According to a consumer survey released by Alfred Kärcher in 2019, almost half of all respondents in Japan estimated they spent up to *** hour per week on cleaning their homes.

Graph and download economic data for Hours Worked for Manufacturing: Soap, Cleaning Compound, and Toilet Preparation Manufacturing (NAICS 3256) in the United States (IPUEN3256L201000000) from 1988 to 2024 about cleaning, hygiene, NAICS, hours, IP, manufacturing, and USA.

According to a consumer survey released by Alfred Kärcher in 2019, the majority of both male and female respondents in Japan estimated they spent up to *** hour per week on cleaning their homes.

Portugal Hours Worked Index: Collection, Treatment, Distribution, Sanitation & Cleaning (CT) data was reported at 87.310 2005=100 in Aug 2013. This records a decrease from the previous number of 96.460 2005=100 for Jul 2013. Portugal Hours Worked Index: Collection, Treatment, Distribution, Sanitation & Cleaning (CT) data is updated monthly, averaging 99.250 2005=100 from Jan 2005 (Median) to Aug 2013, with 104 observations. The data reached an all-time high of 108.470 2005=100 in Oct 2008 and a record low of 87.310 2005=100 in Aug 2013. Portugal Hours Worked Index: Collection, Treatment, Distribution, Sanitation & Cleaning (CT) data remains active status in CEIC and is reported by Statistics Portugal. The data is categorized under Global Database’s Portugal – Table PT.G035: Hours Worked Index: 2005=100.

Data Cleaning Tools Market Outlook

As of 2023, the global market size for data cleaning tools is estimated at $2.5 billion, with projections indicating that it will reach approximately $7.1 billion by 2032, reflecting a robust CAGR of 12.1% during the forecast period. This growth is primarily driven by the increasing importance of data quality in business intelligence and analytics workflows across various industries.

The growth of the data cleaning tools market can be attributed to several critical factors. Firstly, the exponential increase in data generation across industries necessitates efficient tools to manage data quality. Poor data quality can result in significant financial losses, inefficient business processes, and faulty decision-making. Organizations recognize the value of clean, accurate data in driving business insights and operational efficiency, thereby propelling the adoption of data cleaning tools. Additionally, regulatory requirements and compliance standards also push companies to maintain high data quality standards, further driving market growth.

Another significant growth factor is the rising adoption of AI and machine learning technologies. These advanced technologies rely heavily on high-quality data to deliver accurate results. Data cleaning tools play a crucial role in preparing datasets for AI and machine learning models, ensuring that the data is free from errors, inconsistencies, and redundancies. This surge in the use of AI and machine learning across various sectors like healthcare, finance, and retail is driving the demand for efficient data cleaning solutions.

The proliferation of big data analytics is another critical factor contributing to market growth. Big data analytics enables organizations to uncover hidden patterns, correlations, and insights from large datasets. However, the effectiveness of big data analytics is contingent upon the quality of the data being analyzed. Data cleaning tools help in sanitizing large datasets, making them suitable for analysis and thus enhancing the accuracy and reliability of analytics outcomes. This trend is expected to continue, fueling the demand for data cleaning tools.

In terms of regional growth, North America holds a dominant position in the data cleaning tools market. The region's strong technological infrastructure, coupled with the presence of major market players and a high adoption rate of advanced data management solutions, contributes to its leadership. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid digitization of businesses, increasing investments in IT infrastructure, and a growing focus on data-driven decision-making are key factors driving the market in this region.

As organizations strive to maintain high data quality standards, the role of an Email List Cleaning Service becomes increasingly vital. These services ensure that email databases are free from invalid addresses, duplicates, and outdated information, thereby enhancing the effectiveness of marketing campaigns and communications. By leveraging sophisticated algorithms and validation techniques, email list cleaning services help businesses improve their email deliverability rates and reduce the risk of being flagged as spam. This not only optimizes marketing efforts but also protects the reputation of the sender. As a result, the demand for such services is expected to grow alongside the broader data cleaning tools market, as companies recognize the importance of maintaining clean and accurate contact lists.

Component Analysis

The data cleaning tools market can be segmented by component into software and services. The software segment encompasses various tools and platforms designed for data cleaning, while the services segment includes consultancy, implementation, and maintenance services provided by vendors.

The software segment holds the largest market share and is expected to continue leading during the forecast period. This dominance can be attributed to the increasing adoption of automated data cleaning solutions that offer high efficiency and accuracy. These software solutions are equipped with advanced algorithms and functionalities that can handle large volumes of data, identify errors, and correct them without manual intervention. The rising adoption of cloud-based data cleaning software further bolsters this segment, as it offers scalability and ease of

China Home Cleaning Electrical Appliance: Current Asset Turnover Ratio data was reported at 2.248 Times in Oct 2015. This records a decrease from the previous number of 2.263 Times for Sep 2015. China Home Cleaning Electrical Appliance: Current Asset Turnover Ratio data is updated monthly, averaging 2.320 Times from Dec 2006 (Median) to Oct 2015, with 83 observations. The data reached an all-time high of 4.546 Times in Dec 2012 and a record low of 0.367 Times in Feb 2009. China Home Cleaning Electrical Appliance: Current Asset Turnover Ratio data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIA: Home Electrical Apparatus: Home Cleaning Electrical Appliance.

