Logs are an essential part of modern IT systems. They provide detailed insights into the operations, performance, and security of applications and infrastructure. Effective log management helps organizations monitor their systems, troubleshoot issues, and enhance security. In this guide, we present a detailed list of the most popular and effective log management tools available today.

1. Splunk

Splunk is one of the most popular log management tools, offering robust features for log analysis and monitoring.

• Features: Real-time search, analysis, and visualization of machine data. Advanced machine learning capabilities.
• Use Cases: IT operations, security, and DevOps.
• Strengths: Scalable, supports large data volumes, and offers extensive third-party integrations.

2. Elastic Stack (ELK Stack)

The Elastic Stack consists of Elasticsearch, Logstash, and Kibana, forming a powerful open-source log management and analysis suite.

• Features: Centralized logging, real-time analytics, and rich visualization.
• Use Cases: Application performance monitoring, security analytics, and operational monitoring.
• Strengths: Cost-effective, customizable, and open-source.

3. Graylog

Graylog is an open-source log management platform known for its user-friendly interface and efficient performance.

• Features: Centralized log storage, quick search capabilities, and alerting.
• Use Cases: Application debugging, security event management, and compliance reporting.
• Strengths: Simple setup, intuitive UI, and affordable pricing.

4. LogRhythm

LogRhythm provides enterprise-grade log management and security information and event management (SIEM).

• Features: Threat detection, log collection, and machine analytics.
• Use Cases: Security operations, compliance, and IT operations.
• Strengths: Strong security focus, advanced threat intelligence, and user-friendly dashboards.

5. Datadog

Datadog is a comprehensive monitoring and analytics platform that also offers log management capabilities.

• Features: Log ingestion, monitoring, and correlation with metrics and traces.
• Use Cases: DevOps, cloud monitoring, and application performance management.
• Strengths: Easy integration with cloud services and seamless correlation of logs with other metrics.

6. Sumo Logic

Sumo Logic is a cloud-native log management and analytics platform built for scalability.

• Features: Real-time log analytics, security analytics, and predictive insights.
• Use Cases: Cloud monitoring, DevSecOps, and compliance.
• Strengths: Scalable, fast, and designed for modern cloud architectures.

7. Papertrail

Papertrail is a simple and lightweight log management solution suitable for small to medium-sized businesses.

• Features: Real-time log aggregation, live tailing, and alerts.
• Use Cases: Debugging, error tracking, and performance monitoring.
• Strengths: Easy to use, affordable, and quick to set up.

8. SolarWinds Log Analyzer

SolarWinds Log Analyzer is a powerful log management tool that integrates with other SolarWinds products.

• Features: Real-time log streaming, search, and event correlation.
• Use Cases: Network troubleshooting, application monitoring, and compliance.
• Strengths: Seamless integration with SolarWinds ecosystem and easy setup.

9. Fluentd

Fluentd is an open-source data collector that helps with log aggregation and processing.

• Features: Log collection, transformation, and forwarding.
• Use Cases: Unified logging and monitoring for cloud-native applications.
• Strengths: Lightweight, flexible, and open-source.

10. LogDNA

LogDNA is a log management tool designed for modern DevOps teams and cloud environments.

• Features: Centralized log storage, real-time search, and custom parsing.
• Use Cases: Kubernetes log management, security monitoring, and troubleshooting.
• Strengths: Easy Kubernetes integration and intuitive interface.

11. ManageEngine EventLog Analyzer

ManageEngine EventLog Analyzer is a comprehensive tool focusing on log management and compliance.

• Features: Log analysis, audit trails, and compliance reporting.
• Use Cases: Security audits, compliance management, and IT operations.
• Strengths: Affordable and feature-rich for SMBs and enterprises.

12. Humio

Humio is a modern log management solution emphasizing real-time insights and scalability.

• Features: Real-time log data streaming, advanced search, and analytics.
• Use Cases: Security operations, DevOps, and incident response.
• Strengths: High-performance search and unlimited scalability.

13. Syslog-ng

Syslog-ng is an open-source log management tool focusing on log collection and forwarding.

• Features: Log routing, filtering, and centralization.
• Use Cases: Network monitoring, application logging, and system auditing.
• Strengths: Lightweight and flexible configuration options.

14. Splunk Light

A lighter version of Splunk, tailored for small IT environments and startups.

• Features: Basic log collection, monitoring, and search capabilities.
• Use Cases: Small-scale IT operations and troubleshooting.
• Strengths: Cost-effective alternative to full Splunk.

15. Nagios Log Server

Nagios Log Server provides centralized log management and monitoring.

• Features: Log storage, search, and alerting.
• Use Cases: Network monitoring, event logging, and security.
• Strengths: Integrates well with other Nagios tools.

