In this whitepaper read How companies address common big data trends & challenges to gain greater value from their data.
https://www.qubole.com/resources/report/big-data-trends-and-challenges-report
The document discusses how companies can drive business agility through cloud-based big data analytics. It notes that traditional big data approaches are no longer sufficient due to the increasing volume, variety, and velocity of data. The document outlines a reference architecture for analyzing diverse data sources in real-time and iteratively to gain insights. It emphasizes the need for data products to be responsive to disruption, leverage external data sources, and be resilient through cloud elasticity. Examples from Ford and healthcare are provided to illustrate integrating diverse data for predictive analytics and personalized recommendations.
Breakthrough experiments in data science: Practical lessons for successAmanda Sirianni
Leading firms are integrating data science capabilities within their organizations to capture the untapped potential of data science as a source for competitive advantage. Yet, many enterprises are challenged to successfully integrate these capabilities for sustained value and to measure its worth for the organization. This analytics study conducted by the IBM Center for Applied Insights uses practical advice from those seeing the benefits to establish a proven success formula for integrating a data science capability within your organization.
To learn more: www.ibm.com/ibmcai/data-science
The document discusses strategies for organizations to better manage big data when resources are limited. It recommends identifying unused data in the data warehouse in order to reduce costs by moving that data to cheaper platforms like Hadoop. Organizations can save millions by offloading data that is not frequently queried but must be retained for regulatory reasons. The document also suggests purging data that is not needed at all to further reduce storage and management costs. Proper classification and placement of data onto platforms suited to its usage level and type, such as Hadoop for less critical datasets, can help organizations get more value from their data with fewer resources.
Move It Don't Lose It: Is Your Big Data Collecting Dust?Jennifer Walker
The document discusses the rapid growth of big data and challenges of gaining insights from data. Some key points:
- By 2020, the digital universe is projected to reach 40 zettabytes, with 5,200 GB of data for every person on Earth.
- Data is coming from a growing number of sources like IoT devices, mobile devices, social media, and more. Much of this data is unstructured.
- Moving large amounts of data to storage and analytics platforms in a timely manner is challenging using traditional ETL and bulk transfer methods, which can take months.
- Freshness of data is important for insights but current methods result in data becoming stale before it reaches its destination.
As per the PfMP Certification, it is critical to keep track of project progress in order to keep the timetable on track. Six elements included in comprehensive project reports are mentioned here.
Hadoop: Data Storage Locker or Agile Analytics Platform? It’s Up to You.Jennifer Walker
The document discusses how Hadoop is often used primarily as a data storage system rather than an agile analytics platform. It argues that for Hadoop to enable productive analytics, companies need to transform Hadoop into a system that allows for iterative exploration of diverse data sources through intuitive interfaces that leverage machine learning. This requires addressing challenges such as a lack of data understanding, scarce expertise, and time-consuming data preparation processes. Adopting platforms that provide self-service access and leverage business context can help democratize data access and analysis.
Big Data: Real-life examples of Business Value Generation with ClouderaCapgemini
Capgemini has helped multiple organizations to put Big Data to work and create value for their business and their clients.
This prsentation looks at real-world cases of how organizations are using, or planning to use, big data technology. It will look at the different ways in which the technology is being used in a business context.
Examples are drawn from Retail, Telco, Financial Services, Public Sector and Consumer goods.
It will look at a range of business scenarios from simple cost reduction through to new business models looking at how the business case has been built and what value has been realized.
It will also look at some of the practical challenges and approaches taken and specifically the application of Enterprise Data Hubs in collaboration with its prime partner Cloudera.
Written by Richard Brown, Global Programme Leader, Big Data & Analytics, Capgemini
The document discusses how companies can drive business agility through cloud-based big data analytics. It notes that traditional big data approaches are no longer sufficient due to the increasing volume, variety, and velocity of data. The document outlines a reference architecture for analyzing diverse data sources in real-time and iteratively to gain insights. It emphasizes the need for data products to be responsive to disruption, leverage external data sources, and be resilient through cloud elasticity. Examples from Ford and healthcare are provided to illustrate integrating diverse data for predictive analytics and personalized recommendations.
