The document outlines 5 new patterns of innovation that companies can use to create value: 1) Augmenting products to generate data, 2) Digitizing physical assets, 3) Combining data within and across industries, 4) Trading data, and 5) Codifying distinctive service capabilities. It provides examples of each pattern and notes that managers must be skilled in out-of-the-box thinking and supporting new ways of doing business to take advantage of these innovation opportunities using data and analytics.
Slide from my talk at Contech Forum 2021. This update from the November 2020 talk on digital equity work in the Bronx and lessons for Information providers in our changing world. This session will look at the progression of the Bronx Digital Equity Coalition and the development of principles for information and technology access that can also apply to information provider communities.
Data strategy - How & When to Invest (SXSW V2V Core Conversation)Courtney Hemphill
Data strategy is a necessary component of every company but the approach and skills can vary widely as a product and its users grow. Data ensures that each product feature released can be measured as to its impact and effectiveness. Data also surfaces latent market needs that can be leveraged into further product value.
Carbon Five has been using data to solve tricky product problems with companies like Square, Altschool, StitchFix, Prosper, and Fandango for over 15 years. Come join the conversation if you are interested in what skills are necessary to drive data science at your company, how to hire data science talent, and what data strategy looks like for different companies.
- See more at: http://schedule.sxswv2v.com/events/event_V2VP46093#sthash.oPukb2oW.dpuf
10 Enterprise Analytics Trends to Watch in 2020MicroStrategy
As businesses face a 2020 reality check and use this year to hone their strategy for the next decade, MicroStrategy has compiled insights on the top enterprise analytics trends to watch from leading BI, analytics and digital transformation influencers including analysts from Forrester, IDC, Constellation Research, Ventana Research and more.
From artificial intelligence and mobile intelligence, to the explosion of data and data sources, to some very human factors, we hope you’ll find this gathering of insights (plus the patterns and themes that have emerged here) a valuable resource for taking action now, but also looking and planning ahead to become an Intelligent Enterprise.
Business intelligence and data analytics involve analyzing data to extract useful information for decision making. BI tools provide trend analysis from multiple data sources, while BI technologies provide historical, current, and predictive views. BI architecture organizes data, information management, and technology components, while frameworks provide standards. Challenges include continuous availability, data security, cost, increasing users, new areas like operational BI, and performance/scalability. Leading vendors provide solutions like Google, Microsoft, Oracle, SAS, SAP, IBM, EMC, HP, and Teradata.
Why there are so many problems with streamlining data strategy ? What are the major problems ? How do you solve them ?
Using an approach based on Agile and Lean Concepts to achieve the goal of actionable data & analytics
10 Enterprise Analytics Trends to Watch in 2019 MicroStrategy
View insights from Forrester analyst Mike Gualtieri, Constellation Research’s Ray Wang and Doug Henschen, Ventana Research’s Mark Smith and David Menninger, IDC’s Chandana Gopal, Marcus Borba, Ronald van Loon and other top analytics and business intelligence thought leaders.
The document outlines 5 new patterns of innovation that companies can use to create value: 1) Augmenting products to generate data, 2) Digitizing physical assets, 3) Combining data within and across industries, 4) Trading data, and 5) Codifying distinctive service capabilities. It provides examples of each pattern and notes that managers must be skilled in out-of-the-box thinking and supporting new ways of doing business to take advantage of these innovation opportunities using data and analytics.
Slide from my talk at Contech Forum 2021. This update from the November 2020 talk on digital equity work in the Bronx and lessons for Information providers in our changing world. This session will look at the progression of the Bronx Digital Equity Coalition and the development of principles for information and technology access that can also apply to information provider communities.
Data strategy - How & When to Invest (SXSW V2V Core Conversation)Courtney Hemphill
Data strategy is a necessary component of every company but the approach and skills can vary widely as a product and its users grow. Data ensures that each product feature released can be measured as to its impact and effectiveness. Data also surfaces latent market needs that can be leveraged into further product value.