Choosing the Right Log Management Tool

When selecting a log management tool, consider the following:

1. Scale and Volume: Choose a solution that can handle your organization’s data volume.
2. Ease of Use: Prioritize tools with intuitive interfaces and easy deployment.
3. Integrations: Ensure compatibility with your existing systems and applications.
4. Budget: Select a tool that aligns with your budget without compromising essential features.
5. Security and Compliance: Evaluate the tool’s ability to meet regulatory requirements and enhance security.

The post List of Log Management tools appeared first on Artificial Intelligence.

Artificial intelligence (AI) is eating the world, one boring, routine task at a time. 

From navigation apps using AI to crunch a bunch of data at a super-fast speed to determine the best and fastest route from A to B, or automatic spam filters and categorizations that make email more manageable, AI is truly ubiquitous.

It was only a matter of time before AI applications in the content management space arose. And when it comes to content ops, the combination of content management and artificial intelligence is a great tool for giving workers back the time they need to perform more complex tasks that still require a human brain.

The state of play – how content management and AI are already impacting Content Ops 

AI excels at ‘understanding’ vast pools of data and automating routine tasks. This is typically orientated towards objectives of improving consumer experiences, saving the time and money invested in routine processes, and even exposing patterns that can uncover new revenue opportunities. 

These are typically seen in a content operations workflow in four areas:

1. Smart Content Analysis

AI can analyze a piece of content to identify its sentiment and overall tone, very quickly. This is important for helping content managers determine whether a piece of content is right for their audience or if it needs tweaking before it will truly engage the intended consumer. IBM Watson, for example, uses AI to automate content categorization, text labeling, sentiment analysis, keyword extraction, and more.

2. Automatic Image Tagging

A picture still tells a thousand words.  Images enhance content increase engagement. Unfortunately, there is almost nothing less engaging for workers than manually tagging image after image for search and SEO purposes. But it is still a supremely important task. And that is what makes it a great job for AI.

AI-powered automated image recognition is now smart enough to tag images in a matter of seconds—letting content workers get back to deeper work instead of routine classification.

3. Scalable Personalisation and Predictions

AI also brings scalability to another important but nearly impossible task for human staff: tracking and making use of individual user behavior. 

AI can automate the process of watching what each user on a website or app is doing simultaneously. Then, it can compile this data to look for patterns that will help it predict, based on past behavior, what each user might want next.

This information can dramatically improve personalization efforts, from serving dynamic content to making product recommendations and more. And improving personalization has never been more important. In the words of the management consulting firm McKinsey, “Personalization will be the prime driver of marketing success within five years.” In fact, they found that leaders in personalization were already able to increase revenue by 5 to 15 percent and improve efficiency on marketing spend by 10 to 30 percent. Achieving that kind of improvement automatically is a massive competitive advantage.

4. Time-Saving Content Creation Assistance

Controversially, AI can also be a big help when it comes to creating content. 

Whilst artificial intelligence still is not great at coming up with original ideas or creating nuanced pieces of content, it is catching up fast. A well-trained AI tool should be able to contribute to straightforward writing projects such as news articles, factual reports, translations, transcriptions, and editing for accuracy. 

At present, in the content creation workflow, AI is basically a tool for improving the ROI on content marketing, which can often be resource-intensive. Simply put, AI can do the legwork when it comes to research and data while human writers can take this material and do the deep work required to create high-value, relevant content for each target customer.

The shape of things to come 

Based on these areas in which content management and artificial intelligence are already coming together to improve content operations, AI may improve marketing even more in the future.

Interactions Between AI Tools

AI interactions already abound in the consumer space.  A voice-activated smart speaker to controlling house lights or audio is an everyday example. Similar interactions are in store for the future of AI-powered tools in the content operations space. It is only a matter of time before AI-enabled content management systems (CMSs) and other content platforms and tools will be able to interact with each other in smart, automatic ways to provide faster functionality and better experiences for consumers and marketers alike.

On-the-Spot SEO Improvements

Taking the idea of sentiment analysis one step further, soon AI-enabled CMSs may be able to identify opportunities for SEO improvements in real-time. This capability would empower marketing professionals to create more effective content in less time, which will outperform competitors and rank better in search engines.

Content Gap Identification

Whilst AI may not create great content on its own, it can spot a lack of it – especially if it has access to huge pools of data on customer behavior preferences.   This can then alert a business to where its content may be lacking (or where competitors’ content may be lacking). 

Both situations are a huge opportunity to fill those “gaps” and capture more traffic. AI is becoming smart enough to flag gaps and make recommendations so businesses can create fresh content that adds value and generates new leads.

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Customer service is another expensive yet necessary element of business. Chatbots have already come to the for as a way of reducing both the time and money needed to deliver customer service excellence. 

