This document presents a qualitative phenomenological study on critical success factors for implementing information governance. The study explores experiences of 20 information governance professionals from Fortune 500 companies. Key topics covered include the rapid growth of digital data, enterprise information management, information lifecycle management, methodology, emergent themes from interviews, and recommendations. Critical success factors identified include executive sponsorship, communication, technology, risk management, strategy/planning, and change management.
Business analytics in healthcare & life scienceSanjay Choubey
Business analytics for Healthcare, Life Science businesses. Trends, Issues, Challenges, process & steps, Business drivers, Market & compliance, Big data and approach to overcome
Asset management has always involved data-intensive business models, yet today's practitioners are confronted with a deluge of new information arriving in a variety of different formats.
Leader or Laggard: How Data Drives Competitive Advantage in the Investment Co...State Street
In an age where asset owners and managers face vast amounts of data, two distinct groups are emerging. The data leaders are using smart data strategies for valuable insights and a competitive edge, while the laggards struggle to master data complexity. There are key strategies these institutional investors need to go from laggard to leader – and pull ahead of the pack.
Waters USA 2013: Data Leaders vs. Data LaggardsState Street
Originally presented at Waters USA, this presentation features highlights from our Data and Analytics Survey conducted by the Economist Intelligence Unit.
Lecture notes on being Data-Driven and doing Data Science Johan Himberg
Visiting lecture held at Aalto University School of Business on prof. Pekka Malo's course "Data Science for Business". Lecture given by Johan Himberg and Jaakko Särelä (@ReaktorNow)
Business analytics in healthcare & life scienceSanjay Choubey
Business analytics for Healthcare, Life Science businesses. Trends, Issues, Challenges, process & steps, Business drivers, Market & compliance, Big data and approach to overcome
Asset management has always involved data-intensive business models, yet today's practitioners are confronted with a deluge of new information arriving in a variety of different formats.
Leader or Laggard: How Data Drives Competitive Advantage in the Investment Co...State Street
In an age where asset owners and managers face vast amounts of data, two distinct groups are emerging. The data leaders are using smart data strategies for valuable insights and a competitive edge, while the laggards struggle to master data complexity. There are key strategies these institutional investors need to go from laggard to leader – and pull ahead of the pack.
Waters USA 2013: Data Leaders vs. Data LaggardsState Street
Originally presented at Waters USA, this presentation features highlights from our Data and Analytics Survey conducted by the Economist Intelligence Unit.
Lecture notes on being Data-Driven and doing Data Science Johan Himberg
Visiting lecture held at Aalto University School of Business on prof. Pekka Malo's course "Data Science for Business". Lecture given by Johan Himberg and Jaakko Särelä (@ReaktorNow)
This is an interesting paper providing insight on the utilization of data for Asset Managers that you might find useful and informative. We are happy to schedule direct one on one discussions, as you wish.
New Horizons for Official Institutions: Research FindingsState Street
These findings are based on fieldwork conducted during January 2014 by FT Remark. In association with State Street, FT Remark surveyed 62 senior executives at official institutions – defined as central banks, sovereign wealth funds and public pension reserve funds – to explore the opportunities and challenges they face today and in the future.
The Innovator’s Journey: Insurance Sector InsightsState Street
On behalf of State Street, Longitude conducted a global survey of senior executives at investment
organizations during October and November 2014. We asked them to self-assess their confidence and
progress across six data capabilities, including infrastructure, insight, adaptability, compliance, talent and
governance. The 400 respondents were drawn from 11 countries and included insurance companies,
private and public pension funds, fund-of-funds, foundations, central banks, endowments, sovereign
wealth funds and supranationals. One-hundred insurance companies participated in the survey.
The Data-Driven Investor: How Technology Changes the Game for Today’s Asset O...State Street
This presentation highlights why data is driving a major transformation in the way asset owners manage their investments and the role advanced technology will play in the future. The research is based on a survey — conducted by the Economist Intelligence Unit — of more than 400 institutional investors, including 206 asset owners.