Turkey Hours Worked Index: TS: NACE 2: AS: Cleaning Activities data was reported at 138.400 2005=100 in Dec 2012. This records a decrease from the previous number of 147.600 2005=100 for Sep 2012. Turkey Hours Worked Index: TS: NACE 2: AS: Cleaning Activities data is updated quarterly, averaging 167.050 2005=100 from Mar 2009 (Median) to Dec 2012, with 16 observations. The data reached an all-time high of 200.300 2005=100 in Dec 2010 and a record low of 125.100 2005=100 in Jun 2009. Turkey Hours Worked Index: TS: NACE 2: AS: Cleaning Activities data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.G090: Hours Worked Index: 2005=100: by Trade and Services.

Japan E(15-64): FM: AWHW: Carrying, Cleaning, Packaging&RelatedWKRS(CCP) data was reported at 3,873.000 Hour tt in Mar 2025. This records an increase from the previous number of 3,556.000 Hour tt for Feb 2025. Japan E(15-64): FM: AWHW: Carrying, Cleaning, Packaging&RelatedWKRS(CCP) data is updated monthly, averaging 3,819.000 Hour tt from Jan 2018 (Median) to Mar 2025, with 87 observations. The data reached an all-time high of 4,276.000 Hour tt in Nov 2018 and a record low of 3,019.000 Hour tt in Apr 2020. Japan E(15-64): FM: AWHW: Carrying, Cleaning, Packaging&RelatedWKRS(CCP) data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.G: Labour Force Survey: Aggregate Weekly Hours of Work: Employee: JSIC 12th Rev.

Graph and download economic data for Employed: Workers paid hourly rates: Wage and salary workers: Building and grounds cleaning and maintenance occupations: 16 years and over (LEU0204835900A) from 2000 to 2024 about cleaning, maintenance, paid, occupation, salaries, workers, hours, buildings, 16 years +, wages, employment, rate, and USA.

Street sweeping schedule by Ward and Ward sections number. To find your Ward section, visit http://bit.ly/Hz0aCo. For more information about the City's Street Sweeping program, go to http://bit.ly/H2PHUP.

Street sweeping schedule by Ward and Ward section number. To find your Ward section, visit https://data.cityofchicago.org/d/icje-4fmy. For more information about the City's Street Sweeping program, go to http://bit.ly/H2PHUP.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

STREET CLEANING - % of urgent cleansing cases resolved within SLA (2 hours) - (YTD) (SLA set in 2016)

Graph and download economic data for Intermediate Inputs Intensity for Manufacturing: Soap, Cleaning Compound, and Toilet Preparation Manufacturing (NAICS 3256) in the United States (IPUEN3256P061000000) from 1988 to 2022 about cleaning, hygiene, intermediate, purchase, ratio, NAICS, hours, IP, manufacturing, and USA.

Japan E(15-64): M: AWHW: CCP: Cleaning WKRS data was reported at 1,297.000 Hour tt in Mar 2025. This records a decrease from the previous number of 1,424.000 Hour tt for Feb 2025. Japan E(15-64): M: AWHW: CCP: Cleaning WKRS data is updated monthly, averaging 1,331.000 Hour tt from Jan 2018 (Median) to Mar 2025, with 87 observations. The data reached an all-time high of 1,582.000 Hour tt in Jul 2022 and a record low of 1,085.000 Hour tt in Apr 2020. Japan E(15-64): M: AWHW: CCP: Cleaning WKRS data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.G: Labour Force Survey: Aggregate Weekly Hours of Work: Employee: JSIC 12th Rev.

Abstract

National Labor Force Survey (SAKERNAS) is a survey that is designed to observe the general situation of workforce and also to understand whether there is a change of workforce structure between the enumeration period. Since the survey was initiated in 1976, it has undergone a series of changes affecting its coverage, the frequency of enumeration, the number of households sampled and the type of information collected. It is the largest and most representative source of employment data in Indonesia. For each selected household, the general information about the circumstances of each household member that includes the name, relationship to head of household, sex, and age were collected. Household members aged 10 years and over will be prompted to give the information about their marital status, education and employment.

SAKERNAS is aimed to gather informations that meet three objectives: 1.Employment by education, working hours, industrial classification and employment status, 2.Unemployment and underemployment by different characteristics and efforts on looking for work, 3.Working age population not in the labor force (e.g. attending schools, doing housekeeping and others).