While many of today’s chatbots can address very simple questions with answers pulled from a knowledge base, the future will see a large percentage of queries – if not the majority – that do not have to be routed back to human agents. After all, it is the instantaneous and round-the-clock support that consumers are truly looking for when interacting with brand chatbots.

Bridging the gap – going headless 

At the heart of this evolution from where AI and content management is today to where it could be tomorrow, is the need to integrate content management systems with an array of new technologies driven by AI.  

In practical terms that means developing headless architectures that will enable content operations teams to explore and exploit everything from automated content analysis to smart content creation. Adopting this strategy will leave a business ready to capitalize on the next wave of AI-based innovation with minimal disruption, driving return on investment.

The post Content management and Artificial Intelligence – the future of ContentOps appeared first on Artificial Intelligence.

Artificial intelligence (AI) has proven to have several benefits across different industries and businesses. One sector that has benefitted from the use of AI is the healthcare industry. This sector is always full of patient information, health records, and other important data crucial to patients and hospitals. 

Major problems facing healthcare data are cyberattacks, losing the information, and improper handling, leading to mixing up the records. These mistakes always have devastating effects on the healthcare sector as these medical procedures and other treatments are dependent on these data. In addition, there are other procedures outside the health industry that are dependent on these data. Therefore, properly managing healthcare data is fundamental in the healthcare industry.

The importance of these data has led to the adoption of AI in hospitals to help in the management. Here are some of the applications of AI in optimizing data management: 

• Convenient Data Transmission

Health records are constantly subjected to several transfers among patients, hospitals, remote workers, and other legally entitled parties. When transferring this data, there needs to be a convenient and streamlined way to reach all the desired recipients in time. For example, you may opt to use faxing services, like MyFax, and several others to send the faxes digitally without the need for printing and scanning. 

These modes of data transmission ensure that the records are sent faster and securely. This helps reduce cases of alterations or sending to wrong addresses. With AI, the sharing of information is simplified.

• Data Security 

Several cyberattacks are lodged on these records during these transfers as criminals try to steal or change the records. These attacks are a major concern for the healthcare sector. 

Moreover, even when being stored, patient information is always vulnerable to attacks from hackers. Covering all these attack points manually could be next to impossible, considering the amount of data being held by the information system. 

However, with the application of AI, securing health records against any cyberattacks is promising and fruitful. This is because AI can identify possible entry points for hackers and provide possible solutions for correcting them. Moreover, AI can diagnose the system to identify and correct bugs that would otherwise affect the data management system. 

• Automation Of Data Flow

When patients enter a medical facility, their records are always taken by the hospital from time to time. Each process of their treatment is dependent on the information from the previous step to avoid any cases of errors. The number of patients in the hospital could be challenging to handle if the data flow is done manually. Moreover, handling data manually can lead to confusion.

In contrast, AI automates the data flow from one point to the other, streamlining the whole process. Once the information is entered at the first stage, it becomes accessible for authorized personnel in the hospitals. These records are always entered against a patient’s identity, which means very minimal cases of errors. It also becomes easy for return patients to continue their treatment as the complete information is already recorded in the system. 

• Optimizing Data Storage

Traditionally, health records could be stored in paper works and filed for future references. However, this storage has several disadvantages and limitations. 

First, once a record is added, deleting or changing is difficult unless new paperwork is filed. Secondly, paper is limited in storage, and very little information can be stored on a piece of paper. Finally, once you lost these records, it would be difficult to retrieve them due to a lack of backups.

Fortunately, AI changes all these and optimize data storage in many ways. For example, cloud storage can help hospitals store large quantities of data in only one system. In addition, these cloud services have data backup where you can retrieve any lost information. It’s also possible to change any medical data without altering the other record elements when storing it in a system.

• Data Analysis And Decision Making 

Another important use of AI when handling health data, especially in big data, is analyzing and interpreting the data. With AI, it’s possible to deduce important data points from health records, analyze them, and then present them to understand the chart. This can help in decision-making regarding medical procedures or genetic mapping for patients.

Conclusion 

The healthcare sector is crucial due to the information stored in the systems and their value. Therefore, there’s the need to have an efficient data management system that can ensure information security and streamline any process that depends on these data. 

Manual handling of these data has some limitations, unlike AI, which has several applications in health data management. It can be used in automating data flow and aiding in crucial decision making among many others. It’s safe to say that the application of AI in healthcare will improve. 

The post 5 AI APPLICATIONS TO OPTIMIZE HEALTHCARE DATA MANAGEMENT appeared first on Artificial Intelligence.

Gary M. Shiffman, Ph.D. is the Founder and CEO of Giant Oak and Co-Founder and CEO of Consilient. He is the creator of GOST and Dozer. 

According to my company’s research, a full 25% of PPP fraud casesbrought by the Department of Justice could have been easily prevented. The fraud is so obviously clumsy that it is embarrassing to whomever approved the loans.

