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.
The document discusses the need for "NextGen" business intelligence (BI) software that better meets the needs of younger, more tech-savvy employees entering the workforce. Current BI tools are often too difficult to use and not aligned with how newer generations interact with and use technology. NextGen software needs to be highly visual, interactive, collaborative and provide guided experiences for analyzing and sharing information. The document provides ideas for key features of NextGen software, including the ability to create guides to share analytic processes and rapid, seamless data handling and visualization.
1) There is a growing gap in capabilities and performance between companies that invest heavily in data and analytics compared to those that invest less. The capability gap is exacerbated by a shortage of analytical talent.
2) The amount of data being created is growing exponentially, estimated at 2.5 quintillion bytes per day globally. However, most organizations are not effectively using the data they already have.
3) Investing in analytics can provide significant financial benefits across industries. For example, leveraging big data in healthcare could capture $300 billion annually and increase retailers' operating margins by 60%.
The document discusses how IT infrastructure is changing to adapt to new business priorities in the digital age. It introduces the "HEROES" framework for the future of IT infrastructure, which focuses on hybrid cloud architectures, edge computing, robotic process automation, obsolescence of old IT, and enterprise security. Artificial intelligence will be integrated across all areas of the framework and fundamentally change how organizations procure and consume IT infrastructure over the next five years.
Big Data Analytics: The art of the data scientist discusses the evolution of data analytics and roles of data scientists. It explains that while volume is interesting, distinguishing big data requires understanding patterns in incomplete and anonymized data from multiple sources. Effective data science discovers unknown insights, provides business value through predictive models and data products, and builds confidence in decisions through explanation and storytelling.
BIG DATA is having an enormous impact on the profile of workforces around the world. If you've ever seen the technology and experienced the impact it has on the pace of innovation in a business then the predictations made by McKinsey Global Institute will come as no surprise ( and just in case you've been on holiday for around two years, McKinsey is suggesting that by 2018 the US will face a shortfall of close to 200,000 analysts and 1.5 million managers with the right skills. In this presentation I outline the impact of BIG DATA on workforce design. I hope you find it informative and fun to read. Ian.
This document discusses the importance of business process management (BPM) for IT organizations. It provides advice on how to get started with BPM, including piloting high-impact projects and publicizing successes. BPM allows organizations to standardize and improve processes, delivering solutions faster, better, and cheaper. The future of BPM is bright as tools become more intuitive and integrated, allowing businesses to monitor and improve processes in real-time. Choosing a "Best of Value" platform over "Best of Breed" can provide significant cost savings while still meeting needs.
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.
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
The document discusses the need for "NextGen" business intelligence (BI) software that better meets the needs of younger, more tech-savvy employees entering the workforce. Current BI tools are often too difficult to use and not aligned with how newer generations interact with and use technology. NextGen software needs to be highly visual, interactive, collaborative and provide guided experiences for analyzing and sharing information. The document provides ideas for key features of NextGen software, including the ability to create guides to share analytic processes and rapid, seamless data handling and visualization.
1) There is a growing gap in capabilities and performance between companies that invest heavily in data and analytics compared to those that invest less. The capability gap is exacerbated by a shortage of analytical talent.
2) The amount of data being created is growing exponentially, estimated at 2.5 quintillion bytes per day globally. However, most organizations are not effectively using the data they already have.
3) Investing in analytics can provide significant financial benefits across industries. For example, leveraging big data in healthcare could capture $300 billion annually and increase retailers' operating margins by 60%.
The document discusses how IT infrastructure is changing to adapt to new business priorities in the digital age. It introduces the "HEROES" framework for the future of IT infrastructure, which focuses on hybrid cloud architectures, edge computing, robotic process automation, obsolescence of old IT, and enterprise security. Artificial intelligence will be integrated across all areas of the framework and fundamentally change how organizations procure and consume IT infrastructure over the next five years.
Big Data Analytics: The art of the data scientist discusses the evolution of data analytics and roles of data scientists. It explains that while volume is interesting, distinguishing big data requires understanding patterns in incomplete and anonymized data from multiple sources. Effective data science discovers unknown insights, provides business value through predictive models and data products, and builds confidence in decisions through explanation and storytelling.
BIG DATA is having an enormous impact on the profile of workforces around the world. If you've ever seen the technology and experienced the impact it has on the pace of innovation in a business then the predictations made by McKinsey Global Institute will come as no surprise ( and just in case you've been on holiday for around two years, McKinsey is suggesting that by 2018 the US will face a shortfall of close to 200,000 analysts and 1.5 million managers with the right skills. In this presentation I outline the impact of BIG DATA on workforce design. I hope you find it informative and fun to read. Ian.
This document discusses the importance of business process management (BPM) for IT organizations. It provides advice on how to get started with BPM, including piloting high-impact projects and publicizing successes. BPM allows organizations to standardize and improve processes, delivering solutions faster, better, and cheaper. The future of BPM is bright as tools become more intuitive and integrated, allowing businesses to monitor and improve processes in real-time. Choosing a "Best of Value" platform over "Best of Breed" can provide significant cost savings while still meeting needs.
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.