Breakthrough experiments in data science: Practical lessons for successAmanda Sirianni
Leading firms are integrating data science capabilities within their organizations to capture the untapped potential of data science as a source for competitive advantage. Yet, many enterprises are challenged to successfully integrate these capabilities for sustained value and to measure its worth for the organization. This analytics study conducted by the IBM Center for Applied Insights uses practical advice from those seeing the benefits to establish a proven success formula for integrating a data science capability within your organization.
To learn more: www.ibm.com/ibmcai/data-science
The document discusses strategies for organizations to better manage big data when resources are limited. It recommends identifying unused data in the data warehouse in order to reduce costs by moving that data to cheaper platforms like Hadoop. Organizations can save millions by offloading data that is not frequently queried but must be retained for regulatory reasons. The document also suggests purging data that is not needed at all to further reduce storage and management costs. Proper classification and placement of data onto platforms suited to its usage level and type, such as Hadoop for less critical datasets, can help organizations get more value from their data with fewer resources.
Move It Don't Lose It: Is Your Big Data Collecting Dust?Jennifer Walker
The document discusses the rapid growth of big data and challenges of gaining insights from data. Some key points:
- By 2020, the digital universe is projected to reach 40 zettabytes, with 5,200 GB of data for every person on Earth.
- Data is coming from a growing number of sources like IoT devices, mobile devices, social media, and more. Much of this data is unstructured.
- Moving large amounts of data to storage and analytics platforms in a timely manner is challenging using traditional ETL and bulk transfer methods, which can take months.
- Freshness of data is important for insights but current methods result in data becoming stale before it reaches its destination.
As per the PfMP Certification, it is critical to keep track of project progress in order to keep the timetable on track. Six elements included in comprehensive project reports are mentioned here.
Hadoop: Data Storage Locker or Agile Analytics Platform? It’s Up to You.Jennifer Walker
The document discusses how Hadoop is often used primarily as a data storage system rather than an agile analytics platform. It argues that for Hadoop to enable productive analytics, companies need to transform Hadoop into a system that allows for iterative exploration of diverse data sources through intuitive interfaces that leverage machine learning. This requires addressing challenges such as a lack of data understanding, scarce expertise, and time-consuming data preparation processes. Adopting platforms that provide self-service access and leverage business context can help democratize data access and analysis.
Big Data: Real-life examples of Business Value Generation with ClouderaCapgemini
Capgemini has helped multiple organizations to put Big Data to work and create value for their business and their clients.
This prsentation looks at real-world cases of how organizations are using, or planning to use, big data technology. It will look at the different ways in which the technology is being used in a business context.
Examples are drawn from Retail, Telco, Financial Services, Public Sector and Consumer goods.
It will look at a range of business scenarios from simple cost reduction through to new business models looking at how the business case has been built and what value has been realized.
It will also look at some of the practical challenges and approaches taken and specifically the application of Enterprise Data Hubs in collaboration with its prime partner Cloudera.
Written by Richard Brown, Global Programme Leader, Big Data & Analytics, Capgemini
Seven Trends in Government Business IntelligenceTableau Software
Modern business intelligence (BI) trends in government for 2017 include:
1. Modern BI becoming the new normal as more governments adopt self-service analytics platforms.
2. The era of open data in government arriving as more data is released to the public.
3. Collaborative analytics growing from niche to essential as data becomes more accessible and cloud technology enables easier sharing.
4. Data-driven decision making exploding as people can more easily access and explore data to improve outcomes.
Modern Manufacturing: 4 Ways Data is Transforming the IndustryTableau Software
This document discusses how data is transforming the manufacturing industry in 4 key ways: 1) Improving production and plant performance with self-service analytics to empower employees, as shown by Tesla Motors; 2) Enhancing sales and operations planning with data blending and forecasting from multiple systems; 3) Mobilizing supply chains with real-time analytics accessed on mobile devices, as the Coca-Cola Bottling Co. has done; 4) Listening to customer feedback faster through data visualization and taking quicker action, as shown by Trane's improved response times.
