FIMA's latest whitepaper evaluates how financial services companies are managing the challenges posed by data quality management. By analyzing which data types and data characteristics businesses are struggling with, it uncovers the true business costs associated with data quality. It will also gauge how data governance programs are maturing and how they are being measured. Finally, it assesses how data is being managed within financial institutions.
Key findings include:
Data quality has never been more important for financial institutions, but most of those companies feel their data is only mediocre: Quality data serves a myriad of central business goals, from risk reduction to increased productivity. Unfortunately, many businesses continue to struggle with data quality, despite the fact that four-fifths of them have it ranked as a top priority.
The top two business functions impacted by poor data quality are regulatory compliance and risk management: Because these concerns tend to be the most important drivers of data quality, many financial institutions see data governance as a “must-do,” rather than a ROI-boosting activity. Furthermore, the vast majority of financial services companies can not quantify the business cost of poor data quality.
Financial institutions vary greatly in the maturity of their data governance programs: Data governance cannot be overlooked – unsurprisingly, businesses with formalized data governance programs reported that their data was higher quality than most other groups.
Data quality management requires close collaboration between business and IT leaders: That collaboration already exists for 83% of respondents in this study, who say that IT and business leaders work together to manage data quality in their organizations. However, the tools these businesses use to manage their data are not all equal, leading to an uneven allocation of resources.
1. The document discusses shifts in analytics and big data, including that the majority of organizations now realize returns on analytics investments within a year, and that while customer focus remains important, organizations are increasingly using data and analytics to improve operations.
2. It also notes that many organizations are transforming processes by integrating digital capabilities, and that the value driver for big data has shifted from volume to velocity - the ability to quickly move from data to action.
3. Speed is now the key differentiator, as data-driven organizations with capabilities for broad, fast analytics usage and agile technical infrastructure are creating significant business impacts.
This presentation contains our view on how data can be Strategically managed and stewarded in an organization, and the categories where rules can be applied to facilitate that process.
Data Governance That Drives the Bottom LinePrecisely
The financial services sector is investing heavily in data governance solutions to find, understand and trust customer data, while also managing compliance risk around an ever-evolving regulatory landscape more effectively.
But do you still find it difficult to get management support for data governance budgets? Do you have the tools you need to determine the “business cost of data” accurately? Can you show the CFO an ROI projection he can count on? Are you able to answer, “Will I see results on the top line or the bottom line?” Are your business line leaders able to identify areas that are losing money due to data problems?
If you answered no to any of these questions, join Precisely in our upcoming webinar that will focus on how Financial Services companies can monetize the return on investment for data governance and how to relate it to business results that every senior leader understands.
Join this on-demand webinar to learn about:
- How to select data initiatives based on corporate goals and strategy
- How to connect the dots from data challenges (quality, availability, accuracy, currency) to specific business metrics around
- How to quantify the data contribution to improving business performance around
- How to leverage metadata and linage to get a 360-degree understanding of your data
- How to evaluate data assets by assigning measures and defining scores.
- How to assign accountability to assets and processes
- How to define and execute the workflows needed to implement corrective actions
- How to highlight the benefits of data governance
The document discusses a disconnect between IT executives and staff on data strategy and management. While executives understand data's strategic importance, staff who manage data day-to-day have less business focus. This disconnect can hamper an organization's ability to effectively use data. The document also notes business users are taking more control of data initiatives, potentially sidelining IT. Both executives and staff need better communication to align on strategic and operational data issues.
Legal Entity Risk and Counter-Party Exposure April 2016bfreeman1987
Legal entity risk and exposure has become an important issue for financial institutions. This solution aggregates data from multiple internal systems to determine exposure to any given counterparty or legal entity. It identifies issuers and instruments, calculates current exposure values, and monitors public data sources for risk events. The solution detects potential risk events, identifies affected instruments and issuers, and determines exposure to help organizations respond rapidly.
3 Tips to improve supplier information managementSarah Fane
3 tips to improve supplier information management and prepare your company for compliance and risk challenges.
For most companies, supplier information management has always been an afterthought. However over the last few years, increased risk and compliance requirements have made accurate and timely information more important than ever.
Today, many organizations struggle with keeping supplier information up to date, or have not prioritized projects to improve the quality of their data.
sharedserviceslink and Tradeshift conducted a Pulse Survey of the shared services market to understand how important accurate supplier information is, and what steps organizations are taking to improve supplier information and compliance.
Download this report for our results and our 3 tips to improve your supplier information management.
This document discusses the importance of information governance for successful big data analytics projects. It notes that while structured data is usually well-managed, unstructured data which accounts for 90% of enterprise information often lacks proper governance. Without good governance of this unstructured data, big data projects are at risk of using low quality "bad data" which undermines the analysis. The document recommends information governance solutions to help organizations discover, categorize, and manage their unstructured information to ensure the data quality needed for valuable big data analytics outcomes.
1. The document discusses shifts in analytics and big data, including that the majority of organizations now realize returns on analytics investments within a year, and that while customer focus remains important, organizations are increasingly using data and analytics to improve operations.
2. It also notes that many organizations are transforming processes by integrating digital capabilities, and that the value driver for big data has shifted from volume to velocity - the ability to quickly move from data to action.
3. Speed is now the key differentiator, as data-driven organizations with capabilities for broad, fast analytics usage and agile technical infrastructure are creating significant business impacts.
This presentation contains our view on how data can be Strategically managed and stewarded in an organization, and the categories where rules can be applied to facilitate that process.
Data Governance That Drives the Bottom LinePrecisely
The financial services sector is investing heavily in data governance solutions to find, understand and trust customer data, while also managing compliance risk around an ever-evolving regulatory landscape more effectively.
But do you still find it difficult to get management support for data governance budgets? Do you have the tools you need to determine the “business cost of data” accurately? Can you show the CFO an ROI projection he can count on? Are you able to answer, “Will I see results on the top line or the bottom line?” Are your business line leaders able to identify areas that are losing money due to data problems?
