Provide the market supervision and administration punishment case data input and production source statistics.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

Access B2B Contact Data for North American Small Business Owners with Success.ai—your go-to provider for verified, high-quality business datasets. This dataset is tailored for businesses, agencies, and professionals seeking direct access to decision-makers within the small business ecosystem across North America. With over 170 million professional profiles, it’s an unparalleled resource for powering your marketing, sales, and lead generation efforts.

Key Features of the Dataset:

Verified Contact Details

Includes accurate and up-to-date email addresses and phone numbers to ensure you reach your targets reliably.

AI-validated for 99% accuracy, eliminating errors and reducing wasted efforts.

Detailed Professional Insights

Comprehensive data points include job titles, skills, work experience, and education to enable precise segmentation and targeting.

Enriched with insights into decision-making roles, helping you connect directly with small business owners, CEOs, and other key stakeholders.

Business-Specific Information

Covers essential details such as industry, company size, location, and more, enabling you to tailor your campaigns effectively. Ideal for profiling and understanding the unique needs of small businesses.

Continuously Updated Data

Our dataset is maintained and updated regularly to ensure relevance and accuracy in fast-changing market conditions. New business contacts are added frequently, helping you stay ahead of the competition.

Why Choose Success.ai?

At Success.ai, we understand the critical importance of high-quality data for your business success. Here’s why our dataset stands out:

Tailored for Small Business Engagement Focused specifically on North American small business owners, this dataset is an invaluable resource for building relationships with SMEs (Small and Medium Enterprises). Whether you’re targeting startups, local businesses, or established small enterprises, our dataset has you covered.

Comprehensive Coverage Across North America Spanning the United States, Canada, and Mexico, our dataset ensures wide-reaching access to verified small business contacts in the region.

Categories Tailored to Your Needs Includes highly relevant categories such as Small Business Contact Data, CEO Contact Data, B2B Contact Data, and Email Address Data to match your marketing and sales strategies.

Customizable and Flexible Choose from a wide range of filtering options to create datasets that meet your exact specifications, including filtering by industry, company size, geographic location, and more.

Best Price Guaranteed We pride ourselves on offering the most competitive rates without compromising on quality. When you partner with Success.ai, you receive superior data at the best value.

Seamless Integration Delivered in formats that integrate effortlessly with your CRM, marketing automation, or sales platforms, so you can start acting on the data immediately.

Use Cases: This dataset empowers you to:

Drive Sales Growth: Build and refine your sales pipeline by connecting directly with decision-makers in small businesses. Optimize Marketing Campaigns: Launch highly targeted email and phone outreach campaigns with verified contact data. Expand Your Network: Leverage the dataset to build relationships with small business owners and other key figures within the B2B landscape. Improve Data Accuracy: Enhance your existing databases with verified, enriched contact information, reducing bounce rates and increasing ROI. Industries Served: Whether you're in B2B SaaS, digital marketing, consulting, or any field requiring accurate and targeted contact data, this dataset serves industries of all kinds. It is especially useful for professionals focused on:

Lead Generation Business Development Market Research Sales Outreach Customer Acquisition What’s Included in the Dataset: Each profile provides:

Full Name Verified Email Address Phone Number (where available) Job Title Company Name Industry Company Size Location Skills and Professional Experience Education Background With over 170 million profiles, you can tap into a wealth of opportunities to expand your reach and grow your business.

Why High-Quality Contact Data Matters: Accurate, verified contact data is the foundation of any successful B2B strategy. Reaching small business owners and decision-makers directly ensures your message lands where it matters most, reducing costs and improving the effectiveness of your campaigns. By choosing Success.ai, you ensure that every contact in your pipeline is a genuine opportunity.

Partner with Success.ai for Better Data, Better Results: Success.ai is committed to delivering premium-quality B2B data solutions at scale. With our small business owner dataset, you can unlock the potential of North America's dynamic small business market.

Get Started Today Request a sample or customize your dataset to fit your unique...

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

List of open market opportunities from FPDS FAS CASE is targeting for conversion to a FAS solution

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This dataset contains numbers of COVID-19 outbreaks and associated cases, categorized by setting, reported to CDPH since January 1, 2021.

