To Become a Data-Driven Enterprise, Data Democratization is EssentialCognizant
The document discusses how data democratization through an insights marketplace is essential for organizations to become truly data-driven. It defines data democratization as making data accessible across business lines through self-service analytics and predictive platforms. An insights marketplace allows internal users and partners to search, access, and subscribe to shared data assets like reports, models, and raw data. This facilitates collaboration, reduces duplication of efforts, and can help organizations monetize their data internally through improved products and efficiency or externally through partnerships. Examples of Transport for London and educational institutions successfully applying these approaches are provided.
This document discusses the future of big data, including predictions such as machine learning becoming prominent and data scientists being in high demand. It outlines trends like the growth of open source technologies, in-memory computing, machine learning, predictive analytics, intelligent applications, integrating big data with security and the internet of things. Challenges mentioned include dealing with large amounts of data from IoT and high salaries for data professionals.
HPE Building Intelligent & Autonomous Datacenters for the Data EconomySamarn Pannue
Automation is becoming a critical need IDC’s Enterprise Datacenter survey 2018 shows that 45% of the respondents are demanding more shared, agile and flexible resources in the digital transformation era. As a result, IDC sees software defined
and automation becoming the top infrastructure priority for the CIO.
AI will also play a huge role in enabling datacenter automation with enhanced self-learning and predictive capabilities to continuously stay ahead of the problems that cause datacenter failure.
Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?Capgemini
This document is a point of view on how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back. The PoV explores these challenges and suggests actions for banks in order to scale-up to the next level of customer data analytics.
This document provides an overview of predictive analytics and its growing importance. It discusses how advances in technologies like cloud computing and the internet of things are enabling businesses to gather and analyze vast amounts of data. While descriptive and diagnostic analytics describe what happened in the past, predictive analytics uses statistical techniques to create models that forecast future outcomes. The document outlines several key drivers that are pushing predictive analytics towards mainstream adoption over the next few years, including easier-to-use tools, open source software, innovation from startups, and the availability of cloud-based solutions. It concludes that the combination of big data and predictive analytics will continue to accelerate innovation across industries.
Going Big : Why Companies Need to Focus on Operational Analytics Capgemini
Over 70% of organizations surveyed now focus more on operational analytics than customer analytics. While operational analytics offers large potential benefits, few organizations are realizing that potential. Only 18% of organizations have extensively integrated operational analytics across business processes and achieved desired objectives, termed "Game Changers". Game Changers have a robust data strategy including integrated datasets, use of external and unstructured data, and high utilization of operations data. They have made analytics essential to operational decision making.
To Become a Data-Driven Enterprise, Data Democratization is EssentialCognizant
The document discusses how data democratization through an insights marketplace is essential for organizations to become truly data-driven. It defines data democratization as making data accessible across business lines through self-service analytics and predictive platforms. An insights marketplace allows internal users and partners to search, access, and subscribe to shared data assets like reports, models, and raw data. This facilitates collaboration, reduces duplication of efforts, and can help organizations monetize their data internally through improved products and efficiency or externally through partnerships. Examples of Transport for London and educational institutions successfully applying these approaches are provided.
This document discusses the future of big data, including predictions such as machine learning becoming prominent and data scientists being in high demand. It outlines trends like the growth of open source technologies, in-memory computing, machine learning, predictive analytics, intelligent applications, integrating big data with security and the internet of things. Challenges mentioned include dealing with large amounts of data from IoT and high salaries for data professionals.
HPE Building Intelligent & Autonomous Datacenters for the Data EconomySamarn Pannue
Automation is becoming a critical need IDC’s Enterprise Datacenter survey 2018 shows that 45% of the respondents are demanding more shared, agile and flexible resources in the digital transformation era. As a result, IDC sees software defined
and automation becoming the top infrastructure priority for the CIO.
AI will also play a huge role in enabling datacenter automation with enhanced self-learning and predictive capabilities to continuously stay ahead of the problems that cause datacenter failure.
Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?Capgemini
This document is a point of view on how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back. The PoV explores these challenges and suggests actions for banks in order to scale-up to the next level of customer data analytics.