If you answered no to any of these questions, join Precisely in our upcoming webinar that will focus on how Financial Services companies can monetize the return on investment for data governance and how to relate it to business results that every senior leader understands.
Join this on-demand webinar to learn about:
- How to select data initiatives based on corporate goals and strategy
- How to connect the dots from data challenges (quality, availability, accuracy, currency) to specific business metrics around
- How to quantify the data contribution to improving business performance around
- How to leverage metadata and linage to get a 360-degree understanding of your data
- How to evaluate data assets by assigning measures and defining scores.
- How to assign accountability to assets and processes
- How to define and execute the workflows needed to implement corrective actions
- How to highlight the benefits of data governance
The document discusses a disconnect between IT executives and staff on data strategy and management. While executives understand data's strategic importance, staff who manage data day-to-day have less business focus. This disconnect can hamper an organization's ability to effectively use data. The document also notes business users are taking more control of data initiatives, potentially sidelining IT. Both executives and staff need better communication to align on strategic and operational data issues.
Legal Entity Risk and Counter-Party Exposure April 2016bfreeman1987
Legal entity risk and exposure has become an important issue for financial institutions. This solution aggregates data from multiple internal systems to determine exposure to any given counterparty or legal entity. It identifies issuers and instruments, calculates current exposure values, and monitors public data sources for risk events. The solution detects potential risk events, identifies affected instruments and issuers, and determines exposure to help organizations respond rapidly.
3 Tips to improve supplier information managementSarah Fane
3 tips to improve supplier information management and prepare your company for compliance and risk challenges.
For most companies, supplier information management has always been an afterthought. However over the last few years, increased risk and compliance requirements have made accurate and timely information more important than ever.
Today, many organizations struggle with keeping supplier information up to date, or have not prioritized projects to improve the quality of their data.
sharedserviceslink and Tradeshift conducted a Pulse Survey of the shared services market to understand how important accurate supplier information is, and what steps organizations are taking to improve supplier information and compliance.
Download this report for our results and our 3 tips to improve your supplier information management.
This document discusses the importance of information governance for successful big data analytics projects. It notes that while structured data is usually well-managed, unstructured data which accounts for 90% of enterprise information often lacks proper governance. Without good governance of this unstructured data, big data projects are at risk of using low quality "bad data" which undermines the analysis. The document recommends information governance solutions to help organizations discover, categorize, and manage their unstructured information to ensure the data quality needed for valuable big data analytics outcomes.
The document discusses various topics related to organizations and information systems including:
- Definitions of organizations from technical and behavioral perspectives
- Structural characteristics of organizations like division of labor and standard operating procedures
- Changing roles of information systems in organizations and how they lead to automation and virtual organizations
- Decision making processes at different levels and models like rational, political, and "garbage can"
- Use of information technology for competitive advantage at business, firm, and industry levels through strategies
- Importance of managing strategic transitions when adopting new information systems technologies
Survey Results Age Of Unbounded Data June 03 10nhaque
Enterprises today can generate, collect and consider more data than ever before. New types of data can provide insight into previously opaque processes and motivations, but prodigious quantities of data present opportunity, as well as complexity and distraction. nGenera Insight’s 2010 Leading in an Age of Unbounded Data survey garnered responses from over 70 major organizations, including many global corporations, to provide a cross-industry pulse of the state of enterprise data.
From big data overload to business impactMiguel Garcia
The document summarizes the key findings of a survey of 333 North American C-level executives about their organizations' preparedness to manage big data and leverage it effectively. The following were among the main findings:
- Organizations have seen an 86% average increase in data volume in the last 2 years, especially customer, operations, and sales/marketing data.
- However, 60% of executives rated their organizations as unprepared (C or lower), with 29% giving a D or F. Healthcare executives were least confident.
- Top frustrations included lacking the right systems to gather data and inability to give managers timely access to information.
- 93% of executives believe they are losing an average of
This document provides an overview of the history and evolution of information systems and technology. It discusses how computers originally focused on business and defense uses before personal computers became common. Local area networks then allowed people to work together and share information. As technology advanced, larger organizations developed internal IT departments to manage systems and purchase software to coordinate business processes. Information systems are now critical to supporting business operations and management decision making through tools like databases, business process management systems, and decision support systems. When combined with changes to organization and management, information systems can provide a strategic advantage through optimization of costs and processes.
The document discusses key trends in data management identified by global research. It finds organizations are increasingly focused on understanding customers as individuals to offer personalized service. However, inaccurate and incomplete data undermines customer experience for many. Experts recommend using data to develop a single view of each customer by linking all available information. This would allow real-time insights and responses tailored to individual customers, improving relationships and sales. Achieving accurate and comprehensive customer data remains a challenge for most organizations.
The survey of 395 C-level executives from various industries found:
1) Executives have overwhelmingly positive views of big data and its potential, especially for increasing sales, improving efficiency and building customer loyalty.
2) While recognizing big data's potential, three-quarters want a deeper understanding of the underlying technologies. Customer insights and targeting are currently seen as top priorities for big data applications.
3) Lack of understanding of how to apply big data to specific business functions is cited as the top internal obstacle to greater use of big data.
This chapter discusses the relationship between organizations and information systems. It covers key topics such as:
- Organizations are complex systems influenced by factors like structure, culture, politics and the environment. They use routines and business processes to function.
- Information systems can help analyze competitors and value chains to develop strategies. They also allow firms to leverage synergies, competencies and network-based strategies.
- The relationship between organizations and information technology is two-way, with each influencing the other. Information systems impact organizations through changes in costs, quality of information, and economics of information.
Developing & Deploying Effective Data Governance FrameworkKannan Subbiah
This is the slide deck presented at the Customer Privacy and Data Protection India Summit 2019 held in Mumbai, India. The specific topics touched upon are the guiding principles, Aligning with Data Architecture, Data Quality & Compliance.