Carbon Five has been using data to solve tricky product problems with companies like Square, Altschool, StitchFix, Prosper, and Fandango for over 15 years. Come join the conversation if you are interested in what skills are necessary to drive data science at your company, how to hire data science talent, and what data strategy looks like for different companies.
- See more at: http://schedule.sxswv2v.com/events/event_V2VP46093#sthash.oPukb2oW.dpuf
10 Enterprise Analytics Trends to Watch in 2020MicroStrategy
As businesses face a 2020 reality check and use this year to hone their strategy for the next decade, MicroStrategy has compiled insights on the top enterprise analytics trends to watch from leading BI, analytics and digital transformation influencers including analysts from Forrester, IDC, Constellation Research, Ventana Research and more.
From artificial intelligence and mobile intelligence, to the explosion of data and data sources, to some very human factors, we hope you’ll find this gathering of insights (plus the patterns and themes that have emerged here) a valuable resource for taking action now, but also looking and planning ahead to become an Intelligent Enterprise.
Business intelligence and data analytics involve analyzing data to extract useful information for decision making. BI tools provide trend analysis from multiple data sources, while BI technologies provide historical, current, and predictive views. BI architecture organizes data, information management, and technology components, while frameworks provide standards. Challenges include continuous availability, data security, cost, increasing users, new areas like operational BI, and performance/scalability. Leading vendors provide solutions like Google, Microsoft, Oracle, SAS, SAP, IBM, EMC, HP, and Teradata.
Why there are so many problems with streamlining data strategy ? What are the major problems ? How do you solve them ?
Using an approach based on Agile and Lean Concepts to achieve the goal of actionable data & analytics
10 Enterprise Analytics Trends to Watch in 2019 MicroStrategy
View insights from Forrester analyst Mike Gualtieri, Constellation Research’s Ray Wang and Doug Henschen, Ventana Research’s Mark Smith and David Menninger, IDC’s Chandana Gopal, Marcus Borba, Ronald van Loon and other top analytics and business intelligence thought leaders.
This document discusses the importance of business transformation from just managing data to achieving digital agility. It notes that digital disruption is happening more rapidly as new trends emerge and start-ups create disruptive business models. To succeed, businesses need to shift power to customers, change business operations models, and drive organizational change. Data-driven insights and analytics are also key drivers, and the volume of data is exploding. To win, businesses need organizational agility like start-ups through a digital vision and the ability to change and innovate quickly.
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
- Introduction into Big data
- What makes data "big"
- Big data in Marketing
- Big data in politics
- Big data in national intelligence security
- Big data in Shamra.sy
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
The Banking Brand Data Intelligence Report 2016 - Understanding the customer ...MRS
This document discusses big data and how organizations can better utilize the large amounts of data they collect. It argues that most organizations currently only access a small percentage of the data they collect and do not realize its full value. The document advocates for a approach involving deep learning, predictive analytics, discovery analytics, and data visualization to generate real insights from data. It also discusses how predictive analytics can be used to predict the future by analyzing vast amounts of real-time commentary with machine learning algorithms.
5 Steps to Transform into a Data-Driven Organization - Ganes Kesari - Gramen...Ganes Kesari
This session was presented on May 27th, 2021, in a Webinar organized by Gramener.
https://info.gramener.com/5-steps-to-transform-into-data-driven-organization
Session Details:
Today, organizations struggle to get value from data despite significant investments. Did you know that there's one factor that influences the outcomes of all your data initiatives?
This webinar will highlight how an organization's data maturity influences its performance. It will show how you can assess your data maturity and plan the five steps for data-driven business transformation.
Pain points we would be discussing:
Most organizations stagnate midway in their data journey.
Gartner says that over 87% of organizations in the industry are at lower levels of data maturity (levels 1 and 2 on a scale of 5).
