About Dataset

Context

This dataset contains financial information of 1500 companies across 8 different industries scraped from companiesmarketcap.com on May 2024. It contains information about the company's name, industry, country, employees, marketcap, revenue, earnings, etc.

• The scraped program of this dataset can be found in Industry Market Cap Analysis Github repository.
• Tableau visualization of this dataset can also be found in Industry Market Cap: Visualization

Content

The dataset contains 2 files with the same column names. scraped_company_data.csv file is further transformed and cleaned to produce the finaltransformed_company_data.csvfile.

1. Company: Full name of the company
2. Company Path: Website URL of the company
3. Industry: Associated industry of the company
4. Country: Location of the company
5. Employees: Total number of employees of the company
6. Market Cap: Market capital of the company (as of Mar 2024)
7. Revenue: Company's current revenue (31 Mar 2023 - 31 Mar 2024)
8. Earnings: Company's current earnings (31 Mar 2023 - 31 Mar 2024)
9. Operating Margin: Company's current operating margin (at the end of 2023)
10. Total Assets: Company's total assets (as of Mar 2024)
11. Total Liabilities: Company's total liabilities
12. Total Debt: Company's total debt
13. Net Assets: Company’s net assets
14. PE Ratio: Company's current price-to-earnings ratio (31 Mar 2023 - 31 Mar 2024)
15. PS Ratio: Company's current price-to-sales ratio (31 Mar 2023 - 31 Mar 2024)

Acknowledgements

The website companiesmarketcap.com was used to scrape this dataset. Please include citations for this dataset if you use it in your own research.

Inspiration

The dataset can be used to find industries with the highest average market value, most profitable industries, most growth-oriented sectors, etc. More interesting insights can be found in this README file.

The "**Banks Reviews Customer Dataset**" boasts a vast collection of over 1000+ data of user-generated reviews and ratings spanning various banks. It serves as a valuable asset for data scientists, providing a comprehensive view of customer satisfaction, regional banking trends, and the underlying factors that shape banking experiences. This dataset empowers researchers and analysts to uncover meaningful insights within the financial industry, all through the lens of genuine customer feedback, facilitating informed decision-making and data-driven strategies for the banking sector.

Key Features

1. Data Scientists: Utilize the dataset for sentiment analysis, uncover customer satisfaction trends, and create data-driven insights for banking industry improvements.
2. Researchers: Explore historical customer feedback, analyze regional banking patterns, and conduct comparative studies to contribute valuable insights to the financial sector.
3. Banking Professionals: Monitor customer reviews and ratings to enhance customer service, identify areas for improvement, and ensure a better banking experience.
4. Policy Analysts: Use the dataset to assess the impact of policies, monitor economic trends, and support evidence-based decision-making for financial regulations.
5. Interdisciplinary Collaboration: Foster collaboration between data scientists, researchers, banking professionals, and policy analysts to conduct comprehensive studies that benefit research, policy development, and the financial industry as a whole.
   Please upvote and show your support if you find this dataset valuable for your research or analysis. Your feedback and contributions help make this dataset more accessible to the Kaggle community. Thank you!

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Explore the Google Play Store Reviews Database, a comprehensive collection of user reviews for various apps available on the Google Play Store.

This dataset includes millions of reviews across a wide range of categories such as games, productivity, social media, finance, health, and more. Each review entry provides essential details, including app names, user ratings, review texts, review dates, and user feedback, offering valuable insights for developers, data analysts, and market researchers.

Key Features:

• Extensive Review Coverage: Contains millions of user reviews from the Google Play Store, covering various app categories like games, productivity, social media, finance, and health.
• Detailed Review Information: Each review includes key details such as app name, user rating, review text, review date, and user feedback, allowing for in-depth analysis of user sentiment and app performance.
• Ideal for Market Analysis: Perfect for developers, data scientists, and market researchers interested in analyzing user feedback, studying trends in app usage, or optimizing app development strategies based on user reviews.
• Rich Source of User Insights: Provides a comprehensive overview of user experiences and preferences, helping professionals stay updated on the latest trends, popular apps, and user satisfaction levels.

