This dataset is completely based on personal expenses, incomes & savings throughout the year done by my family members. The finance analysis of 2022 year shows how much I had earned, saved and spent on several categories. Here you can see the following attributes like description : It shows the area of finance category: which are the area where money is going and coming sub category: It is the sub part of category category type : Like income, savings & expenses debit amount: includes expenses credit amount: includes incomes and savings Also where I had invested and should I have continue or not, what all things I have to control and what are the savings I can do from my income , this I can analyze from this report very easily. what are my way of spending and how much I'm focusing on my savings after having incomes from several sources. Which sub category shows more expenses and what I can limit that analysis I can do from this and many more to do.

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

Data Security and Privacy Concerns to Restrict Market Growth

One of the key restraints in the Business Intelligence Tools market revolves around persistent concerns regarding data security and privacy. As organizations increasingly rely on BI tools to process and analyze sensitive business information, the risk of data breaches and unauthorized access becomes a prominent challenge. Heightened awareness of regulatory requirements, such as GDPR, has intensified the focus on protecting sensitive data. Businesses face the challenge of implementing robust security measures within BI tools to ensure compliance with regulations and safeguard against potential data vulnerabilities, thereby slowing down the adoption pace.

Impact of COVID-19 on the Business Intelligence market

The COVID-19 pandemic has had a profound impact on the Business Intelligence (BI) market. As organizations grappled with unprecedented disruptions, the need for timely and accurate insights became paramount. The pandemic accelerated the adoption of BI tools as businesses sought to navigate uncertainties and make data-driven decisions. Remote work became a norm, prompting increased demand for BI solutions that support virtual collaboration and enable users to access analytics from anywhere. Moreover, there w...

The Department of the Prime Minister and Cabinet is no longer maintaining this dataset. If you would like to take ownership of this dataset for ongoing maintenance please contact us.

PLEASE READ BEFORE USING

The data format has been updated to align with a tidy data style (http://vita.had.co.nz/papers/tidy-data.html).

The data in this dataset is manually collected and combined in a csv format from the following state and territory portals:

• https://www.cmtedd.act.gov.au/communication/holidays
• https://www.nsw.gov.au/about-nsw/public-holidays
• https://nt.gov.au/nt-public-holidays
• https://www.qld.gov.au/recreation/travel/holidays/public
• https://www.safework.sa.gov.au/resources/public-holidays
• https://worksafe.tas.gov.au/topics/laws-and-compliance/public-holidays
• https://business.vic.gov.au/business-information/public-holidays
• https://www.commerce.wa.gov.au/labour-relations/public-holidays-western-australia

The data API by default returns only the first 100 records. The JSON response will contain a key that shows the link for the next page of records. Alternatively you can view all records by updating the limit on the endpoint or using a query to select all records, i.e. /api/3/action/datastore_search_sql?sql=SELECT * from "{{resource_id}}".

Technological progress has fueled online business activity and companies’ resulting demand for new software tools to enhance operations and customer interactions. Their increased investment in technology has fostered considerable revenue growth over recent years for business analytics and enterprise software publishers. However, macroeconomic factors have also induced volatility in revenue. The e-commerce surge and solid GDP growth amid the pandemic recovery raised corporate profit and, in turn, spending on software from various businesses. Many software providers have also been able to keep prices elevated since the need for software has consistently been very high, pushing profit upward since 2022. At the same time, the Federal Reserve's interest rate hikes between 2021 and 2023 to battle inflation led to fears of a recession. This prompted businesses to limit software investments and slowed revenue growth in 2023 and 2024. In late 2024, many economists reached the consensus that the US had achieved the desired soft landing. The industry has also been impacted by various long-term trends. The shift to cloud-based solutions, accelerated by the need to boost IT security during pandemic-induced lockdowns, has facilitated the use of advanced analytics and AI that allow companies to harness large data efficiently. Major players have incorporated AI features into their platforms to enhance functionality, driving demand for enterprise software providers’ services. Smaller software publishers, lacking the resources to invest heavily in new technologies, have increasingly focused on niche markets. Acquisition activity has also expanded, with companies like Salesforce and Microsoft expanding capabilities by acquiring specialized firms. Overall, revenue for business analytics and enterprise software publishing businesses has surged at a CAGR of 12.8% over the past five years, and is estimated to reach $253.0 billion in 2025. This includes a projected 5.1% rise in revenue in 2025. Moving forward, demand for business analytics and enterprise software across various sectors is expected to remain strong. However, the market is likely to become saturated, slowing revenue growth. Economic uncertainty, marked by the potential for a recession due to tariffs imposed in early 2025, might constrain software demand from the manufacturing and tech sectors. Cybersecurity investment will rise, with big players like Salesforce and Oracle enhancing defenses. AI integration will present new challenges, necessitating advanced infrastructure and skilled workers, which could increase operating costs for software publishers. Overall, revenue for business analytics and enterprise software publishers is anticipated to soar at a CAGR of 7.5% over the next five years, reaching an estimated $363.0 billion in 2030.

