A sanitized version of our presentation to the Teradata Marketing Summit in Los Angeles in March 2014, on how we created $94.95 million in incremental value for a bank by means of a customer-centricity strategy enabled by Big Data and Analytics
Big data & analytics for banking new york lars hambergLars Hamberg
BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015.
Keynote address: "How Predictive Analytics will change the Financial Services Sector”
Speaker : Lars Hamberg
http://www.specialistspeakers.com/?p=8367
Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition
Welcome to the Age of Big Data in Banking Andy Hirst
Big Data in banking presentation from Sibos Dubai 2013 . What are use cases driving deployments in Banking ? See the use cases SAP is involved In banking in 2013
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.
The document discusses big data and big data analytics in banking. It defines big data as large, complex datasets that are difficult to process and store using traditional databases. Sources of big data include social media, sensors, transportation services, online shopping, and mobile apps. Characteristics of big data include volume, velocity, and variety. Hadoop is presented as an open source framework for analyzing big data using HDFS for storage and MapReduce for processing. The benefits of big data analytics in banking include fraud detection, risk management, customer segmentation, churn analysis, and sentiment analysis to improve customer experience.
Analytics in banking preview deck - june 2013Everest Group
This report provides a comprehensive understanding of the analytics services industry with focus on banking domain. Analytics adoption in the banking industry is covered in depth, exploring various aspects such as market size, key drivers, recent analytics initiatives, and challenges. The report also analyses the trends in analytics deals for various banking subverticals (cards, retail, commercial, and lending) and evaluates analytics capabilities of 20+ service providers in the banking space
How advanced analytics is impacting the banking sectorMichael Haddad
The document discusses how advanced analytics is impacting the banking sector. It covers topics like regulatory changes forcing banks to invest in compliance; new digital technologies changing how customers interact with banks; and data analytics helping banks reduce risk, deliver personalized services, and retain skills. It also discusses Hitachi Data Systems' acquisition of Pentaho and how their combined platform can provide unified data integration and business analytics across structured, unstructured, and streaming data sources.
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Future and scope of big data analytics in Digital Finance and banking.VIJAYAKUMAR P
Big data analytics is a powerful tool for banking and finance that can increase revenue, enhance customer engagement, and optimize risk. For example, Reliance Jio was able to gain 100 million users in a short time by collecting customer data to design profitable plans. Banks like ICICI have used analytics to improve debt collection, reduce turnaround time, and automate loan allocation. Leading banks now use analytics to personalize customer service, connect with customers on important dates, and provide a unified customer view across channels. As big data applications and analytics continue to grow, it presents career opportunities for finance professionals to adopt these new skills.
Big data & analytics for banking new york lars hambergLars Hamberg
BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015.
Keynote address: "How Predictive Analytics will change the Financial Services Sector”
Speaker : Lars Hamberg
http://www.specialistspeakers.com/?p=8367
Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition
Welcome to the Age of Big Data in Banking Andy Hirst
Big Data in banking presentation from Sibos Dubai 2013 . What are use cases driving deployments in Banking ? See the use cases SAP is involved In banking in 2013
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.
The document discusses big data and big data analytics in banking. It defines big data as large, complex datasets that are difficult to process and store using traditional databases. Sources of big data include social media, sensors, transportation services, online shopping, and mobile apps. Characteristics of big data include volume, velocity, and variety. Hadoop is presented as an open source framework for analyzing big data using HDFS for storage and MapReduce for processing. The benefits of big data analytics in banking include fraud detection, risk management, customer segmentation, churn analysis, and sentiment analysis to improve customer experience.
Analytics in banking preview deck - june 2013Everest Group
This report provides a comprehensive understanding of the analytics services industry with focus on banking domain. Analytics adoption in the banking industry is covered in depth, exploring various aspects such as market size, key drivers, recent analytics initiatives, and challenges. The report also analyses the trends in analytics deals for various banking subverticals (cards, retail, commercial, and lending) and evaluates analytics capabilities of 20+ service providers in the banking space
How advanced analytics is impacting the banking sectorMichael Haddad
The document discusses how advanced analytics is impacting the banking sector. It covers topics like regulatory changes forcing banks to invest in compliance; new digital technologies changing how customers interact with banks; and data analytics helping banks reduce risk, deliver personalized services, and retain skills. It also discusses Hitachi Data Systems' acquisition of Pentaho and how their combined platform can provide unified data integration and business analytics across structured, unstructured, and streaming data sources.