Whether you're analyzing user feedback, researching market trends, or developing new app strategies, the Google Play Store Reviews Database is an invaluable resource that provides detailed insights and extensive coverage of app reviews on the Google Play Store.

Comprehensive Bitcoin holdings, market data, and treasury information for Orange Pill App, Inc. (null)

This data set is from the One Big Store research project conducted by the App Studies Initiative researchers at the University of Toronto and Manchester Metropolitan University. Have the uneven global flows of capital in the cultural industries changed because of access to distribution platforms like the Apple iOS App Store? Based on a financial analysis of this data set containing three years of game app revenues (2015-2017), our research project asks two questions. First, were game developers and publishers able to generate revenue in their domestic markets in the App Store? Second, to what extent are game app developers from the Global South, historically at the periphery of the global game industry, able to capture value in the US, Canadian, and Dutch instances of the App Store? Our research project advances discussions on the political economy of platform-dependent cultural production by situating this case within broader conversations on cultural imperialism, demonstrating that local app store instances are part of “one big store;” a US-dominated and oriented space of distribution and consumption that effectively captures revenue in regional marketplaces. (2023-10-06)

Financial-Leverage-Ratio Time Series for Allgeier SE. Allgeier SE provides information technology (IT) solutions and software services in Germany. It operates in two segments, Enterprise IT and mgm technology partners. The company provides software lifecycle services, nearshore-/offshore delivery, big data / business intelligence, industry solutions and cloud, managed services & app management, mobile enterprise/apps, process and IT consulting, IT security, enterprise content management, and IT infrastructure services. It is also involved in designing, developing, launching, and operating business software solutions, such as document management; enterprise resource planning; e-commerce, business process management; business digitalization platform and business efficiency solutions; IT services and open-source software development; consultancy, software solutions; cloud transformation and cloudnative application development, as well as provides field service and asset management. The company is engages in Management consultancy and digital consulting, Business analysis and requirements engineering, software modelling and development, design and usability, web and application security, quality assurance, testing automation, SAP integration, process optimization, cloud services. Allgeier SE was founded in 1977 and is headquartered in Munich, Germany.

Alternative Data for Investing Market Outlook

According to our latest research, the global Alternative Data for Investing market size reached USD 6.2 billion in 2024, exhibiting robust momentum with a CAGR of 18.7% from 2025 to 2033. This dynamic market is projected to achieve a value of USD 33.7 billion by 2033, fueled by rising demand for non-traditional data sources that offer actionable investment insights. The rapid expansion is driven by increasing adoption among institutional investors, technological advancements in data analytics, and a growing appetite for alpha generation in competitive financial markets.

The primary growth factor for the Alternative Data for Investing market is the escalating need for unique, real-time information that can provide a competitive edge in investment decision-making. Traditional data sources, such as financial statements and economic indicators, are now widely available and often lead to crowded trades. In contrast, alternative data—ranging from satellite imagery and web-scraped data to mobile app usage and credit card transactions—offers granular, timely insights that are not yet fully priced into the market. As asset managers and hedge funds seek to outperform benchmarks, they are increasingly turning to alternative data to identify emerging trends, assess corporate performance, and anticipate market movements ahead of their peers. This shift is further amplified by advancements in artificial intelligence and machine learning, which enable investors to process and extract value from vast, unstructured datasets with unprecedented speed and accuracy.

Another significant driver is the proliferation of data sources and the democratization of data access. The digital transformation across industries has generated an explosion of data, much of which can be harnessed for investment purposes. Social media platforms, e-commerce sites, geospatial technologies, and IoT devices are continuously generating valuable signals about consumer behavior, supply chain dynamics, and macroeconomic conditions. Financial institutions are increasingly collaborating with data vendors and fintech startups to integrate these alternative datasets into their investment models. Additionally, regulatory changes in data privacy and open banking are enabling greater access to transaction-level data, further broadening the scope of alternative data applications in investing.

The growing sophistication and adoption of data analytics tools are also propelling the Alternative Data for Investing market forward. Cloud-based analytics platforms, advanced visualization tools, and scalable data management solutions have made it easier for investment professionals to ingest, process, and interpret large volumes of alternative data. This has led to the emergence of new investment strategies, such as sentiment-driven trading, real-time supply chain monitoring, and ESG (Environmental, Social, and Governance) analysis using non-traditional metrics. Furthermore, as more retail investors gain access to alternative data through digital platforms, the market is witnessing broader participation and innovation in data-driven investing.