Decision-makers consume a lot of data. The world is awash in data, and data is there to be used — or not used — like at no other time. As a result, risk measurement systems today perform far better than the systems of even just three years ago. But what if yesterday’s performance was poor, in absolute terms? Can improvement over last year justify missing obviously blatant threats to your organization? I want to focus this article on obvious but undiscovered risk and the data not used in analytics.

Artificial Intelligence and Machine Learning (AI/ML) enable qualitative changes to risk management, which deliver large step increases in quantitative performance, leaving a gaping question. If asked, “How much improvement is enough?,” then “any improvement” might sufficiently answer the question. But “any” feels like an inattentive answer. The very existence of data demands decisions most executives have not been trained to make: What data can be excluded from the analysis? And yet these decisions on what data to use and exclude require great care, like receiving a double-edged razor in an unprotected hand. 

About a decade ago, when “big data” was the buzz, I remember joining industry discussions as executives rushed to formulate initiatives and responses. Leaders would often clench their fists while arguing that there is such a thing as too much data. 

The Biden Cybersecurity EO: The Good, The Bad And The Ugly—But Mostly Good

Too much data overwhelms humans, so the reaction of the 2010s made sense at the time. However, data also creates more accurate ML models. Amazon’s market capitalization in 2011 was $78 billion and grew to an astounding $1.7 trillion by 2021; the growth came from understanding the value of more data, not less. Risk professionals in 2021 similarly understand that happiness with less data can cut a career short.

Machine Learning tools are available, posing a new “big data” challenge for the 2020s: missing threats because of data not used. Market leaders have moved from fearing too much data to too little data in analytics. 

To limit the use of data in risk discovery leaves threats undiscovered, exposing decision-makers to ex post facto criticism: “How did you miss that? It was so obvious!” The data is free and publicly available. Read news reports of PPP fraud cases, for example. People who did not have companies or employees received large amounts in Covid-19 relief dollars. “How did they miss that?” you might think. The bank and government screeners used too little data and missed obvious information. They erred in selecting the data not used. 

Critics of using more data, even in 2021, rightly complain that added data still creates too many “false positives,” especially in unstructured data. Like oiling a blade in a sawmill, data helps for a while but eventually gums up the moving parts. Data has a history of gumming up the risk discovery process. 

To prevent these big-data frustrations in the past, data-as-a-service vendors emerged. Firms in these markets use hundreds or thousands of people to filter data, creating highly curated data sets, and they sell this high-cost data at a high price to risk professionals in many industries, financial institutions and law enforcement agencies.  

Unfortunately, human-based filtering absolutely separates risk management professionals from massive amounts of valuable data. For example, financial services firms spend $180.9 billion on financial crime compliance worldwide, according to a 2020 LexisNexis study, and yet financial institutions capture less than 1% of the criminal proceeds. Fifty-seven percent of that $180.9 billion is spent on labor. The large effort masks the lack of progress.

To protect oneself from the double-edged sword of data availability in 2021, use more data in risk measurement to decrease the universe of unused data and use AI/ML to decrease the false positives challenges which vex human screeners and investigators. This is the balance to keep in mind: Use more data and reduce errors by replacing manual human curation with machine learning. 

AI/ML can solve much of the double-edged nature of data abundance. Technology delivers effectiveness with efficiency. The key is reindexing the publicly available information on the internet, a task too massive for a human but easy enough for well-trained ML models, and then to perform entity resolution (ER) on that massive mess of unstructured data.

In addition, organizational changes can be implemented — for example, routine testing of ML model output with measurements of efficiency and effectiveness, such as precision and recall against a known set of test data. To do this, organizations may want to consider training management to better understand the measurement of ML systems. Including someone fluent in AI/ML performance on your company’s board also makes sense in today’s world of important data exclusion decisions. 

If this technology exists, why is it not pervasive across every bank in the U.S.? The answer is that it takes time for the widespread adoption of new technology. There is no villain. There is no government branch or bank CEO fighting adamantly against it — in fact, joint regulatory agencies, FinCEN and the Bank Policy Institute are encouraging it. AI/ML, which is already so pervasive in our cell phones and homes, will soon start impacting the risk world, such as AML/CFT and Customer Due Diligence.

Decision-makers consume a lot of data but need the ability to use more. Entity resolution across massive public and unstructured data will soon be a part of every risk management organization. The most successful risk management managers of the 2020s will find innovative ways to utilize more data, protect privacy and improve both effectiveness and efficiency. 

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AI in healthcare is growing multifold, from diagnostics to pain management

Artificial Intelligence has been playing a growing role in the world in the last few decades. What most don’t understand is artificial intelligence introduces itself in numerous structures that sway everyday life. Signing into your social media, email, car ride services, and online shopping platforms, etc. all include artificial intelligence algorithms to improve customer experience. AI in healthcare is growing quickly; explicitly, in diagnostics and treatment management.

