La base para optimizar y potenciar la toma de decisiones en cualqueir empresa es la información. Pero no la información en bruto, sino aquella de la que podemos obtener valor tras su análisis.
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
1) Big data is defined as large volumes of structured and unstructured data that is growing exponentially. It can be analyzed to provide more accurate insights and better decision making.
2) The key aspects of big data are volume, velocity, variety, and variability of data from multiple sources.
3) Companies that effectively analyze big data can improve marketing ROI by 15-20% and increase productivity and profits by 5-6% over peers.
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
Leveraging Service Computing and Big Data Analytics for E-CommerceKarthikeyan Umapathy
Panel discussions on Leveraging Service Computing and Big Data Analytics for E-Commerce at the Workshop on e-Business (WeB) 2015 held on December 12, 2015 at Fort Worth, Texas, USA.
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASETAM Publications
Big Data is the extremely large sets of data that their sizes are beyond the ability of capturing, managing, processing and storage by most software tools and people which is ever increasing day-by-day. In most enterprise scenarios the data is too big or it moves too fast that extremely exceeds current processing capacity. The term big data is also used by vendors, may refer to the technology which includes tools and processes that an organization requires to handle the large amounts of data and storage facilities. This advancement in technology leads to make relationship marketing a reality for today’s competitive world. But at the same time this huge amount of data cannot be analyzed in a traditional manner, by using manual data analysis. For this, technologies such as data warehousing and data mining have made customer relationship management as a new area where business firms can gain a competitive advantage for identifying their customer behaviors and needs. This paper mainly focuses on data mining technique that performs the extraction of hidden predictive information from large databases and organizations can identify valuable customers and predicts future user behaviors. This enables different organizations to make proactive, knowledge-driven decisions. Data mining tools answer business questions that in the past were too time-consuming, this makes customer relationship management possible. For this in this paper, we are trying explain the use of data mining technique to accomplish the goals of today’s customer relationship management and Decision making for different companies that deals with 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
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
1) Big data is defined as large volumes of structured and unstructured data that is growing exponentially. It can be analyzed to provide more accurate insights and better decision making.
2) The key aspects of big data are volume, velocity, variety, and variability of data from multiple sources.
3) Companies that effectively analyze big data can improve marketing ROI by 15-20% and increase productivity and profits by 5-6% over peers.
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
Leveraging Service Computing and Big Data Analytics for E-CommerceKarthikeyan Umapathy
Panel discussions on Leveraging Service Computing and Big Data Analytics for E-Commerce at the Workshop on e-Business (WeB) 2015 held on December 12, 2015 at Fort Worth, Texas, USA.
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASETAM Publications
Big Data is the extremely large sets of data that their sizes are beyond the ability of capturing, managing, processing and storage by most software tools and people which is ever increasing day-by-day. In most enterprise scenarios the data is too big or it moves too fast that extremely exceeds current processing capacity. The term big data is also used by vendors, may refer to the technology which includes tools and processes that an organization requires to handle the large amounts of data and storage facilities. This advancement in technology leads to make relationship marketing a reality for today’s competitive world. But at the same time this huge amount of data cannot be analyzed in a traditional manner, by using manual data analysis. For this, technologies such as data warehousing and data mining have made customer relationship management as a new area where business firms can gain a competitive advantage for identifying their customer behaviors and needs. This paper mainly focuses on data mining technique that performs the extraction of hidden predictive information from large databases and organizations can identify valuable customers and predicts future user behaviors. This enables different organizations to make proactive, knowledge-driven decisions. Data mining tools answer business questions that in the past were too time-consuming, this makes customer relationship management possible. For this in this paper, we are trying explain the use of data mining technique to accomplish the goals of today’s customer relationship management and Decision making for different companies that deals with 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
This document discusses how businesses can use big data analytics to gain competitive advantages. It explains that big data refers to the massive amounts of data being generated every day from a variety of sources. By applying advanced analytics to big data, businesses can gain deeper insights into customer behavior and operations. The document provides examples of how industries like telecommunications, insurance, and entertainment are using big data analytics to improve customer service, detect fraud, and optimize marketing. It also outlines some of the key technologies that enable businesses to capture, store, and analyze big data at high volumes, velocities, and varieties.
Big Data in the Fund Industry: From Descriptive to Prescriptive Data AnalyticsBroadridge
NICSA’s Technology Committee, including Dan Cwenar, President, Access Data, Broadridge, offer perspectives on the “state of play” of Big Data in the fund industry:
The history of “ Big Data”
The definition of Big Data in the context of industry applications.
The movement from descriptive towards prescriptive analytics in driving decisions
Common misconceptions about the use of predictive analytics.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
Learn about Addressing Storage Challenges to Support Business Analytics and Big Data Workloads and how Storage teams, IT executives, and business users will benefit by recognizing that deploying appropriate storage infrastructure to support a wide range of business analytics workloads will require constant evaluation and willingness to adjust the infrastructure as needed. For more information on IBM Storage Systems, visit http://ibm.co/LIg7gk.
Visit the official Scribd Channel of IBM India Smarter Computing at http://bit.ly/VwO86R to get access to more documents.
This document provides guidance on responsible data collection and application to gain insights about consumers. It recommends focusing on first-party data through social login to get a comprehensive view of consumer identity across channels. It also suggests breaking down data silos by centralizing customer data and tying insights to key performance indicators to measure the impact of data-driven decisions and drive the business. Implementing these strategies can help marketers overcome challenges in accurately analyzing existing data and identifying the right data to collect.