This document discusses the relationship between organizations and information systems. It explains that organizations and information technology influence each other in a complex, two-way relationship. The document outlines several important features of organizations that impact information systems, including routines and business processes, organizational politics and culture, and environmental factors. It also discusses how disruptive technologies and organizational structure affect the use of information systems within companies.
The document discusses assessing an organization's readiness for business intelligence (BI). It defines BI and outlines key considerations like having executive sponsorship, realistic expectations, and dealing with data quality issues. The assessment process should deliver a high-level implementation plan, RFP, BI roadmap, skills gap analysis, and learning plan. Organizations are encouraged to put these deliverables in place to help ensure BI success.
Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?VIRGOkonsult
This document discusses how banks can maximize the value of customer data through big data analytics. It notes that while banks collect vast amounts of customer data, they are not fully exploiting this data due to various challenges. Organizational silos that separate customer data across business units prevent a unified customer view. Additionally, banks lack analytics skills and view big data initiatives as traditional IT projects rather than strategic opportunities. The document outlines how banks can enhance customer retention, acquisition, and share of wallet by leveraging big data analytics across the customer lifecycle from lead generation to risk assessment and personalized marketing. Case studies show analytics improving lead conversion rates by over 100% and top-line growth by ten times.
Big data alchemy - how can banks maximize the value of their customer dataRick Bouter
The document discusses how banks can maximize the value of customer data through big data analytics. It finds that while banks collect vast amounts of customer data, they are not fully exploiting this data due to various challenges. Organizational silos that separate customer data across business units, a lack of analytics talent, and viewing big data projects as just another IT initiative prevent banks from developing a holistic view of customers and gaining full insights from data. Addressing these impediments is key to allowing banks to improve customer experience and gain competitive advantages from big data.
This document discusses how organizations can better link decisions and information to improve organizational performance. It identifies three levels of linking information and decisions: loosely coupled, structured human decisions, and automated decisions. The document provides examples of each approach, such as creating a financial data mart to loosely inform various financial decisions, using analytics and decision process structuring to improve targeted decisions like pricing, and automating operational decisions through embedded rules and algorithms. The goal is to optimize how information supports and improves decision making at all levels of the organization.
The document discusses the importance of data quality and having a data strategy. It notes that poor quality data can lead to skewed analysis, improper campaign targeting, and wasted resources. It also outlines steps for improving data quality such as data audits, profiling data sources, data cleansing, and establishing business rules for data management. Maintaining high quality data requires both internal processes and leveraging external data services and is a key part of building data as a strategic asset for the business.
AIMLEAP #outsourcebigdata.com is a Trusted Partner for #DigitalIT, #BI #Analytics, #Automation & #DataManagement, #dataprocessing. 20 % of CIOs may lose their jobs if they fail to implement a successful framework for #DataGovernance in their organization Source: Gartner.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
State of Salesforce within the Nonprofit SectorJoshua Loomis
This document summarizes the results of a survey of over 300 nonprofits about their use of Salesforce. The key findings are:
- Fundraising and advancement departments use Salesforce more than other departments. Sales Cloud is the most popular product.
- Most nonprofits use the Nonprofit Success Package to track constituents and donations. Integrating additional systems and taking advantage of more Salesforce products is becoming more common.
- Salesforce is helping nonprofits communicate more effectively, raise more funds, increase productivity and better achieve their missions. However, more integration of Salesforce with back-end systems is needed to maximize its benefits.
- Looking ahead, nonprofits aim to raise more funds
Change management success for data governanceReid Elliott
As a data management professional you know that improving data governance is a top priority for many organisations. We know that data governance frameworks, processes and tools only enable benefits to the extent that our stakeholders adopt and use them effectively.
As well as technical proficiency and good project management and delivery, data governance success also requires effective change management. Preparing for change, managing change, and sustaining change are critical steps on the journey to effective data governance. So how can data management professionals best use change management principles and techniques to contribute to the success of our data governance initiatives?
This presentation was prepared to accompany a Data Management Association Australia webinar on change management success for data governance initiatives.
Aims of the facilitated discussion in the webinar were to explore:
How change management can enable the success of your data governance, reporting and analytics initiatives.
Common people change related challenges that many data governance, reporting and analytics initiatives need to navigate.
Change management techniques you can use to drive successful project delivery, change adoption and sustainable use of data governance, and reporting and analytics solutions.
How to identify the top change management priority for your own current project, and the change management techniques that you can use to address it.
Implementing an Effective Third-party & Vendor Risk Management ProgramKannan Subbiah
This document discusses implementing an effective third-party and vendor risk management program. It covers selecting a framework, the relationship lifecycle which includes strategy, due diligence, contracting, ongoing monitoring and re-evaluation. Key focus areas are scope, segmentation, due diligence, control systems, risk assessments, governance, organization, policy, tools and data. Recommendations include building a cross-functional team, being comprehensive without complexity, staying agile with risk-based intelligence, and complementing decision making with risk-based intelligence. The document also discusses compliance challenges, recovering from breach, next steps of integrating the approach and leveraging automation.
Whether you're refining your business ideas, planning to explore your business digitally or looking for a marketing campaign for your business, Webcome Digital brings tools you need to do it right and get it done.
El documento define la ofimática como la automatización de las comunicaciones y procesos de una oficina a través de hardware y software. La ofimática permite tareas como llevar balances financieros en hojas de cálculo y compartir archivos a través de redes e Internet. Las suites ofimáticas incluyen programas como procesadores de texto y hojas de cálculo que facilitan las tareas diarias de una oficina.
The document discusses various topics related to organizations and information systems including:
- Definitions of organizations from technical and behavioral perspectives
- Structural characteristics of organizations like division of labor and standard operating procedures
- Changing roles of information systems in organizations and how they lead to automation and virtual organizations
- Decision making processes at different levels and models like rational, political, and "garbage can"
- Use of information technology for competitive advantage at business, firm, and industry levels through strategies
- Importance of managing strategic transitions when adopting new information systems technologies
Survey Results Age Of Unbounded Data June 03 10nhaque
Enterprises today can generate, collect and consider more data than ever before. New types of data can provide insight into previously opaque processes and motivations, but prodigious quantities of data present opportunity, as well as complexity and distraction. nGenera Insight’s 2010 Leading in an Age of Unbounded Data survey garnered responses from over 70 major organizations, including many global corporations, to provide a cross-industry pulse of the state of enterprise data.
