This document discusses how big data analytics can help revolutionize farming in India by addressing challenges in agriculture. It explains that sensors collect real-time data from fields and equipment that is integrated with other data sources to identify patterns and insights. These reveal existing issues and help form predictive algorithms to prevent future problems and control risks. Benefits of big data in agriculture include useful data collection, managing pests and diseases, identifying hidden patterns, helping cope with climate change, predicting yields, enabling automated agriculture, advanced supply tracking, and risk assessment.
Big data analytics is increasingly important for agriculture as the global population grows. Analytics can reduce farming costs by $2.3 trillion according to researchers, including $250 billion from data analytics alone. Analytics provides real-time soil sensor data to optimize nutrition levels, utilizes GPS-enabled tractors, provides timely pest and disease reports from IoT sensors to efficiently manage threats, helps trace supply chains to reduce food waste, and enables near-accurate yield predictions using satellite data.
Among the new and emerging technologies in agriculture, Big Data is the one that promises the best improvements. Producers and growers want superior yields, cost savings, and better real-time data; consumers want healthier agricultural products at better prices; agriculture scientists need improved seeds and plants to face climate changes and prevent famine.
Today the use of data is having a very revolutionized effect with
cultivatable land in decline demand for food increasing from
developing countries farmers.
Farmers who use data are capable of turning ordinary harvests into
bumper crops and profits behind.This is the precision agriculture hub connecting the world’s biggest agricultural businesses farmers and suppliers using integrated software solutions.
1. The document discusses how aWhere provides agricultural intelligence and data to help farmers increase food production in the face of challenges like increasing weather variability and population growth.
2. aWhere collects data from various sources like weather stations, satellites, and field observations and provides weather and agronomic forecasts, predictions, alerts and recommendations to farmers.
3. This data-driven assistance helps farmers improve yields, reduce risks, and better manage their resources and operations, working towards solving the global challenge of sustainably feeding a growing population.
In this issue of Math in the News we look at the Monarch Butterfly Migration with new data since our last investigation of it. For more math resources go to www.media4math.com.
Proagrica - Big Data to Feed the WorldHPCC Systems
Proagrica uses big data to help drive growth and efficiency in agriculture. They consolidate vast amounts of data from sources like farm machinery, weather, soil, and satellites. Proagrica's HPCC platform integrates this data and provides analytics and insights. Analyzing yield data from UK oilseed rape farms showed that higher yields correlated with warm springs, wet winters, and proper pesticide application. Variety choice did not have a large impact on yields.
This document discusses 3 ways to capture farm data in the Aeros LIVE system: 1) Data entry by flock, which allows entering data for each flock while viewing historical information; 2) Batch data entry, which facilitates entering data for multiple flocks at once for a specific time frame; and 3) Data import, which allows importing data from spreadsheets or third party software using various import types and integration points. Capturing farm data through these methods in Aeros LIVE helps farmers identify issues, make necessary adjustments, and support strategic decision making.
This document discusses how big data analytics can help revolutionize farming in India by addressing challenges in agriculture. It explains that sensors collect real-time data from fields and equipment that is integrated with other data sources to identify patterns and insights. These reveal existing issues and help form predictive algorithms to prevent future problems and control risks. Benefits of big data in agriculture include useful data collection, managing pests and diseases, identifying hidden patterns, helping cope with climate change, predicting yields, enabling automated agriculture, advanced supply tracking, and risk assessment.
Big data analytics is increasingly important for agriculture as the global population grows. Analytics can reduce farming costs by $2.3 trillion according to researchers, including $250 billion from data analytics alone. Analytics provides real-time soil sensor data to optimize nutrition levels, utilizes GPS-enabled tractors, provides timely pest and disease reports from IoT sensors to efficiently manage threats, helps trace supply chains to reduce food waste, and enables near-accurate yield predictions using satellite data.
Among the new and emerging technologies in agriculture, Big Data is the one that promises the best improvements. Producers and growers want superior yields, cost savings, and better real-time data; consumers want healthier agricultural products at better prices; agriculture scientists need improved seeds and plants to face climate changes and prevent famine.
Today the use of data is having a very revolutionized effect with
cultivatable land in decline demand for food increasing from
developing countries farmers.