Just doing more data science projects will not improve your capabilities or outcomes. The fact is that the top challenges reported by CDOs fall into five common areas.
This webinar will show what they are and how you can tackle them.
Who should attend
- Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Managers
What Will You Learn?
- What is data science maturity, and why does it matter?
- How do you assess data science maturity and limitations of the assessment?
- How can data science maturity help your organization level up (explained with an example)?
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
Big data and personalization struggles in banking 092018Intelli-Global
This document discusses challenges that banks face in utilizing big data and personalization. It notes that while usable data and advanced analytics could unlock 40% of revenues in marketing and sales, 94% of US banks cannot deliver personalized experiences due to issues like insufficient data, poor data quality, and lack of technology. Specifically, banks struggle because infrastructure projects consume resources, marketing budgets are strained, and internal expertise is unavailable. The document recommends that banks prioritize personalization, employ a third party with combined data and marketing expertise, deploy a integrated personalization strategy across channels, test it in a pilot region with different customer demographics, and assess results after the pilot.
This document summarizes a presentation about implementing a data analytics function from scratch. It discusses the growth of data and need for analytics, challenges around data quality and governance, and proposes a hybrid governance model. It also outlines building a scalable data platform in the cloud to enable self-service analytics and consumption. Key steps include defining personas, use cases, data sources, and a governance model to balance business and IT needs for a mature analytics capability.
This document discusses the evolution of business intelligence and analytics (BI&A) from BI 1.0 to BI 3.0. It begins with introducing BI as using data and analytics to help executives make informed business decisions. Analytics is defined as exploring data to extract meaningful insights. Big data refers to extremely large and complex data sets.
The document then covers three phases of BI&A evolution. BI 1.0 in the 1990s used structured data and relational databases for descriptive analytics. BI 2.0 emerged in the 2000s adding complex queries and predictive analytics using both structured and unstructured data like social media via NoSQL databases. BI 3.0 integrates traditional BI, big data, and IoT data distributed
Big data and analytics have become increasingly important in the corporate world. Venture capital investments in big data are growing exponentially. Research from MIT shows analytics can increase productivity by 5-6%. However, many companies are unsure how to implement analytics effectively. The document outlines three key capabilities for exploring big data: identify the right data sources, build advanced analytics models, and transform the organization to make better decisions based on data and models. Mastering these skills can help companies gain a competitive advantage.
Discover the business value of Open Data by Majken SanderMajken Sander
Majken explains how to create a hub and start exploiting open data. Majken discusses which data can be found from external sources and how open data can add value by enhancing existing company data to gain new insights. There is a dataset out there for your business to become even more data driven. Join Majken to find it
Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.
The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. However, incorporating this technology in established business is far from obvious: cultural inertia in organizations, lack of transparency in most DL models and the complexity in training these models are some of the issues that will be addressed.
The document discusses how data and rigorous hypothesis testing can provide competitive advantages for businesses, both large and small. It advocates developing data strategies and thoughtful data architecture to extract maximum value from data. Founders should focus hypotheses on key business metrics and create a data-driven culture. While not all companies have large datasets, even small data needs management. The proliferation of tools has made advanced infrastructure accessible. Platform businesses should focus first on proving value for a specific use case before pursuing general platform sales.
DAS Webinar: Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY® survey on Emerging Trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Nicus Summit 2018_Driving Business Transformation with BI and ITFMNicus Software
Lauren Fulton, Fulton Consulting, shares insights on data democratization, Visa's business transformation, and deep dives into cost modeling with Nicus and Tableau.
This document discusses the importance of business transformation from just managing data to achieving digital agility. It notes that digital disruption is happening more rapidly as new trends emerge and start-ups create disruptive business models. To succeed, businesses need to shift power to customers, change business operations models, and drive organizational change. Data-driven insights and analytics are also key drivers, and the volume of data is exploding. To win, businesses need organizational agility like start-ups through a digital vision and the ability to change and innovate quickly.
