Big data feature and scope in future.
Which are the domain we can use the Big data Echo-system.
It can be used in all the data either structured or non-strucutured and easy to store, analyze using Big data.
Fictional study for Allergan. This presentation is a group assignment performed for the Marketing Project Management class at Centennial College - Postgraduate degree in Marketing Management, 2018.
This file has no relationship with Allergan and its intentions to launch products on the market, all information used does not correspond to the reality of the company.
Resume-Predicting Profitability and Customer Preference Presentation-Brian Bu...Brian Burger
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Two data analytics methods were used to predict profitability of new products and customer preferences for Blackwell Electronics. Regression analysis predicted higher profits than similarity analysis. Regression analysis estimated $186,602 in total profit compared to $106,666 using similarity analysis. Customer survey data was also analyzed to predict brand preferences, which showed customers prefer Sony computers to Acer by a 2 to 1 margin. It is recommended that Blackwell pursue a closer partnership with Sony based on these findings.
A high level overview of common Cassandra use cases, adoption reasons, BigData trends, DataStax Enterprise and the future of BigData given at the 7th Advanced Computing Conference in Seoul, South Korea
The document provides an overview of the Hadoop ecosystem, including introductory information on Hadoop and MapReduce, installing and using Hadoop, programming with Pig and Hive, using NoSQL databases like MongoDB, machine learning with Mahout, and moving data in and out of Hadoop systems. It also covers managing Hadoop clusters, running Hadoop on AWS, data structures and algorithms for Hadoop, and testing and debugging Hadoop applications.
Big Data is all about extracting 4Vs
Cloud focuses on On-Demand, Elastic, Scalable, Pay-Per use Self Service models
Big data is not a stand-alone technology; rather, it is a combination of the last 50 years of technology evolution
Wave 1: Creating manageable data structures
Wave 2: Web and content management
Wave 3: Managing big data
Adopting Hadoop to manage your Big Data is an important step, but not the end-solution to your Big Data challenges. Here are some of the additional considerations you must face:
Choosing the right cloud for the job: The massive computing and storage resources that are needed to support Big Data applications make cloud environments an ideal fit, and more than ever, there is a growing number of choices of cloud infrastructure types and providers. Given the diverse options, and the dynamic environments involved, it becomes ever more important to maintain the flexibility for all your IT needs.
Big Data is a complex beast: It involves many and different moving parts, in large clusters, and is continually growing and evolving. Managing such an environment manually is not a viable option. The question is, how can you achieve automation of all this complexity?
The world beyond Hadoop: Big Data is not just Hadoop there is a whole rapidly growing ecosystem to contend with, including NoSQL, data processing, analytics tools As well as your own application services. How can you manage deployment, configuration, scaling and failover of all the different pieces, in a consistent way?
In this session, youll learn how to deploy and manage your Hadoop cluster on any Cloud, as well as manage the rest of your big data application stack using a new open source framework called Cloudify.
Data analysis is one of the revolutionary technique that has been the base for further lot technologies and industries. It is important and is structured in a disciplinary manner in order to produce essential results. Concept includes static algorithms and particular set of work methods but the result is always dynamic.
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Optimizing the value of digital data in the life sciencesSollers College
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In life sciences organizations, digital transformation involves enacting cutting-edge technologies and electronic platforms to improve procedures and make choices. The need for data digitization may vary depending on the drugs life cycle stage.
The document proposes a wellness analytics service to help clients track, measure, and manage workplace wellness programs. It would analyze internal employee wellness data from devices and apps, as well as external data from wellness providers. This would enable better decision-making and returns on wellness investments. Deloitte is well-positioned to deliver the service using its expertise in technology, analytics, and human capital. The proposal estimates $11.7 million in revenue over three years by capturing 1% of the growing Canadian wellness market.
1-2 An Introduction to Clinical Informatics: History, Domains, and CareersCorinn Pope
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2014 Clinical Informatics 際際滷s. Section one part two on an introduction to clinical informatics: history of informatics, domains within informatics, and careers as an informaticist
Data Analytics in Industry Verticals, Data Analytics Lifecycle, Challenges of...Sahilakhurana
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Banking and securities
Challenges
Early warning for securities fraud and trade visibilities
Card fraud detection and audit trails
Enterprise credit risk reporting
Customer data transformation and analytics.
The Security Exchange commission (SEC) is using big data to monitor financial market activity by using network analytics and natural language processing. This helps to catch illegal trading activity in the financial markets.
The Data Analytics Lifecycle is designed specifically for Big Data problems and data science projects. The lifecycle has six phases, and project work can occur in several phases at once. For most phases in the lifecycle, the movement can be either forward or backward. This iterative depiction of the lifecycle is intended to more closely portray a real project, in which aspects of the project move forward and may return to earlier stages as new information is uncovered and team members learn more about various stages of the project. This enables participants to move iteratively through the process and drive toward operationalizing the project work.
Phase 1Discovery: In Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology, time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data.
