This document discusses the opportunities and challenges of big data. It defines big data as huge volumes of structured and unstructured data from various sources that require new tools to analyze and extract business insights. Big data provides both statistical and predictive views to help businesses make smarter decisions. While big data allows companies to integrate diverse data sources and gain real-time insights, challenges include processing large and complex data volumes and ensuring data quality, privacy and management. The document outlines the big data lifecycle and how analytics can be used descriptively, predictively and prescriptively.
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
Big Data & Analytics in the Manufacturing Industry: The Vaasan GroupIBM Analytics
The Vassan Group struggled to accurately forecast fluctuating sales orders across the Nordic region. As a result, they couldn't effectively plan their resource and production schedule. With IBM Big Data & Analytics, Vaasan gained the ability to predict production requirements and prepare for fluctuating orders ultimately fulfilling 30% more orders. http://bit.ly/1bt5yGt
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
Big Data & Analytics in the Manufacturing Industry: The Vaasan GroupIBM Analytics
The Vassan Group struggled to accurately forecast fluctuating sales orders across the Nordic region. As a result, they couldn't effectively plan their resource and production schedule. With IBM Big Data & Analytics, Vaasan gained the ability to predict production requirements and prepare for fluctuating orders ultimately fulfilling 30% more orders. http://bit.ly/1bt5yGt
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...Denodo
Watch full webinar here: https://bit.ly/3xozd5W
Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise.
Watch Now On-Demand!
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Mobile, Wearables, Big Data and A Strategy to Move Forward (with NTT Data Ent...Barcoding, Inc.
Join NTT Data Enterprise Services, Inc.for a discussion on the Internet of Things (IoT), wearables, augmented reality, predictive analytics, and a strategy for using Big Data effectively in your enterprise. Presented at the Barcoding, Inc. Executive Forum 2014
Presentation: Big Data 101, What It Means for Business
Presented by: David Ray, Corporate Vice President, Corporate Internet, New York Life Insurance Company
Big Data is the latest buzzword inside the C-suite, but what does it mean, how are other industries using it to competitive advantage, and what are the real opportunities for business? Does big data require massive amounts of data to be considered or is there success to be found in unifying myriad data sources? Join us for an interesting peek.
www.bdionline.com
Explore how data integration (or “mashups”) can maximize analytic value and help business teams create streamlined data pipelines that enables ad-hoc analytic inquiries. You’ll learn why businesses increasingly focused on blending data on demand and at the source, the concrete analytic advantages that this approach delivers, and the type of architectures required for delivering trusted, blended data. We provide a checklist to assess your data integration needs and capabilities, and review some real-world examples of how blending various data types has created significant analytic value and concrete business impact.
From Business Intelligence to Big Data - hack/reduce Dec 2014Adam Ferrari
Talk given on Dec. 3, 2014 at MIT, sponsored by Hack/Reduce. This talk looks at the history of Business Intelligence from first generation OLAP tools through modern Data Discovery and visualization tools. And looking forward, what can we learn from that evolution as numerous new tools and architectures for analytics emerge in the Big Data era.
Advanced Business Analytics for Actuaries - Canadian Institute of Actuaries J...Kevin Pledge
Presentation given at the Canadian Institute of Actuaries Annual Meeting in June 2013. Covers the direction business intelligence is moving in for insurance.
The benefits of Hadoop for analytics make it a popular option for many companies looking to expand their analytics suite. However, adding Hadoop as an analytics platform to an existing environment based on more traditional data structures and methods poses several key challenges. Review these slides to understand key challenges and strategies to expanding the analytics suite to use Hadoop, such as: architectural integration with existing platforms, skills and organizational readiness, and the importance of a vision and a clear path forward.
TDWI Spotlight: Enabling Data Self-Service with Security, Governance, and Reg...Denodo
Watch full webinar here: https://bit.ly/3xozd5W
Companies today want to realize the value of data and share it across the enterprise. While unlocking the full potential of data for business users, these companies must also ensure that they maintain security requirements. Learn how you can successfully implement self-service initiatives with data governance to enable both business and IT to realize the full potential of any data in the enterprise.
Watch Now On-Demand!
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Mobile, Wearables, Big Data and A Strategy to Move Forward (with NTT Data Ent...Barcoding, Inc.
Join NTT Data Enterprise Services, Inc.for a discussion on the Internet of Things (IoT), wearables, augmented reality, predictive analytics, and a strategy for using Big Data effectively in your enterprise. Presented at the Barcoding, Inc. Executive Forum 2014
Presentation: Big Data 101, What It Means for Business
Presented by: David Ray, Corporate Vice President, Corporate Internet, New York Life Insurance Company
Big Data is the latest buzzword inside the C-suite, but what does it mean, how are other industries using it to competitive advantage, and what are the real opportunities for business? Does big data require massive amounts of data to be considered or is there success to be found in unifying myriad data sources? Join us for an interesting peek.
www.bdionline.com
Explore how data integration (or “mashups”) can maximize analytic value and help business teams create streamlined data pipelines that enables ad-hoc analytic inquiries. You’ll learn why businesses increasingly focused on blending data on demand and at the source, the concrete analytic advantages that this approach delivers, and the type of architectures required for delivering trusted, blended data. We provide a checklist to assess your data integration needs and capabilities, and review some real-world examples of how blending various data types has created significant analytic value and concrete business impact.
From Business Intelligence to Big Data - hack/reduce Dec 2014Adam Ferrari
Talk given on Dec. 3, 2014 at MIT, sponsored by Hack/Reduce. This talk looks at the history of Business Intelligence from first generation OLAP tools through modern Data Discovery and visualization tools. And looking forward, what can we learn from that evolution as numerous new tools and architectures for analytics emerge in the Big Data era.
