Data continues to flood into today’s enterprises in ever-increasing velocity, variety and volume. This big data brings with it challenges – in storing it and in integrating it all into a form that can be used for business tasks. Many organizations try to use technology already on hand to collect, access and integrate big data. But processing manually or using legacy tools is slow and risks creating errors that undermine the value of the information and cause users to lose confidence in it. Automated processes using technology specifically designed for big data integration can overcome these issues and enable businesses to use the information to make decisions.
Data Trends for 2019: Extracting Value from DataPrecisely
To get the most business value from data, you need to keep up with the latest tech trends – or do you?
View this webinar on-demand as we share the results from our 2019 Data Trends Survey! We'll reveal what organizations around the world are really up to at the intersection of technology, big data and business.
Key topics include:
• Business initiatives getting the most IT support in 2019
• Highest-priority IT initiatives
• Tech adoption rates, benefits and challenges
Paradigm4 Research Report: Leaving Data on the tableParadigm4
While Big Data enjoys widespread media coverage, not enough attention has been paid to what practitioners think — data scientists who manage and analyze massive volumes of data. We wanted to know, so Paradigm4 teamed up with Innovation Enterprise to ask over 100 data scientists for their help separating Big Data hype from reality. What we learned is that data scientists face multiple challenges achieving their company’s analytical aspirations. The upshot is that businesses are leaving data — and money — on the table.
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Data Trends for 2019: Extracting Value from DataPrecisely
To get the most business value from data, you need to keep up with the latest tech trends – or do you?
View this webinar on-demand as we share the results from our 2019 Data Trends Survey! We'll reveal what organizations around the world are really up to at the intersection of technology, big data and business.
Key topics include:
• Business initiatives getting the most IT support in 2019
• Highest-priority IT initiatives
• Tech adoption rates, benefits and challenges
Paradigm4 Research Report: Leaving Data on the tableParadigm4
While Big Data enjoys widespread media coverage, not enough attention has been paid to what practitioners think — data scientists who manage and analyze massive volumes of data. We wanted to know, so Paradigm4 teamed up with Innovation Enterprise to ask over 100 data scientists for their help separating Big Data hype from reality. What we learned is that data scientists face multiple challenges achieving their company’s analytical aspirations. The upshot is that businesses are leaving data — and money — on the table.
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Healthcare Analytics Summit Keynote Fall 2017Dale Sanders
The Data Operating System. Changing the Digital Trajectory of Healthcare. Why do we need to change the current digital trajectory? What’s the business case for a Data Operating System? What is a Data Operating System and how did we get here? What difference will DOS make? What should we do with it and what should we expect?
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
Impact of DDOD on Data Quality - White House 2016David Portnoy
"The Impact of Demand-Driven Open Data (DDOD) on Data Quality" was presented on April 27, 2016 at Open Data Roundtable held at the White House Office of Science and Technology Policy.
It discusses the data quality problems prevalent in open data and their impact, the origins of the DDOD concept, how it works, progress towards its goals, several use case examples, and how to implement it at other organizations.
More information:
* DDOD http://ddod.healthdata.gov
* Open Data Roundtables https://www.data.gov/meta/open-data-roundtables/
* White House Office of Science and Technology Policy: https://www.whitehouse.gov/blog/2016/02/05/open-data-empowering-americans-make-data-driven-decisions
Data Management - a top Priority for Healthcare PracticesData Dynamics Inc
The healthcare industry has become increasingly data-driven and poised to take a leap into the future, thanks to an increasingly tech-savvy and demanding patient-consumer base. While the Healthcare Data Ecosystem is presently fragmented and often, insufficient, pioneering firms see vast opportunities to be a part of the Healthcare revolution through proper management of their massive amount of Data.