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
The document discusses the impact and possibilities of artificial intelligence (AI) in business. It explains that AI is rapidly being adopted in business and resulting in a fundamental shift in how businesses operate. The document provides examples of how AI can be applied across various business functions through automation, machine learning, and robotic process automation. It discusses how AI can free up human workers to focus on more strategic tasks by handling repetitive processes. Overall, the document argues that AI can improve business operations, efficiency, customer experience, and decision making when applied appropriately.
How Insurers Can Tame Data to Drive InnovationCognizant
To thrive among entrenched rivals and compete more effectively with digital natives, insurers will need to get their data right. That will mean moving to more responsive, AI-enabled architectures that accelerate data management and deliver insights that drive business performance.
Manufacturers were hard hit by COVID-19, but our research reveals the next best steps to take, based on the investments digital leaders in the industry have made and plan to make.
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.
Finance Crunsh Time Reporting | Deloitte Indiaaakash malhotra
Reporting is an essential function of every organization because it tracks an employee's performance. But superiors are wasting a lot of time in creating and updating reports; rather, they should use that time to communicate with subordinates. This can be possible only when the whole reporting function gets automated in the organization.
In an increasingly data-centric world, a company which fails to leverage the power of AI-powered business intelligence tools often lag behind. Learn from these slides how these tools are affecting businesses today and why should you choose them.
Delivering on the Promise of Digital TransformationBMC Software
This document discusses how digital transformation through technologies like cloud, big data, mobile and social media is changing how companies operate. It makes three key points:
1. Fully adopting these technologies requires transforming a company's operating model in a way that is comparable to the shift from steam to electric power a century ago.
2. For digital transformation strategies to succeed, CIOs must collaborate with business leaders to build a strategic vision, modernize infrastructure to integrate new and existing technologies, and restructure IT organizations to be more responsive.
3. Leading companies approach digital transformation as an enterprise-wide initiative requiring changes across the organization, not just from IT, in order to capitalize on new opportunities and stay
Diginomica 2019 2020 not ai neil raden article links and captionsNeil Raden
The balance of my articles on Diginomica 2019-2020other than AI: HPC/Supercomputers, Quantum, Cognitive, Complexity, Supply Chain, IoT, Edge Intelligence, Data, Telemedicine, healthcare Industry, For Good
Teaching organizations to fish in a data-rich future: Stories from data leadersAmanda Sirianni
This document summarizes interviews with data leaders about challenges they face and best practices for delivering value from data. It discusses three key steps data leaders take: 1) collaborating for an enterprise-wide data strategy, 2) developing skills internally through training programs, and 3) increasing data sharing and integration. Examples are given of how data leaders in industries like insurance, manufacturing, and healthcare have used these steps to drive business benefits such as reducing fraud and accelerating clinical trials.
The 10 Most Admired Analytics Companies to Watch in 2018Merry D'souza
We introduce “The 10 Most Admired Analytics Companies to Watch in 2018”, in order to assist businesses to choose their right analytics companies. Assessing the scenario in versatile perceptions, our magazine has brought light onto the companies, who have flaunted excellence in providing technologically advanced analytics solutions. This list showcases the analytics companies which are creating a better ‘Analytics’ world.
Digital transformation review no 5 dtr - capgemini consulting - digitaltran...Rick Bouter
This document discusses how most organizations have focused their digital transformation efforts on customer-facing areas rather than operations. It highlights emerging technologies like big data, machine learning, robotics, and 3D printing that can automate and improve operational processes. The document features interviews with thought leaders from companies like ABB, UPS, HMRC, edX, and Stratasys discussing how they are leveraging these technologies to digitize their operations and drive efficiencies. It also examines the underutilization of big data analytics and lack of skills in this area among many organizations.
1) The document discusses the need for IT operations teams to provide real-time business value dashboards to business stakeholders to better demonstrate IT's strategic value.
2) It describes the components needed to build an effective business value dashboard, including flexibility, collaboration between IT and business leaders, integration of both IT and business data, and the ability to rapidly deploy customizable views.
3) Examples are provided of different types of business value dashboards for a bank branch manager, financial services executive, and healthcare operations VP that integrate both IT operational metrics and business KPIs in real-time.
Technolony Vision 2016 - Primacy Of People First In A Digital World - Vin Mal...Vin Malhotra
The document discusses emerging technologies and their impact on businesses over the next 3-5 years based on research by Accenture. It identifies 5 technology trends that will be essential for business success: 1) Intelligent Automation using AI to automate tasks, 2) Liquid Workforce to build a flexible workforce, 3) Platform Economy using platforms for business model innovation, 4) Predictable Disruption from digital ecosystems, and 5) Digital Trust to strengthen customer relationships through ethics and security. The research involved input from experts and a global survey of over 3,100 business and IT executives.
In the first interview in this series, which kicks off PwC’s 2018 CEO Survey, chief executive Safra Catz explains the broad culture shift brought on by AI and cloud technologies.
The 10 Most Admired Analytics Companies to Watch in 2018Merry D'souza
"We introduce “The 10 Most Admired Analytics Companies to Watch in 2018”, in order to assist businesses to choose their right analytics companies. Assessing the scenario in versatile perceptions, our magazine has brought light onto the companies, who have flaunted excellence in providing technologically advanced analytics solutions. This list showcases the analytics companies which are creating a better ‘Analytics’ world."