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
- Introduction into Big data
- What makes data "big"
- Big data in Marketing
- Big data in politics
- Big data in national intelligence security
- Big data in Shamra.sy
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
The Banking Brand Data Intelligence Report 2016 - Understanding the customer ...MRS
This document discusses big data and how organizations can better utilize the large amounts of data they collect. It argues that most organizations currently only access a small percentage of the data they collect and do not realize its full value. The document advocates for a approach involving deep learning, predictive analytics, discovery analytics, and data visualization to generate real insights from data. It also discusses how predictive analytics can be used to predict the future by analyzing vast amounts of real-time commentary with machine learning algorithms.
5 Steps to Transform into a Data-Driven Organization - Ganes Kesari - Gramen...Ganes Kesari
This session was presented on May 27th, 2021, in a Webinar organized by Gramener.
https://info.gramener.com/5-steps-to-transform-into-data-driven-organization
Session Details:
Today, organizations struggle to get value from data despite significant investments. Did you know that there's one factor that influences the outcomes of all your data initiatives?
This webinar will highlight how an organization's data maturity influences its performance. It will show how you can assess your data maturity and plan the five steps for data-driven business transformation.
Pain points we would be discussing:
Most organizations stagnate midway in their data journey.
Gartner says that over 87% of organizations in the industry are at lower levels of data maturity (levels 1 and 2 on a scale of 5).
Just doing more data science projects will not improve your capabilities or outcomes. The fact is that the top challenges reported by CDOs fall into five common areas.
This webinar will show what they are and how you can tackle them.
Who should attend
- Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Managers
What Will You Learn?
- What is data science maturity, and why does it matter?
- How do you assess data science maturity and limitations of the assessment?
- How can data science maturity help your organization level up (explained with an example)?
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
Big data and personalization struggles in banking 092018Intelli-Global
This document discusses challenges that banks face in utilizing big data and personalization. It notes that while usable data and advanced analytics could unlock 40% of revenues in marketing and sales, 94% of US banks cannot deliver personalized experiences due to issues like insufficient data, poor data quality, and lack of technology. Specifically, banks struggle because infrastructure projects consume resources, marketing budgets are strained, and internal expertise is unavailable. The document recommends that banks prioritize personalization, employ a third party with combined data and marketing expertise, deploy a integrated personalization strategy across channels, test it in a pilot region with different customer demographics, and assess results after the pilot.
This document summarizes a presentation about implementing a data analytics function from scratch. It discusses the growth of data and need for analytics, challenges around data quality and governance, and proposes a hybrid governance model. It also outlines building a scalable data platform in the cloud to enable self-service analytics and consumption. Key steps include defining personas, use cases, data sources, and a governance model to balance business and IT needs for a mature analytics capability.
This document discusses the evolution of business intelligence and analytics (BI&A) from BI 1.0 to BI 3.0. It begins with introducing BI as using data and analytics to help executives make informed business decisions. Analytics is defined as exploring data to extract meaningful insights. Big data refers to extremely large and complex data sets.
The document then covers three phases of BI&A evolution. BI 1.0 in the 1990s used structured data and relational databases for descriptive analytics. BI 2.0 emerged in the 2000s adding complex queries and predictive analytics using both structured and unstructured data like social media via NoSQL databases. BI 3.0 integrates traditional BI, big data, and IoT data distributed
Big data and analytics have become increasingly important in the corporate world. Venture capital investments in big data are growing exponentially. Research from MIT shows analytics can increase productivity by 5-6%. However, many companies are unsure how to implement analytics effectively. The document outlines three key capabilities for exploring big data: identify the right data sources, build advanced analytics models, and transform the organization to make better decisions based on data and models. Mastering these skills can help companies gain a competitive advantage.