Healthcare has unique data management challenges that other industries do not face, so the solutions that worked in those fields cannot simply be replicated. Challenges in healthcare data management include -
1. Data environment consolidation in acquisitions and mergers
2. Managing the rapid growth of unstructured healthcare data
3. Adhering to the strict healthcare regulations and reforms
On top of this, Healthcare organizations have to ensure that their data management solution must have a dependable & active security protocol to safeguard sensitive information of patients as per HIPAA norms. With the exponential increase in data, risk is only going to amplify.
In case of mergers & acquisitions, a sizable challenge for large healthcare corporates is the Amalgamation and Streamlining Data with the parent company’s processes. This becomes tedious and cost intensive as merging two data environments that are often radically different from each other into a single system, is difficult and tedious.
Healthcare companies need consumer-driven data strategies with patients at the forefront of their planning. How? To know, read on.
Data Dynamics is a leader in intelligent file management solutions that empower enterprises to seamlessly analyze, move, manage and modernize critical data across hybrid, cloud and object-based storage infrastructures for true business transformation.
Intro to Demand-Driven Open Data for Data OwnersDavid Portnoy
This document is intended for use by data owners within government to learn how Demand-Driven Open Data (DDOD) could benefit their organizations.
DDOD is an initiative by the U.S. Department of Health and Human Services (HHS) started in November 2014 as part of its IDEA Lab program. The goal is to leverage the vast data assets throughout HHS’s agencies (CMS, NIH, CDC, FDA, AHRQ and others) to create additional economic and public health value.
DDOD provides a systematic, ongoing and transparent mechanism for anybody to tell HHS and its agencies what data would be valuable to them. With this initiative HHS can move from measuring Open Data in terms of number of datasets released to value in terms of use cases enabled.
DDOD website: http://ddod.us
This document explores the concepts behind how DDOD (Demand-Driven Open Data) can be used in conjunction with FOIA (Freedom of Information Act) requests. It describes how DDOD and FOIA can leverage each other's strengths to help overcome their inherent challenges.
DDOD is an initiative by the U.S. Department of Health and Human Services (HHS) started in November 2014 as part of its IDEA Lab program. The goal is to leverage the vast data assets throughout HHS’s agencies (CMS, FDA, NIH, CDC, NCHS, AHRQ and others) to create additional economic and public health value.
DDOD provides a systematic, ongoing and transparent mechanism for anybody to tell HHS and its agencies what data would be valuable to them. It's the Lean Startup approach to open data. With this initiative HHS can move from measuring Open Data in terms of number of datasets released to value in terms of use cases enabled.
DDOD website: http://ddod.us
Intro to Demand Driven Open Data for Data UsersDavid Portnoy
This document is intended for any commercial or academic organization to learn how Demand-Driven Open Data (DDOD) could benefit them.
DDOD is an initiative by the U.S. Department of Health and Human Services (HHS) started in November 2014 as part of its IDEA Lab program. The goal is to leverage the vast data assets throughout HHS’s agencies (CMS, NIH, CDC, FDA, AHRQ and others) to create additional economic and public health value.
DDOD provides a systematic, ongoing and transparent mechanism for anybody to tell HHS and its agencies what data would be valuable to them. With this initiative HHS can move from measuring Open Data in terms of number of datasets released to value in terms of use cases enabled.
DDOD website: http://ddod.us
Data Governance Talking Points: Simple Lessons From the TrenchesHealth Catalyst
About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
Big Data Tools PowerPoint Presentation SlidesSlideTeam
Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of twenty slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Big Data Tools PowerPoint Presentation Slides complete deck. http://bit.ly/39AwSro
Camssguide Big Data Analytics Solutions, can help you meet and exceed challenges and opportunities for business, industry, and technology solution areas
Big data analytics and its impact on internet usersStruggler Ever
Big Data Analytic tools are promising techniques for a future prediction in many aspects of our life. The need for such predictive techniques has been exponentially increasing. even though, there are many challenges and risks are still of concern of researchers and decision makers, the outcome from the use of these techniques will considerable revolutionize our world to a new era of technology.
Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision making for an organization. Combining multiple operational databases and external data create data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.
Healthcare Analytics Summit Keynote Fall 2017Dale Sanders
The Data Operating System. Changing the Digital Trajectory of Healthcare. Why do we need to change the current digital trajectory? What’s the business case for a Data Operating System? What is a Data Operating System and how did we get here? What difference will DOS make? What should we do with it and what should we expect?
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
Impact of DDOD on Data Quality - White House 2016David Portnoy
"The Impact of Demand-Driven Open Data (DDOD) on Data Quality" was presented on April 27, 2016 at Open Data Roundtable held at the White House Office of Science and Technology Policy.
It discusses the data quality problems prevalent in open data and their impact, the origins of the DDOD concept, how it works, progress towards its goals, several use case examples, and how to implement it at other organizations.
More information:
* DDOD http://ddod.healthdata.gov
* Open Data Roundtables https://www.data.gov/meta/open-data-roundtables/
* White House Office of Science and Technology Policy: https://www.whitehouse.gov/blog/2016/02/05/open-data-empowering-americans-make-data-driven-decisions
Data Management - a top Priority for Healthcare PracticesData Dynamics Inc
The healthcare industry has become increasingly data-driven and poised to take a leap into the future, thanks to an increasingly tech-savvy and demanding patient-consumer base. While the Healthcare Data Ecosystem is presently fragmented and often, insufficient, pioneering firms see vast opportunities to be a part of the Healthcare revolution through proper management of their massive amount of Data.
Healthcare has unique data management challenges that other industries do not face, so the solutions that worked in those fields cannot simply be replicated. Challenges in healthcare data management include -
1. Data environment consolidation in acquisitions and mergers
2. Managing the rapid growth of unstructured healthcare data
3. Adhering to the strict healthcare regulations and reforms
On top of this, Healthcare organizations have to ensure that their data management solution must have a dependable & active security protocol to safeguard sensitive information of patients as per HIPAA norms. With the exponential increase in data, risk is only going to amplify.
In case of mergers & acquisitions, a sizable challenge for large healthcare corporates is the Amalgamation and Streamlining Data with the parent company’s processes. This becomes tedious and cost intensive as merging two data environments that are often radically different from each other into a single system, is difficult and tedious.
Healthcare companies need consumer-driven data strategies with patients at the forefront of their planning. How? To know, read on.
Data Dynamics is a leader in intelligent file management solutions that empower enterprises to seamlessly analyze, move, manage and modernize critical data across hybrid, cloud and object-based storage infrastructures for true business transformation.
Intro to Demand-Driven Open Data for Data OwnersDavid Portnoy
This document is intended for use by data owners within government to learn how Demand-Driven Open Data (DDOD) could benefit their organizations.
DDOD is an initiative by the U.S. Department of Health and Human Services (HHS) started in November 2014 as part of its IDEA Lab program. The goal is to leverage the vast data assets throughout HHS’s agencies (CMS, NIH, CDC, FDA, AHRQ and others) to create additional economic and public health value.
DDOD provides a systematic, ongoing and transparent mechanism for anybody to tell HHS and its agencies what data would be valuable to them. With this initiative HHS can move from measuring Open Data in terms of number of datasets released to value in terms of use cases enabled.
DDOD website: http://ddod.us
This document explores the concepts behind how DDOD (Demand-Driven Open Data) can be used in conjunction with FOIA (Freedom of Information Act) requests. It describes how DDOD and FOIA can leverage each other's strengths to help overcome their inherent challenges.
DDOD is an initiative by the U.S. Department of Health and Human Services (HHS) started in November 2014 as part of its IDEA Lab program. The goal is to leverage the vast data assets throughout HHS’s agencies (CMS, FDA, NIH, CDC, NCHS, AHRQ and others) to create additional economic and public health value.