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Future and scope of big data analytics in Digital Finance and banking.VIJAYAKUMAR P
Big data analytics is a powerful tool for banking and finance that can increase revenue, enhance customer engagement, and optimize risk. For example, Reliance Jio was able to gain 100 million users in a short time by collecting customer data to design profitable plans. Banks like ICICI have used analytics to improve debt collection, reduce turnaround time, and automate loan allocation. Leading banks now use analytics to personalize customer service, connect with customers on important dates, and provide a unified customer view across channels. As big data applications and analytics continue to grow, it presents career opportunities for finance professionals to adopt these new skills.
CaixaBank is using big data and its partnership with Oracle to develop a new technology platform to improve business and better anticipate customer needs with a 360 degree view of customers. CaixaBank consolidated 17 data marts into one centralized data pool built on Oracle technologies. This has improved customer relationships, employee efficiency, and regulatory reporting. The data pool collects data from various sources to power business use cases like deposits pricing, customized ATM menus, online risk scoring, and online marketing automation.
BIG Data & Hadoop Applications in FinanceSkillspeed
Explore the applications of BIG Data & Hadoop in Finance via Skillspeed.
BIG Data & Hadoop in Finance is a key differentiator, especially in terms of generating greater investment insights. They are used by companies & professionals for risk assessment, fraud detection & forecasting trends in financial markets.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Nicolas has a vision of opening a French restaurant using his grandmother's recipes. He is discussing a loan with his banker. The banker not only offers the loan but also provides valuable business insights using data analytics. The banker examines demographic and spending data to recommend the best locations and price points for Nicolas's restaurant. This illustrates how banks can leverage big data to generate new revenue streams by providing business insights to customers.
Big Data Analytics in light of Financial Industry Capgemini
Big data and analytics have the potential to transform economies and competition by delivering new productivity growth. Effective use of big data can increase operating margins over 60% for retailers and save $300 billion in US healthcare and $250 billion in European public sector. Companies that improve decision making through big data have seen a 26% performance improvement over 3 years on average. Emerging technologies like self-driving cars will rely heavily on analyzing vast amounts of real-time sensor data.
A brief overview of the use of big data analytics in retail banking. This basic material is an introduction to the video training series: Retail Banking Analytics, available at briastrategy.com.
How analytics will transform banking in luxembourgTommy Lehnert
This document discusses how analytics will transform banking in Luxembourg. It notes that data is now digital and ubiquitous, creating opportunities for insights through big data analytics. The analytics life cycle is described, from problem identification to model deployment and evaluation. Different levels of analytics usage and culture in organizations are outlined. The document advocates for a hybrid approach to analytics using automated rules, anomaly detection, predictive modeling and other techniques. A case study describes how a bank used analytics for improved risk management, customer insights, and executive decision making. The conclusion is that Luxembourg can become a leader in analytics adoption to transform outdated business models.
Future of Business Intelligence keynotepaul.hawking
The document discusses the future of business intelligence. It provides a brief history of business intelligence, noting it was coined in 1989 to describe how end users could access and analyze company information. It then discusses how the term has been marketed differently over time by vendors. The document also examines emerging technologies like analytics, big data, artificial intelligence, and natural language processing that are shaping the future of business intelligence. It analyzes their position on Gartner's Hype Cycle and provides examples of how these technologies are being applied.
TechConnex Big Data Series - Big Data in BankingAndre Langevin
Big Data in Banking focuses on the use of big data and Hadoop in the Canadian banking sector. The key points are:
1) The RDARR regulatory project is driving major investments in data management by the big six Canadian banks, totaling around $800 million over three years. This has led banks to implement Hadoop data hubs to centralize data.
2) Adoption of Hadoop for risk applications is still in early stages, with a focus on regulatory reporting. Capital markets has led adoption so far.
3) Lessons learned include choosing flexible Hadoop distributions, using native Hadoop tools for best performance, and designing hubs for data engineers rather than casual users. Infrastructure must have
Pi cube banking on predictive analytics151Cole Capital
Predictive analytics can help banks in several key areas:
1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition.
2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk.
3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Understanding Big Data: Strategies to Re-envision Decision-Making
Amy Mayer, Vice President, Capgemini
Oracle Analytics Leader, North America
Presented at Oracle OpenWorld 2012
Big data is an opportunity for communications service providers (CSPs) to create the intelligence for operating their infrastructures more efficiently, to analyze the success of their services, and to create a better personal experience for their customers.
CSP Top executives, Network and IT managers and Marketing, are eager to exploit the large amount of information to achieve better business decisions. They expect their Chief Technical Officer to provide end-to-end analytic solutions based on the data available in their IT and network infrastructure.
This presentation analyzes the complete value chain that can transform CSPs’ data to knowledge. It covers the sources of information, the data collection tools, the analytic platforms providing quick data access, and finally the business intelligence use cases with the presentation and visualization of the results and predictions.
