Data Cleansing Software Market Outlook

The global data cleansing software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 4.2 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 12.5% during the forecast period. This substantial growth can be attributed to the increasing importance of maintaining clean and reliable data for business intelligence and analytics, which are driving the adoption of data cleansing solutions across various industries.

The proliferation of big data and the growing emphasis on data-driven decision-making are significant growth factors for the data cleansing software market. As organizations collect vast amounts of data from multiple sources, ensuring that this data is accurate, consistent, and complete becomes critical for deriving actionable insights. Data cleansing software helps organizations eliminate inaccuracies, inconsistencies, and redundancies, thereby enhancing the quality of their data and improving overall operational efficiency. Additionally, the rising adoption of advanced analytics and artificial intelligence (AI) technologies further fuels the demand for data cleansing software, as clean data is essential for the accuracy and reliability of these technologies.

Another key driver of market growth is the increasing regulatory pressure for data compliance and governance. Governments and regulatory bodies across the globe are implementing stringent data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations mandate organizations to ensure the accuracy and security of the personal data they handle. Data cleansing software assists organizations in complying with these regulations by identifying and rectifying inaccuracies in their data repositories, thus minimizing the risk of non-compliance and hefty penalties.

The growing trend of digital transformation across various industries also contributes to the expanding data cleansing software market. As businesses transition to digital platforms, they generate and accumulate enormous volumes of data. To derive meaningful insights and maintain a competitive edge, it is imperative for organizations to maintain high-quality data. Data cleansing software plays a pivotal role in this process by enabling organizations to streamline their data management practices and ensure the integrity of their data. Furthermore, the increasing adoption of cloud-based solutions provides additional impetus to the market, as cloud platforms facilitate seamless integration and scalability of data cleansing tools.

Regionally, North America holds a dominant position in the data cleansing software market, driven by the presence of numerous technology giants and the rapid adoption of advanced data management solutions. The region is expected to continue its dominance during the forecast period, supported by the strong emphasis on data quality and compliance. Europe is also a significant market, with countries like Germany, the UK, and France showing substantial demand for data cleansing solutions. The Asia Pacific region is poised for significant growth, fueled by the increasing digitalization of businesses and the rising awareness of data quality's importance. Emerging economies in Latin America and the Middle East & Africa are also expected to witness steady growth, driven by the growing adoption of data-driven technologies.

The role of Data Quality Tools cannot be overstated in the context of data cleansing software. These tools are integral in ensuring that the data being processed is not only clean but also of high quality, which is crucial for accurate analytics and decision-making. Data Quality Tools help in profiling, monitoring, and cleansing data, thereby ensuring that organizations can trust their data for strategic decisions. As organizations increasingly rely on data-driven insights, the demand for robust Data Quality Tools is expected to rise. These tools offer functionalities such as data validation, standardization, and enrichment, which are essential for maintaining the integrity of data across various platforms and applications. The integration of these tools with data cleansing software enhances the overall data management capabilities of organizations, enabling them to achieve greater operational efficiency and compliance with data regulations.

Component Analysis

The data cle

Data Cleansing Services Improve the quality of your database With Our End-To-End Data Cleansing Services. Enhance Your Customer Database With Our Data Cleansing Services The quality of data that is evaluated in order to make informed decisions determines a company's efficiency. Most organizations place high importance on maintaining data records of possible leads for future reference and immediate usage. What if, on the other hand, the informa

Dataset Description

This dataset is a collection of customer, product, sales, and location data extracted from a CRM and ERP system for a retail company. It has been cleaned and transformed through various ETL (Extract, Transform, Load) processes to ensure data consistency, accuracy, and completeness. Below is a breakdown of the dataset components: 1. Customer Information (s_crm_cust_info)

This table contains information about customers, including their unique identifiers and demographic details.

Columns:

  cst_id: Customer ID (Primary Key)

  cst_gndr: Gender

  cst_marital_status: Marital status

  cst_create_date: Customer account creation date

Cleaning Steps:

  Removed duplicates and handled missing or null cst_id values.

  Trimmed leading and trailing spaces in cst_gndr and cst_marital_status.

  Standardized gender values and identified inconsistencies in marital status.

1. Product Information (s_crm_prd_info / b_crm_prd_info)

This table contains information about products, including product identifiers, names, costs, and lifecycle dates.

