The Data Preparation Tools market is experiencing robust growth, projected to reach a significant market size by 2033. Driven by the exponential increase in data volume and variety across industries, coupled with the rising need for accurate, consistent data for effective business intelligence and machine learning initiatives, this sector is poised for continued expansion. The 18.5% Compound Annual Growth Rate (CAGR) signifies strong market momentum, fueled by increasing adoption across diverse sectors like IT and Telecom, Retail & E-commerce, BFSI (Banking, Financial Services, and Insurance), and Manufacturing. The preference for self-service data preparation tools empowers business users to directly access and prepare data, minimizing reliance on IT departments and accelerating analysis. Furthermore, the integration of data preparation tools with advanced analytics platforms and cloud-based solutions is streamlining workflows and improving overall efficiency. This trend is further augmented by the growing demand for robust data governance and compliance measures, necessitating sophisticated data preparation capabilities. While the market shows significant potential, challenges remain. The complexity of integrating data from multiple sources and maintaining data consistency across disparate systems present hurdles for many organizations. The need for skilled data professionals to effectively utilize these tools also contributes to market constraints. However, ongoing advancements in automation and user-friendly interfaces are mitigating these challenges. The competitive landscape is marked by established players like Microsoft, Tableau, and IBM, alongside innovative startups offering specialized solutions. This competitive dynamic fosters innovation and drives down costs, benefiting end-users. The market segmentation by application and tool type highlights the varied needs and preferences across industries, and understanding these distinctions is crucial for effective market penetration and strategic planning. Geographical expansion, particularly within rapidly developing economies in Asia-Pacific, will play a significant role in shaping the future trajectory of this thriving market.

This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, after collecting a number of revenues and expenses over the months. Needed to know the answers to a number of questions to make important decisions based on intuition-free data. The Questions:- About Rev. & Exp.
- What is the total sales and profit for the whole period? And What Total products sold? And What is Net profit? - In which month was the highest percentage of revenue achieved? And in the same month, what is the largest day have amount of revenue? - In which month was the highest percentage of expenses achieved? And in the same month, what is the largest day have amount of exp.? - What is the extent of the change in expenditures for each month? Percentage change in net profit over the months? About Distribution - What is the number of products sold each month in the largest state? -The top 3 largest states buying products during the two years? Comparison - Between Sales Method by Sales? - Between Men and Women’s Product by Sales? - Between Retailer by Profit?

What I did? - Understanding the data - preprocessing and clean the data - Solve The problems in the cleaning like missing data or false type data - querying the data and make some calculations like "COGS" with power query "Excel". - Modeling and make some measures on the data with power pivot "Excel" - After finishing processing and preparation, I made Some Pivot tables to answers the questions. - Last, I made a dashboard with Power BI to visualize The Results.

A high-quality, clean dataset simulating global cosmetics and skincare product sales between January and August 2022. This dataset mirrors real-world transactional data, making it perfect for data analysis, Excel training, visualization projects, and machine learning prototypes.

📁 Dataset Overview

🧾 Sample Products

• Hydrating Face Serum
• Vitamin C Cream
• Aloe Vera Gel
• Charcoal Face Wash
• SPF 50 Sunscreen
• Niacinamide Toner
• Anti-Aging Serum
• Face Sheet Masks
• Hair Repair Oil
• Lip Balm Pack
• Body Butter Cream
• Salicylic Acid Cleanser

🌏 Countries Covered

• India
• USA
• UK
• Canada
• Australia
• New Zealand

📊 Quick Stats

• Total Rows: 374
• Date Range: Jan 1, 2022 – Aug 31, 2022
• Revenue Range: Varies from ~$100 to ~$20,000 per order
• Box Quantity Range: 10 – 500 boxes

🎯 Ideal For

• Excel Practice (VLOOKUP, IF, AVERAGEIFS, INDEX-MATCH, etc.)
• Pivot tables & data cleaning tasks
• Power BI / Tableau dashboards
• Sales trend forecasting
• Exploratory Data Analysis (EDA)
• Retail analytics & product demand modeling

📌 Suggested Projects & Questions

• Which salesperson generated the highest revenue overall?
• What’s the average amount per order in each country?
• Which product was most frequently sold?
• What month had the highest total boxes shipped?
• Create a dashboard comparing revenue across countries.