Accenture's report explains how creating effortless experiences are so simple and easy with our data-driven strategy framework to drive growth. Read more.
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.
Benefits of AI-Driven Data Processing Services.pptxAndrew Leo
Integrating AI with data processing workflows proves beneficial for businesses as they can achieve maximum out of minimum. It not only lets them maximize the value proposition of their most valuable business asset, but helps in increasing efficiency. Businesses can gain a plethora of benefits as listed here:
Improve Decision Making
Accelerate Business Processes
Reduced Operational Expenditures
Increased Efficiency
Read here the inspired blog: https://www.damcogroup.com/blogs/fostering-innovation-with-ai-powered-data-processing-services
#dataprocessingservices
#daatprocessingcompany
#dataprocessingcompanies
#onlinedataprocessing
A Guide on How AI Contributes to Businesses in Today’s Era to Watch in 2023.Techugo
Artificial Intelligence and Machine Learning have become the main focus of the scene. Artificial intelligence can be used for a wide variety of uses in business, including streamlining processes and aggregating the performance of companies. Researchers are still determining what AI will mean for businesses shortly. AI is predicted to shift technological advancement away from the traditional two-dimensional screen and towards the three-dimensional physical space surrounding the person.
Although the acceptance by society in general for AI does not mean anything new. The idea itself isn’t. Artificial intelligence is a broad field of business application. Indeed, most of us interact with AI in some way or another. Artificial Intelligence is changing all aspects of business across every industry. To know more, visit the post.
The document discusses the impact and possibilities of artificial intelligence (AI) in business. It explains that AI is rapidly being adopted in business and resulting in a fundamental shift in how businesses operate. The document provides examples of how AI can be applied across various business functions through automation, machine learning, and robotic process automation. It discusses how AI can free up human workers to focus on more strategic tasks by handling repetitive processes. Overall, the document argues that AI can improve business operations, efficiency, customer experience, and decision making when applied appropriately.
How Insurers Can Tame Data to Drive InnovationCognizant
To thrive among entrenched rivals and compete more effectively with digital natives, insurers will need to get their data right. That will mean moving to more responsive, AI-enabled architectures that accelerate data management and deliver insights that drive business performance.
Manufacturers were hard hit by COVID-19, but our research reveals the next best steps to take, based on the investments digital leaders in the industry have made and plan to make.
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.
Finance Crunsh Time Reporting | Deloitte Indiaaakash malhotra
Reporting is an essential function of every organization because it tracks an employee's performance. But superiors are wasting a lot of time in creating and updating reports; rather, they should use that time to communicate with subordinates. This can be possible only when the whole reporting function gets automated in the organization.
In an increasingly data-centric world, a company which fails to leverage the power of AI-powered business intelligence tools often lag behind. Learn from these slides how these tools are affecting businesses today and why should you choose them.
Delivering on the Promise of Digital TransformationBMC Software
This document discusses how digital transformation through technologies like cloud, big data, mobile and social media is changing how companies operate. It makes three key points:
1. Fully adopting these technologies requires transforming a company's operating model in a way that is comparable to the shift from steam to electric power a century ago.
2. For digital transformation strategies to succeed, CIOs must collaborate with business leaders to build a strategic vision, modernize infrastructure to integrate new and existing technologies, and restructure IT organizations to be more responsive.
3. Leading companies approach digital transformation as an enterprise-wide initiative requiring changes across the organization, not just from IT, in order to capitalize on new opportunities and stay
Diginomica 2019 2020 not ai neil raden article links and captionsNeil Raden
The balance of my articles on Diginomica 2019-2020other than AI: HPC/Supercomputers, Quantum, Cognitive, Complexity, Supply Chain, IoT, Edge Intelligence, Data, Telemedicine, healthcare Industry, For Good
Teaching organizations to fish in a data-rich future: Stories from data leadersAmanda Sirianni
This document summarizes interviews with data leaders about challenges they face and best practices for delivering value from data. It discusses three key steps data leaders take: 1) collaborating for an enterprise-wide data strategy, 2) developing skills internally through training programs, and 3) increasing data sharing and integration. Examples are given of how data leaders in industries like insurance, manufacturing, and healthcare have used these steps to drive business benefits such as reducing fraud and accelerating clinical trials.
The 10 Most Admired Analytics Companies to Watch in 2018Merry D'souza
We introduce “The 10 Most Admired Analytics Companies to Watch in 2018”, in order to assist businesses to choose their right analytics companies. Assessing the scenario in versatile perceptions, our magazine has brought light onto the companies, who have flaunted excellence in providing technologically advanced analytics solutions. This list showcases the analytics companies which are creating a better ‘Analytics’ world.
Digital transformation review no 5 dtr - capgemini consulting - digitaltran...Rick Bouter
This document discusses how most organizations have focused their digital transformation efforts on customer-facing areas rather than operations. It highlights emerging technologies like big data, machine learning, robotics, and 3D printing that can automate and improve operational processes. The document features interviews with thought leaders from companies like ABB, UPS, HMRC, edX, and Stratasys discussing how they are leveraging these technologies to digitize their operations and drive efficiencies. It also examines the underutilization of big data analytics and lack of skills in this area among many organizations.