This document summarizes the results of a survey of 298 data management professionals about big data challenges and opportunities. It finds that big data is now present in all organizations, with 11% managing over a petabyte and another 20% having hundreds of terabytes. While bigger companies are dealing with more data currently, most companies will soon have petabyte-sized stores. However, the business does not fully understand big data's potential value yet. Capitalizing on big data does not require replacing existing infrastructure but integrating it. The survey also finds that relational databases and Hadoop are both important technologies for managing big data now and in the future.
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.
Activating Big Data: The Key To Success with Machine Learning Advanced Analyt...Vasu S
A whitepaper of Qubole that How to make all of your data available to users for a multitude of use cases, ranging from analytics to machine learning and artificial intelligence.
https://www.qubole.com/resources/white-papers/activating-big-data-the-key-to-success-with-machine-learning-advanced-analytics
The document summarizes the top 10 trends in analytics according to Tiger Analytics. It discusses how data enrichment is allowing for better forecasts using internal and external data. It also covers how unstructured data like text, images, audio and video are opening new opportunities. Other trends discussed include the growth of speech/voice analytics, data lakes, big data and IoT analytics, the proliferation of analytic tools including open source tools, new visualization tools, full-pipeline analytics automation, and new decision systems using artificial intelligence.
The document discusses Luminar, an analytics company that uses big data and Hadoop to provide insights about Latino consumers in the US. Luminar collects data from over 2,000 sources and uses that data along with "cultural filters" to identify Latinos and understand their purchasing behaviors. This provides more accurate information than traditional surveys. Luminar implemented a Hadoop system to more quickly analyze this large amount of data and provide valuable insights to marketers and businesses.
The document discusses big data, including what it is, its history, current considerations, and importance. It notes that big data refers to large volumes of structured and unstructured data that businesses deal with daily. While the term is relatively new, collecting and storing large amounts of information for analysis has existed for a long time. Big data is now defined by its volume, velocity, and variety. Businesses can gain insights from big data analysis to make better decisions and strategic moves.
Fraud Detection with Graphs at the Danish Business AuthorityNeo4j
Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by forming fraud rings with individuals paid, lured into or unknowingly fronting these activities. To uncover such fraud rings and the people behind them, it is essential to look beyond individual data points to the connections that link them.
Neo4j uncovers difficult-to-detect patterns that far outstrip the power of a relational database. Enterprise organisations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering – and all in real time.
Learn more how to battle fraud with the power of graph databases during this webinar. We are pleased to invite you to hear Marius Hartmann from Danish Business Authority talking about how they are combining graph analysis with machine learning to prevent fraud. In context of the COVID-19 compensation scheme controls, he will present use cases currently in production and explain why graph is a good fit for government authorities.
The rising collection and analysis of data has shifted the way companies do business. Four key ingredients to develop a data strategy, how to leverage next-generation technologies, and three essential steps for rolling out implementation are included. The Data Ecosystem will show you how to develop and implement the strategies that will meet the needs of your business.
DataOps - Big Data and AI World London - March 2020 - Harvinder AtwalHarvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
This document discusses big data analytics projects and some of the challenges involved. It notes that while gaining insights from big data is desirable, it is difficult to do due to the volume, variety and velocity of data, as well as complexity. The document provides advice on questions businesses should consider when developing a big data analytics strategy and system, such as data timeliness, interrelatedness of data sources, historical data needs, and vendor experience. Understanding these issues is key to identifying the right technology to support a big data analytics initiative.
How Do You Improve Data Skills and Data Literacy in your Business?Bernard Marr
Data literacy should be a priority for every organization. Investing in data skills and data literacy is critical for all companies today. Most companies have a deficit in data skills that should be addressed as quickly as possible. There are several ways to improve data skills and data literacy in your business.
Trends in Big Data & Business Challenges Experian_US
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Sushil Pramanick – who is the founder and president of the The Big Data Institute (TBDI).
