The benchmark interest rate in Indonesia was last recorded at 5 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key information about Indonesia Policy Rate

• Indonesia cash rate (Policy Rate: Month End: Indonesia: BI-Rate) was set at 5.75 % pa in Feb 2025, compared with 5.75 % pa in the previous Jan 2025.
• Indonesia Policy Rate averaged 5.25 % pa and is updated monthly, available from Jun 2015 to Feb 2025.
• The data reached an all-time high of 6.25 % pa in Aug 2024 and a record low of 3.50 % pa in Jul 2022.

The Bank of Indonesia announced 7 Days Reverse Repo Rate as new policy rate effective August 19th, 2016. For Policy Rate prior to August 2016, please refer to the 1 Month Bank Indonesia Certificates Auction Rate. From 21 December 2023, Bank Indonesia will change the name of its policy rate to BI Rate, replacing the BI 7-Day Reverse Repo. The new name doesn't change the meaning or objectives of the BI-Rates as the monetary policy stance of Bank Indonesia, with operationalisation still referring to Bank Indonesia reverse repo transaction with a tenor of seven days. [COVID-19-IMPACT]


Related information about Indonesia Policy Rate

• In the latest reports, Indonesia Short Term Interest Rate: Month End: Indonesia: 3 Months Interbank was reported at 6.74 % pa in Jun 2023.
• Its Long Term Interest Rate (Long Term Interest Rate: Month End: Indonesia: Government Bond Yield: 10 Years) was reported at 6.93 % pa in Feb 2025.
• Indonesia Exchange Rate against USD averaged 14,932.00 (USD/IDR) in Jun 2023.
• Its Real Effective Exchange Rate was 111.62 in Jan 2025.

Burundi BI: Bound Rate: Simple Mean: Manufactured Products data was reported at 32.500 % in 2021. This stayed constant from the previous number of 32.500 % for 2020. Burundi BI: Bound Rate: Simple Mean: Manufactured Products data is updated yearly, averaging 32.635 % from Dec 2002 (Median) to 2021, with 18 observations. The data reached an all-time high of 36.410 % in 2003 and a record low of 32.170 % in 2016. Burundi BI: Bound Rate: Simple Mean: Manufactured Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Trade Tariffs. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.;World Bank staff estimates using the World Integrated Trade Solution system, based on data from World Trade Organization.;;The tariff data for the European Union (EU) apply to EU Member States in alignment with the EU membership for the respective countries/economies and years. In the context of the tariff data, the EU membership for a given country/economy and year is defined for the entire year during which the country/economy was a member of the EU (irrespective of the date of accession to or withdrawal from the EU within a given year). The tariff data for the EU are, thus, applicable to Belgium, France, Germany, Italy, Luxembourg, and the Netherlands (EU Member State(s) since 1958), Denmark and Ireland (EU Member State(s) since 1973), the United Kingdom (EU Member State(s) from 1973 until 2020), Greece (EU Member State(s) since 1981), Spain and Portugal (EU Member State(s) since 1986), Austria, Finland, and Sweden (EU Member State(s) since 1995), Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, and Slovenia (EU Member State(s) since 2004), Romania and Bulgaria (EU Member State(s) since 2007), Croatia (EU Member State(s) since 2013). For more information, please revisit the technical note on bilateral applied tariff (https://wits.worldbank.org/Bilateral-Tariff-Technical-Note.html).

This dataset tracks annual white student percentage from 2012 to 2020 for Bi-County Special Educ Coop School District vs. Illinois

In 2023, the real interest rate in Indonesia was 7.28 percent. Between 1986 and 2023, the figure dropped by 11.55 percentage points, though the decline followed an uneven course rather than a steady trajectory.