The Innovator’s Journey: Asset Owners Insights State Street
On behalf of State Street, Longitude conducted a global survey of senior executives at investment
organizations during October and November 2014. We asked them to self-assess their confidence and
progress across six data capabilities, including infrastructure, insight, adaptability, compliance, talent and
governance. The 400 respondents were drawn from 11 countries and included insurance companies,
private and public pension funds, fund-of-funds, foundations, central banks, endowments, sovereign
wealth funds and supranationals. One hundred asset owners participated in the survey.
Keys to extract value from the data analytics life cycleGrant Thornton LLP
Regulatory mandates driving transparency and financial objectives requiring accurate understanding of customer needs have heightened the importance of data analytics to unprecedented levels making it a critical element of doing business.
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...Health Catalyst
In this webinar, Tim and Dale, who worked together at Northwestern Medicine to establish an early-on and leading enterprise data warehouse solution for the hospital, physicians and medical school, will present their unique perspectives creating a thoughtful environment of comparison and contrast. This won’t be a typical corporate dozer—rather it will provide an opportunity for you to think deeply about the novel nature of your organization’s data. Historically, hospital expansion by building a larger footprint was the way to scale and capture market share. While those things still matter, attention has shifted to the expansion of the distribution of care through virtual and physical access points that embody a far more consumer friendly means to deliver care. It is in those entities that enriched data can be used to deliver care outreach that actually makes a difference for patients. That is where the new margins exist.
Data-Driven Business Model Innovation BlueprintMohamed Zaki
In this paper the authors present an integrated framework that could help stimulate an
organisation to become data-driven by enabling it to construct its own Data-Driven Business Model (DDBM) in coordination with the six fundamental questions for a data-driven business. There are a series of implications that may be particularly helpful to companies already leveraging ‘big data’ for their businesses or planning to do so. By utilising the blueprint an existing business is able to follow a step-by-step process to construct its own DDBM centred around the business’ own desired outcomes, organisation dynamics, resources, skills and the business sector within which it sits. Furthermore, an existing business can identify, within its own organisation, the most common inhibitors to constructing and implementing an effective DDBM and plan to mitigate these accordingly. Within the DDBM-Innovation Blueprint inhibitors are colour-coded and ranked from severe (red) to minor (green). This system of inhibitor ranking represents the frequency and severity of inhibitor, as perceived by 41 strategy and data-oriented elite interviewees.
This is an interesting paper providing insight on the utilization of data for Asset Managers that you might find useful and informative. We are happy to schedule direct one on one discussions, as you wish.
New Horizons for Official Institutions: Research FindingsState Street
These findings are based on fieldwork conducted during January 2014 by FT Remark. In association with State Street, FT Remark surveyed 62 senior executives at official institutions – defined as central banks, sovereign wealth funds and public pension reserve funds – to explore the opportunities and challenges they face today and in the future.
The Innovator’s Journey: Insurance Sector InsightsState Street
On behalf of State Street, Longitude conducted a global survey of senior executives at investment
organizations during October and November 2014. We asked them to self-assess their confidence and
progress across six data capabilities, including infrastructure, insight, adaptability, compliance, talent and
governance. The 400 respondents were drawn from 11 countries and included insurance companies,
private and public pension funds, fund-of-funds, foundations, central banks, endowments, sovereign
wealth funds and supranationals. One-hundred insurance companies participated in the survey.
The Data-Driven Investor: How Technology Changes the Game for Today’s Asset O...State Street
This presentation highlights why data is driving a major transformation in the way asset owners manage their investments and the role advanced technology will play in the future. The research is based on a survey — conducted by the Economist Intelligence Unit — of more than 400 institutional investors, including 206 asset owners.