This white paper discusses criteria for evaluating strategic analytics platforms. It identifies 5 key questions: 1) Does the platform combine consumer-like cloud services with sophisticated analytics? 2) Can it access all relevant data? 3) Can the entire analytical process be completed in a single tool? 4) Does it allow for analysis of big data? 5) Does it provide the right analytics for decision making? The document argues that an ideal platform seamlessly integrates data access, analysis, and sharing capabilities to support rapid, data-driven decisions.
Governing Big Data : Principles and practicesPiyush Malik
This document summarizes key points from a special issue of the IBM Journal that focuses on applications, analytics, software, and hardware technologies for massive-scale analytics and processing big data. It begins with an introduction that defines big data and provides examples of how it is used. The remainder of the document provides summaries of 15 articles in the special issue that cover topics like governing big data, trends in massive-scale analytics stacks, designing systems for big data workloads, platforms for extreme analytics, and using big data for applications like social network analysis and underwater acoustic monitoring.
This document discusses big data and data analytics. It begins with quotes about big data and references the story of blind men feeling different parts of an elephant. It then defines big data as datasets that are too large for traditional database tools. Several statistics are provided about the massive growth of data, such as the amount of data collected by the US Library of Congress. Potential value is cited from using big data in sectors like healthcare and public administration. Challenges of data monetization and extracting insights are discussed. The document introduces Rulex as a cognitive machine learning platform that can gain financial value from enterprise data through business decisions, data monetization between companies, and predictive models. It describes how Rulex can automate insight extraction from
The document discusses how most enterprises are investing in big data and real-time analytics initiatives to gain competitive advantages, but many IT organizations lack strategies to align these technologies with business goals. It describes how new data sources can provide richer customer insights and how real-time analytics can enable more timely operational decisions. However, organizations must evaluate whether their specific use cases require real-time data or would benefit more from traditional BI.
Enterprises are facing exponentially increasing amounts of data that is breaking down traditional storage architectures. NetApp addresses this "big data challenge" through their "Big Data ABCs" approach - focusing on analytics, bandwidth, and content. This enables customers to gain insights from massive datasets, move data quickly for high-speed applications, and securely store unlimited amounts of content for long periods without increasing complexity. NetApp's solutions provide a foundation for enterprises to innovate with data and drive business value.
Big data offers companies a big advantage if they can harness enormous data sets that were previously impossible to process. The document discusses how big data is transforming business models through creative destruction, as more data is created every day from various sources. It provides examples of how companies in various industries like retail, banking, and manufacturing are using big data for customer intimacy, product innovation, and improving operations. Specifically, companies are able to better customize products and services, improve supply chain management, and gain real-time insights from vast amounts of structured and unstructured data.
This document discusses worst practices in business intelligence (BI) implementations. It identifies the top four worst practices as: 1) assuming BI tools can be used by average business users, 2) allowing Excel to become the default BI platform, 3) assuming a data warehouse will solve all information needs, and 4) selecting a BI tool without a specific business need in mind. The document provides details on each worst practice and recommends solutions to avoid BI failures and achieve success, such as using BI platforms and applications tailored for business users rather than complex BI tools.
From hype to action getting what's needed from big data agwdeodhar
The document discusses the challenges companies face in realizing value from big data analytics. While big data holds potential for competitive advantage, most companies still struggle with managing vast amounts of data from various sources and finding ways to gain useful insights. Early adopters have found success, but full adoption of big data analytics remains limited due to challenges like lack of skills and understanding how insights can impact organizations. The document argues that in order to benefit, companies need solutions that easily manage the entire data workflow and provide insights to business users in a self-service manner.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
This document discusses how big data is shaping supply chain management. It begins with definitions of big data and a brief history. It then discusses how big data can provide value in supply chains through improved forecasting, optimization, and collaboration. Specific applications mentioned include demand forecasting, inventory management, and supplier performance monitoring. The document also identifies key sources of big data for supply chains like POS data, RFID, and manufacturing sensors. Finally, it discusses how organizations can become big data enabled in supply chain management and the future potential of big data.
How optimize the usage of data to driving innovation and efficiency, focused on Brazilian banking market landscape, highlighting main trends, key challenges, leverage managed data lakes and samples of use cases.
Dark Data Revelation and its Potential BenefitsPromptCloud
Dark data refers to the large amounts of unused data organizations collect during regular business activities. While organizations invest heavily in collecting data, much of it remains unused. There are three main types of dark data: existing unstructured internal data, non-traditional unstructured external data, and data available on the deep web. Analyzing dark data can provide valuable insights but also risks such as privacy issues. Some companies are already leveraging dark data for applications like fraud detection and personalization in retail. Approaching dark data requires getting the right data, augmenting with external sources, building data talent, and using advanced visualization tools.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
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
This document discusses how businesses can use big data analytics to gain competitive advantages. It explains that big data refers to the massive amounts of data being generated every day from a variety of sources. By applying advanced analytics to big data, businesses can gain deeper insights into customer behavior and operations. The document provides examples of how industries like telecommunications, insurance, and entertainment are using big data analytics to improve customer service, detect fraud, and optimize marketing. It also outlines some of the key technologies that enable businesses to capture, store, and analyze big data at high volumes, velocities, and varieties.
Big Data in the Fund Industry: From Descriptive to Prescriptive Data AnalyticsBroadridge
NICSA’s Technology Committee, including Dan Cwenar, President, Access Data, Broadridge, offer perspectives on the “state of play” of Big Data in the fund industry:
The history of “ Big Data”
The definition of Big Data in the context of industry applications.
The movement from descriptive towards prescriptive analytics in driving decisions
Common misconceptions about the use of predictive analytics.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
Learn about Addressing Storage Challenges to Support Business Analytics and Big Data Workloads and how Storage teams, IT executives, and business users will benefit by recognizing that deploying appropriate storage infrastructure to support a wide range of business analytics workloads will require constant evaluation and willingness to adjust the infrastructure as needed. For more information on IBM Storage Systems, visit http://ibm.co/LIg7gk.