Columns:

  prd_id: Product ID

  prd_key: Product key

  prd_nm: Product name

  prd_cost: Product cost

  prd_start_dt: Product start date

  prd_end_dt: Product end date

Cleaning Steps:

  Checked for duplicates and null values in the prd_key column.

  Validated product dates to ensure prd_start_dt is earlier than prd_end_dt.

  Corrected product costs to remove invalid entries (e.g., negative values).

1. Sales Details (s_crm_sales_details / b_crm_sales_details)

This table contains information about sales transactions, including order dates, quantities, prices, and sales amounts.

Columns:

  sls_order_dt: Sales order date

  sls_due_dt: Sales due date

  sls_sales: Total sales amount

  sls_quantity: Number of products sold

  sls_price: Product unit price

Cleaning Steps:

  Validated sales order dates and corrected invalid entries.

  Checked for discrepancies where sls_sales did not match sls_price * sls_quantity and corrected them.

  Removed null and negative values from sls_sales, sls_quantity, and sls_price.

1. ERP Customer Data (b_erp_cust_az12, s_erp_cust_az12)

This table contains additional customer demographic data, including gender and birthdate.

Columns:

  cid: Customer ID

  gen: Gender

  bdate: Birthdate

Cleaning Steps:

  Checked for missing or null gender values and standardized inconsistent entries.

  Removed leading/trailing spaces from gen and bdate.

  Validated birthdates to ensure they were within a realistic range.

1. Location Information (b_erp_loc_a101)

This table contains country information related to the customers' locations.

Columns:

  cntry: Country

Cleaning Steps:

  Standardized country names (e.g., "US" and "USA" were mapped to "United States").

  Removed special characters (e.g., carriage returns) and trimmed whitespace.

1. Product Category (b_erp_px_cat_g1v2)

This table contains product category information.

Columns:

  Product category data (no significant cleaning required).

Key Features:

Customer demographics, including gender and marital status

Product details such as cost, start date, and end date

Sales data with order dates, quantities, and sales amounts

ERP-specific customer and location data

Data Cleaning Process:

This dataset underwent extensive cleaning and validation, including:

Null and Duplicate Removal: Ensuring no duplicate or missing critical data (e.g., customer IDs, product keys).

Date Validations: Ensuring correct date ranges and chronological consistency.

Data Standardization: Standardizing categorical fields (e.g., gender, country names) and fixing inconsistent values.

Sales Integrity Checks: Ensuring sales amounts match the expected product of price and quantity.

This dataset is now ready for analysis and modeling, with clean, consistent, and validated data for retail analytics, customer segmentation, product analysis, and sales forecasting.

This fully synthetic dataset simulates digital sales transactions, marketing channels, and customer feedback for an online store environment. It was designed to support hands-on exploration of sales analytics, marketing attribution, customer segmentation, and business intelligence workflows.

Highlights:

~3,000 transactions with realistic structure while preserving privacy.

Detailed features including product categories, pricing, quantities, calculated revenue.

Customer demographics (country, gender), marketing channels, sales platforms.

Customer feedback captured as Net Promoter Scores (NPS).

Ideal for practicing data cleaning, exploratory data analysis, dashboard design, and predictive modeling in marketing and e-commerce contexts.

File Information

File format: CSV

Total rows: 3,000

Columns: 15

Encoding: UTF-8

Delimiter: Comma (,)

Columns Overview

date: Date of purchase

first_name: Customer names

gender: Gender (Male/Female/Other)

product_name: Name of the digital product

category: Product category (e.g., Course, Template, Webinar)

price: Unit price

quantity: Number of units purchased

revenue: Total revenue = price × quantity

customer_country: Country of the customer

platform: Sales platform (e.g., ClickFunnels, Kajabi)

marketing_channel: Marketing source (e.g., Email, Organic Search)

nps_score: Customer Net Promoter Score (0–10)

Analyzing customers’ characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

Sample data for exercises in Further Adventures in Data Cleaning.