AB 685 (Chapter 84, Statutes of 2020) and the Cal/OSHA COVID-19 Emergency Temporary Standards (Title 8, Subchapter 7, Sections 3205-3205.4) required non-healthcare employers in California to report workplace COVID-19 outbreaks to their local health department (LHD) between January 1, 2021 – December 31, 2022. Beginning January 1, 2023, non-healthcare employer reporting of COVID-19 outbreaks to local health departments is voluntary, unless a local order is in place. More recent data collected without mandated reporting may therefore be less representative of all outbreaks that have occurred, compared to earlier data collected during mandated reporting. Licensed health facilities continue to be mandated to report outbreaks to LHDs.

LHDs report confirmed outbreaks to the California Department of Public Health (CDPH) via the California Reportable Disease Information Exchange (CalREDIE), the California Connected (CalCONNECT) system, or other established processes. Data are compiled and categorized by setting by CDPH. Settings are categorized by U.S. Census industry codes. Total outbreaks and cases are included for individual industries as well as for broader industrial sectors.

The first dataset includes numbers of outbreaks in each setting by month of onset, for outbreaks reported to CDPH since January 1, 2021. This dataset includes some outbreaks with onset prior to January 1 that were reported to CDPH after January 1; these outbreaks are denoted with month of onset “Before Jan 2021.” The second dataset includes cumulative numbers of COVID-19 outbreaks with onset after January 1, 2021, categorized by setting. Due to reporting delays, the reported numbers may not reflect all outbreaks that have occurred as of the reporting date; additional outbreaks may have occurred that have not yet been reported to CDPH.

While many of these settings are workplaces, cases may have occurred among workers, other community members who visited the setting, or both. Accordingly, these data do not distinguish between outbreaks involving only workers, outbreaks involving only residents or patrons, or outbreaks involving both.

Several additional data limitations should be kept in mind:

• Outbreaks are classified as “Insufficient information” for outbreaks where not enough information was available for CDPH to assign an industry code.

• Some sectors, particularly congregate residential settings, may have increased testing and therefore increased likelihood of outbreak recognition and reporting. As a result, in congregate residential settings, the number of outbreak-associated cases may be more accurate.

• However, in most settings, outbreak and case counts are likely underestimates. For most cases, it is not possible to identify the source of exposure, as many cases have multiple possible exposures.

• Because some settings have been at times been closed or open with capacity restrictions, numbers of outbreak reports in those settings do not reflect COVID-19 transmission risk.

• The number of outbreaks in different settings will depend on the number of different workplaces in each setting. More outbreaks would be expected in settings with many workplaces compared to settings with few workplaces.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

The In-Memory Database Market report segments the industry into By Industry Type (Small and Medium, Large), By End-User Industry (Telecommunication and IT, BFSI, Logistics and Transportation, Retail, Entertainment and Media, Healthcare, Other End-User Industries), and Geography (North America, Europe, Asia-Pacific, Rest of the World). Includes five years of historical data and five-year forecasts.

Success.ai’s Beauty & Cosmetics Data for Cosmetics, Beauty & Wellness Professionals Worldwide delivers a powerful dataset tailored to connect businesses with key stakeholders in the global beauty and wellness industries. Covering professionals such as product developers, brand managers, wellness coaches, and salon owners, this dataset provides verified work emails, phone numbers, and actionable professional insights.

With access to over 700 million verified global profiles and detailed insights from 170 million professional datasets, Success.ai ensures your outreach, marketing, and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is ideal for businesses aiming to lead in the competitive beauty and wellness market.

Why Choose Success.ai’s Beauty & Cosmetics Data?

1. Verified Contact Data for Effective Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in cosmetics, skincare, beauty services, and wellness industries.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and improving communication efficiency.

2. Comprehensive Global Coverage

   • Includes profiles of beauty and wellness professionals from regions such as North America, Europe, Asia-Pacific, and emerging markets.
   • Gain insights into global trends in cosmetics innovation, wellness services, and beauty product demand.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, professional roles, and market developments.
   • Stay aligned with the fast-paced nature of the beauty and wellness industry to identify opportunities and maintain relevance.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful use of data for all business initiatives.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with professionals across the beauty, cosmetics, and wellness industries worldwide.
• 170M+ Professional Datasets: Access verified contact information and detailed insights into industry leaders and innovators.
• Business Insights: Understand market trends, product innovations, and consumer preferences driving the beauty industry.
• Decision-Maker Contacts: Engage with CEOs, brand managers, product developers, and wellness leaders driving growth and innovation.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with key players, including beauty brand executives, salon owners, skincare experts, and wellness influencers.
   • Access data on career histories, certifications, and industry expertise to target the right professionals effectively.