The weather station on the campus of Loughborough University, in the East Midlands of the UK, had fallen into disuse and disrepair by the mid-2000s, but in 2007 the availability of infrastructure funding made it possible to re-establish regular weather observation with new equipment. The meteorological dataset subsequently collected at this facility between 2008 and 2021 is archived here. The dataset comes as fourteen Excel (.xlsx) files of annual data, with explanatory notes in each.Site descriptionThe campus weather station is located at latitude 52.7632°, longitude -1.235° and 68 m a.s.l., in a dedicated paddock on a green space near the centre-east boundary of the campus. A cabin, which houses power and network points, sits 10 m to the northeast of the main meteorological instrument tower. The paddock is otherwise mostly open on an arc from the northwest to the northeast, but on the other sides there are fruit trees (mainly varieties of prunus domestica) at distances of 13–16 m, forming part of the university's "Fruit Routes" biodiversity initiative.Data collectionInstruments were fixed to a 3 m lattice mast which is concreted into the ground in the centre of the paddock described above. Up to late July 2013, the instruments were controlled by a solar-charged, battery-powered Campbell Scientific CR1000 data logger, and periodically manually downloaded. From early November 2013, this logger was replaced with a Campbell Scientific CR3000, run from the mains power supply from the cabin and connected to the campus network by ethernet. At the same time, the station's Young 01503 Wind Monitor was replaced by a Gill WindSonic ultrasonic anemometer. This combination remained in place for the rest of the measurement period described here. Frustratingly, the CS215 temperature/relative humidity sensor failed shortly before the peak of the 2018 heatwave, and had to be replaced with another CS215. Likewise, the ARG100 rain gauge was replaced in 2011 and 2016. The main cause of data gaps is the unreliable power supply from the cabin, particularly in 2013 and 2021 (the latter leading to the complete replacement of the cabin and all other equipment). Furthermore, even though the post-2013 CR3000 logger had a backup battery, it sometimes failed to restart after mains power was lost, yielding data gaps until it was manually restarted. Nevertheless, out of 136 instrument-years deployment, only 36 are less than 90% complete, and 21 less than 75% complete.Data processingData retrieved manually or downloaded remotely were filtered for invalid measurements. The 15-minute data were then processed to daily and monthly values, using the pivot table function in Microsoft Excel. Most variables could be output simply as midnight-to-midnight daily means (e.g. solar and net radiation, wind speed). However, certain variables needed to be referred to the UK and Ireland standard ‘Climatological Day’ (Burt, 2012:272), 0900-0900: namely, air temperature minimum and maximum, plus rainfall total. The procedure for this follows Burt (2012; https://www.measuringtheweather.net/) and requires the insertion of additional date columns into the spreadsheet, to define two further, separate ‘Climate Dates’ for maximum temperature and rainfall total (the 24 hours commencing at 0900 on the date given, ‘ClimateDateMax’), and for minimum temperatures (24 hours ending at 0900 on the date given, ‘ClimateDateMin’). For the archived data, in the spreadsheet tabs labelled ‘Output - Daily 09-09 minima’, the pivot table function derives daily minimum temperatures by the correct 0900-0900 date, given by the ClimateDateMin variable. Similarly, in the tabs labelled ‘Output - Daily 09-09 maxima’, the pivot table function derives daily maximum temperatures and daily rainfall totals by the correct 0900-0900 date, given by the ClimateDateMax variable. Then in the tabs labelled ‘Output - Daily 00-00 means’, variables with midnight-to-midnight means use the unmodified date variable. To take into account the