Visit the official Scribd Channel of IBM India Smarter Computing at http://bit.ly/VwO86R to get access to more documents.
This document provides guidance on responsible data collection and application to gain insights about consumers. It recommends focusing on first-party data through social login to get a comprehensive view of consumer identity across channels. It also suggests breaking down data silos by centralizing customer data and tying insights to key performance indicators to measure the impact of data-driven decisions and drive the business. Implementing these strategies can help marketers overcome challenges in accurately analyzing existing data and identifying the right data to collect.
This white paper discusses criteria for evaluating strategic analytics platforms. It identifies 5 key questions: 1) Does the platform combine consumer-like cloud services with sophisticated analytics? 2) Can it access all relevant data? 3) Can the entire analytical process be completed in a single tool? 4) Does it allow for analysis of big data? 5) Does it provide the right analytics for decision making? The document argues that an ideal platform seamlessly integrates data access, analysis, and sharing capabilities to support rapid, data-driven decisions.
Governing Big Data : Principles and practicesPiyush Malik
This document summarizes key points from a special issue of the IBM Journal that focuses on applications, analytics, software, and hardware technologies for massive-scale analytics and processing big data. It begins with an introduction that defines big data and provides examples of how it is used. The remainder of the document provides summaries of 15 articles in the special issue that cover topics like governing big data, trends in massive-scale analytics stacks, designing systems for big data workloads, platforms for extreme analytics, and using big data for applications like social network analysis and underwater acoustic monitoring.
This document discusses big data and data analytics. It begins with quotes about big data and references the story of blind men feeling different parts of an elephant. It then defines big data as datasets that are too large for traditional database tools. Several statistics are provided about the massive growth of data, such as the amount of data collected by the US Library of Congress. Potential value is cited from using big data in sectors like healthcare and public administration. Challenges of data monetization and extracting insights are discussed. The document introduces Rulex as a cognitive machine learning platform that can gain financial value from enterprise data through business decisions, data monetization between companies, and predictive models. It describes how Rulex can automate insight extraction from
The document discusses how most enterprises are investing in big data and real-time analytics initiatives to gain competitive advantages, but many IT organizations lack strategies to align these technologies with business goals. It describes how new data sources can provide richer customer insights and how real-time analytics can enable more timely operational decisions. However, organizations must evaluate whether their specific use cases require real-time data or would benefit more from traditional BI.
Enterprises are facing exponentially increasing amounts of data that is breaking down traditional storage architectures. NetApp addresses this "big data challenge" through their "Big Data ABCs" approach - focusing on analytics, bandwidth, and content. This enables customers to gain insights from massive datasets, move data quickly for high-speed applications, and securely store unlimited amounts of content for long periods without increasing complexity. NetApp's solutions provide a foundation for enterprises to innovate with data and drive business value.
Big data offers companies a big advantage if they can harness enormous data sets that were previously impossible to process. The document discusses how big data is transforming business models through creative destruction, as more data is created every day from various sources. It provides examples of how companies in various industries like retail, banking, and manufacturing are using big data for customer intimacy, product innovation, and improving operations. Specifically, companies are able to better customize products and services, improve supply chain management, and gain real-time insights from vast amounts of structured and unstructured data.
This document discusses worst practices in business intelligence (BI) implementations. It identifies the top four worst practices as: 1) assuming BI tools can be used by average business users, 2) allowing Excel to become the default BI platform, 3) assuming a data warehouse will solve all information needs, and 4) selecting a BI tool without a specific business need in mind. The document provides details on each worst practice and recommends solutions to avoid BI failures and achieve success, such as using BI platforms and applications tailored for business users rather than complex BI tools.
From hype to action getting what's needed from big data agwdeodhar
The document discusses the challenges companies face in realizing value from big data analytics. While big data holds potential for competitive advantage, most companies still struggle with managing vast amounts of data from various sources and finding ways to gain useful insights. Early adopters have found success, but full adoption of big data analytics remains limited due to challenges like lack of skills and understanding how insights can impact organizations. The document argues that in order to benefit, companies need solutions that easily manage the entire data workflow and provide insights to business users in a self-service manner.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
This document discusses how big data is shaping supply chain management. It begins with definitions of big data and a brief history. It then discusses how big data can provide value in supply chains through improved forecasting, optimization, and collaboration. Specific applications mentioned include demand forecasting, inventory management, and supplier performance monitoring. The document also identifies key sources of big data for supply chains like POS data, RFID, and manufacturing sensors. Finally, it discusses how organizations can become big data enabled in supply chain management and the future potential of big data.
How optimize the usage of data to driving innovation and efficiency, focused on Brazilian banking market landscape, highlighting main trends, key challenges, leverage managed data lakes and samples of use cases.
Dark Data Revelation and its Potential BenefitsPromptCloud
Dark data refers to the large amounts of unused data organizations collect during regular business activities. While organizations invest heavily in collecting data, much of it remains unused. There are three main types of dark data: existing unstructured internal data, non-traditional unstructured external data, and data available on the deep web. Analyzing dark data can provide valuable insights but also risks such as privacy issues. Some companies are already leveraging dark data for applications like fraud detection and personalization in retail. Approaching dark data requires getting the right data, augmenting with external sources, building data talent, and using advanced visualization tools.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
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
This document provides an overview of how big data and data science can create value for banks. It discusses how banks generate large amounts of structured and unstructured data from various sources that can be analyzed to improve areas like fraud detection, customer churn analysis, risk management, and marketing campaign optimization. The document also provides case studies of how one company, InData Labs, has helped various banks leverage big data analytics to solve business problems in these areas.