DDOD provides a systematic, ongoing and transparent mechanism for anybody to tell HHS and its agencies what data would be valuable to them. It's the Lean Startup approach to open data. With this initiative HHS can move from measuring Open Data in terms of number of datasets released to value in terms of use cases enabled.
DDOD website: http://ddod.us
Intro to Demand Driven Open Data for Data UsersDavid Portnoy
This document is intended for any commercial or academic organization to learn how Demand-Driven Open Data (DDOD) could benefit them.
DDOD is an initiative by the U.S. Department of Health and Human Services (HHS) started in November 2014 as part of its IDEA Lab program. The goal is to leverage the vast data assets throughout HHS’s agencies (CMS, NIH, CDC, FDA, AHRQ and others) to create additional economic and public health value.
DDOD provides a systematic, ongoing and transparent mechanism for anybody to tell HHS and its agencies what data would be valuable to them. With this initiative HHS can move from measuring Open Data in terms of number of datasets released to value in terms of use cases enabled.
DDOD website: http://ddod.us
Data Governance Talking Points: Simple Lessons From the TrenchesHealth Catalyst
About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
Big Data Tools PowerPoint Presentation SlidesSlideTeam
Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of twenty slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Big Data Tools PowerPoint Presentation Slides complete deck. http://bit.ly/39AwSro
Camssguide Big Data Analytics Solutions, can help you meet and exceed challenges and opportunities for business, industry, and technology solution areas
Big data analytics and its impact on internet usersStruggler Ever
Big Data Analytic tools are promising techniques for a future prediction in many aspects of our life. The need for such predictive techniques has been exponentially increasing. even though, there are many challenges and risks are still of concern of researchers and decision makers, the outcome from the use of these techniques will considerable revolutionize our world to a new era of technology.
Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision making for an organization. Combining multiple operational databases and external data create data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.
60 Top Auto Insurance Keywords by Estimated Cost Per Click Infographic for 2014TPG
60 Top Auto Insurance Keywords by Estimated Cost Per Click Infographic for 2014.
10 keywords were selected per category. Each category represents a stage in the buyer’s journey. In the early stages of the buyer’s journey keyword searches involve more general questions around Car/Auto Insurance. In the end stages of the buyer’s journey keywords are represented by more transactional keywords that have intent to quote/policy.
Celsi® es una plataforma asistida por computadora para simular vías de Transducción de Señales (ST) que ocurren en las células y que derivan de estímulos extracelulares que desencadenan una cascada de señales proteicas al interior de la célula causando la expresión génica y produciendo una respuesta celular.
El objetivo de la Masterclass es introducir los conceptos de Marketing Digital necesarios para el desarrollo de toda una estrategia de Social Media Marketing de éxito, a través de la presentación de uno de los principales medios de comunicación que existen en nuestro país.
De la mano de David Varona, Community Managerde rtve.es, los alumnos conocerán el desarrollo de su estrategia en redes sociales como Facebook, Twitter, Youtube o La Villa, para llegar a más usuarios, gestionar con más eficacia y ganar en viralidad.
IAB Spain, la Asociación que representa al sector de la publicidad, el marketing y la comunicación digital en España, lanza hoy el primer Libro Blanco de Compra Programática y RTB, que se suma a los 14 publicados hasta la fecha por la asociación.
O USBLink® é uma nova ferramenta que pela inovação alia a comunicação/publicidade escrita convencional com a web. Através de um chip USB destacável que é incorporado num folheto ou página de revista, os consumidores são redireccionados para a sua página Web (URL).
Vanson Bourne Research Report: Big DataVanson Bourne
For most organisations, big data is now the reality of doing business. Technological and social innovations are resulting in huge flows of new data every day. As we enter this undeniable era of big data where more information will be captured in ever-finer detail from more sources than ever before does that mean our decision-making is bound to improve?