The document profiles Jeroen ter Heerdt and outlines his expertise in areas such as cultural creativity, agile practices, observation, analysis, and helping others bring data under control to derive insights. It then discusses how data and technology can be leveraged to increase operational efficiency, improve customer experiences, and transform business models across various industries from elevators to healthcare to aviation. The document concludes by providing tips for organizations to facilitate a data-driven culture and take advantage of data through initiatives like "Bring Your Own Data."
Leveraging Big Data to Drive Bank Customer Engagement and LoyaltyJim Marous
The document discusses how banks can leverage big data to drive customer engagement and loyalty. It describes how big data is already being used successfully by companies like Amazon, Netflix, and Pandora to personalize customer experiences. It outlines opportunities for banks to use big data to improve customer targeting, recommendations, cross-selling, sentiment analysis, and churn analysis. Finally, it provides examples of how some banks are using big data for customer acquisition, engagement, loyalty programs, location-based offers, and social media analysis.
Predictive analytics uses statistical techniques and business intelligence technologies to uncover relationships within large datasets to predict future behaviors or outcomes. While predictive analytics can provide benefits like reducing customer churn or improving marketing campaign response rates, it is not widely used due to complexity, underestimating value, high software costs, and reliance on good quality data. The document outlines best practices for predictive analytics including focusing on data management, expecting incremental improvements over time, measuring impact using business metrics, and gaining executive sponsorship for projects.
Big data provides opportunities for financial institutions to gain competitive advantages. It allows them to analyze vast amounts of structured and unstructured data from various sources to better understand customers, identify risks, predict behaviors, and improve financial products and services. While big data implementations face challenges like integrating diverse data sources and developing analytics talent, companies that execute big data strategies are seeing significant benefits like more personalized customer experiences and better risk management. TD Bank is an example of a company revolutionizing IT and banking through big data analytics that can build comprehensive customer profiles and segment their entire customer base within minutes.
1. Big data has the potential to significantly increase operating margins and productivity for retailers.
2. Retailers are investing in big data to improve merchandising, marketing, e-commerce, supply chain operations, and store operations.
3. Getting started with big data requires determining current maturity, identifying high-value use cases, assessing data and analytics capabilities, establishing data management processes, and anticipating business changes.
Fit For Purpose: Preventing a Big Data LetdownInside Analysis
The Briefing Room with Dr. Robin Bloor and RedPoint Global
Live Webcast October 6, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9982ad3a2603345984895f279e849d35
Gartner recently placed Big Data in its “trough of disillusionment,” reflective of many leaders’ struggle to prove the value of Hadoop within their organization. While the promise of enhanced data integration and enrichment is obvious, measurable results have remained elusive. This episode of The Briefing Room will outline how to successfully tie Big Data to existing business applications, preventing your next Hadoop project from being another “Big Data letdown.”
Register today to learn from veteran Analyst Dr. Robin Bloor as he discusses the importance of converging enterprise data integration with intelligence and scalability. He’ll be briefed by George Corugedo of RedPoint Global, who will provide concrete examples of how the convergence of scalable cloud platforms, ever-expanding data sources and intelligent execution can turn the Big Data hype into demonstrable business value.
Visit InsideAnalysis.com for more information.
CaixaBank is using big data and its partnership with Oracle to develop a new technology platform to improve business and better anticipate customer needs with a 360 degree view of customers. CaixaBank consolidated 17 data marts into one centralized data pool built on Oracle technologies. This has improved customer relationships, employee efficiency, and regulatory reporting. The data pool collects data from various sources to power business use cases like deposits pricing, customized ATM menus, online risk scoring, and online marketing automation.
BIG Data & Hadoop Applications in FinanceSkillspeed
Explore the applications of BIG Data & Hadoop in Finance via Skillspeed.
BIG Data & Hadoop in Finance is a key differentiator, especially in terms of generating greater investment insights. They are used by companies & professionals for risk assessment, fraud detection & forecasting trends in financial markets.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Nicolas has a vision of opening a French restaurant using his grandmother's recipes. He is discussing a loan with his banker. The banker not only offers the loan but also provides valuable business insights using data analytics. The banker examines demographic and spending data to recommend the best locations and price points for Nicolas's restaurant. This illustrates how banks can leverage big data to generate new revenue streams by providing business insights to customers.
Big Data Analytics in light of Financial Industry Capgemini
Big data and analytics have the potential to transform economies and competition by delivering new productivity growth. Effective use of big data can increase operating margins over 60% for retailers and save $300 billion in US healthcare and $250 billion in European public sector. Companies that improve decision making through big data have seen a 26% performance improvement over 3 years on average. Emerging technologies like self-driving cars will rely heavily on analyzing vast amounts of real-time sensor data.