From big data overload to business impactMiguel Garcia
The document summarizes the key findings of a survey of 333 North American C-level executives about their organizations' preparedness to manage big data and leverage it effectively. The following were among the main findings:
- Organizations have seen an 86% average increase in data volume in the last 2 years, especially customer, operations, and sales/marketing data.
- However, 60% of executives rated their organizations as unprepared (C or lower), with 29% giving a D or F. Healthcare executives were least confident.
- Top frustrations included lacking the right systems to gather data and inability to give managers timely access to information.
- 93% of executives believe they are losing an average of
This document provides an overview of the history and evolution of information systems and technology. It discusses how computers originally focused on business and defense uses before personal computers became common. Local area networks then allowed people to work together and share information. As technology advanced, larger organizations developed internal IT departments to manage systems and purchase software to coordinate business processes. Information systems are now critical to supporting business operations and management decision making through tools like databases, business process management systems, and decision support systems. When combined with changes to organization and management, information systems can provide a strategic advantage through optimization of costs and processes.
The document discusses key trends in data management identified by global research. It finds organizations are increasingly focused on understanding customers as individuals to offer personalized service. However, inaccurate and incomplete data undermines customer experience for many. Experts recommend using data to develop a single view of each customer by linking all available information. This would allow real-time insights and responses tailored to individual customers, improving relationships and sales. Achieving accurate and comprehensive customer data remains a challenge for most organizations.
The survey of 395 C-level executives from various industries found:
1) Executives have overwhelmingly positive views of big data and its potential, especially for increasing sales, improving efficiency and building customer loyalty.
2) While recognizing big data's potential, three-quarters want a deeper understanding of the underlying technologies. Customer insights and targeting are currently seen as top priorities for big data applications.
3) Lack of understanding of how to apply big data to specific business functions is cited as the top internal obstacle to greater use of big data.
This chapter discusses the relationship between organizations and information systems. It covers key topics such as:
- Organizations are complex systems influenced by factors like structure, culture, politics and the environment. They use routines and business processes to function.
- Information systems can help analyze competitors and value chains to develop strategies. They also allow firms to leverage synergies, competencies and network-based strategies.
- The relationship between organizations and information technology is two-way, with each influencing the other. Information systems impact organizations through changes in costs, quality of information, and economics of information.
Developing & Deploying Effective Data Governance FrameworkKannan Subbiah
This is the slide deck presented at the Customer Privacy and Data Protection India Summit 2019 held in Mumbai, India. The specific topics touched upon are the guiding principles, Aligning with Data Architecture, Data Quality & Compliance.
This document discusses the relationship between organizations and information systems. It explains that organizations and information technology influence each other in a complex, two-way relationship. The document outlines several important features of organizations that impact information systems, including routines and business processes, organizational politics and culture, and environmental factors. It also discusses how disruptive technologies and organizational structure affect the use of information systems within companies.
The document discusses assessing an organization's readiness for business intelligence (BI). It defines BI and outlines key considerations like having executive sponsorship, realistic expectations, and dealing with data quality issues. The assessment process should deliver a high-level implementation plan, RFP, BI roadmap, skills gap analysis, and learning plan. Organizations are encouraged to put these deliverables in place to help ensure BI success.
Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?VIRGOkonsult
This document discusses how banks can maximize the value of customer data through big data analytics. It notes that while banks collect vast amounts of customer data, they are not fully exploiting this data due to various challenges. Organizational silos that separate customer data across business units prevent a unified customer view. Additionally, banks lack analytics skills and view big data initiatives as traditional IT projects rather than strategic opportunities. The document outlines how banks can enhance customer retention, acquisition, and share of wallet by leveraging big data analytics across the customer lifecycle from lead generation to risk assessment and personalized marketing. Case studies show analytics improving lead conversion rates by over 100% and top-line growth by ten times.
Big data alchemy - how can banks maximize the value of their customer dataRick Bouter
The document discusses how banks can maximize the value of customer data through big data analytics. It finds that while banks collect vast amounts of customer data, they are not fully exploiting this data due to various challenges. Organizational silos that separate customer data across business units, a lack of analytics talent, and viewing big data projects as just another IT initiative prevent banks from developing a holistic view of customers and gaining full insights from data. Addressing these impediments is key to allowing banks to improve customer experience and gain competitive advantages from big data.
This document discusses how organizations can better link decisions and information to improve organizational performance. It identifies three levels of linking information and decisions: loosely coupled, structured human decisions, and automated decisions. The document provides examples of each approach, such as creating a financial data mart to loosely inform various financial decisions, using analytics and decision process structuring to improve targeted decisions like pricing, and automating operational decisions through embedded rules and algorithms. The goal is to optimize how information supports and improves decision making at all levels of the organization.
The document discusses the importance of data quality and having a data strategy. It notes that poor quality data can lead to skewed analysis, improper campaign targeting, and wasted resources. It also outlines steps for improving data quality such as data audits, profiling data sources, data cleansing, and establishing business rules for data management. Maintaining high quality data requires both internal processes and leveraging external data services and is a key part of building data as a strategic asset for the business.
AIMLEAP #outsourcebigdata.com is a Trusted Partner for #DigitalIT, #BI #Analytics, #Automation & #DataManagement, #dataprocessing. 20 % of CIOs may lose their jobs if they fail to implement a successful framework for #DataGovernance in their organization Source: Gartner.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
State of Salesforce within the Nonprofit SectorJoshua Loomis
This document summarizes the results of a survey of over 300 nonprofits about their use of Salesforce. The key findings are:
- Fundraising and advancement departments use Salesforce more than other departments. Sales Cloud is the most popular product.