✅ Clean Data Guarantee

• ✅ No missing/null values
• ✅ No duplicates
• ✅ Realistic values
• ✅ Globally relatable product categories
• ✅ Ready for ML, BI, and teaching use cases

The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. It includes deaths which occurred in hospital and deaths which occurred outside of hospital within 30 days (inclusive) of discharge. Deaths related to COVID-19 are excluded from the SHMI. The SHMI gives an indication for each non-specialist acute NHS trust in England whether the observed number of deaths within 30 days of discharge from hospital was 'higher than expected' (SHMI banding=1), 'as expected' (SHMI banding=2) or 'lower than expected' (SHMI banding=3) when compared to the national baseline. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided. The SHMI is composed of 142 different diagnosis groups and these are aggregated to calculate the overall SHMI value for each trust. The number of finished provider spells, observed deaths and expected deaths at diagnosis group level for each trust is available in the SHMI diagnosis group breakdown files. For a subset of diagnosis groups, an indication of whether the observed number of deaths within 30 days of discharge from hospital was 'higher than expected', 'as expected' or 'lower than expected' when compared to the national baseline is also provided. Details of the 142 diagnosis groups can be found in Appendix A of the SHMI specification. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in a new contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there has been a fall in the number of spells for most trusts between this publication and the previous SHMI publication, ranging from 0 per cent to 4 per cent. This is due to COVID-19 impacting on activity from March 2020 onwards and appears to be an accurate reflection of hospital activity rather than a case of missing data. 3. Day cases and regular day attenders are excluded from the SHMI. However, some day cases for University College London Hospitals NHS Foundation Trust (trust code RRV) have been incorrectly classified as ordinary admissions meaning that they have been included in the SHMI. Maidstone and Tunbridge Wells NHS Trust (trust code RWF) has submitted a number of records with a patient classification of ‘day case’ or ‘regular day attender’ and an intended management value of ‘patient to stay in hospital for at least one night’. This mismatch has resulted in the patient classification being updated to ‘ordinary admission’ by the HES data cleaning rules. This may have resulted in the number of ordinary admissions being overstated. The trust has been contacted to clarify what the correct patient classification is for these records. Values for these trusts should therefore be interpreted with caution. 4. On 1 October 2020 Poole Hospital NHS Foundation Trust (trust code RD3) merged with The Royal Bournemouth and Christchurch Hospitals NHS Foundation Trust (trust code RDZ). The new trust is called University Hospitals Dorset NHS Foundation Trust (trust code R0D). This new organisation structure is reflected from this publication onwards. 5. Airedale NHS Foundation Trust (trust code RCF) has submitted an increased number of delivery episode records. HES data cleaning rules have amended some of the records to birth episodes however, most records have not been changed. It is therefore considered likely that the increased number of delivery episodes (and corresponding reduction in ordinary episodes) is incorrect. Values for this trust should therefore be interpreted with caution. 6. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page. 7. This tool is in Microsoft Power BI which does not fully support all accessibility needs. If you need further assistance, please contact us for help.

According to Cognitive Market Research, the global Data Preparation Tools market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Volume of Data and Growing Adoption of Business Intelligence (BI) and Analytics Driving the Data Preparation Tools Market