Farmers who use data are capable of turning ordinary harvests into
bumper crops and profits behind.This is the precision agriculture hub connecting the world’s biggest agricultural businesses farmers and suppliers using integrated software solutions.
1. The document discusses how aWhere provides agricultural intelligence and data to help farmers increase food production in the face of challenges like increasing weather variability and population growth.
2. aWhere collects data from various sources like weather stations, satellites, and field observations and provides weather and agronomic forecasts, predictions, alerts and recommendations to farmers.
3. This data-driven assistance helps farmers improve yields, reduce risks, and better manage their resources and operations, working towards solving the global challenge of sustainably feeding a growing population.
In this issue of Math in the News we look at the Monarch Butterfly Migration with new data since our last investigation of it. For more math resources go to www.media4math.com.
Proagrica - Big Data to Feed the WorldHPCC Systems
Proagrica uses big data to help drive growth and efficiency in agriculture. They consolidate vast amounts of data from sources like farm machinery, weather, soil, and satellites. Proagrica's HPCC platform integrates this data and provides analytics and insights. Analyzing yield data from UK oilseed rape farms showed that higher yields correlated with warm springs, wet winters, and proper pesticide application. Variety choice did not have a large impact on yields.
This document discusses 3 ways to capture farm data in the Aeros LIVE system: 1) Data entry by flock, which allows entering data for each flock while viewing historical information; 2) Batch data entry, which facilitates entering data for multiple flocks at once for a specific time frame; and 3) Data import, which allows importing data from spreadsheets or third party software using various import types and integration points. Capturing farm data through these methods in Aeros LIVE helps farmers identify issues, make necessary adjustments, and support strategic decision making.
Era of Artificial Intelligence Lecture 3 Pietro LeoPietro Leo
This document summarizes a lecture by Pietro Leo on artificial intelligence. Some key points discussed include:
- AI can help industries like agriculture, automotive, and healthcare. For agriculture, precision agriculture using AI is discussed.
- For science, big data acts as a microscope for the 21st century, enabling analysis like wine DNA tracing. Mapping the microbiome can also help protect from harmful bacteria.
- Digital twins of farms can help share insights and data to help farming. AI sensors may also detect foodborne pathogens at home.
- In automotive, self-driving vehicles are discussed as well as predictive maintenance using cloud, AI and connected cars. Damage assessment systems can also help standardize
Big data solutions in agriculture combined with data tools and software have the potential to revolutionize the agricultural sector. To help farmers make better decisions, these technologies should combine data on climate, agronomy, water, farm machinery, supply chain, nutrients and more. It enables real-time streaming of data from multiple sources, helping you uncover important insights based on reliable, high-quality data.
Farming is becoming more data-driven and technology-focused to meet the needs of a growing global population. New technologies like AI, computer vision, IoT sensors, and blockchain are helping farmers increase productivity and efficiency through applications like crop monitoring, yield estimation, equipment management, and ensuring transparency in food supply chains. These innovations are critical to addressing challenges in agriculture and recovering from crises like the COVID-19 pandemic by revolutionizing current farming practices.
151111 BASE ELN 151112 CIO Big Data CollaborationDr. Bill Limond
The document discusses the role of CIOs and big data collaboration. It notes that big data is growing exponentially, with 2.5 quintillion bytes of data created every day from a variety of sources. Big data offers significant value if organizations can analyze it, with potential savings in healthcare, retail, and other sectors. However, big data requires collaboration both internally within organizations and externally with partners. The document provides examples of successful big data collaborations and argues that CIOs will continue playing an important role in facilitating information management and digital transformation through big data initiatives.
Big data solution in agriculture combined with data tools and software can revolutionize the agricultural industry. To help the farmer make better decisions, these technologies should be able to combine data on the climate, agronomy, water, farm machinery, supply chain, weeds, nutrients, and much more. By providing collections of most trusted applications we offer big data service in agriculture for data integration and data integrity.For any better decision farmers need to be able to access authentic information in the form of big data.
For better decision-making, farmers need to be able to access authentic information which is generally in the form of big data. Aerial mapping, field harvesting, weather monitoring, chemical detection, and other technologies that guarantee the correctness of the data acquired are thus required to contact companies that provide big data consulting in agriculture. By providing collections of most trusted applications Rootfacts offer big data service in agriculture for data integration and data integrity.