Materials from workshop conducted for Monroe Library faculty as part of TLT/Faculty Development/Digital Scholarship on 2018-04-05. Objectives:Clean dataAnalyze data using pivot tablesVisualize dataDesign accessible instruction for working with dataAssociated Research Guide at http://researchguides.loyno.edu/data_workshopData sets are from the following:

BaroqueArt Dataset by CulturePlex Lab is licensed under CC0 What's on the Menu? Menus by New York Public Library is licensed under CC0 Dog movie stars and dog breed popularity by Ghirlanda S, Acerbi A, Herzog H is licensed under CC BY 4.0 NOPD Misconduct Complaints, 2016-2018 by City of New Orleans Open Data is licensed under CC0 U.S. Consumer Product Safety Commission Recall Violations by CU.S. Consumer Product Safety Commission, Violations is licensed under CC0 NCHS - Leading Causes of Death: United States by Data.gov is licensed under CC0 Bob Ross Elements by Episode by Walt Hickey, FiveThirtyEight, is licensed under CC BY 4.0 Pacific Walrus Coastal Haulout 1852-2016 by U.S. Geological Survey, Alaska Science Center is licensed under CC0 Australia Registered Animals by Sunshine Coast Council is licensed under CC0

Customer360Insights

The Customer360Insights dataset is a synthetic collection meticulously designed to mirror the multifaceted nature of customer interactions within an e-commerce platform. It encompasses a wide array of variables, each serving as a pillar to support various analytical explorations. Here’s a breakdown of the dataset and the potential analyses it enables:

Dataset Description

• Customer Demographics: Includes FullName, Gender, Age, CreditScore, and MonthlyIncome. These variables provide a demographic snapshot of the customer base, allowing for segmentation and targeted marketing analysis.
• Geographical Data: Comprising Country, State, and City, this section facilitates location-based analytics, market penetration studies, and regional sales performance.
• Product Information: Details like Category, Product, Cost, and Price enable product trend analysis, profitability assessment, and inventory optimization.
• Transactional Data: Captures the customer journey through SessionStart, CartAdditionTime, OrderConfirmation, OrderConfirmationTime, PaymentMethod, and SessionEnd. This rich temporal data can be used for funnel analysis, conversion rate optimization, and customer behavior modeling.
• Post-Purchase Details: With OrderReturn and ReturnReason, analysts can delve into return rate calculations, post-purchase satisfaction, and quality control.

Types of Analysis

• Descriptive Analytics: Understand basic metrics like average monthly income, most common product categories, and typical credit scores.
• Predictive Analytics: Use machine learning to predict credit risk or the likelihood of a purchase based on demographics and session activity.
• Customer Segmentation: Group customers by demographics or purchasing behavior to tailor marketing strategies.
• Geospatial Analysis: Examine sales distribution across different regions and optimize logistics. Time Series Analysis: Study the seasonality of purchases and session activities over time.
• Funnel Analysis: Evaluate the customer journey from session start to order confirmation and identify drop-off points.
• Cohort Analysis: Track customer cohorts over time to understand retention and repeat purchase patterns.
• Market Basket Analysis: Discover product affinities and develop cross-selling strategies.

This dataset is a playground for data enthusiasts to practice cleaning, transforming, visualizing, and modeling data. Whether you’re conducting A/B testing for marketing campaigns, forecasting sales, or building customer profiles, Customer360Insights offers a rich, realistic dataset for honing your data science skills.

Curious about how I created the data? Feel free to click here and take a peek! 😉

📊🔍 Good Luck and Happy Analysing 🔍📊

This dataset provides information about Customer Service Requests relating to City Presentation (Cleaning) that are in the City of Casey.

AI in Data Cleaning Market Outlook

According to our latest research, the global AI in Data Cleaning market size reached USD 1.82 billion in 2024, demonstrating remarkable momentum driven by the exponential growth of data-driven enterprises. The market is projected to grow at a CAGR of 28.1% from 2025 to 2033, reaching an estimated USD 17.73 billion by 2033. This exceptional growth trajectory is primarily fueled by increasing data volumes, the urgent need for high-quality datasets, and the adoption of artificial intelligence technologies across diverse industries.

The surging demand for automated data management solutions remains a key growth driver for the AI in Data Cleaning market. As organizations generate and collect massive volumes of structured and unstructured data, manual data cleaning processes have become insufficient, error-prone, and costly. AI-powered data cleaning tools address these challenges by leveraging machine learning algorithms, natural language processing, and pattern recognition to efficiently identify, correct, and eliminate inconsistencies, duplicates, and inaccuracies. This automation not only enhances data quality but also significantly reduces operational costs and improves decision-making capabilities, making AI-based solutions indispensable for enterprises aiming to achieve digital transformation and maintain a competitive edge.