- Most nonprofits use the Nonprofit Success Package to track constituents and donations. Integrating additional systems and taking advantage of more Salesforce products is becoming more common.
- Salesforce is helping nonprofits communicate more effectively, raise more funds, increase productivity and better achieve their missions. However, more integration of Salesforce with back-end systems is needed to maximize its benefits.
- Looking ahead, nonprofits aim to raise more funds
Change management success for data governanceReid Elliott
As a data management professional you know that improving data governance is a top priority for many organisations. We know that data governance frameworks, processes and tools only enable benefits to the extent that our stakeholders adopt and use them effectively.
As well as technical proficiency and good project management and delivery, data governance success also requires effective change management. Preparing for change, managing change, and sustaining change are critical steps on the journey to effective data governance. So how can data management professionals best use change management principles and techniques to contribute to the success of our data governance initiatives?
This presentation was prepared to accompany a Data Management Association Australia webinar on change management success for data governance initiatives.
Aims of the facilitated discussion in the webinar were to explore:
How change management can enable the success of your data governance, reporting and analytics initiatives.
Common people change related challenges that many data governance, reporting and analytics initiatives need to navigate.
Change management techniques you can use to drive successful project delivery, change adoption and sustainable use of data governance, and reporting and analytics solutions.
How to identify the top change management priority for your own current project, and the change management techniques that you can use to address it.
Implementing an Effective Third-party & Vendor Risk Management ProgramKannan Subbiah
This document discusses implementing an effective third-party and vendor risk management program. It covers selecting a framework, the relationship lifecycle which includes strategy, due diligence, contracting, ongoing monitoring and re-evaluation. Key focus areas are scope, segmentation, due diligence, control systems, risk assessments, governance, organization, policy, tools and data. Recommendations include building a cross-functional team, being comprehensive without complexity, staying agile with risk-based intelligence, and complementing decision making with risk-based intelligence. The document also discusses compliance challenges, recovering from breach, next steps of integrating the approach and leveraging automation.
Whether you're refining your business ideas, planning to explore your business digitally or looking for a marketing campaign for your business, Webcome Digital brings tools you need to do it right and get it done.
El documento define la ofimática como la automatización de las comunicaciones y procesos de una oficina a través de hardware y software. La ofimática permite tareas como llevar balances financieros en hojas de cálculo y compartir archivos a través de redes e Internet. Las suites ofimáticas incluyen programas como procesadores de texto y hojas de cálculo que facilitan las tareas diarias de una oficina.
Digital River was dealing with data quality issues due to having commerce data spread across multiple platforms as a result of acquisitions. They addressed this by centralizing all transaction data into a single source of truth enterprise resource planning (ERP) system with the aid of a data governance program. This involved aligning data from different platforms that had various data capture points, workflows, payment methods and terminology. They established a data governance framework based on the Data Management Body of Knowledge (DMBOK) to define governance processes, roles and technology to manage data quality.
An audio quality evaluation of digital radio systemRojith Thomas
This document provides an audio quality evaluation of digital radio systems. It compares analog radio systems to proposed digital systems in terms of audio quality, channel capacity, spectrum efficiency and more. The evaluation involves collecting audio samples, encoding them using different codecs and bitrates, transmitting them over various digital radio systems, then having listeners rate the quality. The goal is to determine the optimal bitrate needed for a given audio quality across different digital radio systems.
OER: From the Perspective of Quality GovernanceCEMCA
This document discusses quality governance in the context of open educational resources (OER) from three perspectives:
1. It outlines the Indian public policy context around expanding access to education through existing institutions, enhancing capabilities with information and communication technologies, and ensuring equity and excellence.
2. It defines the concepts of "quality" and "governance" in education and discusses the opportunities and challenges of OER from a practitioner's approach using examples from a dual mode university.
3. It argues that quality indicators for OER should incorporate diverse learning processes and realities, be converted into an "index", and that OER should be seen as an integral component of open and distance learning that facilitates quality governance and higher
Richard Palczynski is the Head of Programme Controls at Crossrail. The document discusses creating a world class mindset at Crossrail, including what that looks like and ensuring the project is truly in control. It also references myth busting and delivering the railway on time, on budget, and to a world-class standard. The governance structure at Crossrail is described, focusing on delegation of authority and the information used to drive decision making.
Wen Wen is currently working as the Assistant Manager at Yuan Chinese restaurant in Atlantis The Palm Dubai since 2015. She has over 5 years of experience in hospitality including roles as a Team Leader, F&B Attendant, and internships in banqueting and production. Her responsibilities include ensuring all activities adhere to quality standards, maintaining guest feedback and SOPs, promoting upselling, and handling complaints. She holds a Bachelor's degree in Hotel Management from 2013.
The Linux booting process involves 6 main stages:
1. BIOS performs integrity checks and loads the MBR bootloader.
2. The MBR bootloader loads the Linux bootloader (GRUB).
3. GRUB loads and initializes the Linux kernel.
4. The kernel initializes memory and hardware before starting init.
5. Init reads /etc/inittab to determine the default runlevel and starts programs to launch that runlevel.
6. Each runlevel directory (/etc/rc.d/rc#.d/) contains programs starting with S and K that control services for that runlevel.
RWDG Webinar: Achieving Data Quality Through Data GovernanceDATAVERSITY
Data quality requires sustained discipline around the management of data definition and production. Data Governance is a large part of that discipline. The relationship between how well data is governed and the quality of the data is obvious. You cannot have high quality data without active Data Governance.
This month’s Real-World Data Governance webinar with Bob Seiner addresses how to improve data quality through the application of Data Governance practices. Quality starts with a plan and requires formal execution and enforcement of authority over the data. Attend this webinar and take away a plan to achieve data quality through Data Governance.
In this webinar, Bob will discuss:
• How Data Governance leads to data quality
• Core principles of Data Governance and data quality success
• Quality metrics based on governance practices
• Relationship between quality and governance roles
• Steps to achieve quality through governance
[Business Plan] PT Artoncode Indonesia May 2016 Marlin Sugama
My latest business plan for Artoncode (a game development studio), which at the time explored expanding strategy from international 3rd party contractor to having additional intellectual property development.