1) The document discusses the need for IT operations teams to provide real-time business value dashboards to business stakeholders to better demonstrate IT's strategic value.
2) It describes the components needed to build an effective business value dashboard, including flexibility, collaboration between IT and business leaders, integration of both IT and business data, and the ability to rapidly deploy customizable views.
3) Examples are provided of different types of business value dashboards for a bank branch manager, financial services executive, and healthcare operations VP that integrate both IT operational metrics and business KPIs in real-time.
Technolony Vision 2016 - Primacy Of People First In A Digital World - Vin Mal...Vin Malhotra
The document discusses emerging technologies and their impact on businesses over the next 3-5 years based on research by Accenture. It identifies 5 technology trends that will be essential for business success: 1) Intelligent Automation using AI to automate tasks, 2) Liquid Workforce to build a flexible workforce, 3) Platform Economy using platforms for business model innovation, 4) Predictable Disruption from digital ecosystems, and 5) Digital Trust to strengthen customer relationships through ethics and security. The research involved input from experts and a global survey of over 3,100 business and IT executives.
In the first interview in this series, which kicks off PwC’s 2018 CEO Survey, chief executive Safra Catz explains the broad culture shift brought on by AI and cloud technologies.
The 10 Most Admired Analytics Companies to Watch in 2018Merry D'souza
"We introduce “The 10 Most Admired Analytics Companies to Watch in 2018”, in order to assist businesses to choose their right analytics companies. Assessing the scenario in versatile perceptions, our magazine has brought light onto the companies, who have flaunted excellence in providing technologically advanced analytics solutions. This list showcases the analytics companies which are creating a better ‘Analytics’ world."
Accenture's report explains how creating effortless experiences are so simple and easy with our data-driven strategy framework to drive growth. Read more.
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.
Benefits of AI-Driven Data Processing Services.pptxAndrew Leo
Integrating AI with data processing workflows proves beneficial for businesses as they can achieve maximum out of minimum. It not only lets them maximize the value proposition of their most valuable business asset, but helps in increasing efficiency. Businesses can gain a plethora of benefits as listed here:
Improve Decision Making
Accelerate Business Processes
Reduced Operational Expenditures
Increased Efficiency
Read here the inspired blog: https://www.damcogroup.com/blogs/fostering-innovation-with-ai-powered-data-processing-services
#dataprocessingservices
#daatprocessingcompany
#dataprocessingcompanies
#onlinedataprocessing
A Guide on How AI Contributes to Businesses in Today’s Era to Watch in 2023.Techugo
Artificial Intelligence and Machine Learning have become the main focus of the scene. Artificial intelligence can be used for a wide variety of uses in business, including streamlining processes and aggregating the performance of companies. Researchers are still determining what AI will mean for businesses shortly. AI is predicted to shift technological advancement away from the traditional two-dimensional screen and towards the three-dimensional physical space surrounding the person.
Although the acceptance by society in general for AI does not mean anything new. The idea itself isn’t. Artificial intelligence is a broad field of business application. Indeed, most of us interact with AI in some way or another. Artificial Intelligence is changing all aspects of business across every industry. To know more, visit the post.
The document provides an overview of how data analytics is being used in various fields like accounting, auditing, fraud detection, and journalism. It discusses how data analytics and machine learning are enhancing audit procedures by allowing auditors to analyze large datasets. It also explains how data analytics tools are helping detect fraud by identifying unusual patterns in data. Additionally, it mentions how some news organizations are using automated systems to generate news stories from financial reports to quickly report earnings information.
The rising collection and analysis of data has shifted the way companies do business. Four key ingredients to develop a data strategy, how to leverage next-generation technologies, and three essential steps for rolling out implementation are included. The Data Ecosystem will show you how to develop and implement the strategies that will meet the needs of your business.
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
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.
The document discusses several key trends in analytics for 2015:
1. Data security is a major concern as data volumes grow exponentially, requiring companies to quadruple down on security efforts through innovation, analytics, and tighter integration.
2. The rise of the Internet of Things generates massive sensor data that requires new analytics to extract value, though challenges remain in integrating these systems.
3. Some argue that data should be monetized as an asset, but this brings risks around privacy, ethics, and real costs that companies need to consider carefully.
4. Cognitive analytics is enhancing decision-making by providing users with vast new sources of knowledge, though questions remain about how these systems will impact human roles over time
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.
The document provides an introduction to artificial intelligence (AI), including its history and limitations. It discusses 5 main limitations of AI: data, cultural limitations, bias, emotional intelligence, and lack of a strategic approach. It then discusses 5 key advantages: reduction in human error, taking risks instead of humans, availability 24/7, helping with repetitive jobs, and digital assistance. Finally, it covers 5 disadvantages: high creation costs, making humans lazy, unemployment, lack of emotions, and inability to think outside the box. The document thus provides a broad overview of the history, limitations, advantages and disadvantages of artificial intelligence.
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.
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
COVID-19 has increased the need for intelligent decisioning through AI, but ROI is not guaranteed. Here's how to accelerate AI outcomes, according to our recent study.