effect of missing data, the tab ‘Completeness’ again uses a pivot table to count the numbers of daily and monthly observations where the 15-minute data are not at least 99.99% complete. Values are only entered into the ‘Daily data’ tab of the archived spreadsheets where 15-minute data are at least 75% complete; values are only entered into ‘Monthly data’ tabs where daily data are at least 75% complete.Wind directions are particularly important in UK meteorology because they indicate the origin of air masses with potentially contrasting characteristics. But wind directions are not averaged in the same way as other variables, as they are measured on a circular scale. Instead, 15-minute wind direction data in degrees are converted to 16 compass points (the formula is included in the spreadsheets), and a pivot table is used to summarise these into wind speed categories, giving the frequency and strength of winds by compass point.In order to evaluate the reliability of the collected dataset, it was compared to equivalent variables from the HadUK-Grid dataset (Hollis et al., 2019). HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations, which have been interpolated from meteorological station data onto a uniform grid to provide coherent coverage across the UK at 1 km x 1 km resolution. Daily and monthly air temperature and rainfall variables from the HadUK-Grid v1.1.0.0 Met Office (2022) were downloaded from the Centre for Environmental Data Analysis (CEDA) archive (https://catalogue.ceda.ac.uk/uuid/bbca3267dc7d4219af484976734c9527/). Then the grid square containing the campus weather station was identified using the Point Subset Tool of the NOAA Weather and Climate Toolkit (https://www.ncdc.noaa.gov/wct/index.php) in order to retrieve data from that specific location. Daily and monthly HadUK-grid data are included in the spreadsheets for convenience.Campus temperatures are slightly, but consistently, higher than those indicated by HadUK-grid, while HadUK-Grid rainfall is on average almost 10% higher than that recorded on the campus. Trend-free statistical relationships between campus and HadUK-grid data implies that there is unlikely to be any significant temporal bias in the campus dataset.ReferencesBurt, S. (2012). The Weather Observer's Handbook. Cambridge University Press, https://doi.org/10.1017/CBO9781139152167.Hollis, D, McCarthy, M, Kendon, M., Legg, T., Simpson, I. (2019). HadUK‐Grid—A new UK dataset of gridded climate observations. Geoscience Data Journal 6, 151–159, https://doi.org/10.1002/gdj3.78.Met Office; Hollis, D.; McCarthy, M.; Kendon, M.; Legg, T. (2022). HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.1.0.0 (1836-2021). NERC EDS Centre for Environmental Data Analysis, https://dx.doi.org/10.5285/bbca3267dc7d4219af484976734c9527.

The market size for business intelligence and analytics software applications is forecast to increase worldwide over the next few years from **** billion U.S. dollars in 2021 to more than ** billion in 2026. The business intelligence and analytics software application market is a subsegment of the enterprise application software market. Enterprise application software - a market with worldwide revenues of *** billion U.S. dollars in 2020 - aims at responding to the needs of organizations. These software programs make it easier for companies and businesses to accomplish their corporate goals, by helping to improve supply chain management, manage resources, or interact better with customers, among others. Business intelligence and analytics     Business intelligence applications are used to collect and analyze current, actionable data in order to maintain, optimize or streamline business operations. Business analytics tools, on the other hand, are used to analyze data to be able to predict business trends. The leading companies in the business intelligence and analytics market are Microsoft, SAP and IBM, with revenues of *** billion U.S. dollars, *** billion, and *** billion respectively in 2018.