Another crucial factor propelling market expansion is the growing emphasis on regulatory compliance and data governance. Sectors such as BFSI, healthcare, and government are subject to stringent data privacy and accuracy regulations, including GDPR, HIPAA, and CCPA. AI in data cleaning enables these industries to ensure data integrity, minimize compliance risks, and maintain audit trails, thereby safeguarding sensitive information and building stakeholder trust. Furthermore, the proliferation of cloud computing and advanced analytics platforms has made AI-powered data cleaning solutions more accessible, scalable, and cost-effective, further accelerating adoption across small, medium, and large enterprises.

The increasing integration of AI in data cleaning with other emerging technologies such as big data analytics, IoT, and robotic process automation (RPA) is unlocking new avenues for market growth. By embedding AI-driven data cleaning processes into end-to-end data pipelines, organizations can streamline data preparation, enable real-time analytics, and support advanced use cases like predictive modeling and personalized customer experiences. Strategic partnerships, investments in R&D, and the rise of specialized AI startups are also catalyzing innovation in this space, making AI in data cleaning a cornerstone of the broader data management ecosystem.

From a regional perspective, North America continues to lead the global AI in Data Cleaning market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The region’s dominance is attributed to the presence of major technology vendors, robust digital infrastructure, and high adoption rates of AI and cloud technologies. Meanwhile, Asia Pacific is witnessing the fastest growth, propelled by rapid digitalization, expanding IT sectors, and increasing investments in AI-driven solutions by enterprises in China, India, and Southeast Asia. Europe remains a significant market, supported by strict data protection regulations and a mature enterprise landscape. Latin America and the Middle East & Africa are emerging as promising markets, albeit at a relatively nascent stage, with growing awareness and gradual adoption of AI-powered data cleaning solutions.

Component Analysis

The AI in Data Cleaning market is broadly segmented by component into software and services, with each segment playing a pivotal role in shaping the industry’s evolution. The software segment dominates the market, driven by the rapid adoption of advanced AI-based data cleaning platforms that automate complex data preparation tasks. These platforms leverage sophisticated algorithms to detect anomalies, standardize formats, and enrich datasets, thereby enabling organizations to maintain high-quality data repositories. The increasing demand for self-service data cleaning software, which empowers business users to cleanse data without extensive IT intervention, is further fueling growth in this segment. Vendors are continuously enhancing their offerings with intuitive interfaces, integration capabilities, and support for diverse data sources to cater to a wide r

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

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Cleaning Services Market Size 2024-2028

The cleaning services market size is forecast to increase by USD 21.78 billion at a CAGR of 6.4% between 2023 and 2028.

The market is experiencing significant growth driven by increasing health consciousness in workplaces and a robust residential sector. With the heightened focus on maintaining clean and hygienic environments to prevent the spread of diseases, the demand for professional cleaning services is on the rise. Moreover, the residential sector's expansion, particularly in urban areas, is fueling the market's growth as more people seek convenient and reliable cleaning solutions. However, the market faces challenges, including the scarcity of skilled labor, which could impact service quality and efficiency. Companies seeking to capitalize on market opportunities must invest in training programs and technology to address the labor shortage. Additionally, offering value-added services, such as disinfection and specialized cleaning, can help differentiate offerings and cater to evolving customer needs. Navigating these challenges and leveraging market trends requires strategic planning and a customer-centric approach.

What will be the Size of the Cleaning Services Market during the forecast period?

Request Free SampleThe market encompasses various sectors, including workplace sustainability and hygiene, window washing, healthcare facilities, and residential customers. Workplace sustainability is a growing concern for business entities, leading to an increased focus on employee wellness and safety protocols in office buildings. The economic upturn has boosted the demand for commercial cleaning services from real estate investment firms and retail stores. High competition prevails in the market, with companies offering services such as vacuuming, floor cleaning, and furniture cleaning to cater to diverse customer needs. Working parents and dual-income households prioritize convenience, driving the growth of residential cleaning services. Safety protocols are essential in healthcare facilities, making professional cleaning services indispensable. Additionally, services like air duct cleaning and carpet cleaning cater to specific customer requirements. Building workers and commercial customers seek reliable and efficient cleaning solutions to maintain their operations. Water damage restoration is another segment that experiences significant demand due to unforeseen circumstances. The trend towards sustainability influences the market, with companies focusing on eco-friendly cleaning methods and practices. In summary, the market is dynamic, with various sectors, customer segments, and trends shaping its evolution. Businesses and individuals prioritize cleanliness, safety, and convenience, driving the demand for professional cleaning services.