This document provides an overview of predictive analytics and its growing importance. It discusses how advances in technologies like cloud computing and the internet of things are enabling businesses to gather and analyze vast amounts of data. While descriptive and diagnostic analytics describe what happened in the past, predictive analytics uses statistical techniques to create models that forecast future outcomes. The document outlines several key drivers that are pushing predictive analytics towards mainstream adoption over the next few years, including easier-to-use tools, open source software, innovation from startups, and the availability of cloud-based solutions. It concludes that the combination of big data and predictive analytics will continue to accelerate innovation across industries.
Going Big : Why Companies Need to Focus on Operational Analytics Capgemini
Over 70% of organizations surveyed now focus more on operational analytics than customer analytics. While operational analytics offers large potential benefits, few organizations are realizing that potential. Only 18% of organizations have extensively integrated operational analytics across business processes and achieved desired objectives, termed "Game Changers". Game Changers have a robust data strategy including integrated datasets, use of external and unstructured data, and high utilization of operations data. They have made analytics essential to operational decision making.
Big Data Analytics for Banking, a Point of ViewPietro Leo
This document discusses how big data and analytics can transform the banking industry. It notes that digital transformation, enabled by big data and analytics, is creating pressures on banks from new digital native customers, large amounts of new data, new channels like mobile, and new competitors. It argues that to succeed in this new environment, banks need to build a 360-degree integrated customer view using big data, and ensure analytics are part of closed-loop business processes to create value. New applications and platforms like IBM Watson Analytics aim to make analytics more accessible and valuable to more users.
Evaluating the opportunity for embedded ai in data productivity toolsNeil Raden
The document discusses embedding AI in data productivity tools. It notes that while AI applications get attention, opportunities to embed AI in tools to manage and interpret data may be overlooked. This could help address challenges in preparing large amounts of clean, AI-ready data at scale. The document summarizes experts' views, many of which support embedding AI in data pipelines and tools to improve data quality, integration and analytics. It provides examples of companies like Informatica, UnifiSoftware, Trifacta and Tamr that are infusing AI into their data integration and catalog offerings. The document argues that opportunities for AI to improve data management could boost overall AI success rates.
Banks are increasingly recognizing the importance of customer centricity and analyzing customer data, but many are struggling to maximize the value of their data due to legacy systems, a lack of analytics talent and skills, and privacy concerns. Banks need to establish robust data management frameworks, develop analytics talent through targeted recruitment and training, and promote a culture where data is viewed as a key asset for decision-making rather than just an IT project.
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
Big Data BlackOut: Are Utilities Powering Up Their Data Analytics?Capgemini
Analytics is seeing greater recognition amongst utility executives. Our research showed that 80% of utilities consider big data analytics as a source of new business opportunities and 75% see it as crucial for future success. Big Data indeed offers an exciting opportunity to transform utility operational effectiveness, while at the same time dealing with the historical problem of low customer satisfaction. Take operational efficiency alone. The annual cost of weather-related power outages to the U.S. economy is estimated to be between $18 billion to $33 billion. Organizations can use Big Data analytics to detect operational challenges and prevent outages, substantially reducing costs. Big Data also affords opportunities to utilities for inventing new business models through the data generated by the smart infrastructure.
The analytics opportunity for utilities is clear, but there continues to be a lack of real impetus and value delivery. Only 20% have already implemented big data analytics initiatives. What is putting the brakes on utilities?
In this paper, we highlight the big data opportunities that utilities can leverage and identify the challenges that are currently holding them back. We conclude the paper with concrete recommendations on how to ensure analytics drive business value.
The Work Ahead in Intelligent Automation: Coping with Complexity in a Post-Pa...Cognizant
Intelligent automation continues to be a top driver of the future of work, according to our recent study. To reap the full advantages, businesses need to move from isolated to widespread deployment.
