Comprehensive dataset of 14 Indonesian restaurants in Texas, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Our project, “Indonesian Media Audio Database,” is designed to establish a rich and diverse dataset tailored for training advanced machine learning models in language processing, speech recognition, and cultural analysis.

The Indonesian data center market is experiencing robust growth, driven by the burgeoning digital economy, increasing cloud adoption, and the government's push for digital transformation. The market's substantial size, coupled with a high CAGR (let's assume a conservative 15% based on regional trends), indicates significant investment opportunities. Key growth drivers include the expanding e-commerce sector, the rising demand for digital services in the BFSI and government sectors, and the need for improved digital infrastructure to support Indonesia's large and rapidly growing population. The Greater Jakarta area serves as the primary hotspot, but expansion into other regions is accelerating as demand diversifies. While the market is dominated by larger data centers, the emergence of smaller, edge data centers caters to localized needs and reduces latency. The colocation market is witnessing a shift towards hyperscale providers meeting the needs of large cloud operators. However, challenges such as infrastructural limitations in certain regions and regulatory hurdles can somewhat constrain rapid expansion. Segmentation by Tier type, colocation model, and end-user type offers further granularity in understanding market dynamics and potential investment areas. The presence of both international players and local companies indicates a competitive yet expanding landscape. The forecast period (2025-2033) suggests continued market expansion, with a focus on meeting the growing demand for high-capacity and resilient data center infrastructure. Further growth will likely be fueled by increasing government initiatives promoting digital infrastructure development and the ongoing expansion of 5G networks. Strategic partnerships between international and local companies will be crucial in navigating the Indonesian market's unique challenges and capitalizing on its significant growth potential. Specific market segments to watch include the hyperscale colocation market and data centers located outside of Greater Jakarta, both of which are poised for exponential growth in the coming years. The ongoing development and improvement of supporting infrastructure, such as power grids and fiber optic networks, will act as a significant catalyst for market expansion. Recent developments include: September 2022: The company commenced construction on a 23MW data center in Jakarta, Indonesia, marking the company’s third site in South East Asia as it capitalizes on the region’s rapid digital transformation in the wake of the global pandemic.The new facility will offer 3,430 cabinets and an IT load of 23MW and is designed to cater for the growing demand for high power density applications from cloud-driven hyperscale deployments, local and international network and financial service providers. It is expected to complete by Q4 2023.August 2022: PT Sigma Cipta Caraka (SCA), also known as telkomsigma, transfers its data centre business to PT Telkom Data Ekosistem (TDE), which is worth a total of IDR 2.01 trillion. The parent company PT Telkom Indonesia (Persero) Tbk (TLKM), claimed that this transfer of the data centre business line is related to the business restructuring program held by Telkom Group.June 2022: The company announced the launch of BDx Indonesia, following the completion of a USD 300 million joint venture agreement with PT Indosat Tbk (Indosat Ooredoo Hutchison or IOH) and PT Aplikanusa Lintasarta, Big Data Exchange (BDx).. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

United States Immigrants Admitted: Indonesia data was reported at 1,914.000 Person in 2017. This records a decrease from the previous number of 2,129.000 Person for 2016. United States Immigrants Admitted: Indonesia data is updated yearly, averaging 2,134.000 Person from Sep 1986 (Median) to 2017, with 32 observations. The data reached an all-time high of 4,868.000 Person in 2006 and a record low of 905.000 Person in 1997. United States Immigrants Admitted: Indonesia data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s United States – Table US.G087: Immigration.