A brief overview of the use of big data analytics in retail banking. This basic material is an introduction to the video training series: Retail Banking Analytics, available at briastrategy.com.
How analytics will transform banking in luxembourgTommy Lehnert
This document discusses how analytics will transform banking in Luxembourg. It notes that data is now digital and ubiquitous, creating opportunities for insights through big data analytics. The analytics life cycle is described, from problem identification to model deployment and evaluation. Different levels of analytics usage and culture in organizations are outlined. The document advocates for a hybrid approach to analytics using automated rules, anomaly detection, predictive modeling and other techniques. A case study describes how a bank used analytics for improved risk management, customer insights, and executive decision making. The conclusion is that Luxembourg can become a leader in analytics adoption to transform outdated business models.
Future of Business Intelligence keynotepaul.hawking
The document discusses the future of business intelligence. It provides a brief history of business intelligence, noting it was coined in 1989 to describe how end users could access and analyze company information. It then discusses how the term has been marketed differently over time by vendors. The document also examines emerging technologies like analytics, big data, artificial intelligence, and natural language processing that are shaping the future of business intelligence. It analyzes their position on Gartner's Hype Cycle and provides examples of how these technologies are being applied.
TechConnex Big Data Series - Big Data in BankingAndre Langevin
Big Data in Banking focuses on the use of big data and Hadoop in the Canadian banking sector. The key points are:
1) The RDARR regulatory project is driving major investments in data management by the big six Canadian banks, totaling around $800 million over three years. This has led banks to implement Hadoop data hubs to centralize data.
2) Adoption of Hadoop for risk applications is still in early stages, with a focus on regulatory reporting. Capital markets has led adoption so far.
3) Lessons learned include choosing flexible Hadoop distributions, using native Hadoop tools for best performance, and designing hubs for data engineers rather than casual users. Infrastructure must have
Pi cube banking on predictive analytics151Cole Capital
Predictive analytics can help banks in several key areas:
1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition.
2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk.
3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Understanding Big Data: Strategies to Re-envision Decision-Making
Amy Mayer, Vice President, Capgemini
Oracle Analytics Leader, North America
Presented at Oracle OpenWorld 2012
Big data is an opportunity for communications service providers (CSPs) to create the intelligence for operating their infrastructures more efficiently, to analyze the success of their services, and to create a better personal experience for their customers.
CSP Top executives, Network and IT managers and Marketing, are eager to exploit the large amount of information to achieve better business decisions. They expect their Chief Technical Officer to provide end-to-end analytic solutions based on the data available in their IT and network infrastructure.
This presentation analyzes the complete value chain that can transform CSPs’ data to knowledge. It covers the sources of information, the data collection tools, the analytic platforms providing quick data access, and finally the business intelligence use cases with the presentation and visualization of the results and predictions.
The document profiles Jeroen ter Heerdt and outlines his expertise in areas such as cultural creativity, agile practices, observation, analysis, and helping others bring data under control to derive insights. It then discusses how data and technology can be leveraged to increase operational efficiency, improve customer experiences, and transform business models across various industries from elevators to healthcare to aviation. The document concludes by providing tips for organizations to facilitate a data-driven culture and take advantage of data through initiatives like "Bring Your Own Data."
Leveraging Big Data to Drive Bank Customer Engagement and LoyaltyJim Marous
The document discusses how banks can leverage big data to drive customer engagement and loyalty. It describes how big data is already being used successfully by companies like Amazon, Netflix, and Pandora to personalize customer experiences. It outlines opportunities for banks to use big data to improve customer targeting, recommendations, cross-selling, sentiment analysis, and churn analysis. Finally, it provides examples of how some banks are using big data for customer acquisition, engagement, loyalty programs, location-based offers, and social media analysis.
Predictive analytics uses statistical techniques and business intelligence technologies to uncover relationships within large datasets to predict future behaviors or outcomes. While predictive analytics can provide benefits like reducing customer churn or improving marketing campaign response rates, it is not widely used due to complexity, underestimating value, high software costs, and reliance on good quality data. The document outlines best practices for predictive analytics including focusing on data management, expecting incremental improvements over time, measuring impact using business metrics, and gaining executive sponsorship for projects.
Big data provides opportunities for financial institutions to gain competitive advantages. It allows them to analyze vast amounts of structured and unstructured data from various sources to better understand customers, identify risks, predict behaviors, and improve financial products and services. While big data implementations face challenges like integrating diverse data sources and developing analytics talent, companies that execute big data strategies are seeing significant benefits like more personalized customer experiences and better risk management. TD Bank is an example of a company revolutionizing IT and banking through big data analytics that can build comprehensive customer profiles and segment their entire customer base within minutes.