The Innovator’s Journey: Asset Owners Insights State Street
On behalf of State Street, Longitude conducted a global survey of senior executives at investment
organizations during October and November 2014. We asked them to self-assess their confidence and
progress across six data capabilities, including infrastructure, insight, adaptability, compliance, talent and
governance. The 400 respondents were drawn from 11 countries and included insurance companies,
private and public pension funds, fund-of-funds, foundations, central banks, endowments, sovereign
wealth funds and supranationals. One hundred asset owners participated in the survey.
Keys to extract value from the data analytics life cycleGrant Thornton LLP
Regulatory mandates driving transparency and financial objectives requiring accurate understanding of customer needs have heightened the importance of data analytics to unprecedented levels making it a critical element of doing business.
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...Health Catalyst
In this webinar, Tim and Dale, who worked together at Northwestern Medicine to establish an early-on and leading enterprise data warehouse solution for the hospital, physicians and medical school, will present their unique perspectives creating a thoughtful environment of comparison and contrast. This won’t be a typical corporate dozer—rather it will provide an opportunity for you to think deeply about the novel nature of your organization’s data. Historically, hospital expansion by building a larger footprint was the way to scale and capture market share. While those things still matter, attention has shifted to the expansion of the distribution of care through virtual and physical access points that embody a far more consumer friendly means to deliver care. It is in those entities that enriched data can be used to deliver care outreach that actually makes a difference for patients. That is where the new margins exist.
Data-Driven Business Model Innovation BlueprintMohamed Zaki
In this paper the authors present an integrated framework that could help stimulate an
organisation to become data-driven by enabling it to construct its own Data-Driven Business Model (DDBM) in coordination with the six fundamental questions for a data-driven business. There are a series of implications that may be particularly helpful to companies already leveraging ‘big data’ for their businesses or planning to do so. By utilising the blueprint an existing business is able to follow a step-by-step process to construct its own DDBM centred around the business’ own desired outcomes, organisation dynamics, resources, skills and the business sector within which it sits. Furthermore, an existing business can identify, within its own organisation, the most common inhibitors to constructing and implementing an effective DDBM and plan to mitigate these accordingly. Within the DDBM-Innovation Blueprint inhibitors are colour-coded and ranked from severe (red) to minor (green). This system of inhibitor ranking represents the frequency and severity of inhibitor, as perceived by 41 strategy and data-oriented elite interviewees.
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
It’s been three years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. But what is the state of Data Governance today?
How has it evolved? What’s its role now? Building on prior research, erwin by Quest and ESG have partnered on a new study about what’s driving the practice of Data Governance, program maturity and current challenges. It also examines the connections to data operations and data protection, which is interesting given the fact that improving data security is now the No. 1 driver of Data Governance, according to this year’s survey respondents.
So please join us for this webinar to learn about the:
Other primary drivers for enterprise Data Governance programs
Most common bottlenecks to program maturity and sustainability
Advantages of aligning Data Governance with the other data disciplines
In a post-COVID world, data has the power to be even more transformative, and 84% of business and technology professionals say it represents the best opportunity to develop a competitive advantage during the next 12 to 24 months. Let’s make sure your organization has the intelligence it needs about both data and data systems to empower stakeholders in the front and back office to do what they need to do.
Data-Ed Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Theera-Ampornpunt N. Adopting Health IT: What, Why, and How? Presented at: How to Implement World Standard Hospital IT?; 2010 Nov 3; Srinagarind Hospital, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand. Invited speaker, in Thai.
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand:
- Their current Data Management practices
- Strengths that should be leveraged
- Remediation opportunities
ITS 833 – INFORMATION GOVERNANCEChapter 7Business consider.docxvrickens
ITS 833 – INFORMATION GOVERNANCE
Chapter 7
Business considerations for a successful ig program
Dr. Sandra J. Reeves
[email protected] J. Reeves 2018
1
1
CHAPTER GOALS AND OBJECTIVES
What is the difference between structured and unstructured data?
What is the difference between unstructured and semi-structured information?
Why is unstructured data so challenging?
[email protected] J. Reeves 2018
2
Generally, what is full cost accounting (FCA)?
