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Deep-sea mining could soon be approved how bad is it? – Nature.com

An excavating machine on the Japanese research vessel Hakurei collected cobalt-rich ocean sediments in a 2020 test run.Credit: Kiyoshi Ota/Bloomberg via Getty

Commercial mining of the sea floor could soon get the green light. The International Seabed Authority (ISA), a body associated with the United Nations that oversees deep-sea mining in international waters, is now meeting in Kingston, Jamaica, where it could decide whether companies can begin excavating the sea floor for minerals and metals such as cobalt, nickel and sulfides.

Proponents say that this move could help with meeting the growing demand for rare-earth metals used in batteries both for electric cars and for storing renewable energy, aiding the shift to a low-carbon economy. However, research hints that the potential ecological impacts of deep-sea mining are larger than previously thought. Nature explores just how bad deep-sea mining could be.

Scientists say that very little is known1 about deep-sea ecosystems, making it difficult to assess how they will be affected by mining. However, a few new studies are providing clues to the damage that large-scale mining might cause.

A study2 published today in Current Biology is the first to examine the environmental effects of mining cobalt-rich crusts. These rock-hard, metallic layers, which form on the side of underwater mountains called seamounts, are among three deep-sea resources that have been proposed to the ISA as a target for mining. In 2020, a two-hour operation funded by the Japanese government excavated a roughly 120-metre-long strip of cobalt-rich crust on a seamount in the northwest Pacific Ocean, as a test run for mining activities.

To investigate the operations effects, scientists reviewed video footage collected by a remotely operated vehicle. They found that, in the year after the excavation, the density of active swimming animals, such as fish and shrimp, dropped by 43% in areas directly affected by sediment kicked up by mining, and by 56% in adjacent areas.

Animals observed during a study of the effects of deep-sea mining included those in the categories Actiniaria, Holothuroidea, Pentametrocrinidae (top row, left to right), Euplectellidae, Notocanthiformes and Aspidodiadematidae (bottom row, left to right).Credit: JOGMEC

Travis Washburn, a benthic ecologist and co-author of the study who at the time was at the National Institute of Advanced Industrial Science and Technology in Tsukuba, Japan, says that he didnt expect to see any ecological impacts from such a small mining operation. He suggests that the fish and shrimp swam away from the area because the mining and sediment pollution might have affected their food supply. The results show that effects are felt beyond the mining areas by a pretty substantial amount, he says.

A study3 published last week suggests that tuna near the ocean surface could gravitate to areas likely to be affected by mining. Writing in npj Ocean Sustainability, scientists project that climate change will drive large numbers of the fish into the ClarionClipperton Zone (CCZ), a 4.5-million-square-kilometre area in the eastern Pacific Ocean between Hawaii and Mexico where much of the mining interest is focused. The study predicts that by middle of the century, the zones total biomass of skipjack (Katsuwonus pelamis) will rise by around 31% and yellowfin (Thunnus albacares) by 23%.

Tuna catch rates soared after creation of no-fishing zone in Hawaii

Data about deep-sea minings effects on animals in the upper layers of the sea are scare. But co-author Diva Amon, a marine biologist and a scientific adviser to the Benioff Ocean Science Laboratory at the University of California, Santa Barbara, says that deep-sea mining could harm tuna and other organisms, such as Pacific leatherback turtles (Dermochelys coriacea).

Plumes of sediment stirred up by mining could contaminate sea water and damage fishes gills and filter-feeding apparatus, says Amon. The same problems could occur when mining waste is thrown back into the water. Furthermore, noise from the mining operations could alter the tunas feeding and reproductive behavior, she adds.

Deep-sea mining will potentially have impacts from the sea surface right down to the sea floor, says Amon.

Proponents of deep-sea mining, such as The Metals Company, a mining start-up based in Vancouver, Canada, that is seeking permission to harvest metals on the sea floor, argue that deep-sea mining will benefit the environment by helping the shift to a green economy. They also argue that sediment plumes effects can be minimized and that mining contractors dont currently propose to release waste from exploiting mineral-rich sea floor deposits called polymetallic nodules.

Amon says that deep-sea mining is unlikely to replace terrestrial mining, so comparing one with the other is not helpful. Both will proceed, and well see double destruction in two different parts of the planet, she says.

Washburn says that deep-sea mining might cause less direct damage to people than does terrestrial mining. But by spoiling huge swathes of the sea floor, it could disrupt marine processes such as carbon sequestration, which helps to offset humans greenhouse gas emissions.I dont think anybody has enough information to say which one is better or worse, he says.