As of late, AI applications in healthcare have sent huge waves across medical services, fuelling a conversation of whether AI doctors will in the end supplant human doctors in the future. However, experts believe that human doctors won’t be supplanted by machines soon, yet the use of artificial intelligence in healthcare can help doctors to settle on better clinical choices or even supplant human judgment in certain functional areas of healthcare.

One emerging application of AI in healthcare is a new study led by the research team of Northwestern University faculty and alumni, created and applied artificial intelligence, or machine learning algorithms to physiological information — including respiratory rate, oxygen levels, pulse rate, body temperature, blood pressure, etc. from patients with chronic pain suffering sickle cell illness. Not exclusively did the scientists’ methodology beat baseline models to gauge subjective pain levels, it additionally distinguished changes in pain and abnormal pain fluctuations.

The team of researchers utilized data from 46 adults and kids with sickle cell sickness over a consolidated total of 105 hospital stays, taking a look at the physiological information alongside patient-reported pain scores to create models that could derive pain levels and identify changes in pain level through machine learning

Presently, patients should evaluate their pain on a scale of zero to 10. This can be a troublesome assignment on the grounds that numerous individuals experience pain in an unexpected way, and little kids and unconscious patients can’t rate their pain by any means. The scientists say that these subjective evaluations of pain could be enhanced with a more objective, less intrusive, data-driven approach to help physicians with a more accurate treatment plan.

The researchers then analyzed their new models against existing ones that attempt to evaluate levels of pain but that don’t use physiological estimations. The new models outflanked the current ones.

According to Daniel Abrams at Northwestern University in Illinois, “The big picture is that we want to better understand how people experience pain. We’re hoping that the long-term outcome of this line of research is a more quantitative approach to pain management.”

Prior to this use of AI in pain management, Professor Jeff Hughes, Chief Scientific Officer at PainChek, clarifies how smart automation and AI in healthcare can reform pain assessment in patients with dementia who find it very difficult to communicate.

PainChek was created as a compelling solution for this issue. Its novel blend of automated facial-analysis technology and smart automation empowers care takers and healthcare experts to look for the presence of pain when it isn’t self-evident, to evaluate the intensity of pain and screen the effect of treatment to optimize and witness the overall quality of care.

This information is then integrated with non-facial features seen by the application user and information through a range of digital checklists, which together permits automatic calculation of a total pain score and the task of a pain intensity level.

There are different impacts of AI in healthcare. Commonly, AI used in healthcare leverages a web data set permitting doctors and experts to access a lot of diagnostic resources. As doctors are profoundly knowledgeable in their field and are up-to-date with present research, AI technology in healthcare incredibly builds a quicker result that can be matched with their clinical knowledge.

Nonetheless, artificial intelligence in healthcare presents numerous apprehensions as discussed, particularly in the clinical setting, of in the long-run substituting or lowering down the requirement for human doctors. In any case, so much research and data have shown that it is almost certain that AI in healthcare will benefit and improve clinical diagnostics and decision making as opposed to lessen clinician need.

The opportunities for AI applications in healthcare from emergency clinics and primary care, to home care, are huge. The use of artificial intelligence in healthcare can automate patient assessment and eliminate assessor bias. It can assess patient risk, for example, of a patient building up a specific disease, analyze illness, for instance, by deciphering ECG results and X-ray pictures, select the ideal treatment dependent on a patient’s clinical history and the results of clinical trials, and monitor disease and recognize early warning signs of deterioration.

The use of AI in healthcare will be driven by the availability of big data on which to train predictive algorithms, which help (instead of supplanting) human doctors, encourage curiosity-based reasoning, empower collaboration and eliminate unremarkable tasks, thus, improving patient care.

The post AI IN HEALTHCARE: AI IN PAIN MANAGEMENT, A NEW APPLICATION appeared first on Artificial Intelligence.

AI can be leveraged to build efficient water plants and optimize water resources to reduce energy costs in the long run.

Artificial intelligence is disrupting industries with its wide range of capabilities including augmenting human intelligence and processing huge data chunks. There have been discussions and reports on sustainable AI which can work efficiently while conserving the environment. AI has also proved effective in renewable resources industries. Let us discuss the impact of AI in another sector – the water sector. Water is an imperative need to live life and it has been going through pollution and scarcity for a long time. Climate change is a reality that can increase water stress in many places and increased water contamination will result in a huge water crisis which we are not yet ready to deal with. According to a report by UNICEF and WHO, 1 in 3 people globally does not have access to safe drinking water. This scenario is going to become grave in the coming years if we do not address the issue.