You can learn about upcoming chats and see the archive of past big data tweetchats here
http://www.experian.com/blogs/news/about/datadriven
How to keep pace with changing technology and increase speed-to-value. In order to keep pace in a constantly evolving marketplace, organizations need a new model for sourcin IT services. Sourcing has become one of the most critical functions of the IT organization.
This document outlines 10 business intelligence trends to expect in 2013. It predicts that organizations will recognize data stored in multiple places like Hadoop and transactional databases is optimal. Hadoop will become mainstream for large and unstructured data. Self-service BI will evolve to self-reliance as users gain access to the right data and solutions. The value of unstructured data like text will be recognized as techniques to analyze it mature. Cloud BI will become a primary analytics solution and visual analytics will become common for exploring and communicating data trends.
ADV Slides: Modern Analytic Data Architecture Maturity ModelingDATAVERSITY
Maturity frameworks have varying levels of Data Management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program.
Attendees can self-assess their current information management capabilities as we go through data strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity.
This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for achievement of improved information management maturity, aligned with major initiatives.
Big Data & Analytics Trends 2016 Vin MalhotraVin Malhotra
This document discusses several trends in analytics for 2016:
1. Data security is a major concern as data volumes grow exponentially and security risks increase. Analytics can help secure data but requires integration across innovation, analytics, connectivity and technology.
2. The Internet of Things generates massive sensor data that requires new analytics to extract value, though challenges remain in integrating sensor and structured data in real time.
3. Open source analytics solutions like Hadoop are increasingly used by enterprises but also require careful risk management and a clear strategy to ensure they align with technology needs.
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
Traditional data management approaches are inadequate to handle today's data growth and real-time business needs. Siloed technology stacks have become too complex, slow, and expensive. This is driving many enterprises to rethink their data strategies and build self-service platforms that can consolidate different data sources and provide faster, more flexible access to data. An end-to-end data architecture is needed to collapse silos, integrate data, and support real-time analytics and decision making across the business.
Seven Trends in Government Business IntelligenceTableau Software
Modern business intelligence (BI) trends in government for 2017 include:
1. Modern BI becoming the new normal as more governments adopt self-service analytics platforms.
2. The era of open data in government arriving as more data is released to the public.
3. Collaborative analytics growing from niche to essential as data becomes more accessible and cloud technology enables easier sharing.
4. Data-driven decision making exploding as people can more easily access and explore data to improve outcomes.
Modern Manufacturing: 4 Ways Data is Transforming the IndustryTableau Software
This document discusses how data is transforming the manufacturing industry in 4 key ways: 1) Improving production and plant performance with self-service analytics to empower employees, as shown by Tesla Motors; 2) Enhancing sales and operations planning with data blending and forecasting from multiple systems; 3) Mobilizing supply chains with real-time analytics accessed on mobile devices, as the Coca-Cola Bottling Co. has done; 4) Listening to customer feedback faster through data visualization and taking quicker action, as shown by Trane's improved response times.
This document summarizes the results of a survey of 298 data management professionals about big data challenges and opportunities. It finds that big data is now present in all organizations, with 11% managing over a petabyte and another 20% having hundreds of terabytes. While bigger companies are dealing with more data currently, most companies will soon have petabyte-sized stores. However, the business does not fully understand big data's potential value yet. Capitalizing on big data does not require replacing existing infrastructure but integrating it. The survey also finds that relational databases and Hadoop are both important technologies for managing big data now and in the future.
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.
Activating Big Data: The Key To Success with Machine Learning Advanced Analyt...Vasu S
A whitepaper of Qubole that How to make all of your data available to users for a multitude of use cases, ranging from analytics to machine learning and artificial intelligence.
https://www.qubole.com/resources/white-papers/activating-big-data-the-key-to-success-with-machine-learning-advanced-analytics
The document summarizes the top 10 trends in analytics according to Tiger Analytics. It discusses how data enrichment is allowing for better forecasts using internal and external data. It also covers how unstructured data like text, images, audio and video are opening new opportunities. Other trends discussed include the growth of speech/voice analytics, data lakes, big data and IoT analytics, the proliferation of analytic tools including open source tools, new visualization tools, full-pipeline analytics automation, and new decision systems using artificial intelligence.