Regionally, North America dominates the Alternative Data for Investing market, accounting for the largest share in 2024 due to the presence of leading financial hubs, advanced technology infrastructure, and a high concentration of institutional investors. Europe is also experiencing robust growth, driven by regulatory support for data sharing and the rise of fintech innovation hubs. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding capital markets, and increasing adoption of alternative data by local asset managers and hedge funds. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by rising financial inclusion and the entry of global data providers into these regions.

Data Type Analysis

The data type segment of the Alternative Data for Investing market is highly diverse, encompassing a wide array of sources such as social and sentiment data, web-scraped data, satellite and geospatial data, credit and debit card transactions, mobile application usage, and other emerging categories. Social and sentiment data, derived from platforms like Twitter, Reddit, and financial forums, has become indispensable for gauging market sentiment and predicting short-term price movements. Investment prof

Financial-Leverage-Ratio Time Series for Nhn Entertain. NHN Corporation, an IT company, provides gaming, payment, entertainment, IT, and advertisement solutions in South Korea and internationally. It offers PAYCO, a payment service for online and offline stores; comico, a Webtoon platform; Bugs, a music portal operating music streaming and online music distribution business; and NHN KCP, an integrated e-payment service. The company also provides Ticketlink, a concert, culture, and sports ticket booking service; comico webtoon, an internet webtoon studio; Doctortour, a travel service that offers various travel packages; Dighty, AI-based data solution and services; Dighty Data Market, a data hub that sells web and app data product and data-based insight contents; ACE Counter, a real-time web and app integrated log analysis service; and Audiens, a customer data platform. In addition, it offers ACE Trader, a data-based performance AD platform; ACE eXchange, a digital market place; ADLIB, an advertising network platform to optimize the profit of application developers; NHN Cloud, an integrated cloud service; NHN Dooray!, an integrated business communication platform; NHN Game platform; and NHN Cloud Center, as well as Shop by, an online shopping mall solution, TOAST Cam, a smart cloud IP camera; OPEN Ads, a marketing data curation service; I am School, an education management platform; and Pink Diary, a female health care app for Korean society of obstetricians and gynecologists developed based on consultation by doctors. Further, the company offers games including Crusaders Quest, Friends POP, Compass, Hangame, Hangame Baduk and Omok, Hangame Janggi, Hangame Shin Matgo, Hangame Sutda&Matgo, Hangame Poker Classic, Hangame Poker, Hangame Double A Poker, and GUNS UP! Mobile. The company was formerly known as NHN Entertainment Corp. and changed its name to NHN Corporation in April 2019. NHN Corporation was incorporated in 2013 and is based in Seongnam-si, South Korea.

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

Mobile apps are everywhere. They are easy to create and can be very lucrative from the business standpoint. Specifically, Android is expanding as an operating system and has captured more than 74% of the total market[1].

The Google Play Store apps data has enormous potential to facilitate data-driven decisions and insights for businesses. In this notebook, we will analyze the Android app market by comparing ~10k apps in Google Play across different categories. We will also use the user reviews to draw a qualitative comparision between the apps.

The dataset you will use here was scraped from Google Play Store in September 2018 and was published on Kaggle. Here are the details:

googleplaystore.csv This file contains all the details of the apps on Google Play. There are 9 features that describe a given app. App: Name of the app Category: Category of the app. Some examples are: ART_AND_DESIGN, FINANCE, COMICS, BEAUTY etc. Rating: The current average rating (out of 5) of the app on Google Play Reviews: Number of user reviews given on the app Size: Size of the app in MB (megabytes) Installs: Number of times the app was downloaded from Google Play Type: Whether the app is paid or free Price: Price of the app in US$ Last Updated: Date on which the app was last updated on Google Play

googleplaystoreuserreviews.csv This file contains a random sample of 100 most helpful first user reviews for each app. The text in each review has been pre-processed and passed through a sentiment analyzer. App: Name of the app on which the user review was provided. Matches the App column of the apps.csv file Review: The pre-processed user review text Sentiment Category: Sentiment category of the user review - Positive, Negative or Neutral Sentiment Score: Sentiment score of the user review. It lies between [-1,1]. A higher score denotes a more positive sentiment. From here on, it will be your task to explore and manipulate the data until you are able to answer the three questions described in the instructions.