Close the AI Action Gap in Financial ServicesCognizant
Financial institutions are making progress with AI but have been slow to scale it across their organizations, resulting in an "AI action gap". To close this gap, the article recommends four steps:
1. Identify universal use cases that are well-defined to build AI expertise.
2. Improve data management capabilities, which AI relies on, by developing intelligent data tagging strategies and integrating fragmented systems.
3. Move beyond experimentation to fully implementing more AI initiatives to realize benefits across the enterprise.
4. Mitigate unintended consequences by creating responsible AI applications.
Following these steps can help financial institutions maximize the business value and ROI of AI.
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.
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalCognizant
Utilities are starting to adopt digital technologies to eliminate slow processes, elevate customer experience and boost sustainability, according to our recent study.
In this edition of our Work Ahead study, we explore the increasing primacy of digital within the context of the COVID-19 pandemic and assess what’s next for the future of work.
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.
The Path to Manageable Data - Going Beyond the Three V’s of Big DataConnexica
This document discusses how businesses can gain value from big data through effective analysis and actionable insights. It outlines the traditional "3 Vs" of big data (volume, velocity, variety) and additional "Vs" like veracity, variability, visualization, and value. Effective business analytics software can help validate data quality, analyze diverse data formats, and present insights visually for quick decision making. The document also provides examples like how a local authority used analytics software to transform large volumes of parking, service, and tax records into actionable reports.
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.
EU Data Market study. Presentation at NESSI Summit 2014 IDC & Open EvidenceKasia Szkuta
The study aims to define, assess and measure the European data economy as well as build a genuine stakeholders’ ecosystem. Find us on http://datalandscape.eu and @eudatalandscape
In partnership with IDG, our 2022 Insight Intelligent Technology™ Report examines how companies are making progress on long-term IT strategies to meet the changing, post-pandemic expectations of their businesses, their employees, and the market more broadly.
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
Internet of things: Accelerate Innovation and Opportunity on top The 3rd Plat...Son Phan
The document discusses the growth of technologies like the Internet of Things (IoT) and how they are driving major changes in business and society. It notes that by 2020, IoT technologies will represent the majority of ICT spending growth and will create $19 trillion in economic value over the next 10 years. The IoT is creating new opportunities for businesses to optimize operations, develop new revenue streams from data insights, and transform customer interactions. Key industries like retail, transportation and healthcare will be impacted as physical systems become connected and integrated with digital systems and data analytics. The rise of IoT requires organizations to rethink their strategies and ecosystems to capitalize on emerging opportunities.
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
Patrick Couch - Intelligenta Maskiner & Smartare Tjänster IBM Sverige
Industriföretag, såväl tillverkare som användare av maskiner, fordon och utrustning, står inför ett paradigmskifte drivet av ökad global konkurrens, kunders förändrade efterfrågan samt det faktum att produkterna nu blir instrumenterade, ihopkopplade och mer intelligenta. Stora datamängder är inte ett buzzword för dessa företag, utan en reell verklighet som de behöver förhålla sig till för att säkra sin framtida verksamhet. I bästa fall omvandlar dessa företag denna teknologiska revolution (populärt kallad Internet of Things, Industrial Internet, M2M, Industri 4.0 etc.) till en motor för att utveckla verksamheten mot tillväxt och effektivare produktion. Detta skifte skapar framförallt stora möjligheter att förflytta sig mot leveranser av tjänster som kraftigt ökar mervärdet för kunderna, deras kunders kunder samt för producenten.
Big Data Analytics for Banking, a Point of ViewPietro Leo
This document discusses how big data and analytics can transform the banking industry. It notes that digital transformation, enabled by big data and analytics, is creating pressures on banks from new digital native customers, large amounts of new data, new channels like mobile, and new competitors. It argues that to succeed in this new environment, banks need to build a 360-degree integrated customer view using big data, and ensure analytics are part of closed-loop business processes to create value. New applications and platforms like IBM Watson Analytics aim to make analytics more accessible and valuable to more users.