Discover the business value of Open Data by Majken SanderMajken Sander
Majken explains how to create a hub and start exploiting open data. Majken discusses which data can be found from external sources and how open data can add value by enhancing existing company data to gain new insights. There is a dataset out there for your business to become even more data driven. Join Majken to find it
Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.
The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. However, incorporating this technology in established business is far from obvious: cultural inertia in organizations, lack of transparency in most DL models and the complexity in training these models are some of the issues that will be addressed.
The document discusses how data and rigorous hypothesis testing can provide competitive advantages for businesses, both large and small. It advocates developing data strategies and thoughtful data architecture to extract maximum value from data. Founders should focus hypotheses on key business metrics and create a data-driven culture. While not all companies have large datasets, even small data needs management. The proliferation of tools has made advanced infrastructure accessible. Platform businesses should focus first on proving value for a specific use case before pursuing general platform sales.
DAS Webinar: Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY® survey on Emerging Trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Nicus Summit 2018_Driving Business Transformation with BI and ITFMNicus Software
Lauren Fulton, Fulton Consulting, shares insights on data democratization, Visa's business transformation, and deep dives into cost modeling with Nicus and Tableau.
Five building blocks of digital transformation Maziar Ebrahimi
For the past six years I have been engaged in several digital transformation initiatives, Corona gave me the time to create this presentation on inspirations and reflections on how companies can strengthen the core of their business while reimagining scalable digital futures.
Special thanks to Jeanne Ross of MIT CISR for the wonderful course and book on Organisational Design for Digital Transformation.
DAS Slides: Building a Data Strategy – Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
The document discusses how companies that are leading in analytics use data and analytics to gain competitive advantages and innovate. It profiles "Analytical Innovators" - companies that rely on analytics to compete and innovate. These companies share a belief that data is a core asset, make effective use of more data for faster results, and have senior management support for data-driven decision making. The document provides examples of companies in different industries that are successfully using analytics and a framework for other companies to also become more analytical.
April 11 G20 Presentation - IDB and LinkedIn Nathan Williams
The document summarizes a workshop held by the G20 on building opportunities for an inclusive future of work. It discusses challenges with traditional workforce data sources and opportunities provided by new sources like LinkedIn. It outlines a partnership between LinkedIn and the IDB to provide data insights on emerging jobs, skills, and talent migration trends in G20 countries including Argentina. The summary identifies key participants, their roles, and an action plan to address data shortages and guide education and training strategies.
DUP (DELOITTE UNIVERSITY PRESS)
Social Business Report: Shifting Out of First Gear
Explore the findings of the second annual global study, conducted in collaboration with MIT Sloan Management Review, to gain fresh insight into the social business landscape today and discover how some businesses are reaping value.
Palestra sobre conceitos Big data no evento IDETI em SP. Aborda o que é Big data, debate alguns beneficios e desafios. Debate também o papel do CDO- Chief Data Officer.
Technology is not the Answer: Why "digital" is not the most important aspect ...Megan Hurst
Shortly after its establishment in 1970, researchers at Xerox Parc invented the personal computer, complete with graphical user interface, windows, icons and a mouse. Yet, Xerox completely failed to successfully market and sell the personal computer and is still today known for making photocopiers and mainframes. In 1975, an employee at Kodak built the first digital camera. In 2012, Kodak filed for bankruptcy, having had its photographic film business disrupted by competitors invested heavily in promoting the "new" technology of digital photography. So why do large organizations (including academic institutions) fail to evolve with the times? And what is your strategy for supporting evolution and innovation in your organization? How do you adapt to and benefit from change and new ideas? In 2018, Athenaeum21 was commissioned to conduct an environmental scan of how and why digital strategies in a range of organizations succeed, and also why they "fail." We define "digital strategy" as "a plan of action for the adoption of institutional processes and practices to support and/or transform the organization and culture to effectively and competitively function in an increasingly digital world." Our research included a literature review, web review, and interviews with thought leaders and practitioners in digital transformation and digital skills-building in higher education, non-profits, and corporations. The report we produced provides examples of successful practices undertaken by organizations actively managing digital transformation and benefiting from their investments in innovation in Canada, the United States and Europe, as well as examples of so-called "failed" digital strategies. The answers as to why digital strategies succeed or fail are complex, but all hinge on six key elements that we identified during the research: 1. People, 2. Culture, 3. Leadership, 4. Organizational Alignment, followed by 5. Data, and 6. Technology. We will present our findings and model, with examples of how and why people, culture, leadership, and organizational alignment are more important for digital transformation than data and technology. We would like to have a robust discussion of how this model fits with your own local context.