Price-To-Tangible-Book-Ratio Time Series for Global Infotech Co Ltd. Global Infotech Co., Ltd. provides financial information software products and integrated services in China. It offers business, retail, mobile, and web based credit system; paperless banking solution; unified quota, collateral, asset preservation, and archives management system; integrated contract risk management system; credit risk mitigation consulting and management service; integrated contract and ECIF solution; branch geographic information management and integrated reward system; risk alert system; product management solution; business and retail CRM; distributed memory database; web-based app development kit; security EFIC and institute CRM solutions; and life insurance core business, policy management, products factory, security, claims, new contract, and underwriting systems. The company also provides platforms for microlending and supply chain financial service, web-based retail payment, commercial paper management, risk decisioning, financial sales service, business scenario operation and maintenance, cloud service operation support, automated operation and maintenance, financial big data, data governance, external data integration, image content management, financial sales service, and securities investment, as well as for cloud, multi-cloud, disaster, private cloud, image content, cloud based billing management, auto lease financing, and web based retail payment. Global Infotech Co., Ltd. was founded in 1998 and is headquartered in Beijing, China.

Current-Ratio Time Series for Global Infotech Co Ltd. Global Infotech Co., Ltd. provides financial information software products and integrated services in China. It offers business, retail, mobile, and web based credit system; paperless banking solution; unified quota, collateral, asset preservation, and archives management system; integrated contract risk management system; credit risk mitigation consulting and management service; integrated contract and ECIF solution; branch geographic information management and integrated reward system; risk alert system; product management solution; business and retail CRM; distributed memory database; web-based app development kit; security EFIC and institute CRM solutions; and life insurance core business, policy management, products factory, security, claims, new contract, and underwriting systems. The company also provides platforms for microlending and supply chain financial service, web-based retail payment, commercial paper management, risk decisioning, financial sales service, business scenario operation and maintenance, cloud service operation support, automated operation and maintenance, financial big data, data governance, external data integration, image content management, financial sales service, and securities investment, as well as for cloud, multi-cloud, disaster, private cloud, image content, cloud based billing management, auto lease financing, and web based retail payment. Global Infotech Co., Ltd. was founded in 1998 and is based in Beijing, China. Global Infotech Co., Ltd. operates as a subsidiary of Global InfoTech Holdings, Inc.

This dataset includes a variety of fields related to addresses, airport information, animals, apps, personal and company information, medical data, financial information, construction details, and various other categories. Specific fields include:

Address Line 2 Airport Details: Code, Continent, Country Code, Elevation, GPS Code, Latitude, Longitude, Municipality, Name, Region Code Animal Details: Common Name, Scientific Name App Information: Bundle ID, Name, Version Image Data: Avatar URL, Base64 Image URL Financial Information: Bitcoin Address, Credit Card Number and Type, Currency and Code Vehicle Information: Car Make, Model, Year, VIN Medical Data: ICD10/ICD9 Codes and Descriptions, Drug Names Personal Information: First Name, Last Name, Full Name, Gender, Email Address, SSN, IP Address Company Information: Company Name, Job Title, Industry, Stock Market Cap, Symbol The dataset also includes various metadata like timestamps, GUIDs, and file names, providing a comprehensive overview for various data types and fields.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Description of the Dataset 1. Dataset Overview

Name: Wellness Technology Market Analysis Dataset Purpose: This dataset is designed to analyze various factors influencing the success of wellness technology companies. It aims to identify strategic opportunities and challenges in the wellness tech industry by evaluating market trends, customer behavior, and competitive dynamics. 2. Key Attributes