How is this Cleaning Services Industry segmented?

The cleaning services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. End-userCommercialResidentialGeographyNorth AmericaUSCanadaEuropeGermanyItalyMiddle East and AfricaAPACChinaSouth AmericaRest of World (ROW)

By End-user Insights

The commercial segment is estimated to witness significant growth during the forecast period.The Metal Additive Manufacturing Market is witnessing significant growth, particularly in the commercial segment. This expansion is driven by the increasing demand for cleaning services from commercial office buildings, medical institutions, and other establishments. Hospitality sector entities, such as hotels and resorts, are major contributors to this segment, prioritizing brand awareness and public hygiene. Food service establishments, including restaurants, cafes, bars, and pubs, also require frequent cleaning to maintain health regulations and customer satisfaction. Hospitals and healthcare centers hold substantial importance due to government-mandated cleanliness standards. With many patients undergoing long-term treatment, the need for regular cleaning is crucial. The residential segment also contributes significantly, with dual-income households and aging populations prioritizing workplace sustainability and workplace hygiene. The labor shortage has led to the adoption of advanced cleaning technologies, such as autonomous sweepers and disinfection techniques. Additionally, the growing population and rapid urbanization have increased the demand for eco-friendly products and services. The availability of these products caters to the sustainability concerns of both residential and commercial customers. In the commercial segment, cleaning priorities include floor cleaning, carpet cleaning, and air duct cleaning. Factories and industries focus on maintaining safety protocols and ensuring the skilled labor workforce is well-t

📝 Dataset Description This dataset contains information about customers of a telecommunications company, including their demographic details, account information, service subscriptions, and churn status. It is a modified version of the popular Telco Churn dataset, curated for exploratory data analysis, machine learning model development, and churn prediction tasks.

The dataset includes simulated missing values in some columns to reflect real-world data issues and support preprocessing and imputation tasks. This makes it especially useful for demonstrating data cleaning techniques and evaluating model robustness.

📂 Files Included telco_data_modified.csv: The main dataset with 21 columns and 7043 rows (some missing values are intentionally inserted).

📌 Features Column Name Description customerID Unique identifier for each customer gender Customer gender: Male/Female SeniorCitizen Indicates if the customer is a senior citizen (0 = No, 1 = Yes) Partner Whether the customer has a partner Dependents Whether the customer has dependents tenure Number of months the customer has stayed with the company PhoneService Whether the customer has phone service MultipleLines Whether the customer has multiple lines InternetService Customer's internet service provider (DSL, Fiber optic, No) OnlineSecurity Whether the customer has online security OnlineBackup Whether the customer has online backup DeviceProtection Whether the customer has device protection TechSupport Whether the customer has tech support StreamingTV Whether the customer has streaming TV StreamingMovies Whether the customer has streaming movies Contract Type of contract: Month-to-month, One year, Two year PaperlessBilling Whether the customer uses paperless billing PaymentMethod Payment method: (e.g., Electronic check, Mailed check, etc.) MonthlyCharges Monthly charges TotalCharges Total charges to date Churn Whether the customer has left the company (Yes/No)

🔍 Use Cases Binary classification: Predict customer churn

Data preprocessing and imputation exercises

Feature engineering and importance analysis

Customer segmentation and churn modeling

⚠️ Notes Missing values were intentionally inserted in the dataset to help simulate real-world conditions.

Some preprocessing may be required before modeling (e.g., converting categorical to numerical data, handling TotalCharges as numeric).

🏷️ Tags

telecom #churn #classification #customer-analytics #data-cleaning #feature-engineering

🙏 Acknowledgements This dataset is based on the original Telco Customer Churn dataset (initially provided by IBM). The current version has been modified for academic and practical exercises.