Advanced Business Analytics for Actuaries - Canadian Institute of Actuaries J...Kevin Pledge
Presentation given at the Canadian Institute of Actuaries Annual Meeting in June 2013. Covers the direction business intelligence is moving in for insurance.
The benefits of Hadoop for analytics make it a popular option for many companies looking to expand their analytics suite. However, adding Hadoop as an analytics platform to an existing environment based on more traditional data structures and methods poses several key challenges. Review these slides to understand key challenges and strategies to expanding the analytics suite to use Hadoop, such as: architectural integration with existing platforms, skills and organizational readiness, and the importance of a vision and a clear path forward.
The presentation includes the introduction to the topic, the various dimensions of big data, its evolution from big data 1.0 to bid data 3.0 and its impact on various industries, uses as well as the challenges it faces. The concluding slide gives a brief on the future of big data.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
Lecture 1.13 & 1.14 &1.15_Business Profiles in Big Data.pptxRATISHKUMAR32
The presentation contain the business profiles in big data analytics. through this ppt user can learn about the different case studies such as facebook and walmart. This ppt contain the information and seven characteristics that are required to learn the basics of big data.
Big data includes large volumes of data, both unstructured and structured,however the volume of data is not important but the execution is. How organization's perceive those data and implements the understanding, resulting in change- is what matters. HashCash Consultants assists organization's to analyze the data for insights that result in better decisions and strategic business moves.
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
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
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.
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
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
2. Opportunities and Challenges
• Effective use of data in competition.
• Business needs data from information to make better, smarter
decisions ahead of competitors.
• Big Data is supplementing the data, including structured,
unstructured data, machine and online/mobile data.
• Big Data will change the fundamental way business compete
and operate.
• Big Data provides both statistical and predictive views.
• Even though ability to capture huge amount of data has
grown, the technical capacity to analyze data just started.
• Business requires faster tools to analyze data and provide
business with real time data.
3. What is Big Data
• Big Data refers to huge volumes of data, created by
people/tools/machines. This requires new tools to analyze, in order to
provide business insights that relate to customers, risk, performance and
productivity.
• Big Data is the collection of data which is gathered from internet, phones,
video and voice recordings and data can be either structured or
unstructured.
• Big Data has four properties and typically characterized by FOUR “V”s.
• Volume : The amount of data is so vast when compared to traditional
data.
• Variety : Data comes from different sources, both from machines/people.
• Velocity : Data generated very fast from different sources like 24 X 7.
• Veracity : Data collected from different places and we need to check
quality of data captured.
4. Life Cycle
• Creation : Along with traditional data, new data has been created
from social network/videos/voice, which are never used before and
helped business to understand customers more to launch their
products.
• Processing: Even though organizations has huge data, they were
unwilling to process data, as it was out weighting the benefit of
data being processed. But with the help of new technologies like
Hadoop and with distributed storage, the cost of processing data
came down drastically.
• Output: The data should be available readily to the right people to
make insightful decisions leading to successful outcomes.
Organizations putting more importance to employees(Data
Scientist) who are specialized in analysis of data.
5. Big Data and Analytics
• The goal of Analytics is to improve the efficiency and
effectiveness of every decision or action.
• Analytics is the discovery of communication of meaningful
patterns of business data.
• Analytics enables organizations to meet stakeholder reporting
needs of market, risk etc., and helping organization to
perform better.
• Organization should have right combination of people,
process and technology.
6. Analytics
• Descriptive Analytics: This is Business
Intelligence and deals with what has happened
already like last month sales.
• Predictive Analytics: To predict what is going to
happen in different sceneries depending of past
data.
• Prescriptive Analytics: To determine which
decision/action will produce the most effective
result against a specific set of objectives and
constraints.
7. Big Data Benefits
• Big Data helps to get data from new external sources in a cost
effective manner.
• As per estimation 85% of data is unstructured and Big Data helps in
getting this data processed in lower prices.
• Big Data allows organizations to collect data from various sources.
• It allows the data to be stored at lowest level and for prolonged
period. Possible to integrate massive volumes of data in various
formats.
• Customers can be provided with consolidated reports views which
helps in intelligent business decisions. And monitoring customer
profiles
• Healthcare industry adopted Big Data and resulted in helping
researches further.
• Helps in Helps in company offering services which customers
preferred.
8. Big Data Benefits -- Continued
• Possible to integrate massive volumes of data in various
formats.
• Customers can be provided with consolidated reports views
which helps in intelligent business decisions.
• Helps in monitoring customer profiles
• Helps in company offering services which customers
preferred.
• There is no special storage required and Big Data can be
deployed on commodity hardware.
• With the help of low cost computing number of big data
forecasting ventures have risen which are predicting exact
whether.
9. Big Data Risks
• Modeling, storage and processing challenges arise from the
growing of data volumes.
• Challenge in unifying different data sources.
• Too much of data to be processed which results in noise.
• No ready made tools to process data.
• Not enough skilled employees not available process build big
data solutions.
• Integrated data archicture increases the challenge of data
linkages and matching lagorithms.
• Incerase in complexity of data.
10. Big Data Risks -- Continued
• It should have consistent guidance, procedure
and clear management decisions.
• Data should be shared at right level in the
organization.
• Managers need to embrace new technology.
• Has to consider ownership and privacy of data.
• It may bring intellectual property issues and its
big challenges that employees are not sharing the
data outside of organization.
11. Summary
• Big Data become a success factor for companies by providing
relevant information at right time.
• Big Data helped financial sector(Credit Cards/Banks) with
fraud detection, which prevented revenue loss.
• With Big Data it become possible to integrate different types
of data and get the real time data at right time.
• Increasingly started used in Health Industry and this is helping
especially in Pharmacy and Disease detection.
• Companies started using Big Data in selection process of
employees.