Data Architecture Strategies Webinar: Emerging Trends in Data Architecture – ...DATAVERSITY
A robust data architecture is at the core what’s driving today’s innovative, data-driven organizations. From AI to machine learning to Big Data – a strong data architecture is needed in order to be successful, and core fundamentals such as data quality, metadata management, and efficient data storage are more critical than ever.
With the vast array of new technologies available to support these trends, how do you make sense of it all? Our panel of experts will offer their perspectives on how the latest trends in data architecture can support your organization’s data-driven goals.
This document summarizes a report on how leading asset managers and owners are gaining competitive advantages from data. It finds the industry is dividing between "data leaders" who harness data effectively and "data laggards" who struggle to manage and use their data. Data leaders transform large amounts of data into useful insights, integrate risk and performance analytics, and optimize electronic trading. Data laggards' abilities are less confident and their data challenges distract from priorities. Most firms are investing more in data technologies and capabilities to extract value from data, with priorities including managing risk across multiple asset classes.
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.
This document discusses the need for combining big data and ethnography. It summarizes that big data alone is not sufficient and can lead to issues like bias, incomplete representations, and misinterpretations without context. Ethnography provides thick contextual data through open-ended research methods like observation and interviews. The document advocates a mixed methods approach, using ethnography to understand problems and qualitative insights to complement and validate quantitative big data findings. This leads to more human and reflective understandings that benefit both innovation and optimization.
The role of Organisational Network Analysis in People AnalyticsDavid Green
Copy of my slides from my presentation at UNLEASH in Las Vegas on 15 May 2018
People analytics is increasingly being used by analytically advanced companies to help drive productivity, performance, innovation and collaboration. Organisational Network Analysis (ONA) is at the fulcrum of much of these efforts and is a technique that many analytics and HR professionals want to learn more about. This presentation features research, case studies and guidance covering both active and passive data sources that will demystify ONA and demonstrate how in combination with people analytics it can help improve both business outcomes and employee experience.
SMX West 2016 - Search, Content and Digital Marketing Maturity FrameworksBrightEdge Technologies
The convergence of search, social and content marketing has meant that the silos that once separated marketers have virtually merged. In this session, Brad Mattick will discuss how to transition from search practitioners to organizational leaders in a content marketing industry that is forecast to grow to $300 billion in the next four years. Key takeaways will focus around building a community framework to evangelize search and content as a leading performance channel. Marketers will learn how to integrate search, social and content into wider digital campaigns and develop internal conversations that resonate with key c-level executives.
Big data offers companies a big advantage if they can harness enormous data sets that were previously impossible to process. The document discusses how big data is transforming business models through creative destruction, as more data is created every day from various sources. It provides examples of how companies in various industries like retail, banking, and manufacturing are using big data for customer intimacy, product innovation, and improving operations. Specifically, companies are able to better customize products and services, improve supply chain management, and gain real-time insights from vast amounts of structured and unstructured data.
The rise of the digital consumer has led to an explosion of data collected across all touchpoints in the financial services industry. Real-time online interactions are the new normal. To engage Millennial customers, marketers must leverage innovative solutions. In fact, 75% of Millennials don’t receive many offers from their bank and 43% feel their bank doesn’t communicate through preferred channels. Omni-channel strategies can help you successfully engage your customers with real-time interactions as well as outbound campaigns. In this webinar, learn more about how Amazon Web Services (AWS) and FICO can help you build better customer engagement.