1. Big data has the potential to significantly increase operating margins and productivity for retailers.
2. Retailers are investing in big data to improve merchandising, marketing, e-commerce, supply chain operations, and store operations.
3. Getting started with big data requires determining current maturity, identifying high-value use cases, assessing data and analytics capabilities, establishing data management processes, and anticipating business changes.
Fit For Purpose: Preventing a Big Data LetdownInside Analysis
The Briefing Room with Dr. Robin Bloor and RedPoint Global
Live Webcast October 6, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9982ad3a2603345984895f279e849d35
Gartner recently placed Big Data in its “trough of disillusionment,” reflective of many leaders’ struggle to prove the value of Hadoop within their organization. While the promise of enhanced data integration and enrichment is obvious, measurable results have remained elusive. This episode of The Briefing Room will outline how to successfully tie Big Data to existing business applications, preventing your next Hadoop project from being another “Big Data letdown.”
Register today to learn from veteran Analyst Dr. Robin Bloor as he discusses the importance of converging enterprise data integration with intelligence and scalability. He’ll be briefed by George Corugedo of RedPoint Global, who will provide concrete examples of how the convergence of scalable cloud platforms, ever-expanding data sources and intelligent execution can turn the Big Data hype into demonstrable business value.
Visit InsideAnalysis.com for more information.
7 deliver world class customer experience with big data and analytics and loc...Dr. Wilfred Lin (Ph.D.)
This document discusses how companies can improve customer experience through the use of big data and analytics. It notes that social media and mobile technologies have empowered customers and changed expectations. Most companies lack visibility into the value of customer experience. The document promotes Oracle's customer experience (CX) solutions for smarter sales, commerce anywhere, and connected service through features such as predictive analytics, personalized experiences, and automated decisions. Case studies show how Oracle CX has helped companies increase revenue, reduce costs, and improve customer satisfaction.
DataOps @ Scale: A Modern Framework for Data Management in the Public SectorTamrMarketing
Within the last 6 months, the U.S. agencies have begun defining a “Data Science Occupational Series”.
This means adding the term “(Data Scientist)” at the end of a job title to increase the odds of finding a candidate that understands data.
Watch the full presentation: https://resources.tamr.com/govdataops
RPM2 Selected to the CIO Review "Top 100" Most Promising Big Data CompaniesScott Terry
Rapid Progress Marketing and Modeling, LLC receives recognition as a "Top 100" Most Promising Big Data company for its Data Science and Predictive Analytics Expertise
Introduction to Data Science (Data Summit, 2017)Caserta
This document summarizes an introduction to data science presentation by Joe Caserta and Bill Walrond of Caserta Concepts. Caserta Concepts is an internationally recognized data innovation and engineering consulting firm. The agenda covers why data science is important, challenges of working with big data, governing big data, the data pyramid, what data scientists do, standards for data science, and a demonstration of data analysis. Popular machine learning algorithms like regression, decision trees, k-means clustering and collaborative filtering are also discussed.
Sap increase your return on information by focusing on data governance - ma...Bertille Laudoux
This document discusses information governance and data quality. It begins by defining information governance as a discipline for overseeing enterprise information to improve business value. It then discusses why data quality is important, noting that poor data quality can lead to lower profits, poor customer relations, and low productivity. The document emphasizes that information governance is key to managing data quality and achieving business goals. It also provides an overview of SAP's solutions for information governance and data quality.
Business capability mapping and business architectureSatyaIluri
Business architecture and capabilities mapping captures and encapsulates the essence of a business. Using capabilities enterprises can model their current and desired business capabilities with rich semantics and leverage these as Lego blocks to compose products/ initiatives, overlay them with value streams and processes, and capture requirements to evolve capabilities. Business capability mapping helps companies establish a common language, fosters business/IT alignment, helps reduce redundancy and rework, and aligns execution with strategy.
The document discusses big data and its importance for businesses. It provides several definitions of big data from different sources that commonly refer to large and complex datasets that are difficult to process using traditional methods due to their size and speed. Big data represents an opportunity for businesses to gain valuable insights and optimize their operations, customer service, and decision making. However, it also poses challenges for storage, analysis, and privacy. The document advocates the need for businesses to make full use of all their enterprise data and leverage in-memory and streaming analytics to extract value from big data.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
Big Data Business Transformation - Big Picture and BlueprintsAshnikbiz
Kaustubh Patwardhan, Head of Strategy and Business Development at Ashnik presents the big picture and blueprints of a big data journey for enterprises. The Value of Big Data – Machine Learning and its big impact. He covers a spectrum of Big Data use cases where right data storage, integration & data consolidation plays a big role.
