The statistic shows the problems caused by poor quality data for enterprises in North America, according to a survey of North American IT executives conducted by 451 Research in 2015. As of 2015, 44 percent of respondents indicated that having poor quality data can result in extra costs for the business.

The statistic depicts the causes of poor data quality for enterprises in North America, according to a survey of North American IT executives conducted by 451 Research in 2015. As of 2015, 47 percent of respondents indicated that poor data quality at their company was attributable to data migration or conversion projects.

United States CCI: Present Situation: sa: Business Conditions: Bad data was reported at 15.700 % in Feb 2025. This records an increase from the previous number of 15.200 % for Jan 2025. United States CCI: Present Situation: sa: Business Conditions: Bad data is updated monthly, averaging 19.700 % from Feb 1967 (Median) to Feb 2025, with 635 observations. The data reached an all-time high of 57.000 % in Dec 1982 and a record low of 6.000 % in Dec 1968. United States CCI: Present Situation: sa: Business Conditions: Bad data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H042: Consumer Confidence Index. [COVID-19-IMPACT]

In 2023, more than half of Polish respondents had no opinion on whether ChatGPT would store wrong information in the algorithm's database.

Data Analytics Market Valuation – 2024-2031

Data Analytics Market was valued at USD 68.83 Billion in 2024 and is projected to reach USD 482.73 Billion by 2031, growing at a CAGR of 30.41% from 2024 to 2031.

Data Analytics Market Drivers

Data Explosion: The proliferation of digital devices and the internet has led to an exponential increase in data generation. Businesses are increasingly recognizing the value of harnessing this data to gain competitive insights.

Advancements in Technology: Advancements in data storage, processing power, and analytics tools have made it easier and more cost-effective for organizations to analyze large datasets.

Increased Business Demand: Businesses across various industries are seeking data-driven insights to improve decision-making, optimize operations, and enhance customer experiences.

Data Analytics Market Restraints

Data Quality and Integrity: Ensuring the accuracy, completeness, and consistency of data is crucial for effective analytics. Poor data quality can hinder insights and lead to erroneous conclusions.

Data Privacy and Security Concerns: As organizations collect and analyze sensitive data, concerns about data privacy and security are becoming increasingly important. Breaches can have significant financial and reputational consequences.

The global data quality tools market is anticipated to grow at a CAGR of 12.3% during the forecast period of 2025-2033, reaching a value of $20,340 million by 2033. The rising need to improve data quality for accurate decision-making, increasing data volumes and complexity, and growing adoption of cloud-based data management solutions are some of the key factors driving the market growth. The increasing demand for data governance and compliance, as well as the need to mitigate risks associated with poor data quality, are also contributing to the market's expansion. The data quality tools market is segmented by type (on-premises, cloud), application (enterprise, government), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). The cloud segment is expected to witness the highest growth rate during the forecast period due to the increasing adoption of cloud-based data storage and management solutions. The enterprise application segment is anticipated to dominate the market, as businesses of all sizes are increasingly focusing on improving data quality to drive better decision-making and optimize operations. The North American region is expected to remain the largest market for data quality tools, while the Asia Pacific region is projected to exhibit the highest growth rate during the forecast period.

About this dataset

Context

The dataset tabulates the Bad Axe population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Bad Axe. The dataset can be utilized to understand the population distribution of Bad Axe by age. For example, using this dataset, we can identify the largest age group in Bad Axe.

Key observations

The largest age group in Bad Axe, MI was for the group of age 60 to 64 years years with a population of 317 (10.53%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Bad Axe, MI was the 75 to 79 years years with a population of 79 (2.62%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Bad Axe is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Bad Axe total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Bad Axe Population by Age. You can refer the same here

Historical price and volatility data for Bad Idea AI in Taiwan New Dollar across different time periods.

Freshwater harmful algal bloom (HAB) data from the Freshwater Harmful Algal Bloom (FHAB) data system. The FHAB data system is the California State Water Resources Control Board's data system for data and information voluntarily reported to the agency. Bloom reports are voluntary reports submitted by the public or organization to identify a POTENTIAL HAB for evaluation. Bloom Reports may or may not include a report that is confirmed to be a HAB, regardless, all bloom reports are published. Due to the voluntary basis of information and data included in the database, data and information may include: waterbody name and location, potential algal bloom location and observed characteristics, observed field observations and/or analytical sampling results, waterbody and/or land management, general information, recommended advisory status (if any), and updates regarding bloom status. Refer to Data Dictionary and Data Disclaimer for additional information about this dataset. Please visit the Water Boards FHABS web site for more information and data visualizations https://mywaterquality.ca.gov/habs/index.html.

Ireland - Perceived independence of the justice system: Fairly bad was 15.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Ireland - Perceived independence of the justice system: Fairly bad - last updated from the EUROSTAT on March of 2025. Historically, Ireland - Perceived independence of the justice system: Fairly bad reached a record high of 15.00% in December of 2024 and a record low of 10.00% in December of 2019.

Hattie the bad is a book. It was written by Jane Devlin and published by Puffin in 2009.

This dataset provides information about the number of properties, residents, and average property values for Willis Street cross streets in Bad Axe, MI.

United States CSI: Home Buying Conditions: Bad Time: Bad Investment data was reported at 0.000 % in May 2018. This records a decrease from the previous number of 1.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time: Bad Investment data is updated monthly, averaging 0.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 3.000 % in Feb 2014 and a record low of 0.000 % in May 2018. United States CSI: Home Buying Conditions: Bad Time: Bad Investment data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

This dataset of historical poor law cases was created as part of a project aiming to assess the implications of the introduction of Artificial Intelligence (AI) into legal systems in Japan and the United Kingdom. The project was jointly funded by the UK’s Economic and Social Research Council, part of UKRI, and the Japanese Society and Technology Agency (JST), and involved collaboration between Cambridge University (the Centre for Business Research, Department of Computer Science and Faculty of Law) and Hitotsubashi University, Tokyo (the Graduate Schools of Law and Business Administration). As part of the project, a dataset of historic poor law cases was created to facilitate the analysis of legal texts using natural language processing methods. The dataset contains judgments of cases which have been annotated to facilitate computational analysis. Specifically, they make it possible to see how legal terms have evolved over time in the area of disputes over the law governing settlement by hiring.