What are the 10 key factors that drive the total cost of ownership of unstructured data
How can we better manage information?
How would an IG enabled organization look different from one that is not IG enabled?
2
Understanding the Changing Information Environment
Difficult to Justify
Short term return on investment is nonexistent
Long term view is essential
Reduce exposure to risk over time
Improve quality and security of information
Streamlining information retention
Looking at Information Costs differently
THE BUSINESS CASE FOR INFORMATION GOVERNANCE
[email protected] J. Reeves 2018
3
3
The information environment
[email protected] J. Reeves 2018
4
Challenges of Unstructured Information
Data volumes are growing
“Unstructured Information” is growing at a dramatic rate
Challenges unique to unstructured information
Horizontal nature
Lack of formality
Management location
Identification of ownership
Classification
Calculating Information Costs
Rising Storage Costs (Short sighted thinking)
Labor (particularly knowledge workers)
Overhead costs
Costs of e-discovery and litigation
Opportunity Costs
4
FULL COST ACCOUNTING FOR INFORMATON
Models?
[email protected] J. Reeves 2018
5
Total Cost of Ownership (TCO) Model
Return on Investment Model (ROI)
Full Cost Accounting Model (FCA)
Past, Present, Future Costs
Direct Costs
Indirect Costs
Flexible Application
Triple Bottom Line Accounting – Monetary, Environment, Societal Costs
Full Cost Accounting
General and Administrative Costs
Productivity Gains and Losses
Legal and E-discovery costs
Indirect Costs
Up-Front Costs
Future Costs
5
The politics involved
ITS ALL POLITICAL!
I’m Convinced!
Audience
Argument
Argument
Argument
Tools needed to establish facts about the information environment
Find Unstructured Information across enterprise
Combine Basic Metrics
Provide Sophisticated Analysis
FACTS
Use Dashboards
SOURCES OF Costs of owning unstructured information, cost reducers and cost enhanceRS
[email protected] J. Reeves 2018
8
Outdated, Unenforced politics
Poorly defined information ownership and governance
Open loop, reactive e-discovery processes
Uncontrolled information responsibilities
Modernist, paper focused information rules
Ad hoc, unstructured business processes
Disconnected governance programs
Formal, communicated and enforced policies
Automated classification and organization
Defensible deletion and selection content migration
Data maps
Proactive, repeatable e-discovery procedures
C ...
ITS 833 – INFORMATION GOVERNANCEChapter 7Business consider.docxdonnajames55
ITS 833 – INFORMATION GOVERNANCE
Chapter 7
Business considerations for a successful ig program
Dr. Sandra J. Reeves
[email protected] J. Reeves 2018
1
1
CHAPTER GOALS AND OBJECTIVES
What is the difference between structured and unstructured data?
What is the difference between unstructured and semi-structured information?
Why is unstructured data so challenging?
[email protected] J. Reeves 2018
2
Generally, what is full cost accounting (FCA)?
What are the 10 key factors that drive the total cost of ownership of unstructured data
How can we better manage information?
How would an IG enabled organization look different from one that is not IG enabled?
2
Understanding the Changing Information Environment
Difficult to Justify
Short term return on investment is nonexistent
Long term view is essential
Reduce exposure to risk over time
Improve quality and security of information
Streamlining information retention
Looking at Information Costs differently
THE BUSINESS CASE FOR INFORMATION GOVERNANCE
[email protected] J. Reeves 2018
3
3
The information environment
[email protected] J. Reeves 2018
4
Challenges of Unstructured Information
Data volumes are growing
“Unstructured Information” is growing at a dramatic rate
Challenges unique to unstructured information
Horizontal nature
Lack of formality
Management location
Identification of ownership
Classification
Calculating Information Costs
Rising Storage Costs (Short sighted thinking)
Labor (particularly knowledge workers)
Overhead costs
Costs of e-discovery and litigation
Opportunity Costs
4
FULL COST ACCOUNTING FOR INFORMATON
Models?