These slides—based on the webinar featuring John L Myers, managing research director for data and analytics at leading IT analyst firm Enterprise Management Associates (EMA), and Neil Barton, chief technology officer at WhereScape—highlight how the world of streaming data pipelines and automation practices for analytical environments intersect to provide value to both business stakeholders and corporate technologists.
View these slides to learn about:
- Drivers behind the growth of streaming usage scenarios
- Challenges that streaming data presents
- Value of automation techniques and technologies
- Benefits of applying automation to streaming data pipelines
- How WhereScape® automation with Streaming can fast-track streaming data use in your data landscape
This document discusses IBM's big data and analytics solutions. It describes big data as involving large volumes and varieties of data. The document outlines challenges of traditional IT systems and how new systems of engagement require massive scale, rapid insights, and data elasticity. It promotes investing in IBM's big data and analytics platform, which harnesses all data and analytics paradigms. The platform includes infrastructure, governance, ingestion, warehousing, and analytics capabilities. It is presented as helping organizations be more right more often by understanding what happened, learning from data, discovering current trends, deciding on actions, and predicting outcomes.
Data Strategy - Executive MBA Class, IE Business SchoolGam Dias
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
TD Ameritrade transitioned from a data warehouse to a data lake approach to better meet the needs of their marketing department. A data lake provides greater flexibility, speed, and self-service capabilities compared to a traditional data warehouse. It allows for the ingestion of diverse data types and volumes and supports real-time analytics. TD Ameritrade built a data lake solution using Informatica's data management platform to integrate, govern, and analyze marketing data from various sources to drive better customer insights and business outcomes.
[Strata NYC 2019] Turning big data into knowledge: Managing metadata and data...Kaan Onuk
Discover how Uber thinks about building big data knowledge platforms to allow teams to discover, manage, and govern entities. Explore how to build an extensible metadata management platform and infrastructure to democratize data at Uber's scale
Tableau reseller partner in Albania Bilytica Best business Intelligence compa...Carie John
Email: info@bilytica.com
Bilytica provides best in class services in Business Intelligence, Data-warehousing, Data Governance, Big Data management, Enterprise Applications, Enterprise Performance Management, Mobile Applications & Gaming and Business Consulting Services. Being a Tableau preferred reseller and consulting partner for Middle East, Europe, Turkey, Asia & Russia. Bilytica has helped 500+ small to large enterprises in Tableau implementation and training. We provide End to end Tableau consulting and training services including Tableau Proof of Concepts, Tableau Software license sales ,Tableau dashboard design Services , Onsite and remote Tableau consulting ,Customized onsite Tableau training , Tableau Server hosting ,Tableau integration services, Tableau advanced analytic & Tableau managed services.
Tableau reseller partner in Bahrain Bilytica Best business Intelligence Compa...Carie John
Email: info@bilytica.com
Bilytica provides best in class services in Business Intelligence, Data-warehousing, Data Governance, Big Data management, Enterprise Applications, Enterprise Performance Management, Mobile Applications & Gaming and Business Consulting Services. Being a Tableau preferred reseller and consulting partner for Middle East, Europe, Turkey, Asia & Russia. Bilytica has helped 500+ small to large enterprises in Tableau implementation and training. We provide End to end Tableau consulting and training services including Tableau Proof of Concepts, Tableau Software license sales ,Tableau dashboard design Services , Onsite and remote Tableau consulting ,Customized onsite Tableau training , Tableau Server hosting ,Tableau integration services, Tableau advanced analytic & Tableau managed services.
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Bright and glamourous on the outside, innovation is pretty messy on the inside. In spite of high profile news that makes it seem like most organizations are successful and even disruptive innovators, the reality is that only a fraction of innovation efforts ever reach the market. This article shows how innovation governance increases the rate of successful innovation.
Boosting Cybersecurity with Data Governance (peer reviewed)Guy Pearce
Data Governance has a significant role to play in information security, with special data classes beyond the regular four cyber classes (public, confidential, classified and restricted) being useful in helping the organization identify whether sensitive data was exposed in a breach.
My article published in the Canadian Institute of Corporate Directors journal, Director, outlining why not only the CIO, but also the COO and CHRO have roles to play in effective cybersecurity leadership
Leading enterprise-scale big data business outcomesGuy Pearce
A talk specially prepared for McMaster University. There is more benefit to thinking about big data as a paradigm rather than as a technology, as it helps shape these projects in the context of resolving some of the enterprise's greatest challenges, including its competitive positioning. This approach integrates the operating model, the business model and the strategy in the solution, which improves the ability of the project to actually deliver its intended value. I support this position with a case study that created audited financial value for a major global bank.