Big data solution in agriculture combined with data tools and software can revolutionize the agricultural industry. To help the farmer make better decisions, these technologies should be able to combine data on the climate, agronomy, water, farm machinery, supply chain, weeds, nutrients, and much more. By providing collections of most trusted applications we offer big data service in agriculture for data integration and data integrity.For any better decision farmers need to be able to access authentic information in the form of big data.
Digital Agriculture Services for Cotton - Information BrochureAnthony Willmott
1) The National Farmers' Federation (NFF) and Accenture are introducing a new digital agriculture service called Digital Agriculture Services to help Australian farmers build more efficient farms through the use of digital technology and data analytics.
2) Digital Agriculture Services is a web-based tool that integrates data from various sources like sensors, satellite imagery, drones, and weather forecasts to provide farmers recommendations and insights to improve decision-making and farm operations.
3) The tool is focused initially on the cotton industry and provides functionalities to track crop emergence, growth rates, growing degree days, fuel prices, crop health, and farm data to help farmers optimize yield, boost revenue, and minimize costs.
To help reaching the Sustainable Development Goals, CGIAR must tap into Big Data. Within the programme on Climate Change for Agriculture and Food Security (CCAFS), researchers have already applied Big Data analytics to agricultural and weather records in Colombia, revealing how climate variation impacts rice yields. After defining its Open Data-Open Access strategy, CGIAR has launched an internal call for proposals for big data analytics platforms that will provide services to the Agri-Food system programmes and parners, and will interconnect the CGIAR data to other multi-disciplinary big data. The seminar will present the pespectives of the envisioned platforms.
In this issue of TOP TEN we provide the reader with a wealth of information related to current and future usages of BIG DATA. The reader will get an insight into usages in the realm of education, health, construction, management as well as marketing.
Using Big Data Analytics in the Field of Agriculture A Surveyijtsrd
Big data science plays a major role in the current generation deals with the betterment of agriculture field mainly because of the population growth and climate change importance of big data is increased. Big data include the advanced analytical tools. Big data include the advanced analytical chain. Farming is undergoing a digital revolution. Smart farming is depending by the phenomenon of big data. In the field where the cereals and crop seedling growth as well as status and trends of their growth is estimated. Big data is essentially used a global crop growth monitoring system based on remote sensing is dependent on big data science. Big data analytical is a data driven technology useful in generating significant productivity improvement in various industries by collecting, storing, managing, processing, and analysing various kind of structure and unstructured data. The role of big data in agriculture provide an opportunity to increase economic gain of the formers. Gagana H. S | Arpitha H. M | Gouthami H. S "Using Big Data Analytics in the Field of Agriculture: A Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31015.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31015/using-big-data-analytics-in-the-field-of-agriculture-a-survey/gagana-h-s
The document summarizes the potential of digital innovations to manage climate risks in food systems. It discusses how digital tools can provide timely insights to farmers, how technologies can help manage climate risks across the food supply chain, and how digital innovations in forecasting support climate science. It also outlines some key challenges, such as insufficient digital infrastructure in rural areas and gender divides. Finally, it proposes recommendations like investing in bridging digital divides, strengthening information systems, and coordinating with actors to build digital capabilities.
Worldwide adoption of open data is gaining momentum, particularly for public sector and government data. In 2013, all G7 countries signed the Open Data Charter agreeing that government data should be open by default, and many G20 countries are now introducing similar practices. Tesco used open weather data to improve operational efficiency by predicting demand increases of 300% for barbecues during 18 degree rises in temperature. A Colombian research center used open and private data to create a decision tool that helped rice farmers avoid $3.6 million in losses during a drought.
Fortune 1000 companies' response to covid 19databahn
How Fortune 1000 Companies Have Responded to COVID-19 in the Past 60 Days.
U.S. President, Donald Trump invoked the Defense Production Act to get supplies to these manufacturers so they can build ventilators:
3M
General Motors
Medtronic
Hill-Rom
Res Med
Vyaire Medical
Royal Philips
General Electric
EFRA is an EU-funded project that will develop the first analytics-enabled, green data space for AI-enabled food risk prevention. It will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat.