The data for quarterly SAKERNAS was gathered in 1989 covered all provinces in Indonesia, with 65,440 households, scattered both in rural and urban areas and representative until provincial level. The main household data is taken from core questionnaire of SAK89-AK.

Geographic coverage

National coverage* including urban and rural area, representative until provincial level.

*) Although covering all of Indonesia, there are some circumstances when not all provincial were covered. For example, in year 2000, the Province of Maluku excluded in SAKERNAS because horizontal conflicts occurred there. Also, the separation of East Timor from Indonesia in year 1999 also changed the scope of SAKERNAS for the years to come. After that, due to the expansion of regional autonomy as a consequence, the proportion of samples per Province is also changed, as in 2006 when the number of provinces are already 33. However, the difference is only on the number of influential scope/level but not to the pattern. On the other hand, changes in the methodology (including sample size) over time is likely to affect the outcome, for example in years 2000 and 2001, when sample size is only 32.384 and 34.176 the level of data presentation is only representative to island level, (insufficient sample size even to make it representative to provincial level).

Analysis unit

Individual

Universe

The survey covered all de jure household members (usual residents), aged 10 years and over that resident in the household. However, Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

Kind of data

Sample survey data

Sampling procedure

Quarterly SAKERNAS 1989 was implemented in the whole territory of the Republic of Indonesia , with a total sample of about 65,440 households, both in rural and urban areas and representative until provincial level. Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample. Data in the dataset indicates the combined sample data consisting results of the 4 rounds quarterly SAKERNAS in 1989, i.e. quarter I, quarter II, quarter III, and quarter IV.

Implementation of SAKERNAS 1989 include samples of the previous enumeration activities (rotation method). Sampling method* to be used is similar for implementation of SAKERNAS years 1986 to 1989, which households selected samples from previous quarter will be partly re-enumerated and then again partly from other household ever elected from another previous quarters, so no need to re-enroll in new household. The procedure for the selection of households in the sample are described in more detail in the enumerators/ supervisors manual document.

*) Sampling method used is varied in different years. For example, in SAKERNAS period of 1986-1989 sampling method used is the method of rotation, where most of the households selected at one period was re-elected in the following period. This often happens on quarterly SAKERNAS on that period. At other periods often use multi-stages sampling method (two or three stages depend on whether sub block census / segment group included or not), or a combination of multi stages sampling also with rotation method (e.g. SAKERNAS 2006-2010).

Mode of data collection

Face-to-face

Research instrument

In SAKERNAS, the questionnaire has been designed in a simple and concise way. It is expected that respondents will understand the aim of question of survey and avoid the memory lapse and uninterested respondents during data collection. Furthermore, the design of SAKERNAS's questionnaire remains stable in order to maintain data comparison.

A household questionnaire was administered in each selected household, which collected general information of household members that includes name, relationship with head of the household, sex and age. Household members aged 10 years and over were then asked about their marital status, education and occupation.

Cleaning operations

Stages of data processing in Sakernas are through process of: - Batching - Editing - Coding - Data Entry - Validation - Tabulation

Sampling error estimates

Sampling error results are presented at the end of the publication of The State of Labor Force in Indonesia and in publication of The State of Workers in Indonesia.

Street Cleaning ScheduleDownload Link

Japan E: AWHW: CCP: Cleaning WKRS data was reported at 3,323.000 Hour tt in Mar 2025. This records a decrease from the previous number of 3,353.000 Hour tt for Feb 2025. Japan E: AWHW: CCP: Cleaning WKRS data is updated monthly, averaging 3,158.000 Hour tt from Jan 2018 (Median) to Mar 2025, with 87 observations. The data reached an all-time high of 3,504.000 Hour tt in Nov 2023 and a record low of 2,443.000 Hour tt in Apr 2020. Japan E: AWHW: CCP: Cleaning WKRS data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.G: Labour Force Survey: Aggregate Weekly Hours of Work: Employee: JSIC 12th Rev.

This is a legacy dataset which contains detailed information on the timing and location of street sweeping service throughout the City. Daily street cleaning takes place April 1 to November 30 in most Boston neighborhoods (weather permitting), and over 400 curb miles of streets are maintained under the Daytime Street Sweeping Program.

Street sweeping schedule by Ward and Ward section number. To find your Ward section, visit https://data.cityofchicago.org/d/2cgx-fb86. For more information about the City's Street Sweeping program, go to http://bit.ly/H2PHUP. Because the City of Chicago ward map will change on May 18, 2015, this dataset begins on that date. The dataset for April and the first half of May is https://data.cityofchicago.org/d/waad-z968.

Graph and download economic data for Employed: Percent of hourly paid workers: Paid total at or below prevailing federal minimum wage: Wage and salary workers: Building and grounds cleaning and maintenance occupations: 16 years and over (LEU0204863500A) from 2000 to 2024 about cleaning, maintenance, paid, minimum wage, occupation, salaries, workers, hours, buildings, percent, 16 years +, federal, wages, employment, and USA.