Company ID: A unique identifier for each wellness technology company. Company Name: The name of the company. Product Categories: Types of wellness products offered (e.g., wearables, fitness apps, mental health platforms). Market Share: Percentage of market share held by the company in different regions. Revenue: Annual revenue generated by the company (numerical, in USD). Customer Satisfaction Score: Average customer satisfaction ratings (numerical, e.g., 1 to 10 scale). Investment Amount: Total investment received by the company (numerical, in USD). Product Features: Key features of each product (categorical, e.g., heart rate monitoring, sleep tracking). Competitive Position: Assessment of the company’s position relative to competitors (categorical, e.g., leader, challenger, niche). Innovation Index: An index score representing the level of innovation in the company’s product offerings (numerical). Marketing Spend: Annual expenditure on marketing and promotional activities (numerical, in USD). User Demographics: Age, gender, and location of the users (categorical and numerical). 3. Data Collection Method

Sources: The data was collected from a combination of primary and secondary sources:

Industry Reports: Data was sourced from market research reports and industry analysis published by organizations like Gartner, IDC, and Statista.

Company Financial Statements: Financial information and market share data were obtained from public financial reports and investor relations sections of company websites.

Customer Reviews and Ratings: Customer satisfaction scores and feedback were collected from review platforms such as Trustpilot, Google Reviews, and app store ratings.

Surveys and Interviews: Direct surveys and interviews with industry experts, company executives, and customers were conducted to gather qualitative insights into product features and competitive positioning.

Market Analysis Tools: Tools like Google Trends and social media analytics were used to assess market trends and consumer sentiment.

Collection Tools and Techniques:

Web Scraping: Automated scripts were used to extract data from online reviews and financial websites. APIs: Data was pulled from APIs provided by financial databases and market analysis tools. Surveys: Surveys were administered using platforms like SurveyMonkey to gather direct feedback from stakeholders. Data Quality Assurance:

Data Cleaning: Involves handling missing values, correcting data inconsistencies, and ensuring accurate data entry. Validation: Data was cross-verified with multiple sources to ensure reliability and accuracy. 4. Dataset Size and Format

Size: The dataset comprises data from [number of companies, e.g., 50] wellness technology companies and covers [number of records, e.g., 500] individual data points. Format: The data is stored in [format, e.g., Excel spreadsheets, SQL database] for ease of analysis and integration with analytical tools. 5. Privacy and Compliance

Data Privacy: All data collected is anonymized to ensure the privacy of individuals and companies. Compliance: The data collection process adheres to relevant data protection regulations such as GDPR and CCPA, ensuring proper consent and secure handling of data.

Techsalerator’s Business Technographic Data for Iran: Unlocking Insights into Iran's Technology Landscape

Techsalerator’s Business Technographic Data for Iran offers a comprehensive and detailed dataset crucial for businesses, market analysts, and technology vendors aiming to understand and engage with companies operating in Iran. This dataset provides in-depth insights into the technological environment, capturing and organizing information related to technology stacks, digital tools, and IT infrastructure used by businesses across the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Iran, allowing technology vendors to identify potential clients and enabling analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field details the technologies and software solutions utilized by a company, such as ERP systems, CRM software, and cloud services. Understanding a company's technology stack is crucial for evaluating its digital maturity and operational requirements.

• Deployment Status: This field indicates whether the technology is currently in use, planned for future implementation, or under evaluation. Vendors can use this information to gauge the level of technology adoption and interest among companies in Iran.

• Industry Sector: This field specifies the industry in which the company operates, such as oil and gas, manufacturing, or finance. Knowledge of the industry helps vendors tailor their products to sector-specific needs and emerging trends in Iran.

• Geographic Location: This field identifies the company's headquarters or primary operations within Iran. Geographic information supports regional analysis and helps understand localized technology adoption patterns across the country.

Top 5 Technology Trends in Iran

• Oil and Gas Technology: Given Iran's significant role in the global oil and gas industry, there is a strong focus on advanced technologies such as exploration and production tools, seismic analysis software, and energy management systems.

• Fintech Innovations: The financial technology sector is experiencing rapid growth, with businesses adopting digital payment solutions, mobile banking apps, and blockchain technologies to enhance financial transactions and services.

• E-commerce Growth: The e-commerce sector in Iran is expanding, with companies increasingly leveraging online marketplaces, digital payment gateways, and logistics technology to improve customer reach and operational efficiency.