Evaluating the opportunity for embedded ai in data productivity toolsNeil Raden
The document discusses embedding AI in data productivity tools. It notes that while AI applications get attention, opportunities to embed AI in tools to manage and interpret data may be overlooked. This could help address challenges in preparing large amounts of clean, AI-ready data at scale. The document summarizes experts' views, many of which support embedding AI in data pipelines and tools to improve data quality, integration and analytics. It provides examples of companies like Informatica, UnifiSoftware, Trifacta and Tamr that are infusing AI into their data integration and catalog offerings. The document argues that opportunities for AI to improve data management could boost overall AI success rates.
Banks are increasingly recognizing the importance of customer centricity and analyzing customer data, but many are struggling to maximize the value of their data due to legacy systems, a lack of analytics talent and skills, and privacy concerns. Banks need to establish robust data management frameworks, develop analytics talent through targeted recruitment and training, and promote a culture where data is viewed as a key asset for decision-making rather than just an IT project.
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
Big Data BlackOut: Are Utilities Powering Up Their Data Analytics?Capgemini
Analytics is seeing greater recognition amongst utility executives. Our research showed that 80% of utilities consider big data analytics as a source of new business opportunities and 75% see it as crucial for future success. Big Data indeed offers an exciting opportunity to transform utility operational effectiveness, while at the same time dealing with the historical problem of low customer satisfaction. Take operational efficiency alone. The annual cost of weather-related power outages to the U.S. economy is estimated to be between $18 billion to $33 billion. Organizations can use Big Data analytics to detect operational challenges and prevent outages, substantially reducing costs. Big Data also affords opportunities to utilities for inventing new business models through the data generated by the smart infrastructure.
The analytics opportunity for utilities is clear, but there continues to be a lack of real impetus and value delivery. Only 20% have already implemented big data analytics initiatives. What is putting the brakes on utilities?
In this paper, we highlight the big data opportunities that utilities can leverage and identify the challenges that are currently holding them back. We conclude the paper with concrete recommendations on how to ensure analytics drive business value.
The Work Ahead in Intelligent Automation: Coping with Complexity in a Post-Pa...Cognizant
Intelligent automation continues to be a top driver of the future of work, according to our recent study. To reap the full advantages, businesses need to move from isolated to widespread deployment.
Close the AI Action Gap in Financial ServicesCognizant
Financial institutions are making progress with AI but have been slow to scale it across their organizations, resulting in an "AI action gap". To close this gap, the article recommends four steps:
1. Identify universal use cases that are well-defined to build AI expertise.
2. Improve data management capabilities, which AI relies on, by developing intelligent data tagging strategies and integrating fragmented systems.
3. Move beyond experimentation to fully implementing more AI initiatives to realize benefits across the enterprise.
4. Mitigate unintended consequences by creating responsible AI applications.
Following these steps can help financial institutions maximize the business value and ROI of AI.
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.
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalCognizant
Utilities are starting to adopt digital technologies to eliminate slow processes, elevate customer experience and boost sustainability, according to our recent study.
In this edition of our Work Ahead study, we explore the increasing primacy of digital within the context of the COVID-19 pandemic and assess what’s next for the future of work.
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.
The Path to Manageable Data - Going Beyond the Three V’s of Big DataConnexica
This document discusses how businesses can gain value from big data through effective analysis and actionable insights. It outlines the traditional "3 Vs" of big data (volume, velocity, variety) and additional "Vs" like veracity, variability, visualization, and value. Effective business analytics software can help validate data quality, analyze diverse data formats, and present insights visually for quick decision making. The document also provides examples like how a local authority used analytics software to transform large volumes of parking, service, and tax records into actionable reports.
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.
EU Data Market study. Presentation at NESSI Summit 2014 IDC & Open EvidenceKasia Szkuta
The study aims to define, assess and measure the European data economy as well as build a genuine stakeholders’ ecosystem. Find us on http://datalandscape.eu and @eudatalandscape
In partnership with IDG, our 2022 Insight Intelligent Technology™ Report examines how companies are making progress on long-term IT strategies to meet the changing, post-pandemic expectations of their businesses, their employees, and the market more broadly.