Artificial intelligence is becoming increasingly important for businesses. It can automate tasks like customer service, improve marketing through personalized experiences, and help predict outcomes. As more companies develop new AI technologies, those that don't adopt AI may struggle to keep up with competitors in terms of productivity and efficiency. The document provides several examples of how businesses are using AI for tasks like operational automation, predictive maintenance, fraud prevention, and more. It concludes that AI offers businesses many benefits and opportunities for growth.
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
This AI business checklist is a tool that provides an easy-to-use structure for strategic discussions, goal setting and critical decisions in your leadership team. A structure that you can use as a business leader to guide your decisions towards getting full value out of AI technology in your organisation. It is meant to be a tool that you can return to to guide your progress.
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Inte...Kaleido Insights
This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
A white paper on how BI and Data Analytics should be utilized in small to medium organizations and firms. While a lot many resources focuses on large data sets, it creates a vacuum for smaller organization to leverage the power of analytics for their businesses. This white paper tries to provide some direction and roadmap that can be utilized for such small work places
Similar to Investing in AI: Moving Along the Digital Maturity Curve (20)
Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Pr...Cognizant
Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of the reproduction process iteratively as well as provide transparency in data analytics porojects.
Data Modernization: Breaking the AI Vicious Cycle for Superior Decision-makingCognizant
The document discusses how most companies are not fully leveraging artificial intelligence (AI) and data for decision-making. It finds that only 20% of companies are "leaders" in using AI for decisions, while the remaining 80% are stuck in a "vicious cycle" of not understanding AI's potential, having low trust in AI, and limited adoption. Leaders use more sophisticated verification of AI decisions and a wider range of AI technologies beyond chatbots. The document provides recommendations for breaking the vicious cycle, including appointing AI champions, starting with specific high-impact decisions, and institutionalizing continuous learning about AI advances.
It Takes an Ecosystem: How Technology Companies Deliver Exceptional ExperiencesCognizant
Experience is becoming a key strategy for technology companies as they shift to cloud-based subscription models. This requires building an "experience ecosystem" that breaks down silos and involves partners. Building such an ecosystem involves adopting a cross-functional approach to experience, making experience data-driven to generate insights, and creating platforms to enable connected selling between companies and partners.
Intuition is not a mystery but rather a mechanistic process based on accumulated experience. Leading businesses are engineering intuition into their organizations by harnessing machine learning software, massive cloud processing power, huge amounts of data, and design thinking in experiences. This allows them to anticipate and act with speed and insight, improving decision making through data-driven insights and acting as if on intuition.
The Work Ahead: Transportation and Logistics Delivering on the Digital-Physic...Cognizant
The T&L industry appears poised to accelerate its long-overdue modernization drive, as the pandemic spurs an increased need for agility and resilience, according to our study.
Enhancing Desirability: Five Considerations for Winning Digital InitiativesCognizant
To be a modern digital business in the post-COVID era, organizations must be fanatical about the experiences they deliver to an increasingly savvy and expectant user community. Getting there requires a mastery of human-design thinking, compelling user interface and interaction design, and a focus on functional and nonfunctional capabilities that drive business differentiation and results.
The Work Ahead in Manufacturing: Fulfilling the Agility MandateCognizant
Manufacturers are ahead of other industries in IoT deployments but lag in investments in analytics and AI needed to maximize IoT's benefits. While many have IoT pilots, few have implemented machine learning at scale to analyze sensor data and optimize processes. To fully digitize manufacturing, investments in automation, analytics, and AI must increase from the current 5.5% of revenue to over 11% to integrate IT, OT, and PT across the value chain.
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Engineering the Next-Gen Digital Claims Organisation for Australian General I...Cognizant
The document discusses potential future states for the claims organization of Australian general insurers. It notes that gradual changes like increasing climate volatility, new technologies, and changing customer demographics will reshape the insurance industry and claims processes. Five potential end states for claims organizations are described: 1) traditional claims will demand faster processing; 2) a larger percentage of claims will come from new digital risks; 3) claims processes may become "Uberized" through partnerships; 4) claims organizations will face challenges in risk management propositions; 5) humans and machines will work together to adjudicate claims using large data and computing power. The document argues that insurers must transform claims through digital technologies to concurrently improve customer experience, operational effectiveness, and efficiencies
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The pivot to digital is fraught with numerous obstacles but with proper planning and execution, legacy carriers can update their core systems and keep pace with the competition, while proactively addressing customer needs.
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Investing in AI: Moving Along the Digital Maturity Curve
1. Primary Research Report
Investing in AI: Moving
Along the Digital
Maturity Curve
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.
October 2019
2. 2 / Investing in AI: Moving Along the Digital Maturity Curve
Primary Research Report
Executive Summary
In the years since the term “digital business” first emerged,
so has our understanding of digital maturity. And, it turns out,
you don’t get to the right-hand side of the maturity curve just
by collecting and using operational data to make business
decisions. That was fine in the days of data warehouses and
reporting, but now, companies have access to entirely new
categories of more meaningful data: unstructured data,
Internet of Things (IoT) data, images, social data and more.
Still, high data volumes do not equal digital maturity. At some point, it’s a case of
diminishing returns — too many trees, not enough forest; too much noise, too little signal,
not to mention not enough data scientists. Businesses need to know which data matters,
and they need to be able to access and use it to operate with precision.