Big data governance as a corporate governance imperativeGuy Pearce
Poor data governance impacts reputation risk by data breach, by privacy violations and by acting on poor quality data. Furthermore, there are some important differences in what data governance means for big data compared to data governance for operational data.
That poor data governance impacts reputation risk means it has considerable implications for the Board of Directors, for whom reputation risk is the number one risk according to Deloitte (2013).
This presentation targeting the Board of Directors and the C-Suite and presented at the National Data Governance and Privacy Congress in Calgary, Canada presented some reasons why data governance is critical, from the perspective of both the C-Suite and the Board of Directors.
(Also on YouTube at http://youtu.be/QR4KO3Yx0n4)
The pressure is on marketing to quantify the benefits of the huge spend including brand) it incurs. It\'s not particularly difficult, although it is a fair amount of work. This presentation shows you how! Let me know if you need help!
African Retail Banking Opportunities In The Brics And (1)Guy Pearce
The document discusses opportunities for corporate, SME, and retail banking in Africa from the economic growth of the BRICS (Brazil, Russia, India, China, South Africa) nations. It notes that South Africa's inclusion in the BRICS positions it as a gateway to Africa and the increased trade between Africa and BRICS opens opportunities for transactional banking, asset financing, and growing small businesses. However, banks need to be proactive in leveraging these opportunities by working with governments to develop Africa's manufacturing and service sectors to generate more employment and consumer spending.
The relationship marketing advantage, ICSB Halifax, Canada, 2008Guy Pearce
This document summarizes a bank's strategy called Project Galactica to improve customer service for small and medium enterprises (SMEs). The bank was rated poorly on having competent staff and understanding customers' businesses. Project Galactica aimed to provide more proactive, personalized service through staff gaining deeper industry insights and understanding customers' specific needs. The results included increased sales, better customer satisfaction, and the bank becoming a more "top of mind" choice for SME customers. However, the full benefits may not be realized yet due to sales representatives' focus on short-term targets.
Marketing Science Conference on the SME use of banking products, Vancouver 2008 Guy Pearce
This document summarizes a study on business banking customers' acquisition of transactional, savings, and lending products over time. The study found:
1) Businesses behaved differently in their banking product needs based on their industry, market segment, and the specific product.
2) Many combinations of these factors showed significantly different acquisition trajectories and rates of change over the first 24 months.
3) When looking at the largest industries, some customer segments and product combinations maintained or increased their banking needs over time, while others declined, indicating that needs cannot be assumed to remain the same.
Academy of Marketing International Conference On Brand Management, Birmingham...Guy Pearce
This document summarizes a study conducted by Standard Bank on optimizing brand spending for their business-to-business banking customers. The study developed a "brand value chain" model to understand how brand marketing expenditures impact financial returns. By analyzing customer data across industries, the study found inconsistencies between expected risk and returns for some industry segments. This suggests opportunities to better target branding initiatives. The findings provide a tool to evaluate industry-level strategy effectiveness and optimize brand spending across customer segments. However, the results are specific to one bank and time period. Further analysis of other customer segments is ongoing.
Emerging Market SME Turnaround in a Recession: Theory and Practice. Cincinnat...Guy Pearce
A presentation on the academic context (high level literature review) for business turnaround made to the International Council of Small Business in the US on 27 Jun 2010
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Mind map of terminologies used in context of Generative AI
Creating $100 million from Big Data Analytics in Banking
1. @Teradata_Apps
|#TeradataSummit
How to make $100 million with Big Data
How we achieved a remarkable return for our
investment in total enterprise engagement in
the Big Data paradigm. A case study in true
end-to-end Big Data and customer-centricity.
<Sanitized version for Slideshare>
Guy Pearce
Managing Partner
REData Performance Consulting
Toronto, Canada
info@redata.ca
@pearcegf
@data_roi
3. 3 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
0
10
20
30
40
50
60
70
80
90
100
May Jun Jul Aug Sep Oct Nov
$million
Utilization
Payments
Xactions
Credit
A remarkable outcome, but probably not nearly as
remarkable as the journey!
The results above are but the last chapter in a rich people story,
a story about the Magic of Engagement! So, let me begin…
5. 5 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
Margins under
pressure
Increasing
Competition
Market share
under pressure
MARKET RESEARCH
FINDINGS*
• 3rd for “have competent and
knowledgeable staff”
• 3rd for “understand me”
> The findings included that not
understanding the customer
was a primary reason for
customer attrition
• 3rd for “make an effort to
understand me”
These findings were unacceptable. Something needed to be done!
The best solutions solve a problem. Burning platforms
make compelling cases for change!