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
Internet of things: Accelerate Innovation and Opportunity on top The 3rd Plat...Son Phan
The document discusses the growth of technologies like the Internet of Things (IoT) and how they are driving major changes in business and society. It notes that by 2020, IoT technologies will represent the majority of ICT spending growth and will create $19 trillion in economic value over the next 10 years. The IoT is creating new opportunities for businesses to optimize operations, develop new revenue streams from data insights, and transform customer interactions. Key industries like retail, transportation and healthcare will be impacted as physical systems become connected and integrated with digital systems and data analytics. The rise of IoT requires organizations to rethink their strategies and ecosystems to capitalize on emerging opportunities.
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
Patrick Couch - Intelligenta Maskiner & Smartare Tjänster IBM Sverige
Industriföretag, såväl tillverkare som användare av maskiner, fordon och utrustning, står inför ett paradigmskifte drivet av ökad global konkurrens, kunders förändrade efterfrågan samt det faktum att produkterna nu blir instrumenterade, ihopkopplade och mer intelligenta. Stora datamängder är inte ett buzzword för dessa företag, utan en reell verklighet som de behöver förhålla sig till för att säkra sin framtida verksamhet. I bästa fall omvandlar dessa företag denna teknologiska revolution (populärt kallad Internet of Things, Industrial Internet, M2M, Industri 4.0 etc.) till en motor för att utveckla verksamheten mot tillväxt och effektivare produktion. Detta skifte skapar framförallt stora möjligheter att förflytta sig mot leveranser av tjänster som kraftigt ökar mervärdet för kunderna, deras kunders kunder samt för producenten.
The objective of this module is to take a look into what big data can bring you in the future.
Upon completion of this module you will:
- See what are the predictions for the future of Big Data
- Take a look at some trends that are emerging
- Get an overview of possible opportunities your company can have with Big Data
- Face some of the start up challenges you might have with Big Data
Duration of the module: approximately 1 – 2 hours
The document discusses 25 predictions about the future of big data:
1) Data volumes and ways to analyze data will continue growing exponentially with improvements in machine learning and real-time analytics.
2) More companies will appoint chief data officers and use data as a competitive advantage.
3) Data governance, visualization, and delivery through data fabrics and marketplaces will be key to extracting insights from diverse data sources and empowering partners.
4) Data is becoming a new global currency and companies are monetizing their data through algorithms, services, and by becoming "data businesses."
Top 10 Disruptive Big Data Trends for 2022Kavika Roy
https://www.datatobiz.com/blog/big-data-trends/
The global big data market revenue is projected to hit the 103 billion US dollar mark by 2027. With more than 2.5 quintillion bytes of data being generated daily it is more than safe to assume that Big Data is gearing up for changing the way we think.
Here are Top 10 Big Data Trends for 2022: Revolutionizing the Core of Modern Business Landscape
With enterprises putting digital at the core of their transformation, our annual Data Science & AI Trends Report explores the key strategic shifts enterprises will make to stay intelligent and agile going into 2019. The year was marked by a series of technological advances, including advances in AI, deep learning, machine learning, hybrid cloud architecture, edge computing (with data moving away to edge data centres), robotic process automation, a spurt of virtual assistants, advancements in autonomous tech and IoT.
Data Science & AI Trends 2019 By AIM & AnalytixLabsRicha Bhatia
This document discusses 10 data science and AI trends to watch for in India in 2019. It begins with an executive summary noting that enterprises are putting digital technologies like AI, machine learning, and analytics at the core of their transformations. It then discusses each of the 10 trends in more detail, with quotes from experts about how each trend will impact industries and businesses. The trends include more industries utilizing analytics and AI, deploying models for real-time use cases, using data analysis for informed customer engagement, increasing investment in data infrastructure, analytics becoming more pervasive, the need for greater collaboration, personalized products, making analytics more human-centric, replacing centralized data with a single customer view, and the growth of voice and AI assistants.