AI in water management might come off as a huge revelation but it can change the way we treat and manage water sources around us. Let us see how AI can impact the global water sector.

Managing Water Wastage

An India Today report states that it is estimated that around 40% of piped water in India is lost to leakage. According to a US EPA report, an average family can waste 180 gallons of water per week, or 9400 gallons of water annually, from household leaks, which is equivalent to the amount of water needed to wash more than 300 loads of laundry.

We waste a lot of water through leakages, burst pipes, etc. and AI and IoT can help reduce this wastage. Implementing AI to analyze real-time water loss and automating pipes to shut off whenever there is a leak can improve the amount of water wastage. AI can predict leaks in storage tanks and help in mending them before it is too late. Devices connected through IoT can communicate better and integrate various systems across a city or place.

Wastewater Treatment

AI can be used to reduce pollutants in the water which in turn decreases water contamination and scarcity of clean water. AI can be leveraged to detect the amount and composition of toxic contaminants since AI works on optics, which can increase the efficiency of waste management systems. Water quality can be continuously monitored and it is possible to get real-time data on the quality through machine learning and big data. Neural networks and IoT will reduce the energy costs which otherwise increases when using conventional methods.

Digital Water

AI can make the process of water management easier with data analytics, regression models, and algorithms. These cutting-edge technologies help in building efficient water systems and networks. AI can be used to build water plants and to get the status of water resources. Water managers and government bodies can use AI to build a smart water system that can build efficient infrastructure for water management and can adapt to changing conditions. These systems will be cost-effective and sustainable that can optimize all water management solutions and predict potential damages.

Smart Irrigation

Agriculture is the biggest water-using sector and many lands use a good portion of groundwater for irrigation purposes. Smart Irrigation will leverage AI systems to minimize the use of water and also optimize the water resources without wastage. AI systems can detect the groundwater levels and also estimate the agricultural needs to balance the usage of water by guiding sprinkler systems.

More developed precision-based AI systems can predict the weather conditions, climate, and humidity to enable better management of agriculture. The smart farms will be able to reduce leakages and analyze the soil to determine the condition of plants and their water needs using AI sensors.

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It shows so much wisdom when people say it is not easy for you to manage things you cannot measure. The recent development of digital data is now very important for businesses. With the help of big data, managers can now measure and know more about their business performance because this knowledge will help them to make informed decisions.

The revolution of big data comes with its challenges. Businesses need to learn how to secure their data. Nevertheless, it is a welcome decision that business managers need to leverage.

What is new about big data?

Managers often ask “is big data the same thing as analytics?” The truth is that there is a relationship. The big data evolution, just like analytics, seeks to obtain information and knowledge from data. Apart from these, there are three main differences:

1. Velocity

Data is being created in no time. The process is fast, and you gain rapid insights quickly. These insights help business managers to gain a competitive advantage.

2. Volume

More data now cross the internet every second. The number keeps doubling every 40 months and the story is no longer the same. Big data allows many companies to access many petabytes of data daily.

3. Variety

Big Data is usually in from of GPS signals from phones, images on social networks, updates, messages, and many more. As more businesses go digital, there are newer sources of information from cheaper equipment. Managers no longer use intuition; they now make decisions based on evidence. Variety is the spice of big data. Electronic communications, social networks, mobile phones all produce large amounts of data from their normal operations. We all use them daily, we are now walking data-generators.

Benefits of big data in business

1. It delivers a competitive advantage

Data is a huge part of a modern workplace. Corporations have been able to move forward and far ahead of their peers. A recent survey shows that organizations that use advanced analytics have been able to move ahead of their counterparts. Such companies have been able to drive revenue twice as that of their industry peers. Not only that, but they are also likely to make the best decisions than their market peers.

2. Improvement in decision making

Companies can now back up their decisions with evidence rather than intuition and emotions. It provides a balance where opinions vary. With big data, you can review your products better. 76% of people are likely to purchase a product like the detox kit after seeing its review.

Analytics engines are smarter and unbiased. They can absorb a company’s outcome and connect them with relevant data. The best aspect is, they analyze these data without limitations that human decision-makers experience.

3. Improvement in performance

Big data drive productivity. Analyzing individual and team performance will show business managers the necessary areas for improvement. Instead of conducting a one-time training for employees, the use of big data will continually reveal new areas that need improvement.

Challenges of big data

1. Leadership

Companies that use big data still need leadership teams that can set realistic goals and ask the right question. Big data still need human insight and vision to drive success. The successful companies in the next decade will be those who have good leaders. These leaders will be the ones to understand the market, deal with customers, and also think creatively.

2. Talent management

Data scientists are the most crucial to this revolution. Many of the techniques for using big data are rarely taught in schools. People with these skills are hard to find, and they are currently in high demand. Only data scientists can manipulate and understand big data sets while making sense of the results in management and business terms.