A World Economic Forum meeting at Davos 2019 heralded the dawn of 'Society 5.0' in Japan. Its goal: creating a 'human-centred society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space.' Using Artificial Intelligence (AI), robotics and data, 'Society 5.0' proposes to '...enable the provision of only those products and services that are needed to the people that need them at the time they are needed, thereby optimizing the entire social and organizational system.' The Japanese government accepts that realising this vision 'will not be without its difficulties,' but intends 'to face them head-on with the aim of being the first in the world as a country facing challenging issues to present a model future society.' The UK government is similarly committed to investing in AI and likewise views the AI as central to engineering a more profitable economy and prosperous society.

This vision is, however, starting to crystallise in the rhetoric of LegalTech developers who have the data-intensive-and thus target-rich-environment of law in their sights. Buoyed by investment and claims of superior decision-making capabilities over human lawyers and judges, LegalTech is now being deputised to usher in a new era of 'smart' law built on AI and Big Data. While there are a number of bold claims made about the capabilities of these technologies, comparatively little attention has been directed to more fundamental questions about how we might assess the feasibility of using them to replicate core aspects of legal process, and ensuring the public has a meaningful say in the development and implementation.

This innovative and timely research project intends to approach these questions from a number of vectors. At a theoretical level, we consider the likely consequences of this step using a Horizon Scanning methodology developed in collaboration with our Japanese partners and an innovative systemic-evolutionary model of law. Many aspects of legal reasoning have algorithmic features which could lend themselves to automation. However, an evolutionary perspective also points to features of legal reasoning which are inconsistent with ML: including the reflexivity of legal knowledge and the incompleteness of legal rules at the point where they encounter the 'chaotic' and unstructured data generated by other social sub-systems. We will test our theory by developing a hierarchical model (or ontology), derived from our legal expertise and public available datasets, for classifying employment relationships under UK law. This will let us probe the extent to which legal reasoning can be modelled using less computational-intensive methods such as Markov Models and Monte Carlo Trees.

Building upon these theoretical innovations, we will then turn our attention from modelling a legal domain using historical data to exploring whether the outcome of legal cases can be reliably predicted using various technique for optimising datasets. For this we will use a data set comprised of 24,179 cases from the High Court of England and Wales. This will allow us to harness Natural Language Processing (NLP) techniques such as named entity recognition (to identify relevant parties) and sentiment analysis (to analyse opinions and determine the disposition of a party) in addition to identifying the main legal and factual points of the dispute, remedies, costs, and trial durations. By trailing various predictive heuristics and ML techniques against this dataset we hope to develop a more granular understanding as to the feasibility of predicting dispute outcomes and insight to what factors are relevant for legal decision-making. This will allow us to then undertake a comparative analysis with the results of existing studies and shed light on the legal contexts and questions where AI can and cannot be used to produce accurate and repeatable results.

Displays all invalid point of contact emails in the Data Asset Repository. Emails are considered invalid if they cannot be validated by the trusted identity exchange (TIE). All profile and role information for an invalid email is provided.

Overview

This dataset provides fast response wind and virtual sonic temperature data.

Data Details

Each meteorological (met) station has one sonic anemometer (Gill R3-50, omnidirectional) mounted on top of a 10-m tower. Sensor verticality (within a degree) has been verified by the analog inclinometer mounted on the base plate alongside the sonic anemometer. The sonic anemometer has been oriented to magnetic North.

The serial data stream is transmitted via radio link (9XTend RF modem by MaxStream) to the data acquisition computer housed in a temperature-controlled enclosure at the base of the 80-m tower.

The original data were stored in flat ASCII files in 30-min pieces (".00." level). The current version of the data is ".a0." level. All evidently erroneous and/or broken lines were marked as bad and/or replaced with a "baddata" place holder, the housekeeping data were stripped off, and the data were split into 5-min portions with no internal time stamp. The data have been prepared for processing with EddyPro and stored in ASCII comma delimited files formatted as follows:

u,v,w,T,qc

where:

• "u, v, w" are the three wind components (m/s)
• "T" is the sonic virtual temperature (C)
• "qc" is basic quality control code: 0 - OK, 1 - sonic bad data code, 2 - broken data line, and 3 - missed line.

Baddata place holder is 99.99

NOTE: No attempt has been made to fill gaps in the data.

Data Quality

• Includes raw data with basic quality control (QC) applied.
• All housekeeping fields removed.

Bad stories : what the hell just happened to our country is a book. It was written by Steve Almond and published by Red Hen Press in 2018.

According to INSPIRE transformed development plan “Bad-/Bachstraße” of the city of Kornwestheim based on an XPlanung dataset in version 5.0.

This is the replication package for "Nonprofits in Good Times and Bad Times," accepted in 2022 by the Journal of Political Economy Microeconomics.

The Hadley Centre at the U.K. Met Office has created a global sub-daily dataset of several station-observed climatological variables which is derived from and is a subset of the NCDC's ... Integrated Surface Database. Stations were selected for inclusion into the dataset based on length of the data reporting period and the frequency with which observations were reported. The data were then passed through a suite of automated quality-control tests to remove bad data. See the HadISD web page for more details and access to previous versions of the dataset.