Module 6 The Future of Big and Smart Data- Online caniceconsulting
This document provides an overview of the future predictions and trends related to big data. Some of the key predictions discussed include machine learning becoming prominent in big data analysis, privacy emerging as a major challenge, and the creation of chief data officer positions. Emerging trends covered include the growth of open source solutions like Hadoop, the use of in-memory technologies to speed processing, and the incorporation of machine learning and predictive analytics. The document also discusses opportunities that big data presents for industries like increased productivity and sales.
GoodData: Introducing Insights as a Service (White Paper)Jessica Legg
Crafted and copywrote a new white paper announcing new GoodData product features and positioning as the first entrant in the Insights-as-a-Service category. Led design and development applying new branding.
Summary: BI is entering a new era, an era where purchasing decisions are being led by business units and managers, instead of corporate systems and IT. Learn more about this fundamental market shift and the benefits Insights as a Service can offer your business in this white paper.
Crafted and copywrote a new white paper announcing new GoodData product features and positioning as the first entrant in the Insights-as-a-Service category. Led design and development applying new branding.
Summary: BI is entering a new era, an era where purchasing decisions are being led by business units and managers, instead of corporate systems and IT. Learn more about this fundamental market shift and the benefits Insights as a Service can offer your business in this white paper.
The document discusses 10 trends that will shape the business intelligence (BI) landscape in 2017 according to partners ImproveCX and TechStorm. These trends are: 1) artificial intelligence, 2) mobile BI, 3) modern BI, 4) data quality, 5) internet of things, 6) natural language, 7) data visualization, 8) self-service preparation, 9) big data, and 10) hybrid solutions. Each trend is discussed in 1-2 paragraphs highlighting how it will impact BI tools and analytics in 2017.
Big Data, Trends,opportunities and some case studies( Mahmoud Khosravi)Mahmood Khosravi
Humans have been generating data for thousands of years. More recently we have seen
an amazing progression in the amount of data produced from the advent of mainframes
to client server to ERP and now everything digital. For years the overwhelming amount
of data produced was deemed useless
As 2017 begins, we are seeing big data and data science communities engage with new tools that specifically cater to data scientists and data engineers who aren’t necessarily experts in these techniques. Given rapid technological advances, the question for companies now is how to integrate new data science capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries. Leading companies are using their data science capabilities not only to improve their core operations but also to launch entirely new business models.
- Audi views big data as playing a central role in helping the company achieve its goal of becoming the leading premium brand.
- Audi is pursuing big data projects across its entire value chain, from development to production to after-sales, to generate added value for customers through the intelligent analysis and interpretation of data.
- The company already has business intelligence capabilities but is enhancing areas like data management and analytics to incorporate new sources of big data like vehicle sensor data.
Keeping pace with technology and big data.pdfClaire D'Costa
How IT companies can bridge the gap between ever-increasing talent needs and ever-changing technology?
In this pdf, you will get to know:
1- The technology's part in the play
2- The widening skills gap
3- Ways to fill up the void
4- Future of Big Data
5- Other useful insights
Investing in AI: Moving Along the Digital Maturity CurveCognizant
Digitally mature businesses are more likely to consider themselves at an advanced stage of AI adoption, according to our recent study, enabling them to turn data into insights at the scale and precision required today.
Big Data Trends and Challenges Report - WhitepaperVasu S
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
Modern Data Integration Expert Session Webinar ibi
William McKnight, President of McKnight Consulting Group and Information Builders’ Jake Freivald discuss the tools needed for a successful modern data integration.
Artificial Intelligence Expert Session Webinar ibi
Tom Redman of Data Quality Solutions and Information Builders' CMO Michael Corcoran share the latest on artificial intelligence trends in this webinar.
The document invites the reader to meet up at an event but does not provide any details about the event name, date, time or location. It repeats the call to meet at an unnamed event but gives no other context or information to identify the specifics of what is being referred to.
The Value of Improved Clinical Information Management for Payersibi
Payers can use clinical data to identify gaps in care, alert providers, and optimize network performance, cost and profitability. When data has improved accuracy and consistency, payers can eliminate deficiencies, boost network performance, receive bigger incentive payments, and increase membership. Payers can also help providers make better care decisions by providing feedback, sharing metrics, evaluating performance against peers, and delivering automated alerts. Accurate claims adjudication is achieved through improved clinical data management, enhancing efficiency and satisfaction for providers and members.