To identify the data that’s most relevant to meeting their business goals and make these
data types actionable, organizations today are turning to advances in artificial intelligence
(AI) and deep learning, and they’re using new data architectures to allow all that data to
come together for the first time.
3. Investing in AI: Moving Along the Digital Maturity Curve / 3
Primary Research Report
To learn more about what it takes to attain digital maturity and achieve business success,
Cognizant and ESI ThoughtLab conducted a worldwide survey of 2,491 executives from
April to July 2019. We devised a framework to calculate a digital maturity score, based on
three criteria: self-assessed maturity across 13 digital capabilities, (see Figure 1, page 5, for
the full list), percentage of revenue impacted directly or indirectly by digital technologies,
and current benefits from technology (revenue, savings, market share, valuation impact,
etc.). We assigned respondents to one of four maturity stages: “beginner,” “implementing,”
“advancing” or “leading” (see page 12 for more on our methodology).
We found that even as AI is arguably the most difficult of digital technologies to master,
it’s also the most rewarding and the most indicative of digital maturity. In this report, we
focus on the use of AI, its critical role in enabling businesses to churn through data at the
scale and precision required today and how organizations can prepare themselves for AI
adoption to achieve digital maturity.
(Stay tuned for the full report at www.cognizant.com/latest-thinking)
4. Primary Research Report
4 / Investing in AI: Moving Along the Digital Maturity Curve
Moving up the curve with AI
In our study, we discovered a distinct correlation between digital
maturity and use of AI. Respondents lower on the maturity curve (those
categorized as “beginners”) were far less likely to consider themselves
as advanced in AI vs. more digitally mature organizations, or “leaders”
(see Figure 1, next page). Use of AI signals a shift in focus from the data
collection phase (i.e., initiatives like IoT to generate data) to the data
insights phase (i.e., AI). In three years’ time, not even half of beginners
think they’ll have achieved AI maturity, while half of leaders already
do so today. And where are leaders increasing their investment vs.
beginners? You guessed it: AI (see Figure 2, next page).
What this suggests is that businesses lower on the maturity curve are more likely to focus
on what we consider to be the prerequisites or drivers of AI — initiatives like IoT, cloud
adoption, etc. Involvement in these types of initiatives is a positive sign, maturity-
wise, as it signals a shift to collecting data that matters. However, full maturity
means integrating data, analyzing content, understanding what data matters
most, and using AI to predict and prescribe the best actions. Who cares
about IoT devices generating real-time data unless there’s a system
that uses that data to predict and prevent failure, to protect frozen
food quality, to recommend the best actions to maintain health,
etc.? Businesses need to figure out which data will result in
the best outcomes, what it means, and how to translate
that into actions that will increase
shareholder value.
5. 5Primary Research Report
Investing in AI: Moving Along the Digital Maturity Curve / 5
Digital leaders much more advanced with AI
Percent of organizations in the maturing or advanced stage of each area of the digital maturity framework
Response base: 2,491 business and technology leaders
Source: Cognizant
Figure 1
Tactics that lead the pack
Percent of respondents planning to invest significantly in the next three years. The biggest delta between beginners and leaders was in
improving data management and implementing AI.
Response base: 2,491 business and technology leaders
Source: Cognizant
Figure 2
0 10 20 30 40 50 60 70 80
Beginners
Leaders
13. Enhanced/augmented workers
12. Human centricity
11. Improved consumer/employee experience
10. Aligning operations with customer demands
9. Software deployment
8. Innovation culture
7. Artificial intelligence
6. Modernized core IT
5. Workforce transformation
4. IoT and connected products
3. Automation
2. Data management
1. Digital strategy
0 10 20 30 40 50 60
Beginners
Leaders
Analyze customer needs, expectations, and behaviors
Hire an external strategy consultancy
Outsource more IT (application, infrastructure, etc.)
Implement a managed innovation program
Hire human behavior specialists
Hire a technology service provider
Set up a dedicated digital business team
Replace legacy systems
Build a digital strategy
Improve cybersecurity
Implement AI solutions
Improve data management
6. Primary Research Report
6 / Investing in AI: Moving Along the Digital Maturity Curve
An inhuman job
These capabilities, however, are beyond human scale — businesses need to make decisions continuously,
often based on incomplete or inaccessible information, and always based on limited bandwidth. According
to our estimates, most businesses only see 20% of the data that matters, and a good deal of the rest is
in formats that are difficult to use or even comprehend. Look at Uber or any of the FAANG companies
(Facebook, Amazon, Apple, Netflix, Google) — no human can keep up with and process all that data.
Further, business decision-making itself often exceeds the number of parameters that humans are capable
of mulling over. Research suggests that when humans need to consider optimizing across five variables,
their decision-making is not much better than chance.
This is where AI comes in. Today, most companies apply AI and data to automate menial tasks. Where they’ll
see outsize results, however, is in applying AI to specialized decision-making roles, such as video analytics
and radiobiology, where these tools can perform at a scale that’s 10, 100 or even 1,000 times greater than
human capabilities. AI and machine learning algorithms don’t get overwhelmed with data volumes, they
can detect patterns we can’t, they don’t get fatigued in the late afternoon, they can use simulation instead
of real-world experiments to test decisions faster, and they don’t have an opinion (though they may
develop biases just as people do).