This dataset tracks annual two or more races student percentage from 2013 to 2020 for Bi-County Special Educ Coop School District vs. Illinois

Burundi BI: Primary Completion Rate: Total: % of Relevant Age Group data was reported at 49.196 % in 2020. This records a decrease from the previous number of 53.330 % for 2019. Burundi BI: Primary Completion Rate: Total: % of Relevant Age Group data is updated yearly, averaging 32.150 % from Dec 1971 (Median) to 2020, with 42 observations. The data reached an all-time high of 71.301 % in 2013 and a record low of 5.752 % in 1971. Burundi BI: Primary Completion Rate: Total: % of Relevant Age Group data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Social: Education Statistics. Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

This dataset tracks annual two or more races student percentage from 2019 to 2020 for Bi-county-jr High School Life Skills-rock vs. Illinois and Bi-County Special Educ Coop School District

According to Cognitive Market Research, the global Business Intelligence market size is USD 16.9 million in 2023 and will expand at a compound annual growth rate (CAGR) of 9.50% from 2023 to 2030.

The demand for Business Intelligence s is rising due to the increasing data complexity and rising focus on data-driven decision-making.
Demand for adults remains higher in the Business Intelligence market.
The Business intelligence platform category held the highest Business intelligence market revenue share in 2023.
North American Business Intelligence will continue to lead, whereas the Asia-Pacific Business Intelligence market will experience the most substantial growth until 2030.

Growing Emphasis on Data-Driven Decision-Making to Provide Viable Market Output

In the Business Intelligence Tools market, the increasing recognition of the strategic importance of data-driven decision-making serves as a primary driver. Organizations across various industries are realizing the transformative power of insights derived from BI tools. As the volume of data generated continues to soar, businesses seek sophisticated tools that can efficiently analyze and interpret this information. The ability of BI tools to convert raw data into actionable insights empowers decision-makers to formulate informed strategies, enhance operational efficiency, and gain a competitive edge in a data-centric business landscape.

In June 2020, SAS and Microsoft established a comprehensive technology and go-to-market strategic alliance. As part of the collaboration, SAS's industry solutions and analytical products will be moved to Microsoft Azure, SAS Cloud's preferred cloud provider.

Source-news.microsoft.com/2020/06/15/sas-and-microsoft-partner-to-further-shape-the-future-of-analytics-and-ai/#:~:text=and%20SAS%20today%20announced%20an,from%20their%20digital%20transformation%20initiatives.

Rise in Adoption of Advanced Analytics and Artificial Intelligence to Propel Market Growth

Another significant driver in the Business Intelligence Tools market is the escalating adoption of advanced analytics and artificial intelligence (AI) capabilities. Modern BI tools are incorporating AI-driven functionalities such as machine learning algorithms, natural language processing, and predictive analytics. These technologies enable users to uncover deeper insights, identify patterns, and predict future trends. The integration of AI not only enhances the analytical capabilities of BI tools but also automates processes, reducing manual efforts and improving the overall efficiency of data analysis. This trend aligns with the industry's pursuit of more intelligent and automated BI solutions to derive maximum value from data assets.

In March 2020, IBM created a new, dynamic global dashboard to display the global spread of COVID-19 with the assistance of IBM Cognos Analytics. The World Health Organization (WHO) and state and municipal governments provide the COVID-19 data displayed in this dashboard.

Source-www.ibm.com/blog/creating-trusted-covid-19-data-for-communities/

Market Dynamics of the Business Intelligence tool Market

Key Drivers for Business Intelligence tool Market

Increasing Demand for Data-Driven Decision Making Across Various Sectors: As companies produce vast amounts of data, there is an escalating requirement for tools that can analyze and convert raw data into actionable insights. Business Intelligence (BI) tools facilitate quicker and more precise strategic decisions in areas such as sales, finance, operations, and customer service.

Transition to Cloud-Based BI Solutions for Enhanced Scalability and Accessibility: Organizations are progressively shifting from on-premise BI systems to cloud-based solutions, which provide real-time access, foster collaboration, and reduce infrastructure expenses. This transition enhances scalability and accommodates hybrid or remote work settings.