The document discusses 5 trends for 2018: 1) The continued growth of the Internet of Things. 2) Embedded analytics becoming more common and useful. 3) A shift to providing predictions rather than focusing on predictive analytics. 4) Continued development of artificial intelligence but ensuring it helps rather than replaces humans. 5) Increased monetization of data through new data products and services.
What Employees Think of Working at Information Buildersibi
The employee reviews summarize Information Builders as having a supportive and collaborative work culture, with smart and hard-working coworkers. The management cares about employees and customers. It is described as an innovative company with constant learning opportunities and no politics or red tape. Employees say it is a fun place to work that challenges you every day.
What Customers Are Saying About Information Buildersibi
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UiPath integration with generative AI
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
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Key Topics Covered
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- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
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4. Deployment Using ArgoCD for Edge Devices
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5. Introduction to Apache Kafka and S3
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6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
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8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
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Monitoring and Managing Anomaly Detection on OpenShift.pdf
Ten 2015 Technology Predictions
1. Ten 2015 Technology Predictions
1
Dr. Rado Kotorov
Chief Innovation Officer, Information Builders
Rick F. Van der Lans
Independent Analyst, R20/Consultancy BV
15 January 2015
2. 1: IoT Gains Momentum
Prediction: IoT Will expand
significantly in manufacturing,
energy sector, healthcare,
logistics, and other industries.
Fact: GE has generated $1 billion
in incremental revenues form IoT
and PaaS in 2013.
Action: IoT data can be cost
effectively gathered in columnar
high performance databases (like
Hyperstage) for quick analysis,
discovery, and experimentation.
2
Imagine the possibilities in a hyper-connected world…..
3. 1: IoT Gains Momentum
Connected devices include
thermostats, cars, lights,
alarms, shoe insoles
Car industry example
Currently each vehicle has
60-100 sensors
Future: 200 sensors per car
2020: Total 22 billion sensors
used in the automotive
industry
Cisco: 37 billion new things will
be connected by 2020
3
Imagine the possibilities in a hyper-connected world…..
4. 2: Dealing with the data deluge
Prediction: Most data will be
analyzed before it is fully processed
and put into a data warehouse.
Social and unstructured data are
becoming more analytically
accessible.
Fact: The volume of business data
worldwide, across all companies,
doubles every 1.2 years.
Action: Adopt a data lake approach
– access and analyze first, and
integrate later. Use search-BI tools
to create apps for structured and
unstructured data analytics.
4
Imagine when data flows in from everywhere…
5. 2: Dealing with the data deluge
Tools must allow us to sort and
find quickly
Complex, multi-step
architectures are not flexible
enough
Integrated solutions required
to avoid reinventing the wheel
5
Imagine when data flows in from everywhere…
6. 3: Apps and self-service
Prediction: Most companies will
implement different self service
for different stakeholders – tools
for the analysts and apps for front
line employees.
Fact: BI has a less than 30
percent adoption rate in the
enterprise today.
Action: Turn analysis and insights
into custom InfoApps for on-the-
job decision support.
6
Analysis and insights
create opportunities!
Operational apps create
value by changing behavior!
7. 3: Apps and self-service
Self-Service for the masses
Self-service is moving
upstream and must move
downstream
7
Analysis and insights
create opportunities!
Operational apps create
value by changing behavior!
8. 4: The analytics skills gap
Prediction: Companies will not
be able to fill the skill gap.
Therefore, CDOs and CAOs will
try to commoditize analytics.
Fact: The demand for people
with deep analytical skills is 10
times greater than supply.
Action: Commoditize analytics
with infoapps and appstore
like portals for employees.