EFRA is an EU-funded project that will develop the first analytics-enabled, green data space for AI-enabled food risk prevention. It will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat.
3a. Robotics, big data & precision agro - Robert BerendesIventus
This document discusses digital disruption in the agriculture and food industry. It begins by outlining the context of digital disruption and its implications for the industry. Several examples of digital disruptors are then provided, including companies using technologies like sensors, drones, AI and data analytics to increase farming efficiency and reduce costs across the agricultural value chain. The document concludes by noting that digitization will impact all business functions and the largest effects may be felt beyond the farm, such as in new business systems that emerge across the industry.
1. Apple and IBM have released details of their partnership to allow iPhone and Apple Watch users to share health data to IBM's Watson Health cloud-based analytics service.
2. By 2020, the amount of health data available is expected to double every 73 days according to the University of Iowa, with almost 5 million people worldwide connected to healthcare providers through similar partnerships by next year.
3. The partnerships aim to provide users and healthcare providers with more insight into individuals' health through aggregated analytics of users' personal health data.
Agtech Industry Overview and InvestmentRoger Royse
Royse Agtech offers an indepth panel discussion surrounding the growing agricultural tech atmosphere (08/2016). Panel is in partnerships with Moss Adams LLP.
The Limitless Possibilities in Data Science.pdfUSDSI
The big gamut of the data science opportunities beckons you! Yield big data science career goals with the best data science certifications and skills on offer.
1. The document discusses strategies for maximizing receivables as delinquencies rise due to the pandemic. It recommends prioritizing accounts, assessing needed adjustments, and finalizing a plan to maximize cash flow.
2. Key recommendations include focusing collection efforts on accounts that are most likely to pay slowly but eventually pay based on their credit scores, and accelerating legal action or restricting credit for low-scoring accounts that pose the highest risk of non-payment.
3. Commercial recovery scores can help identify the segments of accounts that are most likely to result in collections, with the top 30% of scores accounting for 72% of potential dollar collections. This allows collection resources to be focused most effectively.
As businesses begin to re-open at varying capacities and timelines across the country, it is more difficult than ever to maximize the return on your marketing spend. Sales and marketing departments are finding it increasingly difficult to prioritize pre-COVID marketing lists and find open and stable businesses. Grab your favorite beverage and join me for a drink as we review a strategic approach to sales during the reopening of the US economy. You will learn three key takeaways, including:
A clear understanding of the operational risk associated with COVID-19
Learn to develop marketing lists that effectively balance risk
See how to quickly and accurately identify low-risk prospects within a sales territory
Watch the recording of this webinar:
https://www.experian.com/business-information/webinar-sip-and-solve-ep-24-business-resiliency
Era of Artificial Intelligence Lecture 3 Pietro LeoPietro Leo
This document summarizes a lecture by Pietro Leo on artificial intelligence. Some key points discussed include:
- AI can help industries like agriculture, automotive, and healthcare. For agriculture, precision agriculture using AI is discussed.
- For science, big data acts as a microscope for the 21st century, enabling analysis like wine DNA tracing. Mapping the microbiome can also help protect from harmful bacteria.
- Digital twins of farms can help share insights and data to help farming. AI sensors may also detect foodborne pathogens at home.
- In automotive, self-driving vehicles are discussed as well as predictive maintenance using cloud, AI and connected cars. Damage assessment systems can also help standardize
Big data solutions in agriculture combined with data tools and software have the potential to revolutionize the agricultural sector. To help farmers make better decisions, these technologies should combine data on climate, agronomy, water, farm machinery, supply chain, nutrients and more. It enables real-time streaming of data from multiple sources, helping you uncover important insights based on reliable, high-quality data.
Farming is becoming more data-driven and technology-focused to meet the needs of a growing global population. New technologies like AI, computer vision, IoT sensors, and blockchain are helping farmers increase productivity and efficiency through applications like crop monitoring, yield estimation, equipment management, and ensuring transparency in food supply chains. These innovations are critical to addressing challenges in agriculture and recovering from crises like the COVID-19 pandemic by revolutionizing current farming practices.