The latest advancements in AI, like evolutionary AI,
1
allow AI to scale with significantly fewer data scientists,
and they enable business users to optimize algorithms across hundreds of parameters, which is well
beyond human limits. AI mechanisms also exist that can take hundreds or even thousands of parameters
and find the 10 that are most worth focusing on — the data that will move the needle on any particular
business goal, whether it’s driving revenue or improving customer satisfaction. Why is revenue off by
5%? Why are customers not completing transactions? This requires an understanding of the data with
the highest causal relationship to an outcome — “the data that matters most” — and how that information
impacts our goals.
The latest advancements in AI, like evolutionary AI,
allow AI to scale with significantly fewer data scientists,
and they enable business users to optimize algorithms
across hundreds of parameters, which is well beyond
human limits.
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Primary Research Report
AI: more specialized than originally thought
First, however, this will require a reset of expectations where it comes
to AI. AI is far more specialized than companies expect. AI is more like
a neurosurgeon than a general practitioner. And, that means you need
to know where to apply it and what it’s good at (and not good at) to get
the best business results.
Think of robots in manufacturing — only now are companies making the product design changes required
to take advantage of this specialized equipment. Tesla just changed the way car wiring is done to optimize
for robotic assembly.
2
Companies should have done that years ago, but it takes time to adapt to the
specialized roles of new technology.
Businesses need to apply this same precision mentality when it comes to their data. They can’t apply
AI to data in its as-is state. What is causal within the data? What are the problems? What is influencing
engagement and a decision to buy? The goal is to extrapolate causal data to understand what impacts
human behaviors that, in turn, affect business outcomes. The first part of that involves figuring out and
then focusing on those behaviors (reflected in data) that drive true business value.
As it turns out, evolutionary AI is very good at not only establishing causality but also incorporating it
into a continuous loop that is followed by prediction (determining the outcome of decisions, even in
contexts that have never been seen before) and prescription (identifying actions that will achieve the
best outcomes).
Businesses need to apply a precision mentality when
it comes to their data. What is causal within the data?
What are the problems? What is influencing
engagement and a decision to buy?
8. 8 / Investing in AI: Moving Along the Digital Maturity Curve
Primary Research Report
Evolutionary AI informs business decisions by providing optimal solutions to incredibly complex problems.
It’s like having thousands, or even millions, of people brainstorming, prototyping and testing ideas. It takes
what works and builds out a new version, discarding those with subpar results. By doing so, it discovers
possibilities that lie outside the knowledge of any one person or team of individuals. It enables businesses
to simulate every possible choice, even the ones we would never even think to try, to find the best choices
for the very best outcome.
This branch of artificial intelligence leverages two powerful concepts:
❙❙ Creating a virtual representation of your business. While modeling is not new, using evolutionary
computation to generate and optimize models of the business is, and it’s a very powerful way to create a
model without requiring large numbers of data scientists to design it.
This approach uses business performance data and past decisions to create a model and refine it to
reflect the most accurate view of the business. And because it evolves, the model changes as businesses
change. Adaptable models are required to accommodate new regulations, modifications to bills of
materials, demographic shifts and all the other factors that impact future decisions.
❙❙ Optimizing your business through simulation. Most business decision-making today is really
an experiment on real people, machines and customers. What if you could test every decision on a
virtual copy of your business and see the impact on revenue, retention, conversions and other
important metrics? And, what if you could test the decisions you never even considered to generate
better outcomes?
Together, modeling and simulation deliver the power of evolutionary computation. Using large numbers
of parallel simulations of the business and possible decisions, organizations can find the best possible
outcomes. And by learning from these simulations — in parallel as they play out — they can uncover
optimal outcomes more quickly and cost-effectively than any other method.
9. Big challenge with big returns
While the impact of AI is great, it’s also among the most difficult of
digital disciplines to master. Not only does it require a modern data
foundation that brings together all the data that matters, but it also
requires new skills to extract meaning from that data.
This explains the higher adoption rates among leaders vs. beginners. Generating business insights is
complex, and AI usually requires human transformation as it changes existing roles and eliminates some
roles as it is successful. AI also has big human impacts on consumers who interact with it.
But respondents in our study who have taken on the complexities of AI are reaping benefits from their
investments (see Figure 3).
High-impact areas of code and data
Percent of respondents realizing high positive impact on revenue.
Response base: 2,491 business and technology leaders
Source: Cognizant
Figure 3
Consider the retail client that has been trying to compete with online companies with a 13-week fixed
forecasting model. The company couldn’t localize its model or adapt in real time to changing local
conditions. The retailer is now embracing evolutionary AI to optimize its supply chain and retail store
operations to balance staffing, pricing, inventory and shelf placement against cost, revenue, profit and
customer satisfaction, all localized to the store in near real-time. That is easier to do in a pure online
business, and it is becoming possible with any business.
0 5 10 15 20 25 30 35
All
Leaders
Artificial intelligence
Software deployment
Automation
Data management and analytics
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10. 10 / Investing in AI: Moving Along the Digital Maturity Curve
Primary Research Report
To succeed, outperformers
in the digital space must
understand what data
matters most and get that
data into a modern data
architecture to enable
AI teams to unlock new
insights. Data without AI is
just 1s and 0s, and AI without
data is just wasted potential.