Scientists first need to know more about what lives in the deep ocean, says Amon. Then they can begin to investigate how extensive mining can be before it causes serious harm to key ecosystem functions, such as the oceans ability to sequester carbon. The challenge, she says, is that deep-ocean science is slow and expensive, and scientists need more time and money to understand minings consequences.

Electric cars and batteries: how will the world produce enough?

Matthew Gianni, co-founder of the Deep-Sea Conservation Coalition, a conservation group based in Amsterdam, says that deep-sea mining could become unnecessary thanks to advances in recycling and the advent of batteries that use iron and phosphate instead of nickel and cobalt. Furthermore, improvements in environmental standards for terrestrial mining will lessen the industrys ecological damage.

Washburn, who started his career studying ecological disasters such as 2010s Deepwater Horizon oil spill, is buoyed by the efforts to assess potential impacts before the mining operations begin. Historically, humanity tends to act first and consider the consequences later, he says.

Were actually trying to figure it out beforehand so thats a pretty good place to be, he says.

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Process Mining Builds On Process Mapping With Data for Greater … – Acceleration Economy

Even post-pandemic, chief procurement officers (CPOs) are still dealing with shortages, long lead times, and inflation. At the same time, businesses are mandating cost reductions (or at minimum, cost containment). Suppliers are having similar challenges. So, if you cant ask suppliers to reduce the price for incoming goods and services, what can you do?

Transforming business processes is the next frontier of savings opportunities for CPOs. Process mining is a great enabler to provide insights needed to transform processes. It can identify opportunities faster, more accurately, and more comprehensively than tools used in the past. Ill go into more detail on how process mining works below, but first its important to understand the difference between process mapping and process mining.

See how process mining pioneer Celonis made the Cloud Wars Horizon list by accessing the Acceleration Economy Cloud Wars Top 10 Course. Cloud Wars Founder Bob Evans and Acceleration Economy Editor-in-Chief Tom Smith outline the criteria and innovations that earned Celonis a spot on the horizon of the biggest cloud platform companies on the planet.

Until recently, when there was a perceived need to improve a process, a business would usually go through a process mapping event. Thats a very people-driven exercise where someone is responsible for documenting a process from start to finish. They talk to the individuals involved in the process, identify the technology used, and create a visual workflow of how something gets done.

Then this map can be analyzed for breaks in the chain, redundant steps, bottlenecks, or other problems. Because it relies on input from humans, its the best representation of what people believe to be true. But it will never be completely accurate because its based on peoples interpretations of how things work. Its qualitative, not quantitative.

Process mining is the digital sibling of process mapping. Where process mapping is all about perceptions, process mining is all about data. In process mining, software containing data-mining algorithms is applied to the event logs of IT systems used by a company. The algorithm creates a workflow model based on the trends it sees in the event logs data.

By harvesting data, process mining can create a more accurate map of whats happening because it is quantitative, not qualitative. Whereas process mapping paints a picture of what the organization believes to be true, process mining reveals what is actually happening.

Research by Celonis, a process mining software provider and member of Acceleration Economys Top 10 AI/Hyperautomation Short List, identifies some great process mining use cases for procurement. These include:

One of my favorite use cases is identifying process automation opportunities based on repetitive tasks where the same kind of input or query is done over and over. Invoice entry is a task that comes to mind; process mining can dig deeper and identify similar opportunities that may not be evident.

Process mining isnt for every business. It relies on data-mining algorithms, which need data to function. The more manual the business, the less reliable process mining results are. But for large or complex organizations, process mining is an opportunity to see what is truly happening and identify opportunities to transform processes.

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Development of a prediction model for the depression level of the … – Nature.com

The results of the demographic characteristics of the study and the average difference in depression levels are shown in Table 2. Females accounted for a higher number than males; females (n=2008, 67.5%) and males (n=967, 32.5%). For the age distribution, ages of 80 or older was the largest with 1475 people (49.6%), followed by ages of 7579 with 755 people (25.4%) and ages of 7074 with 459 people (15.4%). Regarding the level of education, 2107 people (70.8%) had elementary school graduation, and 471 people (15.8%) had middle school graduation. indicating that the majority had a low level of education. When asked about the number of household members, two people accounted for the largest portion with 1459 people (49.0%), showing that the majority lived with one more person. As for having disabilities, 2425 people (81.5%) mentioned no, and 550 people (18.5%) stated yes. In terms of participation in economic activities, not participating accounted for more than half of the participants (n=2042, 68.6%).