151111 BASE ELN 151112 CIO Big Data CollaborationDr. Bill Limond
The document discusses the role of CIOs and big data collaboration. It notes that big data is growing exponentially, with 2.5 quintillion bytes of data created every day from a variety of sources. Big data offers significant value if organizations can analyze it, with potential savings in healthcare, retail, and other sectors. However, big data requires collaboration both internally within organizations and externally with partners. The document provides examples of successful big data collaborations and argues that CIOs will continue playing an important role in facilitating information management and digital transformation through big data initiatives.
Big data solution in agriculture combined with data tools and software can revolutionize the agricultural industry. To help the farmer make better decisions, these technologies should be able to combine data on the climate, agronomy, water, farm machinery, supply chain, weeds, nutrients, and much more. By providing collections of most trusted applications we offer big data service in agriculture for data integration and data integrity.For any better decision farmers need to be able to access authentic information in the form of big data.
For better decision-making, farmers need to be able to access authentic information which is generally in the form of big data. Aerial mapping, field harvesting, weather monitoring, chemical detection, and other technologies that guarantee the correctness of the data acquired are thus required to contact companies that provide big data consulting in agriculture. By providing collections of most trusted applications Rootfacts offer big data service in agriculture for data integration and data integrity.
Big data solution in agriculture combined with data tools and software can revolutionize the agricultural industry. To help the farmer make better decisions, these technologies should be able to combine data on the climate, agronomy, water, farm machinery, supply chain, weeds, nutrients, and much more. By providing collections of most trusted applications we offer big data service in agriculture for data integration and data integrity.For any better decision farmers need to be able to access authentic information in the form of big data.
Digital Agriculture Services for Cotton - Information BrochureAnthony Willmott
1) The National Farmers' Federation (NFF) and Accenture are introducing a new digital agriculture service called Digital Agriculture Services to help Australian farmers build more efficient farms through the use of digital technology and data analytics.
2) Digital Agriculture Services is a web-based tool that integrates data from various sources like sensors, satellite imagery, drones, and weather forecasts to provide farmers recommendations and insights to improve decision-making and farm operations.
3) The tool is focused initially on the cotton industry and provides functionalities to track crop emergence, growth rates, growing degree days, fuel prices, crop health, and farm data to help farmers optimize yield, boost revenue, and minimize costs.
To help reaching the Sustainable Development Goals, CGIAR must tap into Big Data. Within the programme on Climate Change for Agriculture and Food Security (CCAFS), researchers have already applied Big Data analytics to agricultural and weather records in Colombia, revealing how climate variation impacts rice yields. After defining its Open Data-Open Access strategy, CGIAR has launched an internal call for proposals for big data analytics platforms that will provide services to the Agri-Food system programmes and parners, and will interconnect the CGIAR data to other multi-disciplinary big data. The seminar will present the pespectives of the envisioned platforms.
In this issue of TOP TEN we provide the reader with a wealth of information related to current and future usages of BIG DATA. The reader will get an insight into usages in the realm of education, health, construction, management as well as marketing.
Using Big Data Analytics in the Field of Agriculture A Surveyijtsrd
Big data science plays a major role in the current generation deals with the betterment of agriculture field mainly because of the population growth and climate change importance of big data is increased. Big data include the advanced analytical tools. Big data include the advanced analytical chain. Farming is undergoing a digital revolution. Smart farming is depending by the phenomenon of big data. In the field where the cereals and crop seedling growth as well as status and trends of their growth is estimated. Big data is essentially used a global crop growth monitoring system based on remote sensing is dependent on big data science. Big data analytical is a data driven technology useful in generating significant productivity improvement in various industries by collecting, storing, managing, processing, and analysing various kind of structure and unstructured data. The role of big data in agriculture provide an opportunity to increase economic gain of the formers. Gagana H. S | Arpitha H. M | Gouthami H. S "Using Big Data Analytics in the Field of Agriculture: A Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31015.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31015/using-big-data-analytics-in-the-field-of-agriculture-a-survey/gagana-h-s
The document summarizes the potential of digital innovations to manage climate risks in food systems. It discusses how digital tools can provide timely insights to farmers, how technologies can help manage climate risks across the food supply chain, and how digital innovations in forecasting support climate science. It also outlines some key challenges, such as insufficient digital infrastructure in rural areas and gender divides. Finally, it proposes recommendations like investing in bridging digital divides, strengthening information systems, and coordinating with actors to build digital capabilities.