As organizations grow more data-driven, the integration of data preparation tools with Business Intelligence (BI) and advanced analytics platforms is becoming a critical driver of market growth. Clean, well-structured data is the foundation for accurate analysis, predictive modeling, and data visualization. Without proper preparation, even the most advanced BI tools may deliver misleading or incomplete insights. Businesses are now realizing that to fully capitalize on the capabilities of BI solutions such as Power BI, Qlik, or Looker, their data must first be meticulously prepared. Data preparation tools bridge this gap by transforming disparate raw data sources into harmonized, analysis-ready datasets. In the financial services sector, for example, firms use data preparation tools to consolidate customer financial records, transaction logs, and third-party market feeds to generate real-time risk assessments and portfolio analyses. The seamless integration of these tools with analytics platforms enhances organizational decision-making and contributes to the widespread adoption of such solutions. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data preparation tools has significantly improved their efficiency and functionality. These technologies automate complex tasks like anomaly detection, data profiling, semantic enrichment, and even the suggestion of optimal transformation paths based on patterns in historical data. AI-driven data preparation not only speeds up workflows but also reduces errors and human bias. In May 2022, Alteryx introduced AiDIN, a generative AI engine embedded into its analytics cloud platform. This innovation allows users to automate insights generation and produce dynamic documentation of business processes, revolutionizing how businesses interpret and share data. Similarly, platforms like DataRobot integrate ML models into the data preparation stage to improve the quality of predictions and outcomes. These innovations are positioning data preparation tools as not just utilities but as integral components of the broader AI ecosystem, thereby driving further market expansion. Data preparation tools address these needs by offering robust solutions for data cleaning, transformation, and integration, enabling telecom and IT firms to derive real-time insights. For example, Bharti Airtel, one of India’s largest telecom providers, implemented AI-based data preparation tools to streamline customer data and automate insights generation, thereby improving customer support and reducing operational costs. As major market players continue to expand and evolve their services, the demand for advanced data analytics powered by efficient data preparation tools will only intensify, propelling market growth. The exponential growth in global data generation is another major catalyst for the rise in demand for data preparation tools. As organizations adopt digital technologies and connected devices proliferate, the volume of data produced has surged beyond what traditional tools can handle. This deluge of information necessitates modern solutions capable of preparing vast and complex datasets efficiently. According to a report by the Lin...

🧥 Snitch Fashion Sales (Uncleaned) Dataset 📌 Context This is a synthetic dataset representing sales transactions from Snitch, a fictional Indian clothing brand. The dataset simulates real-world retail sales data with uncleaned records, designed for learners and professionals to practice data cleaning, exploratory data analysis (EDA), and dashboard building using tools like Python, Power BI, or Excel.

📊 What You’ll Find The dataset includes over 2,500 records of fashion product sales across various Indian cities. It contains common data issues such as:

Missing values

Incorrect date formats

Duplicates

Typos in categories and city names

Unrealistic discounts and profit values

🧾 Columns Explained Column --Description Order_ID ------Unique ID for each sale (some duplicates) Customer_Name ------Name of the customer (inconsistent formatting) Product_Category ---Clothing category (e.g., T-Shirts, Jeans — includes typos) Product_Name -----Specific product sold Units_Sold --Quantity sold (some negative or null) Unit_Price --Price per unit (some missing or zero) Discount_% ----Discount applied (some >100% or missing) Sales_Amount ------Total revenue after discount (some miscalculations) Order_Date ---------Order date (multiple formats or missing) City -------Indian city (includes typos like "Hyd", "bengaluru") Segment----- Market segment (B2C, B2B, or missing) Profit ---------Profit made on the sale (some unrealistic/negative)

💡 How to Use This Dataset Clean and standardize messy data

Convert dates and correct formats

Perform EDA to find:

Top-selling categories

Impact of discounts on sales and profits

Monthly/quarterly trends

Segment-based performance

Create dashboards in Power BI or Excel Pivot Table

Document findings in a PDF/Markdown report

🎯 Ideal For Aspiring data analysts and data scientists

Excel / Power BI dashboard learners

Portfolio project creators

Kaggle competitions or practice

📌 License This is a synthetic dataset created for educational use only. No real customer or business data is included.

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

Project Overview

This project is aimed at analyzing road accident data and identifying trends and insights related to casualties. The project will leverage several key performance indicators (KPIs) to measure the severity and the total number of casualties by accident severity, vehicle type, road type, and other factors.