Practical analytics john enoch white paperJohn Enoch
This document discusses using data analytics to provide value to businesses. It recommends starting with smaller, more manageable data sets and business intelligence (BI) projects that have clear goals and can yield quick wins, like analyzing travel costs. While big data holds promise, the author advises focusing first on consolidating existing data that is stuck in silos and using BI to improve processes and save costs in areas employees already know need improvement. Starting small builds skills for larger initiatives and ensures analytics provides practical benefits.
Big data analytics involves capturing, storing, processing, analyzing, and visualizing huge quantities of information from a variety of sources. This data is characterized by its volume, variety, velocity, veracity, variability, and complexity. Traditional analytics are not suited to handle big data due to its size and constantly changing nature. By analyzing patterns in big data, businesses can gain insights to improve processes and campaigns. However, specialized software is needed to make sense of big data's different types and formats from numerous sources. The right big data solution depends on an organization's specific data, budgets, skills, and future needs.
Thinking Small: Bringing the Power of Big Data to the MassesFlutterbyBarb
Thinking Small: Bringing the Power of Big Data to the Masses via Adobe with the results of improved access to insights, better user experiences, and greater productivity in the enterprise.
This document discusses how a big box retailer utilized big data to improve its business. It outlines the steps the retailer took:
1) It identified where big data could create advantages, such as predictive analytics to forecast sales declines. This would allow the retailer to be more proactive.
2) It built future capability scenarios to determine how to leverage big data, such as using social media data to predict problems.
3) It defined the benefits and roadmap for implementing big data, including investing millions over 5 years for a positive return. Benefits would include more consistent, faster information and insights.
The document provides details on how the retailer methodically planned and aligned its big data strategy to its business needs
Nuestar "Big Data Cloud" Major Data Center Technology nuestarmobilemarketing...IT Support Engineer
Nuestar Communications provides big data and cloud technology solutions to help organizations analyze large datasets and extract value from data. Their platform allows for tightly coupled data integration across various data sources and analytics to support the entire big data lifecycle. Nuestar helps clients address challenges around managing large and varied data, determining what data is most important, and using all of their data to make better decisions.
Effective Big Data Analytics Use Cases in 20+ IndustriesKavika Roy
Big data analytics enables business organizations to make sense of the data they are accumulating and leverage the insights drawn from it for various business activities. Know more about big data analytics, and its use cases.
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.
Big data is impacting individuals in several ways based on their digital footprint and interactions online. As more data is collected through mobile devices, social media, and ecommerce sites about things like demographics, preferences, and behaviors, companies can use analytics to better target marketing and tailor product offerings. This results in personalized ads and discounts. Additionally, employers are able to gain deeper insights into current and potential employees by analyzing data from HR systems, social profiles, and other sources to inform talent management, succession planning, and career predictions. So in many areas of life, big data collected from individuals online is being used to shape the experiences, opportunities, and interactions they receive.
Big data is playing an increasingly important role in the retail industry. The document discusses how retailers can use big data analytics to gain competitive advantages through improved marketing, merchandising, operations, supply chain management, and new business models. Specifically, big data enables retailers to better understand customer behavior, personalize offerings, optimize pricing and inventory, and process customer information in real-time to improve the shopping experience.
Big Data Update - MTI Future Tense 2014Hawyee Auyong
The Futures Group first wrote about the emerging phenomenon of Big Data in 2010 as it was about to enter the mainstream. It was envisaged that Big Data would create a demand for new skills (Google has identified statisticians as the “sexy job of the decade”) and generate new industries. This report updates on the industry value chain and business models for the data analytics industry, latest developments as well as the opportunities for Singapore.
Big Data is Here for Financial Services White PaperExperian
Conquering Big Data Challenges
Financial institutions have invested in Big Data for many years, and new advances in technology infrastructure have opened the door for leveraging data in ways that can make an even greater impact on your business.
Learn how Big Data challenges are easier to overcome and how to find opportunities in your existing data and scale for the future.
Modernizing Insurance Data to Drive Intelligent DecisionsCognizant
To thrive during a period of unprecedented volatility, insurers will need to leverage artificial intelligence to make faster and better business decisions - and do so at scale. For many insurers, achieving what we call "intelligent decisioning" will require them to modernize their data foundation to draw actionable insights from a wide variety of both traditional and new sources, such as wearables, auto telematics, building sensors and the evolving third-party data landscape.
Data is poised to play an important role in the enterprises of the future, with businesses looking to scale up production and recover costs. Visit: https://www.raybiztech.com/blog/data-analytics/what-are-big-data-data-science-and-data-analytics
Power from big data - Are Europe's utilities ready for the age of data?Steve Bray
European utilities are facing growing volumes of data from smart meters and grids, but many are not yet maximizing the value of the data. While utilities rate themselves highly in collecting data, nearly half say they do not consistently maximize its value. Strategies for leveraging big data are immature, with over 40% having no strategy or just beginning to develop one. Utilities will need to improve at analyzing large amounts of diverse data and developing new business models to gain competitive advantage from big data insights. Talent shortages, organizational silos, and a lack of standards also pose challenges to utilities effectively capturing value from big data.
1) Big data is becoming economically relevant as the volume of data generated and stored grows exponentially. It will transform our lives and become the basis of competition as companies use it to enhance productivity.
2) Leading companies are leveraging big data through advanced analytics to innovate, compete, and capture value across industries like healthcare, manufacturing, and retail. This will create new opportunities and categories of data-focused companies.
3) Consumers stand to significantly benefit from big data applications like smart routing which could save drivers over $500 billion annually through time and fuel savings by 2020.