Worldwide adoption of open data is gaining momentum, particularly for public sector and government data. In 2013, all G7 countries signed the Open Data Charter agreeing that government data should be open by default, and many G20 countries are now introducing similar practices. Tesco used open weather data to improve operational efficiency by predicting demand increases of 300% for barbecues during 18 degree rises in temperature. A Colombian research center used open and private data to create a decision tool that helped rice farmers avoid $3.6 million in losses during a drought.
Fortune 1000 companies' response to covid 19databahn
How Fortune 1000 Companies Have Responded to COVID-19 in the Past 60 Days.
U.S. President, Donald Trump invoked the Defense Production Act to get supplies to these manufacturers so they can build ventilators:
3M
General Motors
Medtronic
Hill-Rom
Res Med
Vyaire Medical
Royal Philips
General Electric
EFRA is an EU-funded project that will develop the first analytics-enabled, green data space for AI-enabled food risk prevention. It will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat.
EFRA is an EU-funded project that will develop the first analytics-enabled, green data space for AI-enabled food risk prevention. It will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat.
3a. Robotics, big data & precision agro - Robert BerendesIventus
This document discusses digital disruption in the agriculture and food industry. It begins by outlining the context of digital disruption and its implications for the industry. Several examples of digital disruptors are then provided, including companies using technologies like sensors, drones, AI and data analytics to increase farming efficiency and reduce costs across the agricultural value chain. The document concludes by noting that digitization will impact all business functions and the largest effects may be felt beyond the farm, such as in new business systems that emerge across the industry.
1. Apple and IBM have released details of their partnership to allow iPhone and Apple Watch users to share health data to IBM's Watson Health cloud-based analytics service.
2. By 2020, the amount of health data available is expected to double every 73 days according to the University of Iowa, with almost 5 million people worldwide connected to healthcare providers through similar partnerships by next year.
3. The partnerships aim to provide users and healthcare providers with more insight into individuals' health through aggregated analytics of users' personal health data.
Agtech Industry Overview and InvestmentRoger Royse
Royse Agtech offers an indepth panel discussion surrounding the growing agricultural tech atmosphere (08/2016). Panel is in partnerships with Moss Adams LLP.
The Limitless Possibilities in Data Science.pdfUSDSI
The big gamut of the data science opportunities beckons you! Yield big data science career goals with the best data science certifications and skills on offer.
1. The document discusses strategies for maximizing receivables as delinquencies rise due to the pandemic. It recommends prioritizing accounts, assessing needed adjustments, and finalizing a plan to maximize cash flow.
2. Key recommendations include focusing collection efforts on accounts that are most likely to pay slowly but eventually pay based on their credit scores, and accelerating legal action or restricting credit for low-scoring accounts that pose the highest risk of non-payment.
3. Commercial recovery scores can help identify the segments of accounts that are most likely to result in collections, with the top 30% of scores accounting for 72% of potential dollar collections. This allows collection resources to be focused most effectively.
As businesses begin to re-open at varying capacities and timelines across the country, it is more difficult than ever to maximize the return on your marketing spend. Sales and marketing departments are finding it increasingly difficult to prioritize pre-COVID marketing lists and find open and stable businesses. Grab your favorite beverage and join me for a drink as we review a strategic approach to sales during the reopening of the US economy. You will learn three key takeaways, including:
A clear understanding of the operational risk associated with COVID-19
Learn to develop marketing lists that effectively balance risk
See how to quickly and accurately identify low-risk prospects within a sales territory
Watch the recording of this webinar:
https://www.experian.com/business-information/webinar-sip-and-solve-ep-24-business-resiliency
Why good match rates mean better ROI - Sip and SolveExperian
Finding the right business is the backbone of what drives each task a risk manager must do – ranging from onboarding new accounts to reviewing their existing portfolio.
Sometimes, identifying that business can be very challenging if you don’t have the right information. Which name should you use for the business? Legal name? DBA? What if the business has multiple locations? What if the business is collocated with another? What about businesses that are merged? What if the business moves to another location?