*Ranking out of the major banks
STRATEGIC
CHALLENGES
Ext
6. 6 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
The problem statement concerned the customer. The
right “solution” would therefore have to solve these
Customer
Centricity
Channel
Innovation
Pricing
InnovationProduct
Innovation
The problem statement showed failing customer engagement. A
data-driven customer-centricity initiative was born
STRATEGIC
ALTERNATIVES
7. 7 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
Analytics were customer-centric. Quality and risk
were managed implicitly, the latter not ideal
Longitudinal
Behavioural
Analytics, by
customer
Risk-Return
Portfolio
Modelling, by
customer
Contribution
Profiling, by
customer
“Next Best”
Predictive
Analytics, by
customer
We integrated diverse analytics to best understand the customer,
and then focused everyone’s efforts on how best to serve them
The latter helped
optimize the sales
force geographically,
by sales potential
*
Geospatial
rendition of
“next best”,
aggregated by
municipality
8. 8 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
Tying it together, we proposed an integrated strategy
for data-driven customer-centricity as a solution
Nearly half of big companies’ data initiatives fail because of poor
integration between operating model and business model KPMG 2014
Strategy, Governance and Stakeholders
Marketing
Finance
Group IT
Measurement
Business Model
Product
Management
Channel
Management
Segment
Management
Operating Model
HR Operations
Big Data analytics
and insights
Objectives
10. 10 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
• MIS
• CRM
• Strategy
• Technology
• Environment
• Data Operations
• Data Integration
• Predictive Analytics
• Descriptive Analytics
• Data Sourcing (int/ext)
There were big lessons in building an action-oriented
big data core team
The degree of strategically accurate innovation and initiative that
drove the team to peak performance is a case study on its own
11. 11 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
• Steering Committee
> Channel Operations (action)
– Provincial and > 1000 branches
across the country
> Credit
> Product
> Strategy
> Marketing
> Segments
> Finance (recording)
> Change Management
> Human resources (training)
> Group IT (group CRM rollout)
Lessons learned were used to engage the
enterprise, an imperative for any enterprise-scale
initiative
Change Management 101: What’s the burning platform? Does it
impact me? What’s in it for me? How do I look good in this?
12. 12 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
Ma‟am, may I suggest…
(Aside
> W = what products you‟ve got
> X = what products a cohort of
customers of a similar profile to
you have
> Y = an estimate of what
products you‟ve got at our
competitors
> Z = an estimate of what
products you may need to fulfil
your aspirations)
(Structured conversation about
unique (diff(X-W) union Y union
Z))
Resulted in a 1:2 strike rate
Renewed, relevant, insightful one-to-one customer
engagement
13. 13 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
Insightful branch and suite staffing strategy, based
on potential, aggregated per customer per centre
Aggregated customer
insights were used as a
guide to set individual sales
targets, to assist with
determining staffing
levels, which in turn had
implications for training and
branch budgets
A case study in holistic strategy
14. 14 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
On adoption: Original slide to the board. Poor choice
of words, and over-simplified, but it worked
The best adoption strategy is for people to want what you’ve got!
16. 16 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
Value
Customers
Front Line
Staff
CRM
Big Data
Analytics
B
A
To create value, Big Data „reached‟ the customer by
means of CRM and the front line staff
B = Stakeholders and Team
A = Strategy Alignment. Purpose
For simplicity, the diagram does not show the feedback loops between the different elements
17. 17 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
• Adjust the annual market research survey to facilitate
measuring how the engagement was impacting market share
• Make governance and risk an explicit track
Two things we should have done, but didn‟t think to
do in the heat of the moment
18. 18 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
Volume
Terabyte
Scale
Variety
One internal
data source
Eight
external
data sources
Velocity
Growing at
up to 1000
rows per
second
Processing
A Teradata
appliance
dramatically
improved
performance
and
reliability
But was it really Big Data?
Yes, by Gartner’s definition. We were unable to achieve real time
and mobile deployment … maybe in our next engagement!
20. 20 3/23/2014 Teradata Confidential
@Teradata_Apps
|#TeradataSummit
Big Data is BIG. Complications aside, Big
Data is less effective if it is not
positioned at the enterprise level:
• The board approves corporate strategy
• The CEO sponsors Big Data as a key
component of strategy enablement
• The CIO‟s team builds it
• The CMO‟s team creates excitement
• The CHRO‟s team upskills staff
• The COO‟s team makes it happen
• The CFO‟s team audits and measures
Sharing some lessons
If the objective of your Big Data project is to create value,
then people engagement is a critical success factor
21. 21 3/23/2014 Teradata Confidential
The $100 million question: Is
Big Data something you should
be doing?
Guy Pearce
Managing Partner
REData Performance Consulting
Toronto, Canada
www.redata.ca
info@redata.ca
@pearcegf
@data_roi