses have been used in the healthcare industry for disease diagnosis and fraud detection, as well as predictive maintenance in manufacturing.

Regression Analysis

Regression analysis is a way to explore the relationship between variables. This allows businesses to predict and understand how one variable affects another. Discover case studies that demonstrate how regression analysis is used to predict customer behavior, forecast sales trends, and optimize pricing strategies.

Benefits and ROI:

Businesses are increasingly realizing the benefits of data mining in the current dynamic environment. The benefits are numerous and tangible, ranging from improved decision-making to increased operational efficiency. We’ll explore these benefits, and how businesses can leverage data mining to achieve significant gains.

• Enhancing Decision Making

Data mining provides businesses with actionable insight derived from massive datasets. Analyzing patterns and trends allows organizations to make more informed decisions. This reduces uncertainty and increases the chances of success. There are many case studies that show how data mining has transformed the decision-making process of businesses in various sectors.

• Operational Efficiency

Data mining is essential to achieving efficiency, which is the cornerstone of any successful business. Organizations can improve their efficiency by optimizing processes, identifying bottlenecks, and streamlining operations. These real-world examples show how businesses have made remarkable improvements in their operations, leading to savings and resource optimization.

• Personalized Customer Experiences

Data mining has the ability to customize experiences for customers. Businesses can increase customer satisfaction and loyalty by analyzing the behavior and preferences of their customers. Discover case studies that show how data mining has been used to create engaging and personalized customer journeys.

• Competitive Advantage

Gaining a competitive advantage is essential in today’s highly competitive environment. Data mining gives businesses insights into the market, competitor strategies, and customer expectations. These insights can give organizations a competitive edge and help them achieve success. Look at case studies that show how companies have outperformed their competitors by using data mining.

Calculating ROI and Benefits

To justify investments, businesses must also quantify their return on investment. Calculating ROI for data mining initiatives requires a thorough analysis of the costs, benefits, and long-term impacts. Let’s examine the complexities of ROI within the context of data-mining.

• Cost-Benefit Analysis

Prior to focusing on ROI, companies must perform a cost-benefit assessment of their data mining projects. It involves comparing the costs associated with implementing data-mining tools, training staff, and maintaining infrastructure to the benefits anticipated, such as higher revenue, cost savings and better decision-making. Case studies from real-world situations provide insight into cost-benefit analysis.

• Quantifying Tangible and intangible benefits

Data mining initiatives can yield tangible and intangible benefits. Quantifying tangible benefits such as an increase in sales or a reduction in operational costs is easier. Intangible benefits such as improved brand reputation or customer satisfaction are also important, but they may require a nuanced measurement approach. Examine case studies that quantify both types.

• Long-term Impact Assessment

ROI calculations should not be restricted to immediate gains. Businesses need to assess the impact their data mining projects will have in the future. Consider factors like sustainability, scalability, and ongoing benefits. Case studies that demonstrate the success of data-mining strategies over time can provide valuable insight into long-term impact assessment.

• Key Performance Indicators for ROI

Businesses must establish KPIs that are aligned with their goals in order to measure ROI. KPIs can be used to evaluate the success of data-mining initiatives, whether it is tracking sales growth, customer satisfaction rates, or operational efficiency. Explore case studies to learn how to select and monitor KPIs strategically for ROI measurement.

Data Mining Ethics

Data mining is a field where ethical considerations are crucial to ensuring transparent and responsible practices. It is important to carefully navigate the ethical landscape as organizations use data to extract valuable insights. This section examines ethical issues in data mining and highlights cases that demonstrate ethical practices.

• Understanding Ethical Considerations

Data mining ethics revolves around privacy, consent, and responsible information use. Businesses are faced with the question of how they use and collect data. Ethics also includes the biases in data and the fairness of algorithms.

• Balance Innovation and Privacy

Finding the right balance between privacy and innovation is a major ethical issue in data mining. In order to gain an edge in the market through data insights and to innovate, organizations must walk a tightrope between innovation and privacy. Case studies will illuminate how companies have successfully balanced innovation and privacy.

• Transparency and informed consent

Transparency in the processes is another important aspect of ethical data mining. This is to ensure that individuals are informed and consented before their data is used. This subtopic will explore the importance of transparency in data collection and processing, with case studies that highlight instances where organizations have established exemplary standards to obtain informed consent.