Phase 2Data preparation: Phase 2 requires the presence of an analytic sandbox, in which the team can work with data and perform analytics for the duration of the project. The team needs to execute extract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the sandbox. The ELT and ETL are sometimes abbreviated as ETLT. Data should be transformed in the ETLT process so the team can work with it and analyze it. In this phase, the team also needs to familiarize itself with the data thoroughly and take steps to condition the data.
Forage Accenture Data Analytics Task 3 - Data Visualization and Storytelling....SahilSharma235241
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Social Buzz asked Accenture to analyze their huge amount of daily user data and content to gain insights. Accenture's analytics team extracted data from Social Buzz's sources and analyzed over 100,000 daily pieces of user content like photos, videos, and GIFs. Their analysis found that animals and science were the most popular content categories. Photos were the most reacted to content type followed by videos and GIFs. The top 5 categories were animals, science, healthy eating, food, and technology. This provides insight into Social Buzz's audience and recommendations on boosting growth in certain categories through influencers, brands, and leveraging food during holiday seasons.
Annual ed performance improvement.4 2010capstonerx
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This document discusses performance improvement in healthcare. It outlines how opportunities for improvement are identified through patient satisfaction surveys, staff input, chart reviews, and other means. Priorities are set annually and focus on clinical care quality, patient/employee satisfaction, and financial performance. The Plan-Do-Check-Act (PDCA) cycle is used to test changes. Staff are encouraged to submit improvement ideas. Performance improvement differs from research in that the former aims to improve local processes while research seeks generalizable knowledge. Reducing catheter-associated urinary tract infections is provided as an example project using the PDCA framework.
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This document describes a data analytics internship under Prof. Sameer Mathur at IIM Lucknow. The internship involves developing an analytics strategy to drive healthcare transformation. Key responsibilities include analyzing articles to identify important insights and explaining their relevance to managers in India. Two important insights from the article are discussed. The first is ways to simplify an analytics strategy, such as accelerating data and delegating work to technologies like business intelligence and machine learning. The second insight describes two approaches - hypothesis-based and discovery-based - for solving business problems depending on the nature and known solutions. The insights are relevant to managers in India as they can help uncover patterns, rely on business intelligence, and use analytics to handle inventories.
These slides use concepts from my (Jeff Funk) course on Business Models at National University of Singapore to analyze the business model of SCIOs molecular sensor. This pocket sensor uses infrared light to determine the molecular makeup of fruits, vegetables, drugs, and other organic materials. As a replacement for bulky spectrometers, it can be used by scientists, engineers, and consumers to determine the sugar content, nutritional value, and other aspects of organic materials. The slides describe the value proposition, method of value capture, customers, scope of activities, and method of strategic control for SCIO.
The document discusses several key issues facing healthcare systems and how information technology can help address them. It identifies quality, safety, rising costs, and workforce issues as primary concerns. IT is presented as a way to help integrate care delivery, automate records, connect different parts of the system, implement evidence-based practices, and close information loops. However, past IT strategies are criticized for being fragmented and short-sighted. The document calls for developing long-term strategic IT plans that treat healthcare as an adaptive system and focus on priorities like eliminating errors, waste, and delays.
Market research plays a crucial role in a businesss success and growth and is an important aspect of starting a new business or developing a new product or even getting to know about your competition. Let's learn it's importance in marketing management and 7 steps of market research process.
The document summarizes two key insights from an article on simplifying analytics strategies:
1. There are methods to simplify complex analytics strategies, such as accelerating data processing, delegating work to analytics technologies like business intelligence and data visualization, data discovery, analytics applications, and machine learning.
2. Companies can take either a hypothesis-based or discovery-based approach to solve business problems depending on how much is known about the problem and potential solutions. A hypothesis-based approach is best for known problems with known solutions, while discovery-based is best for known problems with unknown solutions.
The document discusses how mobile apps are being used in five areas of healthcare: monitoring, diagnosis, education, treatment, and sales/marketing. It provides examples of innovative apps from companies like WellDoc, Mobile MIM, Boston Scientific, and AirStrip Cardiology that are improving patient care and physician efficiency. Medical apps are becoming fundamental healthcare tools, and the document asks readers to share their experiences developing mobile health apps.
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Technology is constantly transforming healthcare for the better, but getting technology right is an understated challenge for the industry. This webinar addresses three of healthcare's top challenges in tapping technology's full potential: cost, privacy and adoption. Experts and providers share tips, strategies and stories to help overcome these challenges to truly harness the power of transformative healthcare technology.
This document discusses big data in nursing and education. It defines big data as large datasets that are too large and complex for traditional database systems to analyze. Some key points:
- Florence Nightingale was an early adopter of data analytics to study mortality rates in the Crimean War.
- Big data has 5 characteristics - volume, velocity, variety, veracity, and value. It is collected from a variety of sources like social media, sensors, videos.
- Big data can benefit education by addressing inequities, providing personalized learning based on student profiles, and improving student outcomes through predictive analytics.