2. Advanced Filters for Precision Targeting

   • Filter professionals by industry focus (cosmetics, wellness, skincare), geographic location, or job function.
   • Tailor campaigns to align with specific market segments, such as luxury cosmetics, wellness services, or mass-market beauty products.

3. Global Trend Insights and Market Data

   • Leverage data on emerging beauty trends, wellness innovations, and skincare demands across regions.
   • Refine product development, marketing campaigns, and customer engagement strategies based on actionable insights.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with beauty and wellness professionals.

Strategic Use Cases:

1. Marketing and Brand Outreach

   • Design targeted campaigns to promote beauty products, wellness services, or skincare innovations to industry professionals.
   • Leverage verified contact data for multi-channel outreach, including email, social media, and direct engagement.

2. Product Development and Innovation

   • Utilize market insights to guide product development and align offerings with consumer demands in cosmetics, beauty, and wellness sectors.
   • Collaborate with product developers and brand managers to refine product lines or launch new offerings.

3. Sales and Partnership Development

   • Build relationships with wellness professionals, salon owners, and beauty distributors seeking innovative tools or products.
   • Present co-branding opportunities, supply chain partnerships, or new market expansion strategies to key decision-makers.

4. Market Research and Competitive Analysis

   • Analyze beauty and wellness trends, consumer preferences, and emerging niches to refine business strategies.
   • Benchmark against competitors to identify gaps, growth opportunities, and high-demand product categories.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality beauty and wellness data at competitive prices, ensuring strong ROI for your marketing, sales, and produc...

ADAM-Data-Repository This repository contains all the data needed to run the case studies for the ADAM manuscript. Biogas production The directory "biogas" contains all data for the biogas production case studies (Figs 13 and 14). Specifically, "biogas/biogas_x" contains the data files for the scenario where "x" is the corresponding Renewable Energy Certificates (RECs) value. Plastic waste recycling The directory "plastic_waste" contains all data for the plastic waste recycling case studies (Figs 15 and 16). Different scenarios share the same supply, technology site, and technology candidate data, as specified by the "csv" files under "plastic_waste". Each scenario has a different demand data file, which is contained in "plastic_waste/Elec_price" and "plastic_waste/PET_price". How to run the case studies In order to run the case studies, one can create a new model in ADAM and upload appropriate CSV file at each step (e.g. upload biogas/biogas_0/supplydata197.csv in step 2 where supply data are specified). This dataset is associated with the following publication: Hu, Y., W. Zhang, P. Tominac, M. Shen, D. Göreke, E. Martín-Hernández, M. Martín, G.J. Ruiz-Mercado, and V.M. Zavala. ADAM: A web platform for graph-based modeling and optimization of supply chains. COMPUTERS AND CHEMICAL ENGINEERING. Elsevier Science Ltd, New York, NY, USA, 165: 107911, (2022).

This dataset covers regional production from the Ministry of Agricuture for Kedougou, Senegal. Production estimates are collected annually for the region by extension agents who combine survey-based yield estimates with direct, in-field yield measurements. Data are aggregated to the department and regional level. Data are available back to the early 1990s, but the years 2010-2012 were selected because before 2010, data were only available for a small number of crops and there were several years with missing data points.

About this dataset

Context

The dataset tabulates the Industry population by race and ethnicity. The dataset can be utilized to understand the racial distribution of Industry.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• Industry, CA Population Breakdown by Race
• Industry, CA Non-Hispanic Population Breakdown by Race
• Industry, CA Hispanic or Latino Population Distribution by Their Ancestries

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the New Market population by race and ethnicity. The dataset can be utilized to understand the racial distribution of New Market.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• New Market, IN Population Breakdown by Race
• New Market, IN Non-Hispanic Population Breakdown by Race
• New Market, IN Hispanic or Latino Population Distribution by Their Ancestries

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This is a case study for the company to improve sales

Business Goal
Date: 2023/09/15
Dataset: Sales quantity of a certain brand from January to December 2022 and from January to September 2023.

Please describe what you observe (no specific presentation format required). Among your observations, identify at least three valuable insights and explain why you consider them valuable.
If more resources were available to you (including time, information, etc.), what would you need, and what more could you achieve?