The document discusses Luminar, an analytics company that uses big data and Hadoop to provide insights about Latino consumers in the US. Luminar collects data from over 2,000 sources and uses that data along with "cultural filters" to identify Latinos and understand their purchasing behaviors. This provides more accurate information than traditional surveys. Luminar implemented a Hadoop system to more quickly analyze this large amount of data and provide valuable insights to marketers and businesses.
The document discusses big data, including what it is, its history, current considerations, and importance. It notes that big data refers to large volumes of structured and unstructured data that businesses deal with daily. While the term is relatively new, collecting and storing large amounts of information for analysis has existed for a long time. Big data is now defined by its volume, velocity, and variety. Businesses can gain insights from big data analysis to make better decisions and strategic moves.
Fraud Detection with Graphs at the Danish Business AuthorityNeo4j
Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by forming fraud rings with individuals paid, lured into or unknowingly fronting these activities. To uncover such fraud rings and the people behind them, it is essential to look beyond individual data points to the connections that link them.
Neo4j uncovers difficult-to-detect patterns that far outstrip the power of a relational database. Enterprise organisations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering – and all in real time.
Learn more how to battle fraud with the power of graph databases during this webinar. We are pleased to invite you to hear Marius Hartmann from Danish Business Authority talking about how they are combining graph analysis with machine learning to prevent fraud. In context of the COVID-19 compensation scheme controls, he will present use cases currently in production and explain why graph is a good fit for government authorities.
The rising collection and analysis of data has shifted the way companies do business. Four key ingredients to develop a data strategy, how to leverage next-generation technologies, and three essential steps for rolling out implementation are included. The Data Ecosystem will show you how to develop and implement the strategies that will meet the needs of your business.
DataOps - Big Data and AI World London - March 2020 - Harvinder AtwalHarvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
This document discusses big data analytics projects and some of the challenges involved. It notes that while gaining insights from big data is desirable, it is difficult to do due to the volume, variety and velocity of data, as well as complexity. The document provides advice on questions businesses should consider when developing a big data analytics strategy and system, such as data timeliness, interrelatedness of data sources, historical data needs, and vendor experience. Understanding these issues is key to identifying the right technology to support a big data analytics initiative.
How Do You Improve Data Skills and Data Literacy in your Business?Bernard Marr
Data literacy should be a priority for every organization. Investing in data skills and data literacy is critical for all companies today. Most companies have a deficit in data skills that should be addressed as quickly as possible. There are several ways to improve data skills and data literacy in your business.
Trends in Big Data & Business Challenges Experian_US
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Sushil Pramanick – who is the founder and president of the The Big Data Institute (TBDI).
You can learn about upcoming chats and see the archive of past big data tweetchats here
http://www.experian.com/blogs/news/about/datadriven
How to keep pace with changing technology and increase speed-to-value. In order to keep pace in a constantly evolving marketplace, organizations need a new model for sourcin IT services. Sourcing has become one of the most critical functions of the IT organization.
This document outlines 10 business intelligence trends to expect in 2013. It predicts that organizations will recognize data stored in multiple places like Hadoop and transactional databases is optimal. Hadoop will become mainstream for large and unstructured data. Self-service BI will evolve to self-reliance as users gain access to the right data and solutions. The value of unstructured data like text will be recognized as techniques to analyze it mature. Cloud BI will become a primary analytics solution and visual analytics will become common for exploring and communicating data trends.
ADV Slides: Modern Analytic Data Architecture Maturity ModelingDATAVERSITY
Maturity frameworks have varying levels of Data Management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program.
Attendees can self-assess their current information management capabilities as we go through data strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity.
This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for achievement of improved information management maturity, aligned with major initiatives.