11. Investing in AI: Moving Along the Digital Maturity Curve / 11
Primary Research Report
Increasing AI maturity
Businesses today need digital tools that can surpass human abilities
to keep up with the complexity, velocity and scale of change. That’s
where AI comes in. To move further along the digital maturity curve,
businesses should:
❙❙ Start thinking beyond data warehouses and cloud migration and start to focus on modernizing data:
integrating and virtualizing the data that matters and using new tools to assess data quality and causality.
❙❙ Embrace AI (including evolutionary AI) to get predictive and prescriptive analytics driving the
business. It’s critical to enable data and AI to scale beyond the limits of what’s been possible with
reporting and data warehouse technologies.
❙❙ Establish new roles and governance for data, including the formation of a chief data officer as part of
the leadership team.
❙❙ Assess your data and create a data modernization roadmap.
❙❙ Establish an AI center of excellence and an AI strategy, identifying needed skills, prioritizing use
cases, initiating AI projects, and setting AI standards and governance.
❙❙ Scale AI as part of your digital initiatives and programs.
To ascend the digital maturity curve, organizations will need new ways of working and new skills. This starts
with new leadership styles that seek value not just in tools or people but also in how data can be applied for
competitive advantage.
Great business leaders have historically been able to call upon a variety of leadership styles — from
coaching to visionary to laissez-faire — to drive product improvements and motivate people to perform.
But as AI becomes a part of the organization’s decision-making processes, it effectively becomes a part
of the team.
This will require leaders to challenge AI systems to achieve results, by translating business goals into data
science and managing AI performance as they would any critical organizational role. They will also need
to consider that data is a source of value, and that not all data is equal. To succeed, outperformers in the
digital space must understand what data matters most and get that data into a modern data architecture
3
to enable AI teams to unlock new insights. Data without AI is just 1s and 0s, and AI without data is just
wasted potential.
With AI, the best time to have planted a tree was 10 years ago. The second best time is now. Armed with
new skills and technologies (and new top-down leadership styles), encourage your teams to start small with
test cases that identify data weaknesses. Only then can your organization’s new AI skills pay dividends.
12. Primary Research Report
12 / Investing in AI: Moving Along the Digital Maturity Curve
Methodology
To learn what companies are doing to succeed in the digital economy, Cognizant and ESI ThoughtLab
conducted a worldwide survey from April to May 2019 of 2,491 C-level executives and their direct
reports across regions and industries and from a mix of enterprise functions. In addition to the survey, ESI
ThoughtLab conducted interviews with senior executives in more than 20 companies across industries
and world locations. Our main research objective was to help organizations develop an evidenced-based
roadmap to digital leadership, validated by performance metrics already achieved by companies and
enriched through valuable executive insights.
The digital maturity score was derived from three criteria:
❙❙ Ranking on a digital transformation framework: Created by ESI ThoughtLab, this framework scored
companies across 13 key areas of digital transformation (digital strategy, automation, data management,
IoT, workforce transformation, innovation culture, software deployment, modernized core IT, artificial
intelligence, aligning operations with customer demands, improved consumer/employee experience,
human centricity, enhanced/augmented workers)
❙❙ Ability to influence revenue through digital methods: Drawing on self-reported data, we analyzed
the level of revenue influenced directly or indirectly by digital channels.
❙❙ Range of benefits generated through digital transformation: This included operational benefits,
such as speed to market and improving cost efficiencies, and more strategic ones, such as greater
shareholder value and market share.
We calculated an index score of “digital maturity” for each respondent and assigned each to one of four
maturity stages: beginner, implementer, advanced and leader.
Endnotes
1 For more on evolutionary AI, see our website www.cognizant.com/ai/evolutionary-ai.
2 Alam Khalid, “Tesla Modifies the Wiring to Help Robots Build Cars Like Model Y,” Techacker, July 24, 2018, www.techacker.
net/tesla-modifies-the-wiring-to-help-robots-build-cars-like-model-y/.
3 Arun Varadarajan, “Lessons from the Front Lines of Data Modernization,” Digitally Cognizant, Sept. 10, 2019, https://
digitally.cognizant.com/lessons-from-the-front-lines-of-data-modernization-codex4972/.
13. About the author
Bret Greenstein
Global Vice-President and Head of Digital Business AI Practice, Cognizant
Bret Greenstein is Global Vice-President and Head of Cognizant’s Digital Business
AI Practice, focusing on technology and business strategy, go-to-market and
innovation, helping clients realize their potential through digital transformation.
Prior to Cognizant, Bret led IBM Watson’s Internet of Things offerings, establishing
new IoT products and services for the Industrial Internet of Things. He built his
career in technology and business leadership across a range of roles throughout
IBM in software, services, consulting, strategy and marketing, and served as IBM’s
CIO for Asia-Pacific. He has worked globally in these roles, including living in China for five years, working with
clients and transforming IBM’s IT environment.
Bret holds patents in the area of collaboration systems. He holds a bachelor’s degree in electrical engineering
and a master’s degree in manufacturing systems engineering from Rensselaer Polytechnic Institute. He can be
reached at Bret.Greenstein@cognizant.com.
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Primary Research Report