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Metadata of the file Data Period: January 2022 - September 2023 Data Fields: - item - store_id - sales of each month

Sample question & answer 1. Product insights: identify the product sales analysis, such as BCG matrix 2. Store insights: identify the sales performance of the sales 3. Supply chain insights: identify the demand 4. Time series forecasting: identify tread, seasonality

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

Introduction

The Job Market Insights Dataset offers a comprehensive view of job postings worldwide, providing critical data on job roles, salaries, qualifications, locations, and company profiles. This dataset serves as a valuable resource for understanding global employment trends and patterns in various industries.

Objective

The primary objective of analyzing this dataset is to gain actionable insights into job market dynamics, including in-demand skills, salary ranges by role, preferred qualifications, and geographical job distributions. This analysis can empower job seekers, recruiters, and businesses to make informed decisions.

Key Features

1. Diverse Job Roles: Includes details for various professions like Network Engineers, Software Testers, UX/UI Designers, and more.
2. Global Scope: Covers jobs from diverse locations, spanning countries and industries worldwide.
3. Comprehensive Data Points: Provides salary ranges, qualifications, job types, company profiles, and benefits offered.
4. Temporal Data: Captures job posting dates to understand trends over time.
5. Skills and Responsibilities: Details required skills and responsibilities, aiding in understanding role-specific requirements.

Benefits for Data Science

• Predictive Modeling: Build models to predict salaries, skill demands, or the probability of job fulfillment.
• Trend Analysis: Identify trends in job roles, qualifications, and compensation.
• Geospatial Analysis: Map job distributions to uncover opportunities in specific regions.
• Clustering & Segmentation: Segment jobs by industry, role, or qualifications for targeted insights.
• Skill Gap Identification: Analyze skill requirements to identify gaps between current offerings and market demands.

This dataset is a goldmine for extracting insights that can optimize recruitment strategies, guide career planning, and inform educational initiatives.

US Healthcare NPI Data is a comprehensive resource offering detailed information on health providers registered in the United States.

Dataset Highlights:

• NPI Numbers: Unique identification numbers for health providers.
• Contact Details: Includes addresses and phone numbers.
• State License Numbers: State-specific licensing information.
• Additional Identifiers: Other identifiers related to the providers.
• Business Names: Names of the provider’s business entities.
• Taxonomies: Classification of provider types and specialties.

Taxonomy Data:

• Includes codes, groupings, and classifications.
• Facilitates detailed analysis and categorization of providers.

Data Updates:

• Weekly Delta Changes: Ensures the dataset is current with the latest changes.
• Monthly Full Refresh: Comprehensive update to maintain accuracy.

Use Cases:

• Market Analysis: Understand the distribution and types of healthcare providers across the US. Analyze market trends and identify potential gaps in healthcare services.
• Outreach: Create targeted marketing campaigns to reach specific types of healthcare providers. Use contact details for direct outreach and engagement with providers.
• Research: Conduct in-depth research on healthcare providers and their specialties. Analyze provider attributes to support academic or commercial research projects.
• Compliance and Verification: Verify provider credentials and compliance with state licensing requirements. Ensure accurate provider information for regulatory and compliance purposes.

Data Quality and Reliability:

• The dataset is meticulously curated to ensure high quality and reliability. Regular updates, both weekly and monthly, ensure that users have access to the most current information. The comprehensive nature of the data, combined with its regular updates, makes it a valuable tool for a wide range of applications in the healthcare sector.

Access and Integration: - CSV Format: The dataset is provided in CSV format, making it easy to integrate with various data analysis tools and platforms. - Ease of Use: The structured format of the data ensures that it can be easily imported, analyzed, and utilized for various applications without extensive preprocessing.

Ideal for:

• Healthcare Professionals: Physicians, nurses, and other healthcare providers who need to verify information about their peers.
• Analysts: Data analysts and business analysts who require detailed and accurate healthcare provider data for their projects.
• Businesses: Companies in the healthcare sector looking to understand market dynamics and reach out to providers.
• Researchers: Academic and commercial researchers conducting studies on healthcare providers and services.

Why Choose This Dataset?

• Comprehensive Coverage: Detailed information on millions of healthcare providers across the US.
• Regular Updates: Weekly and monthly updates ensure that the data remains current and reliable.
• Ease of Integration: Provided in a user-friendly CSV format for easy integration with your existing systems.
• Versatility: Suitable for a wide range of applications, from market analysis to compliance and research.