In terms of depression, 2553 people (85.8%) reported no and 422 people (14.2%) reported yes. Lastly, the average difference between the sociodemographic characteristics and the depression level of the participants was evaluated. As a result, there were significant gender differences (t=3.547, p<0.001) and participation in economic activities (F=7.326, p<0.001), but no differences were found in other factors.

The descriptive statistical results of the main factors are shown in Table 3. Considering that the range of scores for health promoting behavior is from a minimum of 0 to a maximum of 3, an average of 2.2 points can be regarded as a high value. On the other hand, having a standard deviation of 0.72, it can be understood that there was no significant difference in health promotion behavior by the elderly in low-income households. With reference to subjective health awareness, it was found that numerous elderly people had a higher awareness than the average, with an average of 2.8 points. Regarding the level of medical expenses, it was found that the average monthly expenditure was 158,000 won, and the standard deviation was 20.17, indicating a high difference in expenditure among the elderly in low-income households. Family support, social support, and leisure life satisfaction showed average scores of 2.7, 2.6, and 2.3, respectively, which were verified to be in good standing, considering that the range of scores was at least 0 to up to 4.

The relative importance of the predictive factors that contributed to predicting depression in low-income seniors utilizing the feature selection, is shown in Table 4. The higher the order of importance of a predictor, the greater the influence of that factor in predicting the level of depression; the highest ranking was identified as 'leisure life satisfaction.' This result can be interpreted as having the greatest effect on satisfaction in leisure life than other factors when predicting the level of depression of the elderly in low-income households. Furthermore, the factors of subjective health awareness, family support, and social support were found to be in the upper ranks. However, it was noted that the factors of presence or absence of chronic diseases, educational level, disability, and health behavior were distributed in the low ranking. A SHAP summary plot was created (Fig.2), a visualization of how much each explanatory variable affects the prediction of depression. A yellow bar indicates a positive influence on the occurrence of depression. The red and orange bars indicate a negative impact on the occurrence of depression. The red bars were found to be the most influential variables. Regarding leisure life satisfaction, it can be used as an explanatory or a dependent variable. This study used it as an explanatory variable because the subjects were low-income elderly. The relationship between leisure life satisfaction and depression in low-income elderly is often reported as causal, with leisure life satisfaction affecting depression29.

The summary plot of the SHAP values.

In this study, the classification techniques used to develop the most accurate predictive model, predicting the level of depression of the elderly in low-income households, were artificial neural networks, decision trees, logistic regression and random forest analysis. Table 5 is the result of the classification analysis by sequentially applying the wrapper's stepwise method to the relative importance of the factors identified in Table 4. Based on the analysis, it was identified that the decision tree algorithm showed higher predictive power than the other three algorithms. In the case of logistic regression analysis, the prediction accuracy was 73.2%, and the artificial neural network showed 81.8%. On the other hand, the decision tree shows a tendency to increase predictive accuracy as the number of factors increases, except when there is only one input factor. When all 13 factors were input, an accuracy of 97.3%, a sensitivity of 100%, and a specificity of 94.6% were presented. Finally, when forming the decision-making tree, the factor that had the greatest impact was the subjective health awareness factor, followed by leisure life satisfaction, family support, and social support. To ensure that the main outcome was reliable and robust, a sensitivity analysis was conductedby dividing the dependent variable, depression incidence, into two thresholds (15 points or less, 16 points or more); the analysis revealed that the main outcome did not change in Tables 6, 7.

Logistic regression analysis was performed to seek the influence of the predictors of high risk of depression in the elderly from low-income households, and the results are shown in Table 8. The factors that affected the level of depression were gender, number of household members, subjective health awareness, family support, social support, and satisfaction with leisure life. In the case of gender, the probability of developing depression in women was confirmed to be 1.86 times (OR=1.861, 95% CI=1.1732.954) higher than in men. As the number of household members increased by each level, the probability of depression decreased by 0.69 times (OR=0.692, 95% CI=0.5130.933). In subjective health awareness, an increase of each level was associated with a 0.40-fold (OR=0.403, 95% CI=0.3120.522) lower probability of depression. Further, family support (OR=0.613, 95% CI=0.4940.759), social support (OR=0.711, 95% CI=0.5520.916), and leisure life satisfaction (OR=0.425, 95% CI=0.3280.425) showed that the probability of depression decreased by 0.61 times, 0.71 times, and 0.42 times, respectively, as the level increased by each level.