Big Data & Analytics Trends 2016 Vin MalhotraVin Malhotra
This document discusses several trends in analytics for 2016:
1. Data security is a major concern as data volumes grow exponentially and security risks increase. Analytics can help secure data but requires integration across innovation, analytics, connectivity and technology.
2. The Internet of Things generates massive sensor data that requires new analytics to extract value, though challenges remain in integrating sensor and structured data in real time.
3. Open source analytics solutions like Hadoop are increasingly used by enterprises but also require careful risk management and a clear strategy to ensure they align with technology needs.
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
Traditional data management approaches are inadequate to handle today's data growth and real-time business needs. Siloed technology stacks have become too complex, slow, and expensive. This is driving many enterprises to rethink their data strategies and build self-service platforms that can consolidate different data sources and provide faster, more flexible access to data. An end-to-end data architecture is needed to collapse silos, integrate data, and support real-time analytics and decision making across the business.
IBM's InfoSphere software helps organizations successfully leverage big data by providing an understanding of their data. It addresses the challenges of big data's four V's (volume, variety, velocity, and veracity) by automating data integration and governance. This helps boost confidence in big data by establishing standard terminology, tracing data lineage, and separating useful "good" data from unnecessary "bad" data. As a result, organizations can more accurately analyze big data and act on the insights with confidence.
Magenta advisory: Data Driven Decision Making –Is Your Organization Ready Fo...BearingPoint Finland
It’s nice to have loads of data. Nevertheless, many managers start to sweat when it comes to genuinely fact-based decision making. This study reveals the keys to leveraging big data successfully.
Big Data Means Big Business
Big data has the potential to disrupt existing businesses and help create new ones by extracting useful information from huge volumes of structured and unstructured data. To realize this promise, organizations need cheap storage, faster processing, smarter software, and access to larger and more diverse data sets. Big data can unlock new business value by enabling better-informed decisions, discovering hidden insights, and automating business processes. While the technology is available, organizations must also invest in skills, cultural change, and using information as a corporate asset to fully leverage big data.
Data foundation for analytics excellenceMudit Mangal
The document discusses predictive analytics and business insights. It covers what data analytics is and its challenges, the importance of data foundation and governance, security issues with data, and a retail use case. The future of data analytics is also discussed, with more structured, human interaction, and machine data expected to be analyzed. Establishing a robust data foundation is key to enabling trusted reporting and analytics.
This white paper discusses how organizations can transform big data into business value by connecting various data sources, analyzing data at scale, and taking action. It outlines the challenges of dealing with exponentially growing data in today's digital world. The paper introduces Actian's solutions for enabling an "action-driven enterprise" through its DataCloud Platform for invisible integration and ParAccel Platform for unconstrained analytics. These platforms allow organizations to connect diverse data, analyze it without constraints, and automate actions based on insights gleaned from big data analytics. Use cases demonstrate how companies are leveraging Actian's technology to gain competitive advantages.
This document summarizes key findings from a survey of 200 IT professionals about big data analytics. The main findings are:
- Big data and data center infrastructure updates are the top strategic priorities for most respondents. Big data is the number one priority for 21% of respondents.
- Most respondents have a formal big data analytics strategy in place or plan to have one within the next six months. The majority will have a strategy within a year.
- Three quarters of respondents are currently processing both structured and unstructured data or plan to within six months.
- Adoption of tools like Apache Hadoop continues to rise, with over half of respondents having deployed or implementing a Hadoop distribution, half of which use
This document summarizes key findings from a survey of 200 IT professionals about big data analytics. The main findings are:
- Big data and data center infrastructure updates are the top strategic priorities for IT managers. Big data is the number one priority for 21% of respondents.
- Most organizations already have a formal big data analytics strategy in place or plan to have one within the next six months. The majority will have a strategy within a year.
- Over half of respondents have already deployed or are currently implementing the Apache Hadoop framework. Half of those use an internal private cloud.