3. Security and data privacy

There are potential risks in terms of security and privacy. Tools used for data analysis store the data in disparate sources. This leads to exposure of data, making it vulnerable to breach. The more data you get, the deeper are your privacy and security concerns.

Companies should make use of Data Lake to store a vast amount of data. Data Lake is different from a traditional warehouse. It protects data from unwanted manipulation and enables businesses to make decisions accurately.

Conclusion

Despite its challenges, it is clear that data-driven companies make better decisions than their industry peers. Companies that figure out how to use it effectively will move ahead of their rivals. Most importantly, there is now an exponential growth in the amount of data. Businesses need to leverage the combination of data, the right people, and tools to drive change.

Source: Big Data: The Data Management Revolution

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June 16, 2020 – A clinical decision support system that leverages machine learning techniques could help patients control their glucose levels and enhance type 1 diabetes management, according to a study published in Nature Metabolism.

People with type 1 diabetes do not produce their own insulin, so they have to take it continuously throughout the day using an insulin pump or with multiple daily injections. Dosing errors can result in life-threatening hypoglycemia events and hyperglycemia, which increases an individual’s risk of neuropathy, retinopathy, and nephropathy.

Additionally, because patients with type 1 diabetes can typically go three to six months between appointments with their endocrinologist, they can be at high risk of these dangerous complications if their glucose levels rise too high or fall too low.

To improve diabetes management, researchers from Oregon Health & Science University (OHSU) leveraged a machine learning algorithm that could generate insulin injection recommendations.

The team trained the algorithm using over 50,000 glucose observations. The algorithm was trained to identify causes of hypoglycemia or hyperglycemia and determine necessary insulin adjustments from a set of 12 potential recommendations. When paired with a smartphone app called DailyDose, the recommendations from the algorithm were shown to be in agreement with physicians 67.9 percent of the time.

Researchers then validated the system by monitoring 16 people with type 1 diabetes over the course of four weeks, showing that the model can help reduce hypoglycemia.

“Our system design is unique,” said lead author Nichole Tyler, an MD-PhD student in the OHSU School of Medicine. “We designed the AI algorithm entirely using a mathematical simulator, and yet when the algorithm was validated on real-world data from people with type 1 diabetes at OHSU, it generated recommendations that were highly similar to recommendations from endocrinologists.”

The researchers noted that their study advances previous findings on using machine learning tools to help patients manage glucose levels.

“There are other published algorithms on this, but not a lot of clinical studies,” said Peter Jacobs, PhD, associate professor of biomedical engineering in the OHSU School of Medicine and senior author on the study.

“Very few have shown a statistically relevant outcome – and most do not compare algorithm recommendations with those of a physician. In addition to showing improvement in glucose control, our algorithm-generated recommendations that had very high correlation with physician recommendations with over 99 percent of the algorithm’s recommendations delivered across 100 weeks of patient testing considered safe by physicians.” 

Investigators have recognized the potential for artificial intelligence to improve diabetes management. A 2019 study from Rensselaer Polytechnic Institute leveraged AI and big data analytics to evaluate information from thousands of glucose monitors and insulin pumps. The team will use the data to enhance the algorithms that control these devices, resulting in better quality of life for people with type 1 diabetes.

“If we look at hundreds of people we can say, ‘Oh, certain problems occur more often in this age group, this type of population, or with this particular type of sensor,’” said Wayne Bequette, professor of chemical and biological engineering at Rensselaer Polytechnic Institute.

“If, for example, you find that it’s more likely that people 8 to 12 years old have these types of irregularities, then you can account for that in your algorithm, and provide more personalized control while reducing burden.”

Going forward, the OHSU team will continue to refine and develop the clinical decision support tool to further improve patients’ management of type 1 diabetes.

“We have plans over the next several years to run several larger trials over eight and then 12 weeks and to compare DailyDose with other insulin treatment strategies, including automated insulin delivery,” said co-author Jessica Castle, MD, associate professor of medicine (endocrinology, diabetes and clinical nutrition) in the OHSU School of Medicine.

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There’s no doubt that we’re living in the age of big data. The numbers tell the story:

• 2.5 quintillion bytes of data are created each day.
• 90% of all data in the world was created in the last two years.
• 50 billion connected devices and sensors are expected by 2020.
• 82% of executives say their organizations are increasingly using data to drive critical and automated decision-making, on an unprecedented scale.
• 89% of companies believe that big data will revolutionize business operations in the same way that the internet did.

Of course, there are many risks associated with managing an organization and its projects in the age of big data. Risk is inherent in all human endeavors, and we need to identify and understand big data risks and know how to manage them effectively.