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Development of a prediction model for the depression level of the ... - Nature.com

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Alkymi launches Alpha, a Generative AI-powered solution built for … – PR Newswire

Alpha enables financial services firms to get instant answers utilizing next-generation large language models.

NEW YORK, July 12, 2023 /PRNewswire/ --Alkymi, the leading business system for unstructured data, today announced the launch of Alpha, a generative AI-powered solution that allows financial services teams to uncover instant answers from documents and proprietary data sets through an interactive chat experience and structured workflows. Using Alpha, customers will be able to accelerate critical business activities such as conducting due diligence, interpreting investment performance, crafting quarterly reports, as well as answering client inquiries.

Until now there has been no way to securely apply large language models (LLMs) to targeted documents and data sets in financial services with a turnkey workflow. Due to these concerns, financial services firms have had to sacrifice access to insights that could have a major impact on business outcomes.

Alkymi has solved this challenge by allowing firms to create their own unique, private, and secure data sets from over 25 supported file formats for data mining and insight generation. Alpha helps customers systematically generate answers from these document data sets in a user friendly way. Alkymi is empowering customers to use LLMs with confidence, keeping their data encrypted and ensuring it is not used to train a model. To build confidence in model output, answers can be traced directly back to their sources in documents.

Customers get insights instantly and can continue to drill down for more context where needed while being assured of the security of their proprietary data. Alpha features include:

"One of the key obstacles that financial services firms face when adopting generative AI is the lack of control - how you apply the technology safely and transparently," says Harald Collet, CEO of Alkymi. "We built Alpha to remove these obstacles through an industry-focused user experience that empowers customers to capture the massive opportunity of generative AI."

"We see tremendous potential in using LLM-powered tools," says Robin Williams, Managing Director of Asset Vantage. "Alkymi's focus on financial services use cases makes Alpha particularly exciting because they understand the business outcomes clients like us need, including the security and governance requirements of financial services firms and their data."

Alpha by Alkymi enables deeper discovery and empowers customers to generate insights, all within a secure environment and with full traceability. It is currently widely available and more information can be found at alkymi.io/product/alpha.

About AlkymiAlkymi is the business system for financial services firms to unlock their unstructured data utilizing the most advanced machine learning and AI technology. Alkymi helps leading firms like Interactive Brokers, SimCorp, and Strategic Investment Group accelerate decision making by giving them the tools to understand, transform and leverage critical data found in emails and documents. For more information, visit http://www.alkymi.io.

Media ContactBethany Walsh[emailprotected]

SOURCE Alkymi

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Ault Alliance Explores Paying Dividends in Bitcoin From its Michigan … – DBusiness

Bitcoin subsidiary BitNile Inc.s Michigan data center could play a major role in the issuing of a special dividend in Bitcoin. // File photo

Ault Alliance in Nevada, a diversified holding company whose BitNile Inc. (BNI) subsidiary has a Bitcoin mining data center in Dowagiac, is exploring a pathway that will allow it to issue a special dividend payable in Bitcoin to stockholders.

The planned dividend would be paid from Bitcoin generated by Ault Alliances mining operation located southwest of Kalamazoo.

The company says it intends to collaborate with regulatory authorities, its transfer agent, and others, which may include a trusted custodian, to determine what is required to pay a special dividend in Bitcoin.

BNI recently reported that its Bitcoin mining facilities are currently operating at an operational hash rate of 2.1 exahashes per second, with approximately 9,000 of its Bitcoin miners at its Michigan data center and 10,000 Bitcoin miners that are being hosted through its strategic collaboration with Core Scientific Inc.

The annualized gross value of Bitcoin currently being mined utilizing BNIs miners is more than $55 million, or approximately 1,800 Bitcoin, based on current market conditions, including a current trading price of Bitcoin at $30,400 and a mining difficulty of 50.65 trillion.

With the increasing popularity and adoption of cryptocurrencies, Ault Alliance says it recognizes the potential of Bitcoin as a valuable asset for its stockholders.

The idea of potentially issuing a Bitcoin dividend is aimed at providing a forward-thinking approach to stockholder value enhancement. By using its Bitcoin mining operations, Ault Alliance seeks to provide an alternative investment opportunity and potential long-term value appreciation for its stockholders.