- The leading current uses of big data relate to understanding staffing levels and productivity, and generating competitive intelligence. Future uses
Big Data; Big Potential: How to find the talent who can harness its powerLucas Group
Big Data is in its infancy but it holds great promise. The key to success is finding and keeping the talent with the skills necessary to obtain and analyze the data, ask the right questions, and present findings in a compelling fashion that makes sense for your organization.
Big data is growing rapidly due to new sources of customer data from online platforms, mobile devices, and machine-to-machine communication. This creates challenges for companies around managing increasing data volumes, varieties, and velocities. The document discusses how some companies are using big data to better understand customers, increase profits, and gain a competitive advantage. It also notes that big data initiatives require business leadership and clear use cases to be successful.
Big Data - Bridging Technology and HumansMark Laurance
The document discusses big data and how organizations can leverage it. It defines big data and notes the rapid growth in data. It outlines five ways big data can create value for organizations, including making information more transparent and usable, improving performance through data collection, narrow customer segmentation, improved decision making, and better product development. The document also warns of a potential shortage of analytics talent as organizations seek to take advantage of big data.
Who needs Big Data? What benefits can organisations realistically achieve with Big Data? What else required for success? What are the opportunities for players in this space? In this paper, Cartesian explores these questions surrounding Big Data.
www.cartesian.com
Big Data projects require diverse skills and expertise, not a single person. Harnessing large and complex datasets can provide significant benefits for organizations, such as better decision making and new revenue opportunities, but also challenges. Successful Big Data initiatives require the right technology, skilled staff, and effective presentation of insights to decision makers. While technology enables exploitation of Big Data, information management practices and a mix of technical and analytical skills are needed to realize its full potential.
The objective of this module is to provide an overview of what the future impacts of big data are likely to be.
Upon completion of this module you will:
Gain valuable insight into the predictions for the future of Big Data
Be better placed to recognise some of the trends that are emerging
Acquire an overview of the possible opportunities your business can have with Big Data
Understand some of the start up challenges you might have with Big Data
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
1) The document discusses the Integrated Insights Platform, which breaks down data silos and integrates data from multiple sources to generate insights.
2) It addresses the challenges organizations face with data complexity and silos, and how doing nothing can be costly as it risks the organization becoming irrelevant.
3) The platform employs best-of-breed technology to source data internally and externally, apply advanced analytics, and provide real-time insights through customizable dashboards.
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Big Data Trends and Challenges Report - Whitepaper
1. 1
An In-Depth Look at Big Data
Trends and Challenges
Results from a Global Survey by Dimensional Research
August 2018
2. 1
Global Survey of Business,
IT and Big Data Professionals
O V E R V I E W
The realization of value from big data in on-premises environments has
lagged expectations, largely due to complexity, cost overruns, steep
demands for specialized talent, and need for powerful computing
platforms.
As a result, companies are rapidly moving big data processing to the
cloud, where powerful, cost-effective self-service platforms and elastic
computing are readily available.
The move to the cloud has turned big data into a profit center with use
cases centered around artificial intelligence (AI), machine learning (ML),
and analytics. But as new big data opportunities emerge, so do new
challenges.
Sponsored by Qubole, Dimensional Research launched a survey in
June 2018 of 401 data professionals with big data responsibilities in
enterprises across the globe.
This report provides insights into big data processing trends, challenges,
and solutions across enterprises worldwide.
Global survey
of big data
professionals
1
3. 2
Big Data is Skyrocketing in Size
As the digital world provides an ever-rising flood of
information, big data lakes in modern organizations are
growing to immense sizes and will continue to do so.
Forecasts call for future datasets that far exceed the
sizes of today’s big data repositories.
Organizations with Average Data Lake Size
Over 100 Terabytes
What size
is your
data lake?
4. 3
Types of Big Data Processing
Big data is a vital component of everyday information
processing and a key asset for enterprises. Enterprises
process big data continuously for multiple purposes,
from analytics to ML, to application integration and more.
Big data plays an essential role in delivering new insights,
innovating, increasing productivity, and realizing new
revenue opportunities.
Types of Big Data Processing Performed
What types
of big data
processing do
you perform?