• Cybersecurity: With the rise in digital transactions and online activities, there is a heightened emphasis on cybersecurity. Companies in Iran are investing in data protection solutions, encryption technologies, and secure communication systems to protect against cyber threats.

• Smart Manufacturing: The push towards Industry 4.0 is evident in Iran, with companies adopting smart manufacturing technologies such as IoT-enabled machinery, automated production systems, and advanced data analytics to enhance operational efficiency.

Top 5 Companies with Notable Technographic Data in Iran

• National Iranian Oil Company (NIOC): As a major player in the oil and gas sector, NIOC utilizes advanced exploration and production technologies, digital asset management, and energy management solutions.

• Bank Melli Iran: A leading financial institution, Bank Melli Iran is implementing digital banking services, mobile apps, and fintech solutions to enhance customer experience and streamline operations.

• Digikala: Iran's largest e-commerce platform, Digikala, leverages sophisticated online shopping technologies, digital payment systems, and logistics solutions to serve a growing customer base.

• Iran Telecommunications Company (TCI): TCI plays a critical role in providing telecommunication services, focusing on expanding its network infrastructure, improving connectivity, and investing in next-generation technologies.

• Khorasan Industrial Group: A significant player in the manufacturing sector, Khorasan Industrial Group is adopting smart manufacturing technologies, automation, and data analytics to optimize production processes and improve product quality.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Iran, please contact info@techsalerator.com with your specific requirements. Techsalerator offers customized quotes based on the number of data fields and records needed, with datasets available for delivery within 24 hours. Ongoing access options can also be arranged upon request.

Included Data Fields

• Company Name
• Technology Stack
• Deployment Status
• Ind...

Techsalerator’s Business Technographic Data for Tanzania: Unlocking Insights into Tanzania's Technology Landscape

Techsalerator’s Business Technographic Data for Tanzania provides a detailed and comprehensive dataset essential for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating within Tanzania. This dataset offers in-depth insights into the technological landscape, capturing and organizing data related to technology stacks, digital tools, and IT infrastructure used by businesses in the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Tanzania, enabling technology vendors to target potential clients and allowing analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field outlines the technologies and software solutions a company uses, such as accounting systems, customer management software, and cloud services. Understanding a company's technology stack is key to evaluating its digital maturity and operational needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under evaluation. Vendors can use this information to assess the level of technology adoption and interest among companies in Tanzania.

• Industry Sector: This field specifies the industry in which the company operates, such as tourism, agriculture, or retail. Knowing the industry helps vendors tailor their products to sector-specific demands and emerging trends in Tanzania.

• Geographic Location: This field identifies the company's headquarters or primary operations within Tanzania. Geographic information aids in regional analysis and understanding localized technology adoption patterns across the country.

Top 5 Technology Trends in Tanzania

• Mobile Financial Services: Mobile money and financial services are rapidly expanding in Tanzania, with many businesses adopting mobile payment solutions to reach a broader customer base and facilitate transactions in areas with limited access to traditional banking.

• Agricultural Technology: With agriculture being a significant part of Tanzania’s economy, there is a growing adoption of agri-tech solutions, including precision farming tools, crop management software, and supply chain technology to enhance productivity and efficiency.

• Renewable Energy: Tanzania is investing in renewable energy solutions such as solar and wind power to address the energy needs of its growing population and support sustainable development goals.

• E-commerce Growth: The e-commerce sector is gaining momentum in Tanzania, with businesses increasingly utilizing online platforms and digital payment systems to cater to the expanding market and enhance consumer experiences.

• Healthcare Technology: There is a rising interest in healthcare technology, including telemedicine solutions, electronic health records, and mobile health apps, aimed at improving healthcare delivery and accessibility across the country.

Top 5 Companies with Notable Technographic Data in Tanzania

• Vodacom Tanzania: A leading telecom provider, Vodacom is enhancing connectivity across Tanzania through advanced mobile services, high-speed internet, and digital solutions, driving the country’s digital transformation.