The company also is exploring ways to educate stockholders who may not be familiar or comfortable with receiving a dividend in Bitcoin itself.

Ault Alliance would, with the intention of addressing the preferences of all its stockholders, accomplish this by offering its stockholders as of the ex-dividend date a choice of receiving actual Bitcoin or a cash payment equal to the dollar value of the Bitcoin as of such ex-dividend date.

By exploring a possible Bitcoin dividend, we aim to stay at the forefront of technological advancements and provide additional value to our stockholders, says Milton Todd Ault III, founder and executive chairman of Ault Alliance. We believe that cryptocurrencies, especially Bitcoin, hold tremendous potential for the future, and we want our stockholders to benefit from this exciting opportunity.

To ensure compliance with regulatory requirements and promote transparency, Ault Alliance will be working closely with relevant regulatory authorities throughout the process.

The company is committed to adhering to the standards of corporate governance and regulatory compliance in all its operations. At this time, the company has not declared a dividend and there can be no assurances that it will declare a dividend payable in Bitcoin or a Bitcoin-denominated cash payment, if at all.

The company has not yet determined what procedures would be required to permit stockholders to receive a dividend in Bitcoin, and even if the company is able to pay a special dividend in Bitcoin, there are no guarantees that all stockholders will be permitted by their local governmental bodies to receive the dividend in Bitcoin.

Ault Alliance notes that all estimates and other projections are subject to the volatility in Bitcoin market price, the fluctuation in the mining difficulty level, the ability to build out and provide the necessary power for miners, and other factors that may impact the results of Bitcoin mining production or operations.

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South African rand flat; manufacturing and mining data due this week – Reuters

JOHANNESBURG, July 10 (Reuters) - The South African rand was flat on Monday, at the start of a week in which local manufacturing and mining data will be released.

At 1513 GMT, the rand traded at 18.8675 against the dollar , 0.04% weaker than its previous close.

Statistics South Africa will release May manufacturing output (ZAMAN=ECI) on Tuesday and mining figures (ZAMNG=ECI) on Thursday.

Like other emerging market currencies, the risk-sensitive rand also takes cues from big global drivers such as U.S. monetary policy and the dollar.

"The rand will remain beholden to U.S. data releases, exhibiting high sensitivity," Investec analyst Annabel Bishop said in a research note.

On the stock market, the Top-40 (.JTOPI) and the broader all-share (.JALSH) indices were down more than 0.5%.

South Africa's benchmark 2030 government bond was weaker, the yield up 3.5 basis points to 10.805%.

Reporting by Tannur Anders and Anait Miridzhanian;Editing by Alexander Winning and David Evans

Our Standards: The Thomson Reuters Trust Principles.

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South African rand flat; manufacturing and mining data due this week - Reuters

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Enterprise Data Warehouse Market to Grow by 20.7% from 2022 to 2027 | Accur8 Software, Alphabet Inc., Amazon.com Inc., and more to emerge as key…

NEW YORK, July 14, 2023 /PRNewswire/ -- The enterprise data warehouse market is estimated to grow at a CAGR of 20.7% between 2022 and 2027. The size of the market is forecast to increase by USD 18,645.51 million, according to Technavio.The growing competition in the market is compelling vendors to adopt various growth strategies, such as promotional activities and spending on advertisements to improve the visibility of their services. Technavio report analyzes the market's competitive landscape and offers information on several market vendors, including - Accur8 Software, Alphabet Inc., Amazon.com Inc., Amitech Solutions Inc., AtScale Inc., CitiusTech Inc., Cloudera Inc., Fusion Consulting AG, HCL Technologies Ltd., Health Catalyst Inc., International Business Machines Corp., Micro Focus International Plc, Microsoft Corp., Oracle Corp., SAP SE, Snowflake Inc., Solver Inc., Tata Sons Pvt. Ltd., Teradata Corp., and Veeva Systems Inc.- Download Sample Report in minutes.

Technavio has announced its latest market research report titled Global Enterprise Data Warehouse Market

Enterprise Data Warehouse Market- Market Insights

Vendors: 15+, Including Accur8 Software, Alphabet Inc., Amazon.com Inc., Amitech Solutions Inc., AtScale Inc., CitiusTech Inc., Cloudera Inc., Fusion Consulting AG, HCL Technologies Ltd., Health Catalyst Inc., International Business Machines Corp., Micro Focus International Plc, Microsoft Corp., Oracle Corp., SAP SE, Snowflake Inc., Solver Inc., Tata Sons Pvt. Ltd., Teradata Corp., and Veeva Systems Inc., among others.