Similar to Mejorar la toma de decisiones con Big Data (20)
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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Mejorar la toma de decisiones con Big Data
1. 1
It used to be that retailers could consider them-
selves customer friendly if they put charcoal on the
shelf next to beer and ketchup in the summer. Nat-
urally enough, customers who bought one of these
items would tend to buy the others as well.Today,
retailers have to do a bit more than that in the bat-
tle to find favor with customers. In e-commerce it
is now common practice for retailers to know what
their customers like, their order history, discount
preferences and, wherever possible, where they live
as well. But only if they know how to interpret this
data or interlink it intelligently are they able to
present customized offerings to their customers
and target them wherever they currently are –
online, at home on the sofa or on the roadr.
To do this, they need the right analytics tools – as
well as the right data for the purpose. In an era of
global competition and volatile markets, this data
is essential to decision-makers seeking to optimize
their business. It gives retailers, for instance, dedi-
cated information about the purchasing behavior
of their customers. For companies, data is the key
to understanding their markets better, uncovering
hidden trends and identifying new business oppor-
tunities in good time. Decisions can thus be made
more quickly and with greater precision – with
the aim of gaining a greater understanding of
customers and being better able to meet their
requirements.
Data analytics puts marketing departments, for
example, in the position of being able to create
fine-grained demographic or customer segments
and customize products and services to suit their
requirements. Detailed segmentation of target
groups makes it easier to address them, reduces
waste and thus cuts the cost of marketing cam-
paigns. A telecommunications provider, for
example, can use data analytics to find out why
customers are leaving and counter it with targeted
measures.
The role of data
Many decision-makers and executives now recog-
nize the strategic value of data, exploit relevant
sources of data that give them information about
their products and customers and use business
intelligence tools to analyze purchasing frequency
for different products or changes in stock levels,
for example. According to a study by software
vendor Artegic, 75 percent of companies believe
that they can be significantly more successful if
they make use of personal data obtained from
online marketing.
Business intelligence tools allow them to adapt and
control their business and adopt a well-targeted
approach. A company’s management benefits
significantly from the information obtained and
BETTER DECISIONS THROUGH
BIG DATA
EXECUTIVE BRIEFING
To enable the correct business decisions to be made quickly, large
quantities of structured and unstructured data now have to be analyzed.
Analytics using big data technologies helps us to find the right answers.
2. 2
can use it as a strategic compass to identify
changes in the market and customer behavior
in good time in order to be proactive.
Data becomes big data
But however many dashboards, graphics and tables
executives have, it doesn’t mean they can just sit
back and relax. In recent years, the world of busi-
ness intelligence has been really shaken up – trig-
gered by the sheer quantity of data. Not long ago,
the amount of information available on which to
base business decision-making was relatively easy
to grasp, but in the last few years it has simply bal-
looned. Everything is essentially now digitized, and
new types of transaction data and real-time data
are emerging. Machines and computers are also
producing enormous quantities of data, and this
can be stored and analyzed on hardware that is
becoming increasingly reasonably priced and
dynamic. A modern aircraft, for example, generates
up to 10 terabytes of data for every 30 minutes of
flying time.With 25,000 flights a day, petabytes
of data are generated.
The transition toward digital business models and
new applications is also contributing to data
growth.Technologies such as cloud computing,
RFID, transactional systems, data warehouses,
document management systems and enterprise
content management systems are important
developments in the context of big data. Many of
these systems are continuously generating new
data streams. However, the critical factors in this
explosion of data are the Internet, the increasing
number of mobile devices and, above all, social
media such as Facebook,Twitter andYouTube.
Facebook alone, for example, generates 2.7 billion
“likes” and 300 million photos a day and scans
105 TB of data every half hour.
In addition, not only are the sheer volumes of data
generated these days huge; the data is also signi
ficantly less structured than the typical kinds of
business data generated in ERP systems. Social
media information such as text, photographs, audio
files or videos can no longer be allocated tidily to
rows and columns, as required by the relational
database model: this data is unstructured. Accord-
ing to an IDC study of data storage in Germany in
2013, 90 percent of data is now unstructured and
has to be captured and analyzed using quite new
techniques. (Source: IDC Storage*)
What that means is that companies now have to
deal with large volumes of unwieldy structured,
semi-structured and unstructured data from many
different sources.
These days, companies can no longer ignore
unstructured data from social networks, in par
ticular. A great deal can be learned from emails,
feedback forms, comments and ratings in social
networks and discussions in forums.The huge
volume of tweets generated every day – currently
amounting to around 12 terabytes of data –
provides a solid basis for trend research or product
development.
Typical types of data today
Structured data Data that is suitable for the tables and structures of relational databases
Semi-structured data Data that is often generated as a result of data interchange between
companies and is therefore often based on XML
Unstructured data Data from text files, speech-to-text applications, PDFs, scanned mail,
presentations, photographs, videos, audio files
3. 3
Which industries benefit from big data?
Depending on the technology at their disposal, com-
panies can get relatively easy access to large volumes
of useful market and customer data – and they want
to extract as much value as they can from this data.
According to an international IDC study commis-
sioned byT-Systems, every second company has al-
ready implemented big data projects or has concrete
plans to do so. In anSAS survey, three out of every
four companies that had launched big data projects
described business analytics as an effective aid to
decision-making. (Source:SAS Decision Making*)
According to the study, they benefit most from
increased profitability, reduced costs, more tar
geted risk management, process optimization,
more rapid decision-making and performance
improvements.
The outlay associated with big data pays off in
terms of hard cash, according to McKinsey. If big
data is analyzed correctly and in good time, retail-
ers, for example, can improve their margins by up
to 60 percent, and European public authorities can
save 250 million euros a year through more effi-
cient processes, according to the consulting firm.
If companies knew more about the locations
of their customers, they would be able to sell
additional products worth 600 million dollars.