• Tigo Tanzania: As a major player in telecommunications, Tigo offers a range of mobile and internet services, along with digital financial solutions, contributing to the growth of digital infrastructure in Tanzania.

• NMB Bank: A prominent financial institution, NMB Bank is leveraging digital banking services, mobile apps, and data analytics to provide efficient financial services and enhance customer experience.

• CRDB Bank: Known for its innovation in digital banking, CRDB Bank is adopting technology solutions such as online banking platforms and mobile financial services to meet the evolving needs of its customers.

• Dar es Salaam Stock Exchange (DSE): The DSE is implementing advanced trading platforms and financial technology solutions to streamline operations and provide a robust trading environment for investors in Tanzania.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Tanzania, please contact info@techsalerator.com with your specific needs. Techsalerator offers customized quotes based on the required number of data fields and records, with datasets available for delivery within 24 hours. Ongoing access options can also be arranged upon request.

Included Data Fields

• Company Name
• Technology Stack
• Deployment Status
• Industry Sector
• Geographi...

Summary

Project that treats data from opendata-rncs.inpi.fr. The database provided content 100 000 profit and loss account of French compagnies. The overall project is meant to be low-code and open source. Aim to provide ethical indicators on companies.

Context

Economics is too serious a thing to leave to economists, yet economic information is often far too complex and plentiful to be comprehensible. Our ambition with Enthic is to reduce as much as possible the barriers to accessing and understanding this data in order to broaden the profiles that delve into it.

On the one hand, financiers, experts and professional consultants manipulate economic data in order to find out which economic actors can bring in big profits. And on the other hand, the general public (or citizens) are subjected to all kinds of more or less true statements made in the media, in order to convince, and not to inform.

This information asymmetry plays a deleterious role for democracy, consumption, the job market and ecological transition. This is why we believe in allowing as many people as possible to reappropriate publicly available economic data, and above all to have access to the information it contains. Enthic is therefore a collaborative, opendata, opensource and free platform that centralizes the public financial data of companies in a single database. This database is accompanied by a website allowing data to be explored, viewed and downloaded: https://enthic-dataviz.netlify.app/

Content

The project currently only deals with the National Trade and Companies Register (RNCS) dataset provided by the National Institute of Industrial Property (INPI), and more particularly the data on the profit and loss accounts of French companies. The database provided content 100 000 profit and loss account of French compagnies, such as annual sales, operating result.

The data comes https://data.inpi.fr/swagger, the extraction has been done in December 2020. Companies can distinguish themselves by their sector of activity (code APE or NAF)

data_kaggle.csv The file contains 100 000 profit and loss of companies. Each line correspond to the profit and loss of a company for one year. It means that a company can have many lines, corresponding to the profit and loss of the year 2016, 2017, 2018 and 2019. Some columns can be obtained by the sum or the subtraction of other columns.

ape_fusion.csv APE makes it possible to identify the main branch of activity of the company. For instance : 47.11C can be interpret as : Section : G Commerce ; réparation d'automobiles et de motocycles Division : 47 Commerce de détail, à l'exception des automobiles et des motocycles Groupe : 47.1 Commerce de détail en magasin non spécialisé Classe : 47.11 Commerce de détail en magasin non spécialisé à prédominance alimentaire Sous classe : 47.11C Supérettes

Profit and loss - Onthology.csv The file contains the description of the columns of 'data_kaggle.csv', the name in French, the translation in English, the liasse is the official ID of the column in the INPI database.

Acknowledgements

We would like to thank PapIT, Leonarf and Briac and Maxime Apercé, who made this dataset possible.

Inspiration

We are interested in Data Visualization and Data Correction. As the project is Open Source and Open data, your help is welcomed on the project. Our suggestion : - a map of France with all the compagnies - data visualization of the link between the compagnies - data visualization of financial flow - correct missing values - identify and correct the outliers - get a tree of the APE code (json)

License

The license to reuse information from the National Institute of Industrial Property (INPI) is approved by the approval decision of April 17, 2019 for data from the National Register of Commerce and Companies of the INPI until April 16 2022. https://www.data.gouv.fr/es/licences

Link

Front : https://enthic-dataviz.netlify.app/ Github : https://github.com/Enthic API : http://api.enthic.fr/