Coverage:Parent market analysis; key drivers, major trends, and challenges; customer and vendor landscape; vendor product insights and recent developments; key vendors; and market positioning of vendors

Segments:Product type (information and analytical processing and data mining), Deployment (cloud-based and on-premise), and geography (North America, Europe, APAC, Middle East and Africa, and South America).

To understand more about the enterprise data warehouse market,request a sample report

Enterprise Data Warehouse Market Market Dynamics

Key Driver

Data explosion across industries is a key factor driving market growth. The amount of data being generated by industries worldwide is constantly increasing. For instance, Facebook uploads around 100 terabytes of data daily, while Walmart manages over one million transactions per hour and stores the corresponding data in its database. Organizations capture and store both financial and non-financial transactions as part of their operations.

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The digitization of organizations adds complexity and diversity to the datasets they handle. Additionally, the adoption of advanced technologies like the Internet of Things (IoT) in industries further contributes to the data volume. In order to process and analyze such massive amounts of data, the utilization of enterprise data warehouse solutions is important. Moreover, these solutions help enhance the overall handling and analysis of the data. Therefore, these factors are expected to drive market growth during the forecast period.

Major Trends

Native machine data generation is the major trend in the market. The adoption of echo state networks (ESN), ML, and IoT is driving an increase in data volume within organizations. Advanced devices and sensors are generating native machine data for machine-to-machine (M2M) communication and collaboration. While many systems already produce native data, legacy systems struggle to handle such data effectively. However, the introduction of advanced sensors and IoT is expected to make this data more accessible and usable in the future.

Consequently, enterprise data warehouse software solutions are anticipated to undergo a transformation, being redesigned and reconfigured to incorporate the ingestion and analysis of native machine data. This transformation aims to enhance data analytics and process efficiency and support critical business systems.

Significant Challenge

Data security concerns are a major challenge restricting growth in the market. The data security threats in the market include distributed denial-of-service (DDoS) attacks, data breaches, unsecured application programming interfaces (APIs), data loss, and account hijacking. Phishing, cross-site scripting, and social engineering techniques can be used to easily hijack backup account credentials, resulting in potential data breaches and loss.

Furthermore, the frequency of cyberattacks on enterprises is on the rise, making vendors collaborate with enterprises in establishing service-level agreements (SLAs) during storage implementation. Data management software and security appliances are used in data center environments to monitor data security and detect any potential threats. Therefore, these security threats are expected to restrict market growth during the forecast period.

Drivers,Trends & Challenges have an impact onmarket dynamics and can impact businesses.Find some insights from a sample report!

The enterprise data warehouse market report provides critical information and factual data, with a qualitative and quantitative study of the market based on market drivers and limitations as well as prospects.

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What are the key data covered in this Enterprise Data Warehouse Market report?

CAGR of the market during the forecast period

Detailed information on factors that will drive the growth of the enterprise data warehouse market between 2023 and 2027

Precise estimation of the size of the enterprise data warehouse market and its contribution to the market with a focus on the parent market

Accurate predictions about upcoming trends and changes in consumer behavior

Growth of the enterprise data warehouse market across North America, Europe, APAC, Middle East and Africa, and South America

A thorough analysis of the market's competitive landscape and detailed information about vendors

Comprehensive analysis of factors that will challenge the growth of enterprise data warehouse market vendors

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Related Reports

The cloud data warehouse market is estimated to decline at a CAGR of 27.38% between 2022 and 2027. The size of the market is forecast to increase by USD 17.74 billion. Furthermore, this report extensively covers market segmentation by deployment (public and private) and geography (North America, Europe, APAC, Middle East and Africa, and South America). The growing penetration of IoT-enabled devices is a key factor driving the market growth during the forecast period.

The enterprise data management market size is estimated to grow by USD 96.98 billion between 2022 and 2027 accelerating at a CAGR of 15.07% during the forecast period. Furthermore, this report extensively covers market segmentation by end-user (BFSI, healthcare, manufacturing, retail, and others), deployment (on-premise and cloud-based), and geography (North America, Europe, APAC, South America, and Middle East and Africa). The growing demand for data integration and visual analytics is a key factor driving the market growth during the forecast period.