Two risks currently appear to be the most critical, and they demand focused attention from any organization that is serious about surviving and thriving in the age of big data:

1. Data Governance

Today’s organizations recognize that managing data is central to their success. They recognize the value of their data and seek to leverage that value. As the human capacity to create and exploit data has increased, so, too, has the need for reliable data management practices.

This makes data governance really essential. There is a major risk if we are trying to exploit the benefits of big data without having data governance that is aligned with business strategy. If your organization currently doesn’t have data governance in place, then now is a good time to start.

Data governance:

Defines a set of guiding principles for data management and describes how these principles can be applied within data management functional areas.

Provides a functional framework for the implementation of enterprise data management, including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics.

Establishes a common vocabulary for data management concepts and serves as the basis for best practices for data management professionals.

DAMA International has developed “The DAMA Guide to the Data Management Body of Knowledge” (DAMA-DMBOK), which can help your company in establishing excellent data governance.

2. Talent

To manipulate, analyze, and leverage the insights available from big data, companies must hire people with skills and knowledge in data science, mathematics, statistics, artificial intelligence (machine learning and deep learning), and storytelling.

The demand for data scientists, data engineers and programmers can only grow stronger. It will not be possible to exploit the value that big data can generate for your business without having these professionals working in your company. Some businesses are forming talent teams, investing in courses to develop the skills that are needed.

Smart companies are already reaping the benefits of guiding their business from the valuable insights available from big data. They are now investing in business intelligence to analyze historical data and are developing advanced artificial intelligence algorithms to enable predictive analysis.

Companies without a data-driven culture in this age of big data will be increasingly exposed to the two risks of inadequate data governance and lack of available talent, making them less likely to survive and thrive than their competitors.

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The latest research on Human Capital Management Market both qualitative and quantitative data analysis to present an overview of the future adjacency around Human Capital Management Market for the forecast period, 2019-2024. The Human Capital Management market’s growth and developments are studied and a detailed overview is been given. Human Capital Management market will register a 5.9% CAGR in terms of revenue, the global market size will reach US$ 19000 million by 2024, from US$ 14300 million in 2019.

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A thorough study of the competitive landscape of the global Human Capital Management Market has been given, presenting insights into the company profiles, financial status, recent developments, mergers and acquisitions, and the SWOT analysis. It provides a refined view of the classifications, applications, segmentations, specifications and many more for Human Capital Management market. This market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. Regulatory scenarios that affect the various decisions in the Human Capital Management market are given a keen observation and have been explained.

Some of the leading market players include: SAP SE, Automatic Data Processing, LLC, Ultimate Software Group, Inc., Linkedin (Microsoft), Oracle Corporation.

Reports Intellect projects detail Human Capital Management Market based on elite players, present, past and futuristic data which will offer as a profitable guide for all Human Capital Management Market competitors. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis..

Segmentation by Type: Talent Acquisition, Talent Management, HCM.

Segmentation by application: Healthcare, Financial Services, Government/Non-Profit, Retail/Wholesale, Professional/Technical Services, Manufacturing.

Major Regions: North America, Europe, Asia-Pacific, South America, Middle East and Africa.Top of Form

Table of Contents         

2019-2024 Global Human Capital Management Market Report (Status and Outlook)

1 Scope of the Report
1.1 Market Introduction
1.2 Research Objectives
1.3 Years Considered
1.4 Market Research Methodology
1.5 Economic Indicators
1.6 Currency Considered

2 Executive Summary
2.1 World Market Overview
2.1.1 Global Human Capital Management Market Size 2014-2024
2.1.2 Human Capital Management Market Size CAGR by Region
2.2 Human Capital Management Segment by Type
2.2.1 Talent Acquisition
2.2.2 Talent Management
2.2.3 HR Core Administration
2.2.4 HCM
2.3 Human Capital Management Market Size by Type
2.3.1 Global Human Capital Management Market Size Market Share by Type (2014-2019)
2.3.2 Global Human Capital Management Market Size Growth Rate by Type (2014-2019)
2.4 Human Capital Management Segment by Application
2.4.1 Healthcare
2.4.2 Financial Services
2.4.3 Government/Non-Profit
2.4.4 Retail/Wholesale
2.4.5 Professional/Technical Services
2.4.6 Manufacturing
2.5 Human Capital Management Market Size by Application
2.5.1 Global Human Capital Management Market Size Market Share by Application (2014-2019)
2.5.2 Global Human Capital Management Market Size Growth Rate by Application (2014-2019)

3 Global Human Capital Management by Players

Continued.

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1. Estimates 2019-2024 Human Capital Management Market development trends with the recent trends and SWOT analysis.
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3. Understand regional Human Capital Management Market supply scenario.
4. Identify opportunities in the global Human Capital Management Market industry with the help of upcoming projects and capital expenditure outlook.
5. Facilitate decision making on the basis of strong historical and forecast of Human Capital Management Market capacity data.

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