Incorporation of AI and Machine Learning for Enhanced Predictive Analytics: Sophisticated BI tools are incorporating artificial intelligence and machine learning technologies to deliver predictive forecasting, anomaly detection, and natural language querying—thereby improving the accuracy of business forecasts and enhancing user accessibility.

Key Restraints for Business Intelligence tool Market

High Initial Setup and Customization Costs for SMEs: Small and medium-sized...

This dataset tracks annual asian student percentage from 1993 to 2020 for Mulberry/Pleasant View Bi-County Schools School District vs. Arkansas

Burundi BI: Labour Force Participation Rate: National Estimate: Female: Aged 15-24 data was reported at 58.083 % in 2020. This records an increase from the previous number of 57.661 % for 2014. Burundi BI: Labour Force Participation Rate: National Estimate: Female: Aged 15-24 data is updated yearly, averaging 70.895 % from Dec 1979 (Median) to 2020, with 8 observations. The data reached an all-time high of 93.050 % in 1979 and a record low of 54.030 % in 2008. Burundi BI: Labour Force Participation Rate: National Estimate: Female: Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Labour Force. Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.;International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Historical Dataset of Bi-county Sp Ed-transition Life S is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2019-2020),Total Classroom Teachers Trends Over Years (2019-2023),Student-Teacher Ratio Comparison Over Years (2019-2020),Hispanic Student Percentage Comparison Over Years (2019-2020),White Student Percentage Comparison Over Years (2019-2020),Two or More Races Student Percentage Comparison Over Years (2019-2020),Diversity Score Comparison Over Years (2019-2020),Free Lunch Eligibility Comparison Over Years (2019-2020)

Burundi BI: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data was reported at 99.130 % in 2020. This records a decrease from the previous number of 102.487 % for 2014. Burundi BI: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data is updated yearly, averaging 100.508 % from Dec 1978 (Median) to 2020, with 12 observations. The data reached an all-time high of 104.470 % in 1984 and a record low of 79.123 % in 1980. Burundi BI: Labour Force Participation Rate: National Estimate: Ratio of Female to Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.;World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization, ILOSTAT at https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Burundi BI: GDP: USD: Gross Value Added at Factor Cost data was reported at 2.748 USD bn in 2020. This records an increase from the previous number of 2.645 USD bn for 2019. Burundi BI: GDP: USD: Gross Value Added at Factor Cost data is updated yearly, averaging 850.611 USD mn from Dec 1960 (Median) to 2020, with 61 observations. The data reached an all-time high of 2.850 USD bn in 2015 and a record low of 149.502 USD mn in 1965. Burundi BI: GDP: USD: Gross Value Added at Factor Cost data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Gross Domestic Product: Nominal. Gross value added at factor cost (formerly GDP at factor cost) is derived as the sum of the value added in the agriculture, industry and services sectors. If the value added of these sectors is calculated at purchaser values, gross value added at factor cost is derived by subtracting net product taxes from GDP. Data are in current U.S. dollars.; ; World Bank national accounts data, and OECD National Accounts data files.; Gap-filled total;

This dataset tracks annual white student percentage from 2019 to 2020 for Bi-county-jr High School Life Skills-rock vs. Illinois and Bi-County Special Educ Coop School District

Burundi BI: Suicide Mortality Rate: per 100,000 Population data was reported at 7.650 Ratio in 2021. This records an increase from the previous number of 6.670 Ratio for 2020. Burundi BI: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 7.265 Ratio from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 10.930 Ratio in 2000 and a record low of 6.670 Ratio in 2020. Burundi BI: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Social: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/].