There are numerous challenges behind finding the right business with significant upside in doing so. If you’re able to increase your ability to find the right business by just 10%, it could have a significant impact on your bottom line. Learn how Experian tackles these challenges and gain an understanding on best practices for how you can solve for them, as well as:
How improving match rate will improve ROI
Strategies to identify the best business information to use
How accuracy & timeliness of contributed data improves match quality (and improves ROI)
Watch the recording of this webinar:
https://www.experian.com/business-information/webinar-sip-and-solve-ep-22-why-good-match-rates-mean-better-roi
Best practices in machine learning for small business lending - Sip and SolveExperian
In today’s world where petabytes of data are created on a daily basis, harnessing the potential value from the growing volume of data becomes a major challenge. One way to overcome this challenge is by applying machine learning techniques to data analysis. For small business lending, there are several barriers of entry to easy adoption and application of such techniques, such as the lack of explainability, interpretability, transparency, and deployability.
Join us for a 15-minute Sip and Solve as our resident data scientist addresses these barriers and uncovers how small business lenders can apply appropriate methods, modern infrastructures, and best-in-class partnerships to integrate machine learning into their business processes.
Watch the recording of this webinar:
https://www.experian.com/business-information/webinar-sip-and-solve-ep-19-best-practices-machine-learning-smb
3 tips to increase response rates when marketing to a business - Sip and SolveExperian
Finding new prospects and customers in the market for business credit has never been more difficult. Even with hyper-personalized and targeted campaigns, marketing response rates can still be stagnant.
During this webinar you will learn how to:
Get the right message to the right prospect using data beyond traditional marketing firmographics
Use analytics to optimize marketing spend
Prioritize your outreach by identifying who is likely in the market to buy
Watch the recording of this webinar:
https://www.experian.com/business-information/webinar-sip-and-solve-ep-16-3-tips-to-increase-response-rates
Combating digital fraud attacks - Sip and SolveExperian
In today's digital evolution, traditional methods of verification while still vital is ineffective in combating today's fraudsters. There's now an ever-increasing need to apply a layered approach and leverage probabilistic techniques such as machine learning, device and telephony intelligence to help combat fraudsters who have access to the same traditional data you're verifying as part of your existing fraud strategy.
We cover:
Understanding why traditional identity data is insufficient in the digital world.
Applying a layered approach will be more effective to combating today's fraud techniques.
New alternative data sources to consider within your layered strategy.
Watch the recording of this webinar here:
https://www.experian.com/business-information/webinar-sip-and-solve-ep-14-combating-digital-fraud-attacks
Modernizing the credit approval process in 3 simple steps - Sip and SolveExperian
Modernizing your credit approval process can feel daunting in the beginning stages, but it doesn't have to be. By starting small with some basic automation principals and tips, you can begin to implement change more easily over time in order to drive incremental returns for your business. As you and your stakeholders become more comfortable with these changes and can see the tangible benefits, you can continue to automate more pieces of the process to drive even more value. Eventually, you'll be left wondering why you didn't automate sooner.
Watch the webinar recording here:
https://www.experian.com/business-information/webinar-sip-and-solve-ep-12-modernizing-credit-approval
Minimizing Supplier Disruption In Your Supply ChainExperian
During this session Li Mao from our product management team, and Tom Hayes from Solutions Consulting present a real world example of failure in the supplier risk assessment process, and how it caused significant disruption for a major healthcare provider.
We talk through how procurement professionals can:
1. Learn from this example and apply simple checks as part of their on-going process.
2. Avoid common pitfalls many organizations encounter.
3. Collaborate with their compliance peers to gain a big-picture view of potential disruption risks.
Watch the recording of this talk on our archive page:
https://www.experian.com/business-information/landing/sip-and-solve.html
Choosing The Right Credit Decisioning ModelExperian
This document discusses different types of models used to predict customer behavior and make business decisions. Generic models use broad customer data to predict outcomes, while custom models are tailored to a specific company's portfolio. The document compares the two approaches and provides an example of validating a custom model's ability to accurately predict which accounts will become delinquent. It also demonstrates how a model can be used to maximize profits by adjusting credit limits or requiring deposits based on risk scores.
Make Smarter Collections Decisions with AnalyticsExperian
Are you developing your collection strategies with the entire customer lifecycle in mind? With the right tools and analytics, you can proactively make smarter collections decisions every step of the way.