Comprehensive dataset of 6 Indonesian restaurants in Georgia, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Home Indonesian DatasetKumpulan Data IndonesiaHigh-Quality Indonesian General Conversation and Podcast Dataset for AI & Speech Models Contact Us General Conversation Data Podcast Data General Conversation Data .elementor-58567 .elementor-element.elementor-element-91938a9{padding:20px 0px 50px…

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Indonesia is 1062.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

The Indonesian data center construction market is experiencing robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 14.09% between 2019 and 2033. This burgeoning market, valued at XX million (Value Unit) in 2025, is driven by several key factors. The rapid expansion of e-commerce, digital services, and cloud computing within Indonesia is significantly increasing the demand for data center infrastructure. Government initiatives promoting digitalization and the establishment of digital economies further fuel this market expansion. Rising investments from both domestic and international players, coupled with the increasing adoption of advanced technologies like 5G and the Internet of Things (IoT), are also contributing to market growth. Key trends shaping the market include a shift towards hyperscale data centers, a growing preference for sustainable and energy-efficient solutions (like immersion cooling), and the increasing adoption of modular and prefabricated data center designs to accelerate deployment. However, the market faces certain restraints, including high initial investment costs associated with data center construction, potential regulatory hurdles, and the need for skilled labor to operate and maintain these complex facilities. Market segmentation reveals a diverse landscape, with significant investments across infrastructure types, including electrical (power distribution solutions, power backup systems), mechanical (cooling systems, racks), and general construction. Specific segments like power distribution solutions (PDUs, transfer switches, switchgears) and cooling systems (in-row, in-rack, immersion cooling) show particularly strong growth potential. The market is further segmented by tier type (Tier I-IV), reflecting varying levels of redundancy and resilience, and end-user industries, with banking, financial services, insurance, IT and telecommunications, and government sectors representing major consumers of data center capacity. Key players such as SAS Institute Inc, L&T, Fortis Construction, IBM, Brookfield Infrastructure, Schneider Electric, Turner Construction, Delta Power Solutions, Legrand, NTT Facilities, AECOM, and Iris Global are actively shaping the competitive dynamics of the market. The Indonesian data center construction market's regional focus is entirely on Indonesia, providing a concentrated analysis of this specific nation's growth trajectory. The study period spans from 2019 to 2033, with 2025 serving as both the base and estimated year. The forecast period extends from 2025 to 2033, while historical data covers 2019-2024. This detailed analysis allows for accurate predictions regarding future market size, potential investment opportunities, and the evolving needs of different market segments. The increasing demand for robust and reliable data center infrastructure, driven by a rapidly growing digital economy, positions Indonesia as a lucrative market for data center construction companies. Furthermore, the government's commitment to digital transformation and the presence of major multinational corporations further strengthens the positive market outlook. However, effective mitigation strategies will be crucial to address the challenges of high investment costs and skill shortages to ensure sustainable and inclusive growth within this dynamic market. Further research into specific regional variations within Indonesia, detailed analysis of regulatory environments, and competitive benchmarking of technology adoption rates will provide even more granular insights into this high-growth market. Recent developments include: September 2022: The company commenced construction on a 23 MW data center in Jakarta, Indonesia, marking the company's third site in South East Asia as it capitalizes on the region's rapid digital transformation in the wake of the global pandemic. The new facility will offer 3,430 cabinets and an IT load of 23 MW and is designed to cater to the growing demand for high-power density applications from cloud-driven hyperscale deployments, local and international networks, and financial service providers. It is expected to complete by Q4 2023., August 2022: PT Sigma Cipta Caraka (SCA), also known as Telkomsigma, transfers its data center business to PT Telkom Data Ekosistem (TDE), which is worth a total of IDR 2.01 trillion. The parent company PT Telkom Indonesia (Persero) Tbk (TLKM) claimed that this transfer of the data center business line is related to the business restructuring program held by Telkom Group.. Key drivers for this market are: Major ICT Indicators Contributing to the Growth of Data Centre in Indonesia, Rise of Green Data Centers; Government Support in the Form of Tax Incentives for Development of Data Centers. Potential restraints include: Higher Initial Investments and Low Availability of Resources. Notable trends are: Major ICT Indicators Contributing to the Growth of Data Centre in Indonesia.