Analysis of ‘Credit Card Customer Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aryashah2k/credit-card-customer-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

A Customer Credit Card Information Dataset which can be used for Identifying Loyal Customers, Customer Segmentation, Targeted Marketing and other such use cases in the Marketing Industry.

A few tasks that can be performed using this dataset is as follows: - Perform Data-Cleaning,Preprocessing,Visualizing and Feature Engineering on the Dataset. - Implement Heirarchical Clustering, K-Means Clustering models. - Create RFM (Recency,Frequency,Monetary) Matrix to identify Loyal Customers.

Content

The Attributes Include: - Sl_No - Customer Key - AvgCreditLimit - TotalCreditCards - Totalvisitsbank - Totalvisitsonline - Totalcallsmade

--- Original source retains full ownership of the source dataset ---

The data and analytical support the Master's thesis submitted by Hana Remesova at the University of Primorska
Faculty of Mathematics, Natural Sciences, and Information Technologies. The .csv files are data files, the .Rmd file is an R markdown which can be run. The product of knitting the .Rmd file is the .html.

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviour: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

This dataset represents a thoroughly transformed and enriched version of a publicly available customer shopping dataset. It has undergone comprehensive processing to ensure it is clean, privacy-compliant, and enriched with new features, making it highly suitable for advanced analytics, machine learning, and business research applications.

The transformation process focused on creating a high-quality dataset that supports robust customer behavior analysis, segmentation, and anomaly detection, while maintaining strict privacy through anonymization and data validation.

➡ Data Cleaning and Preprocessing : Duplicates were removed. Missing numerical values (Age, Purchase Amount, Review Rating) were filled with medians; missing categorical values labeled “Unknown.” Text data were cleaned and standardized, and numeric fields were clipped to valid ranges.

➡ Feature Engineering : New informative variables were engineered to augment the dataset’s analytical power. These include: • Avg_Amount_Per_Purchase: Average purchase amount calculated by dividing total purchase value by the number of previous purchases, capturing spending behavior per transaction. • Age_Group: Categorical age segmentation into meaningful bins such as Teen, Young Adult, Adult, Senior, and Elder. • Purchase_Frequency_Score: Quantitative mapping of purchase frequency to annualized values to facilitate numerical analysis. • Discount_Impact: Monetary quantification of discount application effects on purchases. • Processing_Date: Timestamp indicating the dataset transformation date for provenance tracking.

➡ Data Filtering : Rows with ages outside 0–100 were removed. Only core categories (Clothing, Footwear, Outerwear, Accessories) and the top 25% of high-value customers by purchase amount were retained for focused analysis.

➡ Data Transformation : Key numeric features were standardized, and log transformations were applied to skewed data to improve model performance.

➡ Advanced Features : Created a category-wise average purchase and a loyalty score combining purchase frequency and volume.

➡ Segmentation & Anomaly Detection : Used KMeans to cluster customers into four groups and Isolation Forest to flag anomalies.

➡ Text Processing : Cleaned text fields and added a binary indicator for clothing items.

➡ Privacy : Hashed Customer ID and removed sensitive columns like Location to ensure privacy.

➡ Validation : Automated checks for data integrity, including negative values and valid ranges.

This transformed dataset supports a wide range of research and practical applications, including customer segmentation, purchase behavior modeling, marketing strategy development, fraud detection, and machine learning education. It serves as a reliable and privacy-aware resource for academics, data scientists, and business analysts.

Graph and download economic data for Expenditures: Laundry and Cleaning Supplies by Size of Consumer Unit: Two People in Consumer Unit (CXULAUNDRYLB0504M) from 1984 to 2022 about laundry, cleaning, consumer unit, supplies, expenditures, persons, and USA.

Overview

This dataset contains detailed metadata and user reviews for movies. It includes information such as movie titles, genres, user scores, certifications, metascores, directors, top cast members, plot summaries, and user reviews. The data was scraped from IMDb and may contain some inconsistencies and missing values, making it a great resource for practicing data cleaning and preprocessing.

Columns Description

• Name: The title of the movie.
• Year: The release year of the movie.
• Genres: The genres associated with the movie (e.g., Action, Adventure, Sci-Fi).
• Users-Score: Average user score.
• Certification: Movie certification rating (e.g., PG-13, R).
• Metascore: Metacritic score.
• Director: The director of the movie.
• Top-Cast: Main cast members.
• Plot-Summary: A brief summary of the movie's plot.
• Users-Reviews: User-submitted reviews.