Exploring Data Mining Ethics is crucial as data usage evolves. Businesses must balance innovation, privacy, and transparency while gaining informed consent. Real-world cases show how ethical data mining protects privacy and builds trust.

Implementing Data Mining is complex yet rewarding. This guide helps set goals, choose data sources, and use algorithms effectively. Challenges like data security and resistance to change are common but manageable.

Considering ethics while implementing data mining shows responsibility and opens new opportunities. Organizations prioritizing ethical practices become industry leaders, mitigating risks and achieving positive impacts on business, society, and technology. Ethics and implementation synergize in data mining, unlocking its true potential.

• Q. What ethical considerations are important in data mining?

Privacy and consent are important ethical considerations for data mining.

• Q. How can companies avoid common pitfalls when implementing data mining?

By ensuring the security of data, addressing cultural opposition, and encouraging continuous learning and adaptation.

• Q. Why is transparency important in data mining?

Transparency and consent to use collected data ethically are key elements of building trust.

• Q. What are the main steps to implement data mining in businesses?

Define your objectives, select data sources, select algorithms and monitor continuously.

• Q. How can successful organizations use data mining to gain a strategic advantage?

By taking informed decisions, improving operations and staying on top of the competition.

Use data warehouse and data mining to predict student academic performance in schools: A case study (perspective application and benefits)

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A CASE STUDY ON DATA MINING AND DATA WAREHOUSE

This paper shows information and new application in data mining and data warehouse as well as the advantage of data mining and data warehouse the purpose of data mining and data warehouse to make decision making system in any field for business and organization. This project for how the business will organize in marketing field and what are the advantage or disadvantage for data mining and data warehouse it represent the new techniques in the top of the market for accessing and increase the value for the database. Thus the paper shows that what is data mining and data warehouse and how is use in market and what all are the advantage for market value. It is familiar that the strategic level of decision usually does not use business information on a daily basis but instead, derivative data from specific time. It is necessary in decision-making process to consider the large amounts of database so that the quality of decision-making is satisfied. Data Warehouse and Data Mining concept are any as a good base for business decision-making.

Related Papers

Md. Nayem Khan

This paper provides an overview of Data warehousing and OLAP-OLTP technology exploring the significance of Industrial work like decision support. Data warehousing and OLAP (On-line Analytical Processing) tools are essential for decision-making and has the ability to focus on databases of industry. Today we can easily see the difference between the requirements of database technology compared to OLTP (On-line Transaction Processing) application. OLAP is market oriented where OLTP is customers transaction oriented. Moreover, we have some of the descriptions of back end tools for extracting and cleaning of data, multidimensional data models, front-end client tools, database query and analysis, metadata management of warehouse, industrial database management and some research about data warehousing and mining with OLAP and OLTP. In addition, here will be consideration of basic need of OLAP and OLTP in industry or business management and OLAP-OLTPs advantages and disadvantages.

Dr. Seun Ebiesuwa

This paper aims to give a superficial exposé of Data Warehousing technology as a possible effective tool for organizations Business Intelligence. The key components of a Data Warehouse will be discussed as they offer a part of the core requirements for successful Business Intelligence deployment in an organization. Universally accepted Data Warehousing and Business Intelligence Models will be discussed and highlighted in order to ascertain the effectiveness of Data Warehousing as a tool for efficient Business Intelligence deployment. Traditionally, data warehouses are designed to collect and organize historical business data so it can be properly analyzed to enable management make optimal business decisions. Effective Business Intelligence can help companies gain a comprehensive understanding of the factors affecting their business, enabling them to make informed decisions for the competitive edge (Gutierrez, 2007)

– In this paper I intend the concept of implementation of data warehousing & data mining in E-governance for god governance. India is a large autonomous nation having multilevel administrative. Vast amount of data is generated and circulated by different government departments. The primary duty of government is to provide accurate and clear information to citizens. Make use of efficient Data Warehousing and Data Mining techniques may surely enhance government to do better for people. There are many methodologies used increase the efficiency of E-governance. One of them is Data warehousing and Data mining. It is must to unite all the departments in terms of data sharing so that all departments can work under a single controlling authority. It is important to develop a structure for creating a centralized countrywide data warehouse which has horizontal as well as vertical interconnections having limited accessibility at lower level authorities and can be fully accessed at the higher levels. Proper and accurate data can provide the information to better support for government decision, and also provides the enhanced services for public and achieves humanist truthfully.