5. 4
Extensive Enterprise Value from Big Data
Big data is being used across a wide and growing
spectrum of departments and functions in modern
organizations. The insights and intelligence provided by
big data translate directly into operational efficiencies
and competitive advantage.
Business Processes Receiving Value from Big Data
Over the
next 12 months,
which business
processes will
receive insights
from your
big data
initiatives?
6. 5
Sources Feeding the Big Data Firehose
Big data is pouring in from across the extended
enterprise, the Internet, and third-party data sources.
The staggering volume and diversity of the information
mandates the use of frameworks for big data processing.
Sources of Big Data
Which of these
data sources
does your
company use
in its big data
processing today?
7. 6
Big Data Frameworks Are Constantly Evolving
While no single software framework dominates the big
data landscape, Apache Spark and Presto are showing
impressive gains. Survey data also shows organizations
moving from homegrown approaches to open source
technologies.
Frameworks in Use in 2018
Which big data
software
framework
does your
company
currently use?
Percent Change from 2017
8. 7
Big Data Processing is Moving to the Cloud
The overwhelming majority of organizations are
moving their big data processing to the cloud to take
advantage of its convenience, cost, integration, and
performance benefits.
Organizations Using Cloud for Big Data Processing
Is your
big data
processing
performed
in the cloud?
9. 8
The Need for Self-Service Environments
By empowering users to build their own data
pipelines to perform analytics and utilize machine
learning, big data teams can control staffing costs and
raise the productivity of business professionals across
the enterprise.
Plans to Support Self-Service Big Data Analytics
Do you plan
to move to
a big data
self-service
analytics model?
10. 9
Not Enough Dedicated Tools and Resources
Three-quarters of survey respondents noted a sizable
gap between the dedicated tools and resources
available, and the potential value of their big data
projects. This mismatch triggers an inability to
transform data into profits and reduce associated
operational costs.
Gap Between Big Data Value and Dedicated Resources
Is there a gap
between the
potential value
of big data
and the tools
and people
dedicated
to deliver it?
11. 10
The Shortage of Big Data Talent is Very Real
As big data becomes a vital part of information
strategies, IT organizations would like to grow
headcount to meet project demands. But many IT
groups have flat budgets and face trouble finding
qualified big data talent. The answer lies in maximizing
the productivity of existing big data teams.
Big Data Headcount
Growth Plans
How much will
your big data
headcount
increase over
the next
twelve months?
Is it difficult
to find data
professionals with
the right skills
and experience?
Ease of Finding
Qualified Big Data Talent
12. 11
Big Data Admins Must Serve More Users
Only 40 percent of big data administrators are able to
support more than 25 users—a startling number, since
today’s flat budgets call for admins to serve more than
100 users. As big data strategies and implementations
mature, organizations require platforms that enable
much higher admin-to-user ratios.
What is your
current ratio
of big data
administrators
to users?
Users Supported per Big Data Administrator
13. 12
Challenges Faced by Big Data Teams
Big data strategies are not without their challenges,
which most often are triggered by overwhelming
data and computing demands that require special
technologies and skill sets. Most big data teams face
a variety of issues that need to be addressed with
education, headcount, and better tools.
Which of these
challenges
is your
big data team
experiencing
today?
Most Common Big Data Challenges
14. 13
Machine Learning Presents Its Own Obstacles
Machine learning is being used in diverse initiatives that
include crucial security, maintenance, customer-care,
and lead-generation applications. But like most data-
intensive solutions, machine learning presents a variety
of implementation challenges.
Most Common Machine Learning Obstacles
What are the
major obstacles
to your current
machine learning
objectives?
15. 14
Concerns About Big Data Time-to-Value
The most common complaints cited by executives and
stakeholders focus on the time and costs required to
derive real business value from big data initiatives.
These comments illuminate the need for smarter,
more productive platforms, tools, and technologists.
What is the
top complaint
you hear from
executives and
stakeholders
about big data
initiatives?
Common Complaints About Big Data Initiatives