Burundi BI: Tariff Rate: Applied: Weighted Mean: All Products data was reported at 8.960 % in 2022. This records an increase from the previous number of 8.530 % for 2021. Burundi BI: Tariff Rate: Applied: Weighted Mean: All Products data is updated yearly, averaging 8.530 % from Dec 2002 (Median) to 2022, with 19 observations. The data reached an all-time high of 21.100 % in 2005 and a record low of 5.390 % in 2013. Burundi BI: Tariff Rate: Applied: Weighted Mean: All Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Trade Tariffs. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead.;World Bank staff estimates using the World Integrated Trade Solution system, based on tariff data from the United Nations Conference on Trade and Development's Trade and Development's Trade Analysis and Information System (TRAINS) database and global imports data from the United Nations Statistics Division's Comtrade database.;;The tariff data for the European Union (EU) apply to EU Member States in alignment with the EU membership for the respective countries/economies and years. In the context of the tariff data, the EU membership for a given country/economy and year is defined for the entire year during which the country/economy was a member of the EU (irrespective of the date of accession to or withdrawal from the EU within a given year). The tariff data for the EU are, thus, applicable to Belgium, France, Germany, Italy, Luxembourg, and the Netherlands (EU Member State(s) since 1958), Denmark and Ireland (EU Member State(s) since 1973), the United Kingdom (EU Member State(s) from 1973 until 2020), Greece (EU Member State(s) since 1981), Spain and Portugal (EU Member State(s) since 1986), Austria, Finland, and Sweden (EU Member State(s) since 1995), Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, and Slovenia (EU Member State(s) since 2004), Romania and Bulgaria (EU Member State(s) since 2007), Croatia (EU Member State(s) since 2013). For more information, please revisit the technical note on bilateral applied tariff (https://wits.worldbank.org/Bilateral-Tariff-Technical-Note.html).

Burundi BI: Labour Force Participation Rate: National Estimate: Male: % of Male Population Aged 15+ data was reported at 78.940 % in 2020. This records an increase from the previous number of 77.894 % for 2014. Burundi BI: Labour Force Participation Rate: National Estimate: Male: % of Male Population Aged 15+ data is updated yearly, averaging 78.210 % from Dec 1978 (Median) to 2020, with 12 observations. The data reached an all-time high of 92.530 % in 1979 and a record low of 51.740 % in 1978. Burundi BI: Labour Force Participation Rate: National Estimate: Male: % of Male Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.;International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed January 07, 2025. https://ilostat.ilo.org/data/.;Weighted average;The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

This dataset tracks annual black student percentage from 2012 to 2020 for Bi-County Special Educ Coop School District vs. Illinois

Burundi BI: Tariff Rate: Applied: Weighted Mean: Primary Products data was reported at 16.010 % in 2022. This records a decrease from the previous number of 16.210 % for 2021. Burundi BI: Tariff Rate: Applied: Weighted Mean: Primary Products data is updated yearly, averaging 13.350 % from Dec 2002 (Median) to 2022, with 19 observations. The data reached an all-time high of 29.810 % in 2009 and a record low of 6.690 % in 2016. Burundi BI: Tariff Rate: Applied: Weighted Mean: Primary Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Trade Tariffs. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).;World Bank staff estimates using the World Integrated Trade Solution system, based on tariff data from the United Nations Conference on Trade and Development's Trade and Development's Trade Analysis and Information System (TRAINS) database and global imports data from the United Nations Statistics Division's Comtrade database.;;The tariff data for the European Union (EU) apply to EU Member States in alignment with the EU membership for the respective countries/economies and years. In the context of the tariff data, the EU membership for a given country/economy and year is defined for the entire year during which the country/economy was a member of the EU (irrespective of the date of accession to or withdrawal from the EU within a given year). The tariff data for the EU are, thus, applicable to Belgium, France, Germany, Italy, Luxembourg, and the Netherlands (EU Member State(s) since 1958), Denmark and Ireland (EU Member State(s) since 1973), the United Kingdom (EU Member State(s) from 1973 until 2020), Greece (EU Member State(s) since 1981), Spain and Portugal (EU Member State(s) since 1986), Austria, Finland, and Sweden (EU Member State(s) since 1995), Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, and Slovenia (EU Member State(s) since 2004), Romania and Bulgaria (EU Member State(s) since 2007), Croatia (EU Member State(s) since 2013). For more information, please revisit the technical note on bilateral applied tariff (https://wits.worldbank.org/Bilateral-Tariff-Technical-Note.html).