Driving change in banking bank marketing pov may2015 v 3.5 updated final Chris Yaldezian
This document discusses the disruption of traditional retail banking by non-traditional banking options. It notes billions lost in revenue, challenges to brand loyalty, and rapidly changing customer expectations and technologies. It states that established tech companies and fintech startups are entering financial services with new offerings that appeal to diverse groups like millennials. Peer-to-peer lending platforms are allowing customers to bypass retail banks for loans. The document advocates that banks improve understanding of individual customers, accelerate digital transformation, and deliver improved mobile banking experiences to meet changing customer needs.
- Poor data quality costs the US economy $600 billion annually or 5% of GDP, so it significantly impacts business bottom lines. It also hinders effective customer segmentation and strategic decision making.
- Data quality is defined by how accurate, complete, timely, and consistent the information is. It matters because it affects profits and an executive's ability to make good strategic decisions.
- To ensure good data quality, companies need to build quality processes into gathering, integrating, and leveraging data from multiple sources on an ongoing basis. Outsourcing some of these functions to specialized data partners can complement internal efforts.
Reference data management in financial services industryNIIT Technologies
This white paper analyse s the need for Reference Data Management in the financial services industry and elucidates the challenges associated with its implementation. The paper also focuses on the critical elements of RDM implementation and some of the major benefits an organization can derive by implementing a robust Reference Data Management into its IT infrastructure.
Stewarding Data : Why Financial Services Firms Need a Chief Data OfficierCapgemini
The document discusses the need for Chief Data Officers (CDOs) in financial services firms. While some financial firms were early to appoint CDOs, their roles have typically been limited to data compliance and management. The document argues that for firms to fully leverage big data opportunities and improve data governance, they need "Big Data Ready" CDOs who have responsibility for both data compliance and strategic big data initiatives. It provides recommendations for how different types of CDOs can expand their roles to become more effective big data leaders.
Stewarding data why financial services need a chief data officerRick Bouter
- The document discusses the need for financial services firms to appoint Chief Data Officers (CDOs) to address data management challenges and leverage opportunities from big data.
- While some financial firms were early to create CDO roles, most CDOs currently focus only on compliance and risk management rather than overall data strategy. Their roles are often limited in scope and centered in support functions rather than aligning with business needs.
- For financial firms to fully realize benefits from big data, the document argues they need "Big Data Ready" CDOs who have mandates covering both data compliance and value creation from big data across the entire organization.
Data governance, Information security strategyvasanthi4ever
Data governance refers to decision making and authority over organizational data. It requires cross-functional teams to identify data issues and communication between business and IT. As data volumes double every 1-2 years and data breaches increase, data governance is necessary to prevent potential disasters like data loss. Initial attempts at data governance in the 1970s failed due to lack of data stewards and executive involvement. Reasons for implementing a data governance program include when an organization grows large or complex, and to meet regulatory requirements. The goals of a data governance program are to ensure transparency, reduce costs, and enable better decision making.
The document discusses how investing in data quality can provide a significant return on investment for companies. It outlines five tenets that leading companies embrace to realize this ROI from quality data: 1) view data quality as a business issue, not just an IT issue, 2) establish an explicit data governance strategy, especially at the point of data entry, 3) use a third-party data provider to consolidate and cleanse data, 4) address the challenge of maintaining accurate data given the rapid rate of data changes, and 5) strive for a 360-degree view of customers and suppliers across the organization.
Today’s consumer and how contact data affects relationships - An Experian QAS...Steven Duque
This document discusses a study on how data quality affects customer experience. Some key findings:
1) Businesses are motivated to improve data quality to increase efficiency, enhance customer satisfaction, and enable better decisions. However, many still struggle with inaccurate contact data.
2) On average, businesses believe 17% of their customer data may be inaccurate, most commonly due to incomplete, outdated, or duplicate records. Inaccuracies waste an estimated 12% of departmental budgets.
3) Improving data quality can positively impact the customer experience across channels by preventing errors, consolidating duplicate records, and enabling personalized outreach. But accuracy must be established before leveraging data analytics.
Businesses face a multitude of challenges in today’s environment. The overall speed of business is constantly increasing. Decisions are made within minutes and channels are diversifying rapidly. Perhaps most importantly, face-to-face interaction has started to become a luxury, rather than a necessity or consequence of everyday behavior.
1) Data management is crucial for financial firms to manage risk and generate returns, but new regulations have increased the amount of data firms must handle.
2) The document discusses challenges financial firms face in data management, including legacy systems, changing a focus to data quality, and establishing consistent data definitions across business units and regulations.
3) Interviewees note key processes like risk management, compliance, and reporting require clean, consistent data without room for error, but data transformations across systems introduce reconciliation issues and inconsistencies.
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.
Top 5 Data Analytics And Business Intelligence Trends in 2022.docxSameerShaik43
You may perhaps be interested to know what is happening in business management this 2022. It is more concerned with BI, data and analytics. It will be essential to understand the latest trends. Getting to know them will help derive valuable insights into BI’s future and development.
https://www.tycoonstory.com/resource/top-5-data-analytics-and-business-intelligence-trends-in-2022/
Infographic | Quality of Data & Cost of Bad Data | Sapience AnalyticsSapience Analytics
As the quality of data becomes more and more crucial to the success of an organization, the cost of bad data goes staggeringly high.
Read this Infographic and understand the dependence of organizations on data in terms of:
Importance of data
Quality of data
Cost of bad data
Reasons for bad data quality
Bridging the Gap Between Business Objectives and Data StrategyRNayak3
Explore the fundamental elements of a robust data strategy that aligns with business objectives, from defining goals to prioritizing data architecture.