Enterprise Data Warehouse Market Scope

Report Coverage

Details

Base year

2022

Historic period

2017-2021

Forecast period

2023-2027

Growth momentum & CAGR

Accelerate at a CAGR of 20.7%

Market growth 2023-2027

USD 18,645.51 million

Market structure

Fragmented

YoY growth 2022-2023(%)

20.6

Regional analysis

North America, Europe, APAC, Middle East and Africa, and South America

Performing market contribution

North America at 33%

Key countries

US, China, India, UK, and Germany

Competitive landscape

Leading Vendors, Market Positioning of Vendors, Competitive Strategies, and Industry Risks

Key companies profiled

Accur8 Software, Alphabet Inc., Amazon.com Inc., Amitech Solutions Inc., AtScale Inc., CitiusTech Inc., Cloudera Inc., Fusion Consulting AG, HCL Technologies Ltd., Health Catalyst Inc., International Business Machines Corp., Micro Focus International Plc, Microsoft Corp., Oracle Corp., SAP SE, Snowflake Inc., Solver Inc., Tata Sons Pvt. Ltd., Teradata Corp., and Veeva Systems Inc.

Market dynamics

Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID-19 impact and recovery analysis and future consumer dynamics, and Market condition analysis for the forecast period.

Customization purview

If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized.

Table of Contents

1 Executive Summary

2 Market Landscape

3 Market Sizing

4 Historic Market Size

5 Five Forces Analysis

6 Market Segmentation by Product Type

7 Market Segmentation by Deployment

8 Customer Landscape

9 Geographic Landscape

10 Drivers, Challenges, and Trends

11 Vendor Landscape

12 Vendor Analysis

13 Appendix

About UsTechnavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provide actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

ContactTechnavio ResearchJesse MaidaMedia & Marketing ExecutiveUS: +1 844 364 1100UK: +44 203 893 3200Email: media@technavio.comWebsite: http://www.technavio.com

Global Enterprise Data Warehouse Market

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How Predictive Analytics Can Help Forecast Energy Needs – BizTech Magazine

Forecasting Supply and Demand Is Essential for Energy

Between changes in weather, occupancy, foot traffic, business workloads and more, consumer energy needs can fluctuate significantly. This makes it difficult for energy providers to ensure supply will meet the demand. For example, if an energy provider generates more energy than needed, it has wasted time and money. Conversely, if it fails to meet consumer demand, customers can lose trust in the provider.

Thats why many energy providers are turning to predictive analytics to draw from dozens of variables and stay on target. These analytics, according to a 2022 report by Popmodo, help forecast consumption and improve a companys chance at achieving their sustainability goals.

READ MORE:What trends to watch for in energy and utilities in 2023.

Predictive analytics uses data on what has happened in the past to make highly educated guesses about what is likely to happen in the future. More specifically, and often through the use of statistical models, machine learning algorithms and other data analysis techniques, predictive analytics finds patterns in historical data to identify risks and opportunities and forecast potential scenarios. In this way, predictive analytics helps drive strategic decision-making.

For energy companies, trying to forecast without predictive analytics can be particularly daunting due to the numerous variables at play, including energy sources, weather conditions and inconsistent workloads. Predictive analytics efforts greatly improve the accuracy of these forecasts, especially when they involve regression analysis.

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OU Analyse Team wins top prize for educational dataset – OU News

The OU Analyse team, which works to identify students at the university who may need extra support, has been awarded the prestigious Educational Dataset Prize by the Educational Data Mining Society for their remarkable work with the OULAD dataset.

OU Analyse is a system powered by machine learning methods for early identification of students at risk of failing. All students at risk of failing their next assignment are updated weekly and made available to the course tutors and the Student Support Teams to consider appropriate support.

The OULAD dataset, which consists of anonymised student interactions with the Virtual Learning Environment (VLE) at The Open University, combined with demographics and study results, has had a major impact on the field of learning analytics.

It has fostered the development of a global community of researchers, educators, and students who utilise the data set to gain insights into learning processes, evaluate new research ideas, and introduce learning analytics to the next generation of scholars.

Researchers worldwide have used the dataset to develop a wide range of learning analytics tools and technologies.

The OULAD dataset, explored in a paper published in the Nature Scientific Data journal, is freely available for download further contributing to its widespread use and impact in educational research.

The recognition received by the OU Analyse team highlights their ground-breaking contributions to the field of learning analytics and their dedication to advancing educational data mining.