AWS Speaker: Peter Williams, Partner Technology Lead
FICO Speaker: Manish Pathak, Director, Product Management
Big data offers opportunities for companies to gain competitive advantages through improved customer intimacy, product innovation, and operations. The document discusses how various companies are leveraging big data across industries. It notes that 45% of companies have implemented big data initiatives in the past two years and over 90% of Fortune 500 companies will have initiatives underway soon. Harnessing big data's potential requires understanding where it can create value within a company and having the right organizational structure, technology investments, and plan to capture those benefits.
Isn’t it time you got married? Why subscription commerce is key to B2B success
Relationships underpin every business. In today’s always-on digital era, you can’t assume your customers will remain loyal – there are so many options, programs, and suppliers they can choose from for any range of needs. Aggressive disrupters are luring them away. Discover how Subscription Commerce can help preserve and grow your B2B business.
Speaker: Dave Harrelson, Capgemini
https://www.capgemini.com/events/sap-cx-live/
Similar to Breaking New Ground: Transformational Data Leadership (20)
The Future Is Now: Are You A Data Professional Or A Data VisionaryBrendan Aldrich
This document discusses higher education in California. It provides an overview of the three main public higher education systems in California: the University of California system, the California State University system, and the California Community College system. It details the roles and functions of each system, as well as enrollment and degree information. It also briefly discusses the private higher education associations and institutions in California.
Delivered as the closing keynote of PentahoWorld 2017, Ivy Tech Community College's Chief Data Officer, Brendan Aldrich, discussed ways in which practitioners can break routine and begin to innovate new ways to use data in support of their organizations.
Are you your company’s chief data officer? Given the scarcity of the official role, it’s likely that you’re not — at least in title. But that doesn't mean that you shouldn't operate like one. Do you approach data leadership as a C-level executive or a senior data head? Is your team’s output strategic or just operational? In this interactive keynote, one of the Windy City’s foremost data leaders will lead an interactive discussion on what it takes to lead like a chief, what it looks like, and how to get there and get it done.
Founding a Data Democracy: How Ivy Tech is Leading a Revolution in Higher Edu...Brendan Aldrich
Is your data reliable, intuitive, interactive, and immediately available to everyone who needs it? This presentation explores how Ivy Tech, the nation's largest singly-accredited community college system, coupled cloud-based and open-source platforms with predictive analytics and sustainable data practices to create a cost-effective governed data democracy that's helping administrators, staff, and faculty access the data they need to drive student success.
R. Brendan Aldrich, Executive Director of Data Warehousing at City Colleges of Chicago, discussed moving from a data dictatorship or aristocracy model to a data democracy model where all employees have access to data. This involves providing interactive reporting instead of static reports, using dynamic data environments tailored to user roles, integrated training and data dictionaries, and rethinking expensive licensing models. The City Colleges of Chicago is taking these approaches using a custom system built by Zogotech on Microsoft SQL Server to empower its over 5,800 employees and 120,000 students.
Talking about Big Data generates a lot of questions; however, most of the focus is on the technologies and skills required to collect and store this volume of information as opposed to the insight that companies need to derive from it. What factors should organizations consider in order to ensure that they are capitalizing on their investments with these technologies? How do you break through business silos to enable sharing of data to increase organizational value? Leveraging his cross-industry experience at companies like The Walt Disney Company, Travelers Insurance and Demand Media, Brendan Aldrich will discuss the question of “big value” with industry examples and a particular focus on his current work to deploy a “data democracy” within the City Colleges of Chicago.