Business Case: The business case for this project is to help transportation authorities, law enforcement agencies, and other stakeholders improve traffic safety and reduce the number of casualties caused by road accidents. By analyzing accident data and identifying trends and patterns, these stakeholders can implement targeted interventions to improve road safety and reduce the risk of accidents.

Goal: The goal of this project is to analyze traffic accident data and identify key trends and insights related to casualties. By doing so, the project aims to inform policy and decision-making related to road safety, and ultimately help reduce the number of casualties caused by accidents.

KPIs: Primary KPI: Total Casualties and Total Accident values for Current Year (CY) and YoY growth

Primary KPI's Total Casualties by Accident Severity for CY and YoY growth

Secondary KPI's Total casualties with respect to vehicle types for CY

Monthly trend showing comparision of casualties for Current Year and Previous Year

Causalities by Road Type for CY

CY Causalities by Area/Location & by Day/Night

Total Causalities and Total Accident by Location

I have executed this project using Power BI

Activities Carried Out

1 Getting dataset from client

Data is available in googledrive link below: https://drive.google.com/drive/folders/1pCNs-TRPznlbAn712gAGy7XfBnWs2QJm

2 Stakeholders in Project

KPI's was studied in respect to dataset, also stakeholders holders were determined

3 Raw Data Overview 4 Connecting Data with Power BI 5 Data Cleaning 6 Data Processing 7 Data Modelling 8 Background design in Power Point 9 Data Visualization/ Chart Design 10 Insights

What is IPGOD? The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD. # How do I use IPGOD? IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar. # IP Data Platform IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform # References The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset. * Patents * Trade Marks * Designs * Plant Breeder’s Rights # Updates ### Tables and columns Due to the changes in our systems, some tables have been affected. * We have added IPGOD 225 and IPGOD 325 to the dataset! * The IPGOD 206 table is not available this year. * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use. ### Data quality improvements Data quality has been improved across all tables. * Null values are simply empty rather than '31/12/9999'. * All date columns are now in ISO format 'yyyy-mm-dd'. * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0. * All tables are encoded in UTF-8. * All tables use the backslash \ as the escape character. * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

This dataset contains historical sales data for chocolate products across various countries and sales representatives. It is designed to support retail sales analysis and business intelligence use cases such as performance tracking, sales forecasting, and market trend identification.

🔢 Key Columns: Column Description Sales Person Name of the salesperson who made the sale Country Country where the sale was made Product Type of chocolate product sold Date Date of the transaction Amount Sales value in currency (cleaned to decimal) Boxes Shipped Quantity of chocolate boxes sold and shipped

📊 Key Use Cases: Sales Forecasting – Predict future revenue based on past sales data

Performance Tracking – Analyze top-performing salespeople and countries

Product Analysis – Identify best-selling chocolate products

Geographic Insights – Compare regional sales across markets

Inventory Planning – Estimate product demand using historical trends

📌 Tools Used: Power BI Desktop for data modeling, cleaning, and dashboard visualization

DAX for calculations (Total Sales, Average Sales, Forecasting, etc.)

Power Query for data transformation and cleaning

I'm excited to share my recent project where I dived deep into the world of data analysis to gain valuable insights into Tata Motors' sales data for the fiscal year 2021-2022. 📈

Project Highlights:

1. Data Processing and Cleaning: I meticulously cleaned and processed the dataset, ensuring accuracy and reliability in the analysis.

2. In-Depth Analysis: Through advanced analytical techniques, I uncovered patterns, trends, and key metrics within the data, helping to reveal critical business insights.

3. Data Visualization: I transformed the complex sales data into clear and insightful visual representations, making it easier for stakeholders to grasp the findings.

4. Interactive Dashboard: I designed an interactive dashboard that allows users to explore the data dynamically, facilitating a deeper understanding of the sales performance.

5. Findings: Tata Motors achieved 105% growth in sales, marking an impressive 126% profit increase compared to the year 2021.

This remarkable growth not only showcases the company's resilience but also the effectiveness of their strategies and operations. It's a testament to the hard work and dedication of the entire Tata Motors team.