By leveraging the US Healthcare NPI & Taxonomy Data, users can gain valuable insights into the healthcare landscape, enhance their outreach efforts, and conduct detailed research with confidence in the accuracy and comprehensiveness of the data.

Summary:

• This dataset is an invaluable resource for anyone needing detailed and up-to-date information on US healthcare providers. Whether for market analysis, research, outreach, or compliance, the US Healthcare NPI & Taxonomy Data offers the detailed, reliable information needed to achieve your goals.

Introduction

Since Investing.com does not have an API, I decided to develop this Python package in order to retrieve historical data from the companies that integrate the Continuous Spanish Stock Market. So on, I decided to generate, via investpy, the datasets for every company so that any Data Scientist or Data Enthusiastic can handle it and abstract their own conclusions and research.

The main purpose of developing investpy, the package from which these datasets have been retrieved, was to use it as the Data Extraction tool for its namesake section, for my Final Degree Project at the University of Salamanca titled "*Machine Learning for stock investment recommendation systems*". The package end up being so consistent, reliable and usable that it is going to be used as the main Data Extraction tool by another students in their Final Degree Projects named "*Recommender system of banking products*" and "*Robo-Advisor Application*".

License

MIT License

Additional Information

investpy, the Python package from which datasets were generated is currently in a development beta version, so please, if needed open an issue to solve all the possible problems the package may be causing or any dataset error. Also, any new ideas or proposals are welcome, and will be gladly implemented in the package if the are positive and useful.

For further information or any question feel free to contact me via email at alvarob96@usal.es

You can also check my Medium Publication, where I upload weekly posts related to Data Science and mainly on Data Extraction techniques via Web Scraping. In this case, you can read "investpy — a Python package for historical data extraction from the Spanish stock market" where I explain the basics on investpy development and some insights on Web Scraping with Python.

Disclaimer

This Python Package has been made for research purposes in order to fit a needs that Investing.com does not cover, so this package works like an Application Programming Interface (API) of Investing.com developed in an altruistic way. Conclude that this package is not related in any way with Investing.com or any dependant company, the only requirement for developing this package was to mention the source where data is retrieved.

Integration Software Market size was valued at USD 301.04 Billion in 2024 and is projected to reach USD 735.85 Billion by 2031, growing at a CAGR of 11.84% during the forecast period 2024-2031.

Integration Software Market Drivers

Integration software is the process in which heterogeneous data is combined or retrieved from different sources to form meaningful or valuable information. Integration software primarily supports the analytical processing of large data sets by combining, aligning, and merging each data set from different sources or organizational departments. Integration software is extremely useful in the case of merging systems of two different companies to provide a unified view of the company’s data assets.

It primarily supports the analytical processing of large data sets by combining, aligning, and merging each data set from different sources or organizational departments. It is extremely useful in the case of merging systems of two different companies to provide a unified view of the company’s data assets. In each business enterprise, there is a constant requirement for data storage and processing, fueled by the continuous increase in the use of computers and smartphones. This data can increase from an enterprise’s operations, people, technology, and procedures.

This table covers the investment expectations and realisations of enterprises in the Dutch manufacturing industry. Every February Statistics Netherlands asks enterprises in this sector about their investment expectations for the current calendar year and the realisations for the previous year. This table is compiled with cofinancing of the Ministry of Economic Affairs and Climate Policy.

Data available from: 2013 The data collected for publication in May 2020 is partial (approximately three quarters) from before March 12. March 12 is the turning point for the measures for the COVID-19 pandemic. In view of the impact of this, the data from before 12 March have been corrected, based on additional requests and the response after 12 March.

Status of the figures: All figures for the expectations for 2013 to 2024 are final. The figures for achievements from 2013 to 2022 are final and those for 2023 are revised provisional.

Changes as of November 22, 2024: The provisional figures for the realization of 2023 have been adjusted to revised provisional.

Changes as of July 5, 2024: The description of SIC 2008 coding was accidentally published in Dutch, this has been corrected in this version.

Changes as of June 5, 2024: The dates for the realization of 2022 have been adjusted to more definitive, the provisional dates of the realization of 2023 and the expectations for 2024 have been added. The SBI codes 06 to 09 and B, D, E and F with underlying codes have been added.

When will new figures be published? The expected investment data of year T have the following publication timetable: • In the middle of year T: Expected investments for T (definitive) Realised investments for T minus 1 (preliminary) Realised investments for T minus 2 (definitive) • At the end of year T: Realised investments for T minus 1 (preliminary)