No. of Worker: Petroleum & Natural Gas Mining: Indonesian data was reported at 21,402.000 Person in 2017. This records an increase from the previous number of 20,752.000 Person for 2015. No. of Worker: Petroleum & Natural Gas Mining: Indonesian data is updated yearly, averaging 21,385.000 Person from Dec 2007 (Median) to 2017, with 10 observations. The data reached an all-time high of 25,946.000 Person in 2008 and a record low of 20,468.000 Person in 2012. No. of Worker: Petroleum & Natural Gas Mining: Indonesian data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Energy Sector – Table ID.RBA005: Energy Statistics: Worker: Oil & Gas.

Jumlah Simpanan Indonesia dilaporkan sebesar 500.228 USD bn pada 2025-01. Rekor ini turun dibanding sebelumnya yaitu 500.326 USD bn untuk 2024-12. Data Jumlah Simpanan Indonesia diperbarui bulanan, dengan rata-rata 271.226 USD bn dari 1999-01 sampai 2025-01, dengan 313 observasi. Data ini mencapai angka tertinggi sebesar 528.941 USD bn pada 2024-09 dan rekor terendah sebesar 68.499 USD bn pada 1999-03. Data Jumlah Simpanan Indonesia tetap berstatus aktif di CEIC dan dilaporkan oleh CEIC Data. Data dikategorikan dalam Global Economic Monitor World Trend Plus – Table: Total Deposits: USD: Monthly.

Explore the dynamics and future of the Indonesia Big Data Analytics Software Market with a size at USD 40 billion in 2023, highlighting growth potential, key players, and regional insights featuring emerging trends.

Indonesia Imports: Oil and Gas: Annual data was reported at 29.869 USD bn in 2018. This records an increase from the previous number of 24.316 USD bn for 2017. Indonesia Imports: Oil and Gas: Annual data is updated yearly, averaging 6.019 USD bn from Dec 1984 (Median) to 2018, with 35 observations. The data reached an all-time high of 45.266 USD bn in 2013 and a record low of 909.000 USD mn in 1988. Indonesia Imports: Oil and Gas: Annual data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.JAA001: Trade Statistics.

Comprehensive dataset of 99 Indonesian restaurants in France as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Commercial Banks: KBMI 2: Source of Funds: Liabilities to BI data was reported at 0.307 IDR bn in Feb 2025. This records a decrease from the previous number of 557.468 IDR bn for Jan 2025. Commercial Banks: KBMI 2: Source of Funds: Liabilities to BI data is updated monthly, averaging 0.307 IDR bn from Oct 2021 (Median) to Feb 2025, with 41 observations. The data reached an all-time high of 1,755.594 IDR bn in Jan 2023 and a record low of 0.134 IDR bn in Oct 2021. Commercial Banks: KBMI 2: Source of Funds: Liabilities to BI data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Global Database’s Indonesia – Table ID.KAF002: Sources and Uses of Fund: by Bank.

Saudi Arabia Imports from Indonesia was US$3.57 Billion during 2024, according to the United Nations COMTRADE database on international trade. Saudi Arabia Imports from Indonesia - data, historical chart and statistics - was last updated on August of 2025.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 1.8582 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.1766 and 0.1278 (in million kms), corressponding to 9.5052% and 6.877% respectively of the total road length in the dataset region. 1.5538 million km or 83.6178% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0237 million km of information (corressponding to 1.5266% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Comprehensive dataset of 3 Indonesian restaurants in Iran as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Indonesia Bank Industries: Aggregate NPL, Gross data was reported at 2.081 % in Dec 2024. This records a decrease from the previous number of 2.189 % for Nov 2024. Indonesia Bank Industries: Aggregate NPL, Gross data is updated monthly, averaging 2.727 % from Jan 2014 (Median) to Dec 2024, with 132 observations. The data reached an all-time high of 3.354 % in Aug 2021 and a record low of 1.898 % in Jan 2014. Indonesia Bank Industries: Aggregate NPL, Gross data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI001: Financial System Statistics: Summary.

Comprehensive dataset of 301 Indonesian restaurants in Taiwan as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 8 Indonesian restaurants in Poland as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.