(Source: McKinsey Big Data*)
Whereas up until recently only banks, financial ser-
vices companies and selected large corporations –
typical users of data warehousing and business
intelligence – had given any thought to automated
decision-making processes, now, according to the
Experton Group, retailers, utility companies and
companies in the life sciences, healthcare industry
and many other markets are increasingly also
recognizing that data is an important business
asset. (Source: Experton Big Data*)
In terms of departments within companies, the
benefits are felt, above all, in research and develop-
ment, sales and marketing, production, distribution
and logistics and finance and risk management. In
these five areas the business benefits of big data
are particularly marked.
Analyzing big data
Despite the undisputed benefits, converting the data
collected into useful information is still a challenge for
many companies.According to market research com-
panyGartner, over 85 percent of Fortune 500 compa-
nies will not be in a position to use big data effectively
in order to secure a competitive advantage by 2015.
“In terms of technology and administration, most
companies are poorly prepared for the challenges
associated with big data,” say theGartner analysts.
“Consequently, only a few of them will be able to
exploit this trend effectively and secure themselves
a competitive advantage.” (Source:Gartner PI*)
Three factors – the sheer volume of data involved,
the heterogeneity of the data and the processing
speed required – present a major challenge com-
pared with conventional data processing and analy-
sis. Given their origins and architecture, relational
databases can only be used efficiently for applica-
tions involving frequent transactions at the level of
data records or for scenarios with low to moderate
volumes of data.They are not designed for the pro-
cessing and analysis of data quantities measured in
petabytes or exabytes. Above all, it is not possible,
or at least very difficult, to store unstructured data
in table-based relational database systems.
Given the increasing volumes of data available for
analysis, companies need new approaches and
technologies, according to Gartner in its study Big
Data Opportunities, New Answers and New Ques-
tions. (Source: Gartner Big Data*) Not only do new
“big data systems” have to cope with these huge
quantities of data, they also have to analyze un-
structured data reliably – and as quickly as possible.
These real-time analyses require systems with ex-
tremely fast database access and efficient parallel-
ization so that tasks can be distributed across large
numbers of computers – an approach known in the
past as grid computing.
Google has been the pioneer of big data tools for the
analysis of unstructured data.With its MapReduce
programming module, the company subdivides the
processing of huge volumes of data in such a way
that the infrastructure can be adapted with flexibi
lity, depending on the volumes of data involved.This
resulted in the popular open-source project Hadoop,
which is now the standard for big data technology
together with in-memory and NoSQL databases for
unstructured data. In the context of business appli
cations, SAP set things in motion with its SAP HANA
database (High-PerformanceAnalyticAppliance)
based on in-memory technology.
Big data analytics relies on models and algorithms
designed to search through mountains of data in
4. 4
order to find connections and identify patterns
and similarities. Not only do these predictive or
business analytics solutions help to quickly give
an accurate picture of the current situation, they
also permit predictions and forecasts about
future developments. This is done on the basis
Source:
How Organisations are
approaching Big Data,
IDG, September 2013
(200 decision-makers
from companies with
over 100 employees in
the USA, Brazil, the
Netherlands, Austria,
South Africa and
Switzerland)
Source:
How Organisations are
approaching Big Data,
IDG, September 2013
Business goals related to decision-making capabilities and agility/speed are significantly
connected to a majority of respondents’ big data strategies and initiatives.
Increasing speed of decision-making
Increasing business agility
Improving the quality of decision-making
Improving the speed of response to IT security issues
Improving planning and forecasting capabilities
Meeting regulatory/compliance requirements
New customer acquisition/retention
Using immediate market feedback to improve customer satisfaction
Building new business partnerships
Improving internal communication
Developing new products/services and revenue streams
Strengthening existing business partnerships
Improving finance/accounting and procurement processes
Reducing CAPEX
Reducing OPEX
35 34 23 5 3
35 32 26 5 3
31 37 28 2 2
31 31 29 6 3
29 35 28 4 3
26 33 30 8 3
26 33 27 8 5
26 32 32 6 4
25 34 32 6 4
25 32 35 5 3
25 32 34 6 3
25 29 35 6 4
23 30 33 9 5
19 23 41 12 5
18 28 41 8 5
(5)To a significant extent (4) (3)To a moderate extent (2) (1)To a limited extent
To what extent is your organization’s big data strategy/big data initiatives connected
to each of the following business goals?
Base: 155 qualified respondents who have implemented or have plans to implement big data projects (figures in percent)
About half of all respondents have either already deployed
or are in the process of implementing big data projects at their organizations.
Already deployed/implemented big data initiatives
In the process of implementing big data projects
Planning to implement big data projects over the
next 12 months
Planning to implement big data projects within the
next 13 – 24 months
We have no immediate plans to implement big data
projects
At what stage is your organization currently with the planning and rollout of big data projects?
Base: 200 qualified respondents (figures in percent)
25
23
21
10
23
of statistical and stochastic methods, data mod-
els and simulations with best- and worst-case
scenarios.
People with job titles such as “data scientist” are
required for this entirely new set of activities.
5. 5
How is meaningful information obtained from
large quantities of unstructuredTwitter and
Facebook text, video and consumer data? A lot
of work has to be done before the data that
finds its way into a company can be turned into
information on which executives can base their
decision-making. Countless selection, process-
ing and analysis steps are involved.
Based on the analysis of numerous case studies,
analytics expert Ken McLaughlin in his blog
“Data to Decisions” suggests six concrete steps
for data-driven decision-making using business
analytics.
Step 1: Establish a goal
A clearly defined goal must meet two re
quirements: It must be both achievable and
measurable. “Reduce product shipping costs
by 15 percent” would be a clearly formulated
goal, for example.