The team members leading this work sit within the OUs Knowledge Media Institute (KMi), a diverse and multidisciplinary research and development lab that has been at the forefront of innovation since 1995, conducting research in computing technologies for social and environmental good.

Professor Miriam Fernandez, member of the OU Analyse team said

We are thrilled to have won this award, recognizing the teams outstanding work in the Learning Analytics field.

Our dedication over the past decade has placed the OU at the forefront of Learning Analytics, and this award is a testament to the teams hard work and valuable contributions to the research community.

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Time for short-term rental operators to pay up in San Antonio – San Antonio Report

My family often uses short-term rentals listed on Airbnb or VRBO rather than hotels when traveling. The experience allows us to live more like locals. When we travel in the company of our adult children, we save a lot of money by not reserving multiple hotel rooms.

Yet I remain wary of the short-term rental industry. Its infected with property owners skilled at misrepresenting their offerings. Some obscure property defects like worn-out mattresses or poor water pressure, or they fib about the convenience of their location to area attractions in a given city or outdoor recreational area.

Many property owners, of course, offer detailed information about their rentals and happen to be wonderful people who want their tenants to enjoy a memorable stay. I repeatedly return to the same Airbnb rental in Brownsville when I visit my godson Philip True. Its clean, affordable, centrally located and the hosts respond quickly to my communications.

Still, there is plenty of space for people who hype their sites and leave visitors disappointed. Renters can leave a negative review, but if they do, the property owner could retaliate in kind, which can affect future access to other properties unwilling to rent to anyone with a low rating.

All of this comes to mind after reading San Antonio Report Business Reporter Tracy Idell Hamiltons article published Wednesday revealing that as many as two-thirds of short-term rental property owners in San Antonio are not registered and are not paying the hotel occupancy tax they owe the City of San Antonio. They are shortchanging the city of millions of dollars in annual revenue and undercutting others in the competitive hospitality industry.

San Antonio passed an ordinance governing short-term rentals in 2018. It was first put forward by then-City Councilman Mike Gallagher (D10). Hosts are required to register with the city, show proof of property insurance, acquire a permit for $100 that must be renewed every three years and conform to local standards. Properties need to have a working smoke and carbon monoxide detector and meet other basic indoor and outdoor safety standards that apply to any homeowner. Party and wedding rentals are prohibited, and violators are subject to fines of $200-$500 a day for failing to maintain a permit or abide by the ordinance.

Most importantly, the city permit number must be listed in all advertisements or online advertising. Try a random neighborhood search on a short-term rental site, and good luck finding registered properties that list their permit number. The citys short-term rentals page does include a map displaying the nearly 3,500 registered properties, but it takes many steps to reach it, more than I can detail here.

I doubt any of the short-term operators in San Antonio are violating the ordinance out of ignorance, but if they are, the Short Term Rental Association of San Antonio is a user-friendly guide for responsible operators to stay current on local regulations and obtain a permit. For example, the city changed vendors contracted to collect taxes and is using Virginia-based Avenu Insights as of July 1.

One valuable data mining job Avenu Insights can do for the city is to identify the estimated 6,500 unregistered short-term rental operators in San Antonio. The city can take that report and announce a brief grace period to allow offenders to register or face enforcement and fines. The city also could inform short-term rental platforms like Airbnb and VRBO that if they do not begin to require operators to post their permit numbers, the companies will be prohibited from operating in the city. That latter suggestion might be wishful thinking on my part.

People operating in the tech industry are fond of pointing out how new applications offer solutions to old problems. There is less talk about the new problems they create. Look at the scooter industry and all the fanfare about the usefulness of getting people out of their polluting vehicles or offering mass transit users that last mile solution to come and go. Unregulated scooter companies littered city streets and sidewalks with toppled scooters, while some individuals angered by their presence vandalized them, even throwing scooters into the San Antonio River. Scooters are regulated now, and there are far fewer of them, but teenagers and children are regularly seen riding the scooters, dangerously weaving in and out of traffic, on and off sidewalks, despite the ordinance prohibiting underage use.

Short-term rentals at their best offer an experience few hotels can match: making visitors feel like they are living like locals. But they also have taken thousands of residences off the market, either for rent or sale, and this is contributing to the citys acute housing shortage. The least city officials can do is redouble efforts to make operators register and pay for the privilege.

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Time for short-term rental operators to pay up in San Antonio - San Antonio Report

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