[email protected] J. Reeves 2018
5
Total Cost of Ownership (TCO) Model
Return on Investment Model (ROI)
Full Cost Accounting Model (FCA)
Past, Present, Future Costs
Direct Costs
Indirect Costs
Flexible Application
Triple Bottom Line Accounting – Monetary, Environment, Societal Costs
Full Cost Accounting
General and Administrative Costs
Productivity Gains and Losses
Legal and E-discovery costs
Indirect Costs
Up-Front Costs
Future Costs
5
The politics involved
ITS ALL POLITICAL!
I’m Convinced!
Audience
Argument
Argument
Argument
Tools needed to establish facts about the information environment
Find Unstructured Information across enterprise
Combine Basic Metrics
Provide Sophisticated Analysis
FACTS
Use Dashboards
SOURCES OF Costs of owning unstructured information, cost reducers and cost enhanceRS
[email protected] J. Reeves 2018
8
Outdated, Unenforced politics
Poorly defined information ownership and governance
Open loop, reactive e-discovery processes
Uncontrolled information responsibilities
Modernist, paper focused information rules
Ad hoc, unstructured business processes
Disconnected governance programs
Formal, communicated and enforced policies
Automated classification and organization
Defensible deletion and selection content migration
Data maps
Proactive, repeatable e-discovery procedures
C.
As businesses generate and manage vast amounts of data, companies have more opportunities to gather data, incorporate insights into business strategy and continuously expand access to data across the organisation. Doing so effectively—leveraging data for strategic objectives—is often easier said
than done, however. This report, Transforming data into action: the business outlook for data governance, explores the business contributions of data governance at organisations globally and across industries, the challenges faced in creating useful data governance policies and the opportunities to improve such programmes.
Federated data organizations in public sector face more challenges today than ever before. As discovered via research performed by North Highland Consulting, these are the top issues you are most likely experiencing:
• Knowing what data is available to support programs and other business functions
• Data is more difficult to access
• Without insight into the lineage of data, it is risky to use as the basis for critical decisions
• Analyzing data and extracting insights to influence outcomes is difficult at best
The solution to solving these challenges lies in creating a holistic enterprise data governance program and enforcing the program with a full-featured enterprise data management platform. Kreig Fields, Principle, Public Sector Data and Analytics, from North Highland Consulting and Rob Karel, Vice President, Product Strategy and Product Marketing, MDM from Informatica will walk through a pragmatic, “How To” approach, full of useful information on how you can improve your agency’s data governance initiatives.
Learn how to kick start your data governance intiatives and how an enterprise data management platform can help you:
• Innovate and expose hidden opportunities
• Break down data access barriers and ensure data is trusted
• Provide actionable information at the speed of business
Event Report - SAP Sapphire 2024 Orlando - lots of innovation and old challengesHolger Mueller
Holger Mueller of Constellation Research shares his key takeaways from SAP's Sapphire confernece, held in Orlando, June 3rd till 5th 2024, in the Orange Convention Center.
Premium MEAN Stack Development Solutions for Modern BusinessesSynapseIndia
Stay ahead of the curve with our premium MEAN Stack Development Solutions. Our expert developers utilize MongoDB, Express.js, AngularJS, and Node.js to create modern and responsive web applications. Trust us for cutting-edge solutions that drive your business growth and success.
Know more: https://www.synapseindia.com/technology/mean-stack-development-company.html
Buy Verified PayPal Account | Buy Google 5 Star Reviewsusawebmarket
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Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
Recruiting in the Digital Age: A Social Media MasterclassLuanWise
In this masterclass, presented at the Global HR Summit on 5th June 2024, Luan Wise explored the essential features of social media platforms that support talent acquisition, including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok.
6. Problem Statement
The general problem is the difficulty seen in trying to
manage the ever-growing amount of data (Gantz &
Reinsel, 2011).
4.4
Trillion
GB
44
Trillion
GB