Cognizant Analytics for Banking & Financial Services FirmsCognizant
The document discusses how analytics can help banking and financial services firms address challenges from increased data, cloud computing, and customer demands. It provides examples of how firms have used analytics in customer analytics, risk management, and operations. Specific solutions discussed include fraud detection, risk modeling, marketing optimization, and spend analysis. Cognizant is positioned as an analytics partner that can help firms unlock insights from data through scalable solutions and industry expertise.
Achieving Operational Efficiency and Effectiveness with Digital Transformatio...Appian
This document discusses how digital transformation can help achieve operational efficiency and effectiveness in healthcare payers. It describes how legacy systems, manual processes, and rising costs are driving the need for digital transformation. New approaches like workflow automation, integrating clinical and financial data on new software platforms, and low-code application development can help drive cost savings and efficiencies. However, two-thirds of digital transformations fail due to barriers like outdated culture and skills in IT organizations. The document advocates using a low-code platform approach to accelerate digital transformation across various functions like provider data management, utilization review, and claims modernization.
The document discusses the role of a Chief Data Officer in establishing a data governance structure and data quality management program. It notes that currently, data ownership and management is fragmented across different departments with no single party responsible. A CDO would create rules and policies for data governance, establish a data quality team, and ensure standards and accountability for high quality data as a strategic asset. This would help address issues like high costs of poor data quality and system failures due to bad data.
The document summarizes key findings from the 2015 Uptime Institute Data Center Industry Survey of 1,000 data center operators. It finds that while third-party/colocation data center budgets are increasing, enterprise data center budgets are growing more slowly. Many enterprises are outsourcing workloads but maintaining significant capacity on-site. The survey also reveals inefficiencies in enterprise management including lack of energy planning, low server utilization rates due to infrequent hardware refreshes, and focus on reactive firefighting over strategic planning.
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.
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办理美国UNCC毕业证书制作北卡大学夏洛特分校假文凭定制Q微168899991做UNCC留信网教留服认证海牙认证改UNCC成绩单GPA做UNCC假学位证假文凭高仿毕业证GRE代考如何申请北卡罗莱纳大学夏洛特分校University of North Carolina at Charlotte degree offer diploma Transcript
Vicinity Jobs’ data includes more than three million 2023 OJPs and thousands of skills. Most skills appear in less than 0.02% of job postings, so most postings rely on a small subset of commonly used terms, like teamwork.
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Madhya Pradesh, the "Heart of India," boasts a rich tapestry of culture and heritage, from ancient dynasties to modern developments. Explore its land records, historical landmarks, and vibrant traditions. From agricultural expanses to urban growth, Madhya Pradesh offers a unique blend of the ancient and modern.
In a tight labour market, job-seekers gain bargaining power and leverage it into greater job quality—at least, that’s the conventional wisdom.
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Confirmation of Payee (CoP) is a vital security measure adopted by financial institutions and payment service providers. Its core purpose is to confirm that the recipient’s name matches the information provided by the sender during a banking transaction, ensuring that funds are transferred to the correct payment account.
Confirmation of Payee was built to tackle the increasing numbers of APP Fraud and in the landscape of UK banking, the spectre of APP fraud looms large. In 2022, over £1.2 billion was stolen by fraudsters through authorised and unauthorised fraud, equivalent to more than £2,300 every minute. This statistic emphasises the urgent need for robust security measures like CoP. While over £1.2 billion was stolen through fraud in 2022, there was an eight per cent reduction compared to 2021 which highlights the positive outcomes obtained from the implementation of Confirmation of Payee. The number of fraud cases across the UK also decreased by four per cent to nearly three million cases during the same period; latest statistics from UK Finance.
In essence, Confirmation of Payee plays a pivotal role in digital banking, guaranteeing the flawless execution of banking transactions. It stands as a guardian against fraud and misallocation, demonstrating the commitment of financial institutions to safeguard their clients’ assets. The next time you engage in a banking transaction, remember the invaluable role of CoP in ensuring the security of your financial interests.
For more details, you can visit https://technoxander.com.
A toxic combination of 15 years of low growth, and four decades of high inequality, has left Britain poorer and falling behind its peers. Productivity growth is weak and public investment is low, while wages today are no higher than they were before the financial crisis. Britain needs a new economic strategy to lift itself out of stagnation.
Scotland is in many ways a microcosm of this challenge. It has become a hub for creative industries, is home to several world-class universities and a thriving community of businesses – strengths that need to be harness and leveraged. But it also has high levels of deprivation, with homelessness reaching a record high and nearly half a million people living in very deep poverty last year. Scotland won’t be truly thriving unless it finds ways to ensure that all its inhabitants benefit from growth and investment. This is the central challenge facing policy makers both in Holyrood and Westminster.
What should a new national economic strategy for Scotland include? What would the pursuit of stronger economic growth mean for local, national and UK-wide policy makers? How will economic change affect the jobs we do, the places we live and the businesses we work for? And what are the prospects for cities like Glasgow, and nations like Scotland, in rising to these challenges?
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Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
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2. 2 Modernizing Data Quality & Governance
Executive Summary
Evaluating how financial services companies are managing the
challenges posed by data quality management.
Table of Contents
Ever since the advent of the computer, financial services companies have been
faced with increasingly significant data quality challenges. As the decades
passed and computers became further ensconced in the everyday operations
of financial services companies, those data quality challenges became even
more pronounced and their effects more wide-ranging. Now, good data quality
is absolutely essential to help organizations minimize risk while better informing
business decisions. Unfortunately for many organizations, data quality tools that
were purchased for IT to fix data issues have not keep pace with the ascendance
of data governance programs that require business and IT to co-manage the quality
of data as a business asset.
Financial services companies accounted for many early adopters of data quality
tools back in the 1990’s and 2000’s However, those first generation tools were
very IT-oriented: they were designed to be used by IT personnel to fix data issues
during development projects. Those tools evolved over time, enabling businesses
to profile data errors, define their own data quality rules, and effectively monitor
and manage exceptions. Gradually, the emergence of data governance programs
shifted responsibility for managing data quality from the IT department to business
leaders. In this context, business users became responsible for the definition of
data quality rules, while IT focused more on the execution of those rules across the
enterprise architecture.