In this session we share how to:
Test collection strategies early in the lifecycle
Identify early warning signs pointing to collection
Develop collections strategies that will help you maintain the relationship for future engagement
Watch the recording of this talk on our archive page:
https://www.experian.com/business-information/landing/sip-and-solve.html
Best Practices to Identify Companies in the Market for Business CreditExperian
In today’s saturated lending market, identifying businesses looking for commercial financing is the key to ensuring you target the right audience at the right time. In fact, it’s your competitive advantage. Join us over your coffee break as we share best practices on how you can find new prospects or recognize existing customers in the market for business credit.
Watch the recording of this talk on our archive page:
https://www.experian.com/business-information/landing/sip-and-solve.html
Collections prioritization using a scorecardExperian
The rate of market growth is beginning to slow and discussions around an economic downturn is starting to pick up. This means, now is the time to reassess your account management and collection strategies to prepare for what may come.
In this 15-minute session, get an overview of how you can stabilize and enhance your bottom line with a collection score, all while protecting the customer relationships you fought to build.
Watch the recording of this talk on our archive page:
https://www.experian.com/business-information/landing/sip-and-solve.html
In this Sip and Solve session I provide an overview of data visualization and the process of creating data visualizations, even when your data seems too unwieldy!
We cover:
- Why visualize your portfolio?
- How to prepare your data for more efficient use within visualization software
- How to get started building dashboards in Tableau
Watch the recording of this talk on our archive page:
https://www.experian.com/business-information/landing/sip-and-solve.html
Using Blended Business Owner Data in Credit Decision MakingExperian
Have you ever wondered whether you should consider using a blended scorecard in your small business decisioning?
In this Sip and Solve session I provide an overview of blended decision making, why it’s important and what data can be leveraged to start or enhance your blended data analysis.
Watch the recording of this talk on our archive page:
https://www.experian.com/business-information/landing/sip-and-solve.html
1. The document discusses how small businesses can have difficulties obtaining loans from traditional lenders due to having little credit history. It suggests that using alternative data sources in lending models could help assess risk for these businesses.
2. Two new alternative data approaches are described: alternative lending models that use alternative lending data to build predictive models, and social media scores that analyze social media data about businesses.
3. The models showed improved approval rates and reduced bad rates compared to traditional credit scoring. Around half of businesses analyzed had their risk assessment significantly impacted by social media data.
Experian Commercial Data Sciences analyzed 3.1 million commercial entities from June 2016 to June 2018. We focused our research on approximately 2.8 million of those entities with either a male-owned or a female-owned designation.
Women are capitalizing on the economy by opening businesses at a record pace. According to a 2018 study, an average of 1,821 women-owned businesses were launched daily.* Women-owned businesses are an essential part of the economy and the communities they serve.
Because women-owned businesses are a significant portion of the overall small-business sector, they have quickly become a vital source of job growth. Businesses owned by women provide job opportunities for millions of professionals. Additionally, these businesses have a high growth potential to expand, which increases job opportunities and positively contributes to the economy.
Across all industries, women-owned businesses have shown success in terms of revenue and longevity. When Experian studied women-owned businesses, however, we found significant differences in how women-owned and male-owned businesses approach expansion. The impact of how women manage credit compared with men can affect the business’s success and eventually the economy.
Download the full report http://bit.ly/317EXjI
In this Infographic we show highlights from our latest data study comparing attributes of 2.5 million business owners to general consumers. Learn more at:
http://bit.ly/2cLKozS
Experian provides steps for businesses to become data reporters of consumer and business credit information:
1. Contact an Experian data specialist to discuss secure file transmission methods and get help with a test file.
2. Review data verification samples and reports, then approve the packet.
3. Choose a Metro 2 reporting strategy and format to build files directly, use vendor software, or partner with a processor.
4. Register for e-OSCAR if reporting consumer data and start monthly reporting to Experian.
The State of Minority Owned Small BusinessExperian
According to a study by Experian of minority-owned small businesses:
- Nearly 21% of all small businesses in the US are minority-owned.
- Over 45% of minority-owned small businesses are located in just three states: California, Texas, and Florida.
- The average credit score for minority-owned small businesses was 707, slightly lower than the average consumer credit score of 749.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!