8
Finding and hiring good data scientists…
9. 4: The analytics skills gap
Data is still considered a by-
product
Data is produced for internal
consumption only
Data must be regarded as a
key product
9
Finding and hiring good data scientists…
10. 5: Machine learning
Prediction: To bridge the skills
gap and to cope with highly
dimensional data deluge
companies will adopt machine
learning
Fact: IBM Watson is here and
ready for business
Action: Use machine learning
in combination with data
discovery to explore the field
and provide faster time to
market analytics
10
“Robots will be smarter than humans within 15 years,
Google’s new chief on artificial intelligence has claimed.”
11. 5: Machine learning
Many BI systems only do
reporting
ROI of reporting hard to
calculate
Analytics is the way to go
11
“Robots will be smarter than humans within 15 years,
Google’s new chief on artificial intelligence has claimed.”
12. 6: Master data management (MDM)
12
The quest for the golden record…
Prediction: The implementation
cycles for MDM will shrink
drastically from a couple of years
to a few months with new and
innovative approaches.
Fact: Miscoding and billing errors
from doctors and hospitals
totaled $20 billion in USA.
Fact: The average billion-dollar
company is losing $130 million a
year due to poor data
management.
Action: Adopt an MDM platform
with built in templates, wizards &
best practices approach.
13. 6: Master data management (MDM)
13
The quest for the golden record…
MDM will only be a success if
it’s setup in a flexible way,
technologically and
organizationally
15. 7: Data warehouse decline
Prediction: Unmodelled data
analytics will grow due to
competitive pressure. NoSQL,
Columnar and in-memory offer
alternatives to DW for many use
cases.
Fact: Relational databases still
dominate the market, but 30% to
35% of enterprises have invested
in big data. Is it a tipping point?
Action: Conduct powerful
analytics against columnar, in-
memory, and Hadoop using
standard query and analysis tools.
15
Imagine how quickly data can be analyzed if data modeling
and schemas were not necessary….
16. 7: Data warehouse decline
The future is for the Logical
Data Warehouse
Multiple data sources using
different storage
technologies together
forming one logical database
Big data is too big to move
16
Imagine how quickly data can be analyzed if data modeling
and schemas were not necessary….
17. 8: Tech gets personal
Prediction: The benefits of
predictive analytics are great, but
many companies will be lured to
buy easy to use tools, ignore the
pitfalls, and fail.
Fact: Deloitte research shows
more than 60% of companies
have experienced project failure.
Action: Implement verification
processes and commoditize
analytics with expert certified
InfoApps.
17
Is your prediction scientifically sound?
19. 9: Mobile workforce
Prediction: Gartner predicts that
over 50% of BI users will be
mobile users.
Fact: BI Scorecard: “BI adoption as
a percentage of employees
remains flat at 22%, but
companies that have successfully
deployed mobile BI show the
highest adoption at 42% of
employees.”
Action: Offer self-service BI with
an appstore like portal and
InfoApps.
19
If BI and analytics could be downloaded from an appstore?
20. 9: Mobile workforce
The ROI of mobile analytics is
not clear
Mobile analytics and
consumer-driven analytics
could become a marriage
made in heaven
20
If BI and analytics could be downloaded from an appstore?
21. Vote:
What percentage of your users do you think will
be accessing BI on mobile devices in 2 years
time?
21
22. 10: The CIO transformed
Prediction: Successful CIOs will
transform their roles into
business leadership roles and
eventually become CEOs.
Fact: Of 384 hospitals only one
selected the CIO as the next CEO
in 2014.
Fact: GE CEO says, “Every
company will be a software
company.”
Action: Use software to transform
processes, organizational culture,
customer facing experience, and
to monetize data.
22
The rise of the techno-leader
23. 10: The CIO transformed
More in-depth knowledge of
technology needed on c-level
What can we learn from the
CEOs of Google, Facebook, and
Twitter?
23
The rise of the techno-leader
25. Further Resources
Blog post: Gartner’s 2015 Tech Trends Lead To
Pervasive BI
Webinar: Big Data + Enterprise Data = Big
Information, 15 January 2015, 14.00 GMT /
15:00 CET
25
26. Questions?
26
Rick F. van der Lans, R20/Consultancy BV
@rick_vanderlans
Rado Kotorov, Information Builders
@rado_kotorov