Data Cleaning and Preprocessing

The dataset may include the following issues:

• Missing Values: Some columns have missing values.
• Inconsistent Delimiters: Certain rows may have inconsistent delimiters.
• Duplicate Entries: There might be duplicate records.
• Formatting Issues: Some columns may contain improperly formatted data.

Steps for Data Cleaning:

• Identify and handle missing values.
• Correct delimiter issues using text processing techniques.
• Remove duplicate records to ensure data integrity.
• Standardize formats for categorical variables.

Potential Use Cases

• Movie Recommendation Systems: Use the metadata to build recommendation algorithms.
• Sentiment Analysis: Analyze user reviews to gauge audience sentiment.
• Trend Analysis: Explore trends in movie genres, ratings, and user reviews.

License

This dataset is shared under the MIT License. If you use this data, please attribute IMDb as the source.

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Data Wrangling Market Size 2024-2028

The data wrangling market size is forecast to increase by USD 1.4 billion at a CAGR of 14.8% between 2023 and 2028. The market is experiencing significant growth due to the numerous benefits provided by data wrangling solutions, including data cleaning, transformation, and enrichment. One major trend driving market growth is the rising need for technology such as the competitive intelligence and artificial intelligence in the healthcare sector, where data wrangling is essential for managing and analyzing patient data to improve patient outcomes and reduce costs. However, a challenge facing the market is the lack of awareness of data wrangling tools among small and medium-sized enterprises (SMEs), which limits their ability to effectively manage and utilize their data. Despite this, the market is expected to continue growing as more organizations recognize the value of data wrangling in driving business insights and decision-making.

What will be the Size of the Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for data management and analysis in various industries. The market is experiencing significant growth due to the increasing volume, variety, and velocity of data being generated from various sources such as IoT devices, financial services, and smart cities. Artificial intelligence and machine learning technologies are being increasingly used for data preparation, data cleaning, and data unification. Data wrangling, also known as data munging, is the process of cleaning, transforming, and enriching raw data to make it usable for analysis. This process is crucial for businesses aiming to gain valuable insights from their data and make informed decisions. Data analytics is a primary driver for the market, as organizations seek to extract meaningful insights from their data. Cloud solutions are increasingly popular for data wrangling due to their flexibility, scalability, and cost-effectiveness.

Furthermore, both on-premises and cloud-based solutions are being adopted by businesses to meet their specific data management requirements. Multi-cloud strategies are also gaining traction in the market, as organizations seek to leverage the benefits of multiple cloud providers. This approach allows businesses to distribute their data across multiple clouds, ensuring business continuity and disaster recovery capabilities. Data quality is another critical factor driving the market. Ensuring data accuracy, completeness, and consistency is essential for businesses to make reliable decisions. The market is expected to grow further as organizations continue to invest in big data initiatives and implement advanced technologies such as AI and ML to gain a competitive edge. Data cleaning and data unification are key processes in data wrangling that help improve data quality. The finance and insurance industries are major contributors to the market, as they generate vast amounts of data daily.

In addition, real-time analysis is becoming increasingly important in these industries, as businesses seek to gain insights from their data in near real-time to make informed decisions. The Internet of Things (IoT) is also driving the market, as businesses seek to collect and analyze data from IoT devices to gain insights into their operations and customer behavior. Edge computing is becoming increasingly popular for processing IoT data, as it allows for faster analysis and decision-making. Self-service data preparation is another trend in the market, as businesses seek to empower their business users to prepare their data for analysis without relying on IT departments.

Moreover, this approach allows businesses to be more agile and responsive to changing business requirements. Big data is another significant trend in the market, as businesses seek to manage and analyze large volumes of data to gain insights into their operations and customer behavior. Data wrangling is a critical process in managing big data, as it ensures that the data is clean, transformed, and enriched to make it usable for analysis. In conclusion, the market in North America is experiencing significant growth due to the increasing demand for data management and analysis in various industries. Cloud solutions, multi-cloud strategies, data quality, finance and insurance, IoT, real-time analysis, self-service data preparation, and big data are some of the key trends driving the market. Businesses that invest in data wrangling solutions can gain a competitive edge by gaining valuable insights from their data and making informed decisions.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

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