IEEE International Conference on Research, Innovation and Vision for the Future

Publisher ijmra.us UGC Approved

Data warehouse is the requisite of all present competitive business communities " i.e. profitable and non-profitable as well as educational institutions where data is complex, huge and dynamic. The technology advent is not in a day but emerging from late 1980s. Data warehouse is a central repository that merges data from various sources: internal & external along with the web data and upholding the traditional database. Data mining is a tool of data warehousing to extract valuable information, choose a right alternate and make predictions to support the managerial tasks. Though the technologies are still not mature however, due to developments in Information Technologies, high performance softwares & tools and artificial intelligence, it is easier to implement these technologies to face the current challenges. The current paper highlights certain journeys of data warehouse, warehousing and data mining in a gradual and comprehensive way.

International Journal IJRITCC

— Data mining is a process which is used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies as well as increase sales and decrease costs. It depends on constructive data collection and warehousing as well as computer processing. Data mining used to analyze patterns and relationships in data based on what users request. For example, data mining software can be used to create classes of information. When companies centralize their data into one database or program, it is known as data warehousing. Accompanied a data warehouse, an organization may spin off segments of the data for particular users and utilize. While, in other cases, analysts may begin with the type of data they want and create a data warehouse based on those specs. Regardless of how businesses and other entities systemize their data, they use it to support management's decision-making processes.

International Journal of Engineering Sciences & Research Technology

Ijesrt Journal

IJESRT Journal

Today in organizations, the developments in the transaction processing technology requires that, amount and rate of data capture should match the speed of processing of the data into information which can be utilized for decision making. A data warehouse is a subject- oriented, integrated, time-variant and non-volatile collection of data that is required for decision making process. Data mining involves the use of various data analysis tools to discover new facts, valid patterns and relationships in large data sets. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. OLTP is customer-oriented and is used for transaction and query processing by clerks, clients and information technology professionals. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives and analysts. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications.

alok ranjan

Data Mining is the process of analyzing the data from different perspective and summarizing it into meaningful information. In fact, Data Mining is a technique involving non-trivial extraction of novel, implicit, and actionable knowledge from a huge sets of data, and is an emerging technology in the recent era, with the increased use of databases to store and retrieve information efficiently. This is also termed as ‘Knowledge Discovery in Database’, which enables the data exploration, data analysis, and data visualization of huge database at a high level of abstraction. These techniques are used to predict future trends and behaviors, thus allowing the business to make proactive and knowledge driven decisions. This paper focus on the Data Mining techniques, its architecture, its process, methodologies employed, kinds of data mined, functionalities, general and technical issues, its significance and application areas in relevance to today’s business environment, and the scope for research in the related areas.

International Journal of …

Ravindra Hegadi

Data quality is a critical factor for the success of data warehousing projects. If data is of inadequate quality, then the knowledge workers who query the data warehouse and the decision makers who receive the information cannot trust the results. In order to obtain clean and reliable data, it is imperative to focus on data quality. While many data warehouse projects do take data quality into consideration, it is often given a delayed afterthought. Even QA after ETL is not good enough the Quality process needs to be incorporated in the ETL process itself. Data quality has to be maintained for individual records or even small bits of information to ensure accuracy of complete database. Data quality is an increasingly serious issue for organizations large and small. It is central to all data integration initiatives. Before data can be used effectively in a data warehouse, or in customer relationship management, enterprise resource planning or business analytics applications, it needs to be analyzed and cleansed. To ensure high quality data is sustained, organizations need to apply ongoing data cleansing processes and procedures, and to monitor and track data quality levels over time. Otherwise poor data quality will lead to increased costs, breakdowns in the supply chain and inferior customer relationship management. Defective data also hampers business decision making and efforts to meet regulatory compliance responsibilities. The key to successfully addressing data quality is to get business professionals centrally involved in the process. We have analyzed possible set of causes of data quality issues from exhaustive survey and discussions with data warehouse groups working in distinguishes organizations in India and abroad. We expect this paper will help modelers, designers of warehouse to analyse and implement quality warehouse and business intelligence applications.

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Published: 5 April 2024 Contributors: Tim Mucci, Cole Stryker

Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.