- Challenges to big data use include technical issues in handling large datasets, privacy and ethics concerns,
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Social Buzz asked Accenture to analyze their huge amount of daily user data and content to gain insights. Accenture's analytics team extracted data from Social Buzz's sources and analyzed over 100,000 daily pieces of user content like photos, videos, and GIFs. Their analysis found that animals and science were the most popular content categories. Photos were the most reacted to content type followed by videos and GIFs. The top 5 categories were animals, science, healthy eating, food, and technology. This provides insight into Social Buzz's audience and recommendations on boosting growth in certain categories through influencers, brands, and leveraging food during holiday seasons.
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This document discusses performance improvement in healthcare. It outlines how opportunities for improvement are identified through patient satisfaction surveys, staff input, chart reviews, and other means. Priorities are set annually and focus on clinical care quality, patient/employee satisfaction, and financial performance. The Plan-Do-Check-Act (PDCA) cycle is used to test changes. Staff are encouraged to submit improvement ideas. Performance improvement differs from research in that the former aims to improve local processes while research seeks generalizable knowledge. Reducing catheter-associated urinary tract infections is provided as an example project using the PDCA framework.
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Hi Friends
This is supa bouy
I am a mentor, Friend for all Management Aspirants, Any query related to anything in Management, Do write me @ supabuoy@gmail.com.
I will try to assist the best way I can.
Cheers to lyf!!!
Supa Bouy
This document describes a data analytics internship under Prof. Sameer Mathur at IIM Lucknow. The internship involves developing an analytics strategy to drive healthcare transformation. Key responsibilities include analyzing articles to identify important insights and explaining their relevance to managers in India. Two important insights from the article are discussed. The first is ways to simplify an analytics strategy, such as accelerating data and delegating work to technologies like business intelligence and machine learning. The second insight describes two approaches - hypothesis-based and discovery-based - for solving business problems depending on the nature and known solutions. The insights are relevant to managers in India as they can help uncover patterns, rely on business intelligence, and use analytics to handle inventories.
These slides use concepts from my (Jeff Funk) course on Business Models at National University of Singapore to analyze the business model of SCIOs molecular sensor. This pocket sensor uses infrared light to determine the molecular makeup of fruits, vegetables, drugs, and other organic materials. As a replacement for bulky spectrometers, it can be used by scientists, engineers, and consumers to determine the sugar content, nutritional value, and other aspects of organic materials. The slides describe the value proposition, method of value capture, customers, scope of activities, and method of strategic control for SCIO.
The document discusses several key issues facing healthcare systems and how information technology can help address them. It identifies quality, safety, rising costs, and workforce issues as primary concerns. IT is presented as a way to help integrate care delivery, automate records, connect different parts of the system, implement evidence-based practices, and close information loops. However, past IT strategies are criticized for being fragmented and short-sighted. The document calls for developing long-term strategic IT plans that treat healthcare as an adaptive system and focus on priorities like eliminating errors, waste, and delays.
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1. There are methods to simplify complex analytics strategies, such as accelerating data processing, delegating work to analytics technologies like business intelligence and data visualization, data discovery, analytics applications, and machine learning.
2. Companies can take either a hypothesis-based or discovery-based approach to solve business problems depending on how much is known about the problem and potential solutions. A hypothesis-based approach is best for known problems with known solutions, while discovery-based is best for known problems with unknown solutions.
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Technology is constantly transforming healthcare for the better, but getting technology right is an understated challenge for the industry. This webinar addresses three of healthcare's top challenges in tapping technology's full potential: cost, privacy and adoption. Experts and providers share tips, strategies and stories to help overcome these challenges to truly harness the power of transformative healthcare technology.
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Big data feature and scope in future
1. Big Data Feature and Scope in
Future
1) Health Care
2) Management
3) Product Improvement
4) Environment Study
5) Education Improvements etc.
All above can be achieved using Big data
technologies, because easy to store and analyze
it, improving things using technology.
2. 1)Health Care
If person want to take care of there future
health and diseases it can be cured before he
get diseased by analyzing his history data.
He/she has to share information of his health
changes and weekly activity.
3. 2)Management
By Gathering information from past changes and
comparing the current changes.
What thing has changed company like number of
employs, technologies and domain expertise,
projects.
How many required for future projects
accordance with data and we can train and ready
for it.
After analyzing , what thing are effected we will
come to know by past data, by comparing current
data
4. 3)Product Improvement
Once product released in market we can
improve it by taking a comments and feedback
from the customer.
By using the current , history data and
suggesting the new product which are related
to the product which people buys .
By analyzing product will be improved and
relevant product sale by giving adds
suggestion.
5. 4)Environment
We can predict the kind to changes in future
environment .
By using the same history current data
By this we can take the preposition before it
occur
At least we can guise what to do, before it may
happen
6. 5)Education
Taking current and past data of staff from
each State and analyzing on it.
From this data get there education.
Taking every parent reviews , suggestion and
in which subjects there kid is weak.
After analyzing the data, we can improve both
staff and kids knowledge .