Step 2: Model alternatives
The goal determines the direction, the alterna-
tives and how the goal is to be achieved. Exam-
ple: “Costs of a reasonably priced shipper” ver-
sus “costs of an automated handling process”
would be possible alternatives.
Step 3: Identify the required data
Identify the data and metrics required to model
the alternative. In the example: previous ship-
ping costs and software and hardware costs for
automated processes.
Step 4: Collect and organize data
Before the models can be evaluated, data has
to be collected and organized.
Step 5: Analyze data
To evaluate the data, the appropriate analytical
techniques and then the best alternative have
to be selected.
Step 6: Decide and execute
Finally, the action that delivers the best results
should be executed and the real results observed.
What are the risks?
A central question in connection with big data is
that of data quality. Does data occur more than
once, does it contain errors or inconsistencies, or
are entire records missing? Users are generally
aware of the importance of this question, as a study
by Omikron Data Quality shows.Thirty-nine per-
cent of those surveyed said they believed that a
big data approach is condemned to failure if the
data is of poor quality.
“It is clear that, when there is a larger volume of
data, statistical significance increases and the re-
sults of BI analytics are more reliable,” according to
the study. “However, if the initial data is incorrect,
duplicated or inconsistent, this significance is mis-
leading: in the worst-case scenario, you get appar-
ently clear results that are mathematically sound –
but in fact incorrect. If actions are then taken based
on the results of analytics, which is, of course, the
goal of BI, negative consequences are inevitable.”
(Source: Omikron Data Quality*).
If the analyses and forecasts are to be accurate,
the foundation (i.e., the data) must be correct. In
typical BI, there are proven processes and methods
in the ETL (extract, transform, load) process for
tidying up data before the information is stored
in the data warehouse.These include profiling,
cleansing, enriching and comparing with reference
data.
Data to Decisions: the six steps
6. The challenge of data silos
A further fundamental challenge (or key question)
when dealing with big data is the distribution of the
data to parallel systems. On the one hand, for his-
torical reasons, data silos – from CRM, ERP or other
systems, for example – have mastered the architec-
ture of data storage and increasingly also have to
handle the archiving of historical data. On the other
hand, given rising data volumes, many companies
merely allocate the data flooding in to different
storage locations – without processing or trans-
forming it beforehand.
These distributed and heterogeneous data process-
ing and storage structures are neither cost effective
nor expedient for potential data analyses.They
prevent the exchange and integration of data and
make it difficult to maintain a holistic view of data
management.
Modern integration technologies can be used here
that turn the structured, unstructured and semi-
structured data from a variety of sources into an
integral part of the enterprise-wide data manage-
ment strategy.
To this end, software solutions tap sources of data
throughout the company, read and extract it and
load it into the storage system provided. In the next
step, this data is loaded into data models, enriched
with further data from other sources and then ana-
lyzed. Cloud-based systems help to provide storage
capacity for large volumes of data.
No big data without skilled staff
Successful big data analytics requires not just suit-
able technologies but also skilled staff. Big data an-
alytics can only be implemented with the help of
highly qualified specialists who can handle the rele-
vant tools and technologies and are also able to un-
derstand the requirements of specific departments
and ensure that the technology that is put in place
meets these requirements.
For some time now, a chief data officer (CDO) has
been included in the list of C-level executives in
many US companies.The focus of the CDO’s activi-
ties is on managing data as an asset and converting
it into something with a concrete business value.
Capital One appointed the first CDO in the industry
in the year 2003.
Since then, CDOs have become increasingly com-
mon in lists of top executives, above all in large
public institutions that are overwhelmed with data.
According to Gartner, there are CDOs in 2 percent
of companies around the world and in 6 percent of
large companies.This is forecast to increase to 20
percent of large companies by 2017. In Europe the
CDO is still relatively unknown.Whether it is really
necessary to establish a CDO is a matter of debate,
particularly since the role is not precisely defined.
However, there is an urgent need for big data
experts who are able to work with data effectively.
These IT experts have to have different skills from
those required for conventional IT systems. In addi-
tion to meeting the technical requirements, these
specialists must be able to work with statistical and
stochastic methods as well as analytical models and
have sound industry expertise.
The Experton Group therefore demands that new
types of jobs are created with titles such as data
scientist or data artist.The data scientist is the data
expert who selects the analytical methods and
analyzes the data. A data scientist requires a good
general education with knowledge of mathematics
and stochastics, programming fundamentals,
SQL and databases, information technology and
networks.
Presentation and visualization of the data is then
handled by the data artist, whose training includes
graphic design, psychology, some mathematics,
IT and communications.These jobs form what you
might call the core of big data staff. Other new jobs
are being added to this core group.The table on the
next page shows all of these.
6
7. 7
Big data job descriptions
Position Responsibilities Required expertise
Data scientist Decides which forms of analysis
are most suitable and which
raw data is required and then
analyzes it
Mathematics, stochastics, pro-
gramming, SQL and databases,
information technology and
networks
Data artist Presents the analyses clearly in
the form of charts and graphics
Graphic design, psychology,
mathematics, IT and communi
cations
Data architect Creates data models and decides
which analytical tools are to be
used
Databases, data analysis, BI
Data engineer Looks after the hardware and
software, in particular the ana
lytical systems and the network
components
Hardware and software
knowledge, programming
Information broker Obtains information and makes it
available, for example by providing
customer data or in-house data
from a variety of sources
Databases, communications,
psychology
Who is going to train big data specialists?
Until now, however, companies have hardly ever
been able to call on staff resources like these. “Data
scientist and data artist are jobs for which a two- to
three-year period of training would be required, but
due to the cross-cutting nature of the work, they
scarcely exist today,” says Holm Landrock, a senior
advisor at the Experton Group.