Session Discovery Topics:
• Big value - keeping an eye on the forest (assumptions, judgment and bias)
• Data democracy - increasing productivity with data transparency and open access
The design of data systems within education can be challenging due to a lack of easily accessible information and a large variety of stakeholders with differing needs. Architecting Academic Intelligence is the process of centralizing and making accessible the student administrative information to the every member of the administration, faculty and staff of the City Colleges of Chicago so as to more efficiently promote student success.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
2. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
Breaking New Ground:
Transformational Data Leadership
R. Brendan Aldrich
Chief Data Officer
California State University, Chancellor’s Office
3. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
3
R. Brendan Aldrich
Chief Data Officer
California State University, Chancellor’s Office
20 years working specifically in the fields of data warehousing, business intelligence, analytics & research
Led data transformation and modernization initiatives for private and public organizations, including The Walt
Disney Company, Travelers Insurance, Demand Media, City Colleges of Chicago and Ivy Tech Community College
Transformative work in data has been recognized with national & international awards.
Twitter:
@CalStateCDO
LinkedIn:
www.linkedin.com/in/brendanaldrich/
4. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
• The Largest
and most diverse
system of 4-year
higher education
in the Nation
• 23 Campuses
• 43K Employees
• 500k Enrolled
Students Each Year
5. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
Leadership
Leadership is a process of social
influence, which maximizes the efforts of
others, towards the achievement of a goal.
- Kevin Kruse, Author of “Employee Engagement 2.0”
noun | leadership | ʹlē-der- ship
• Leadership stems from social influence, not authority or power
• Leadership requires others, and that implies they don’t need to be “direct reports”
• No mention of personality traits, attributes, or even a title; there are many styles,
many paths, to effective leadership
• It includes a goal, not influence with no intended outcome
7. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
Systemic Barriers for Education
James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh and Angela Hung Byers, McKinsey Global
Institute, “Big data: The next frontier for innovation, competition and productivity”, 5/11,
http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
8. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
Data Regimes
Data Dictatorship:
Data is controlled and its use is restricted. There is asymmetric distribution of
information based on your position.
Data Aristocracy:
Data analysts, scientists and PhDs are needed to do anything meaningful.
Power concentrates in the hands of these employees and their supervisors.
Data Anarchy:
Business users feel underserved and take matters into their own hands. They
create “shadow IT” systems and work around the “unresponsive” IT group.
Data Democracy:
Everybody gets timely and equitable access to data. Line of business users
are empowered and “own” the data. Executives and IT get out of the way.
1 Shash Hegde, Mariner, “The Rise of Data Regimes”, 9/12/13, http://www.mariner-
usa.com/rise-data-regimes/ (image substitution for Mao Zedong)
9. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
Placeholder for Survey
What “Data Regime” is most representative of your organization
today?
• Data Dictatorship
• Data Aristocracy
• Data Anarchy
• Data Dictatorship
16. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
Transformational Leadership
No mention of personality
traits, attributes, or even a
title; there are many styles,
many paths, to effective
leadership
Leadership stems
from social influence, not
authority or power
It includes a goal, not
influence with no intended
outcome
Leadership requires others, and
that implies they don’t need to
be “direct reports”
17. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
CSU: The First Six Months
Technical Design Challenge:
Statewide consistency with
local ownership
Campus Visits:
Building a Community of Practice
Building on the Possible:
Data Democracy to support
Operational and Student
Success
Competition or Support:
The Existing Data Teams
18. REAL BUSINESS INTELLIGENCE 2018 | JUNE 27-28, 2018 | MIT TANG CENTER
Thank You!
18
R. Brendan Aldrich
Chief Data Officer
California State University, Chancellor’s Office
Twitter:
@CalStateCDO
LinkedIn:
www.linkedin.com/in/brendanaldrich/
Editor's Notes
The CSU awards nearly half of the state’s baccalaureate degrees.
1 in 10 employed graduates came from CSU… representing 1 in 20 college degree holders nationwide!
We produce in the neighborhood of 120,000 graduates per year and our more than 3.4 MILLION living alumni are employed in every field across the world