Survey Results Age Of Unbounded Data June 03 10nhaque
Enterprises today can generate, collect and consider more data than ever before. New types of data can provide insight into previously opaque processes and motivations, but prodigious quantities of data present opportunity, as well as complexity and distraction. nGenera Insight’s 2010 Leading in an Age of Unbounded Data survey garnered responses from over 70 major organizations, including many global corporations, to provide a cross-industry pulse of the state of enterprise data.
Analyst report from Forrester on how leading organizations are investing in integrated collaborative and social solutions, and find out how to develop a strategy that will drive the most value for your business.
Views From The C-Suite: Who's Big on Big DataPlatfora
he way that big data pervades most organizations today creates a dynamic environment for C-level executives to explore how it can and should be used strategically to add business value.
While each C-level executive views big data through a unique lens, a strong consensus exists among them about the need for effective big data analytics across their organizations.
This Economist Intelligence Unit report shows that senior executives are optimistic about both the capabilities of big data and the impacts such data can have on their businesses.
Download the report to get the whole story.
Making the Leap: Exploring the Push for Cloud AdoptionGov BizCouncil
For a growing number of public and private sector organizations, cloud is the future — a game-changer for mitigating risk, enhancing effectiveness, and initiating new capabilities. To learn more about ongoing progress and challenges associated with cloud adoption, Government Business Council and Salesforce launched an in-depth research study in May 2017.
We're moving into an era of big data, where more information is captured in ever-finer detail from
ever-more sources which means our decision-making is bound to improve. Doesn’t it? We interviewed 100 ITDMs from organisations with 1000 or more employees to find out.
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalCapgemini
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalSubrahmanyam KVJ
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
Integrated Demand Management-When Will We Start Using Downstream Data-7 Nov 2012Lora Cecere
For the purposes of this report, downstream data is defined as data that originates downstream on the demand side of the value chain. It can include point-of-sale data, T-log data, distributor data, social and unstructured data sources, retail withdrawal data and retail forecasts. Integrated demand signal management is the use of this data in a more holistic and integrated demand management process.
The use of channel data is evolving and this report is designed to give the industry an update on progress. Data for this report is based on two inputs: quantitative survey data from twenty-nine respondents (manufacturers) that use downstream data for integrated demand signal management, and qualitative input from attendees at an Integrated Demand Signal Management event that was attended by eleven manufacturers and four retailers. Data was collected in the fall of 2012.
While the study demographic is a small number, the respondents represent an experienced panel group. In the study, 90% of the respondents were using downstream data. The average time of usage is four years.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
Similar to Ventana Research Big Data Integration Benchmark Research Executive Report (20)
Ventana Research 2015 Technology Innovation AwardsVentana Research
The Ventana Research Technology Innovation Awards recognize vendors that have introduced noteworthy innovations in technology that advance business and IT.
The Technology Innovation Awards showcase advances in the productivity and potential of business applications as well as technology that contributes significantly to the improved efficiency, productivity, and the performance of an organizations.
Customer service is more complicated than ever. Today’s consumers have more channels available to them, and as a result, more opportunity to go elsewhere for their products and services. To understand their high expectations, companies must know their customers better. Doing so requires a complete view of customers and their interactions with the company. This is challenging because customers interact through many channels, and information from each often is stored separately and hard to bring together. Thus companies need better tools than most have on hand. Using appropriate systems to integrate, analyze and manage customer information can make it possible to personalize the customer experience, engage with customers more fully, build loyalty and increase revenue.
Deploying Predictive Analytics for Competitive AdvantageVentana Research
Predictive analytics is a relatively straightforward idea: It is the use of past data to evaluate the likelihood of future outcomes. Predictive analytics can support multiple lines of business and can improve effectiveness.
Organizations are making analytics the highest-priority
innovative technology. It’s no wonder why. The demands
placed upon businesses today are unprecedented. To
succeed in this volatile environment companies will have
to use analytics continuously. Top performing companies
have implemented the next generation of analytics with
more intuitive real-time information and presentation
through visualization for discovery and exploration.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.