Only a few companies and organizations are com-
mitted to training data scientists and data artists in
any way, but what they offer is far from a compre-
hensive program of training. IT Companies such as
SAS, EMC and Oracle do offer training in this direc-
tion.The Fraunhofer also offers training for data
scientists.
But short courses like this are just a drop in the
ocean.The Experton Group therefore recommends
that the ICT industry should get together with
education providers – such as vocational acade-
mies, technical colleges, industry associations and
chambers of industry and commerce – to create
new job profiles as quickly as possible.Training
staff for a role as a data scientist or one of the other
new jobs types is not some kind of Good Samaritan
project but a foundation stone for future big data
projects and the resulting sustainable business
success.
8. 8
What big data solutions exist?
There is no standard solution, but some processing
methods have emerged in recent years that serve
as the basis for big data analytics today and will
continue to do so in the next few years.
The ideal solution for coming to grips with huge
volumes of data is the old principle of “divide and
conquer”. Arithmetic calculations are subdivided
into many small calculations and distributed to
multiple servers. Google’s MapReduce algorithm
has emerged as the de facto standard for distri
buted computing. A typical MapReduce application
calculates multiple terabytes of data on thousands
of machines.
MapReduce is implemented in practice by means of
the software library Apache Hadoop. By subdivid-
ing the data into smaller chunks and processing
them in parallel on standard computers, Hadoop
has emerged as the current industry standard for
big data environments.
The Chinese mobile phone provider China Mobile,
for example, was able to use Hadoop to analyze the
phone usage of all of its customers and the proba-
bility of them churning.The “scale-up” solution it
was using prior to this enabled the company to
analyze the data of only around ten percent of its
customers. Now, however, all customer data can
be taken into account, and targeted marketing
measures have been introduced to reduce churn.
Source:
How Organisations
are approaching
Big Data, IDG,
September 2013
In-memory permits real-time analytics
However, a Hadoop cluster is not capable of han-
dling all big data tasks. If the data is on a hard disk,
slow database accesses cannibalize the gains made
through parallelization.This is why in-memory
databases have established themselves for the
accelerated processing of extremely large quan
tities of data.These databases store the data in
working memory (RAM) and call it from there.
That makes them faster than that use conventional
disk technology by a factor of around 1,000.
To obtain the maximum in terms of performance,
wherever possible in-memory databases therefore
load the entire volume of data – together with the
database applications – into main memory, which
has to be large enough to cope. Business data ana-
lytics can thus be carried out virtually in real time
rather than taking days or weeks.
SAP’s highly popular HANA (High-Performance
Analytic Appliance), for example, a database
About two-thirds of respondents are extremely/very likely to consider using
or to continue to use in-memory databases.
In-memory databases
(e.g., SAP HANA, Oracle Exadata)
Log file analysis software
NoSQL databases
Columnar databases
Hadoop/MapReduce
(5) Extremely likely (4)Very likely (3) Somewhat likely (2) Not very likely
(1) Not at all likely Not familiar with this type of solution
How likely are you to consider using or to continue to use each of the following big data solutions?
Base: 155 qualified respondents who have implemented or have plans to implement big data projects
(figures in percent)
28 38 15 9 3
20 32 26 10 3
20 31 26 9 7
17 28 28 12 4
15 25 26 12 6
6
9
6
11
15
9. 9
system based on in-memory technology, was
unveiled as a high-performance platform for the
analysis of large volumes of data in mid-2010 by
Hasso Plattner and SAP technology bossVishal
Sikka. Database specialist Oracle also now offers
a database system based on in-memory techno
logy: Exadata.
In-memory databases are no longer a niche
product. According to a study by TNS-Infratest
commissioned by T-Systems, 43 percent of
German companies are already using in-memory
technologies for data analytics or plan to do so
in the near future. Ninety percent of users say
their experience with the technology has been
good or very good. (Source: T-Systems New
Study*)
However, the majority of German companies
regard in-memory technology as complementary
to time-critical analytics as things stand. But almost
20 percent of companies see it as an important
response to the challenges of big data.They expect
in-memory systems to become a central element
of data analytics environments.
In addition, there are technologies such as NoSQL
databases for unstructured data. NoSQL is the
collective term for “non-relational” database
systems and also the term used to describe a shift
away from relational databases to new or forgotten
database models. NoSQL database systems are
an efficient way to store and process unstructured
data such as text, audio files, videos and photo-
graphic material.
Source:
How Organisations are
approaching Big Data,
IDG, September 2013
Overall, respondents believe that in-memory databases best address big data’s
challenges, but there are significant differences by region.
Which of the following solutions do you believe would best address the challenges associated with big data?
Base: 147 qualified respondents who are familiar with two or more big data solutions shown in Q.3
(figures in per cent)
Make or buy?
The current market situation for big data solutions
presents a final challenge on the way to big data
success. Numerous providers are offering software
tools based on Hadoop.These include Cloudera,
Hortonworks, Datameer and HStreaming as well as
big names such as IBM, Intel and EMC. But they are
all coming up against the same limitation: none of
them have standardized industry solutions that can
be customized quickly to suit customers’ require-
ments.They often have to specially develop these
systems in joint projects together with their cus-
tomers.
Companies wanting to use the technology are
faced with a typical “make or buy” decision.When
analytics is carried out on a one-off basis, or there
Respondents in EMEA are significantly more likely to favor in-memory databases (60%),
compared to only 22% in the US and 14% in Brazil.
In-memory databases
(e.g., SAP, HANA, Oracle Exadata)
NoSQL databases
Log file analysis software
Columnar databases
Hadoop/MapReduce
Not sure
30
19
15
12
11
14