Although this new model asked business users to become much more involved in
managing data quality, existing IT-oriented data quality tools were not designed
for self service data quality management by business users. Given the ongoing
regulatory and revenue pressures across all sectors of financial services, managing
data quality required business users to actively participate in this process.
BCBS239 for example calls out specific principles that require well defined
processes and responsibilities related to managing data quality by the business.
Hence the need arose for solutions that allow data owners, stewards, analysts,
and IT developers to manage the quality of data more effectively with each other.
Many financial services companies are still in the process of implementing data
quality processes that are rigorous and open enough to support the information
needs of today.
This paper will evaluate how financial services companies are managing the
challenges posed by data quality management. By analyzing which data types
and data characteristics businesses are struggling with, we will uncover the
true business costs associated with data quality. We will also gauge how data
governance programs are maturing and how they are being measured. Finally,
we will asses how data is being managed within financial institutions.
Executive Summary.............2
Key Findings.......................3
Research Findings
Unlocking Better Performance
Through
Data Quality....................4
Facing Down the Business
Cost of Data Quality..........6
Data Governance –
Maturity and
Measurement...................8
How Data
is Managed...................10
Recommendations To
Improve Data Quality
Management.................... 11
Appendices...................... 12
Research Partner:
Informatica....................... 13
WBR Digital..................... 14
3. 3 Modernizing Data Quality & Governance
Key Findings
Data quality has
never been more
important for financial
institutions, but most
of those companies
feel their data is only
mediocre
Financial institutions
vary greatly in the
maturity of their data
governance programs
The top two business
functions impacted by
poor data quality are
regulatory compliance
and risk management
Data quality
management requires
close collaboration
between business and
IT leaders
Quality data serves a
myriad of central business
goals, from risk reduction
to increased productivity.
Unfortunately, many
businesses continue to
struggle with data quality,
despite the fact that
four-fifths of them have it
ranked as a top priority.
Data governance
cannot be overlooked –
unsurprisingly, businesses
with formalized data
governance programs
reported that their data
was higher quality than
most other groups.
Because these concerns
tend to be the most
important drivers of data
quality, many financial
institutions see data
governance as a “must-do,”
rather than a ROI-boosting
activity. Furthermore, the
vast majority of financial
services companies can not
quantify the business cost
of poor data quality.
That collaboration
already exists for 83%
of respondents in this
study, who say that IT
and business leaders
work together to manage
data quality in their
organizations. However,
the tools these businesses
use to manage their data
are not all equal, leading
to an uneven allocation of
resources.
4. 4 Modernizing Data Quality & Governance
Research Findings
Although many financial institutions have been working to improve the quality of
their data for more than two decades, never has data quality been more important
than it is today. Data is at the center of critical regulations, including Dodd Frank,
CCAR, BCBS 239, Solvency II, and MifiD II, all of which require financial services
firms to provide accurate and complete views of their risk and capital positions.
Quality data also helps to reduce risk and improve underwriting processes. This
enables organizations to more accurately price policies while minimizing buy-backs
on defective loans, among other benefits. Furthermore, better quality data can
improve sales and marketing productivity by unlocking relevant client relationship
information and creating better-informed marketing campaigns. Finally, quality data
can even help reduce costs associated with client onboarding.
Unlocking Better Performance
Through Data Quality
How would you rank the quality of your enterprise data?
1
poor quality
2 3
high quality
4 5
very high quality
slide 3
On a scale of 1 to 5, how would you rank the quality of your enterprise data?
4%
10%
60%
25%
1%
1
poor
quality
2 3
high
quality
4 5
very high
quality
Comparison Data
How organizations rated
their data quality in 2015
slide 4
How organizations rated their data quality in 2015
3%
18%
48%
30%
1%
The majority of respondents believe their enterprise data
is of average quality, with very few of the mind that their
data is extremely high quality
5. 5 Modernizing Data Quality & Governance
Despite the clear importance of data quality, quality remains a persistent issue for
many financial institutions. This problem is compounded by the fact that financial
firms are collecting new data at a nearly exponential rate. As a result, a strong
majority of the institutions surveyed believe that their data is of mediocre quality,
with only 1% asserting that their data is extremely high quality. It is perhaps even
more troubling that respondents do not seem to have improved their data quality
since this survey was taken in 2015; rather, these institutions have trended even
further toward the mean. Given these struggles, it is no surprise that 82% of leaders
surveyed see data quality as a vital issue for their businesses to address over the
next 12 months.
While survey respondents almost universally agree that they can (and must) take
steps to improve their data quality, they do not all share common data struggles.
About a third of organizations are struggling to standardize their data quality rules
across all systems, making standardization the most common problem. Still, about
a quarter of respondents feel they need to improve their ability to identify errors,
while the remainder are split between identifying data rules and monitoring for
exceptions. In practice, managing all of these components of data quality means
integrating quality assurance procedures into workflows and applying those rules
throughout the data’s lifecycle.
Standardizing data
quality rules is the
most-cited data quality
struggle, although about
a quarter of respondents
are also having difficulty
identifying data errors
Data quality is a top
priority for four-fifths of
businesses
Where does your organization struggle most with data quality?
How important is it to address your organization’s data quality issues in the next 12 months?
slide 5
Where does your organization struggle most with data quality?
slide 6
How important is it to address your organization’s data quality issues in the next 12
months?
39% Standardize a common set of data quality rules
across all systems
24% Identifying data errors in your source systems
19% Defining data quality rules to fix discovered data
issues
18% Monitor for data errors and exceptions
82% Very important
17% Somewhat important
1% Not important
6. 6 Modernizing Data Quality & Governance
slide 2 Comparison Data
Data quality struggles based on biggest data concern
Despite the investments financial services companies have made to bolster their
data governance programs, organizations both large and small continue to face
data quality issues within their business systems and applications. Quality issues
impact a vast array of data types, including sales and marketing data (such as
client contact information, account relationships, and transactions) and risk and
compliance data (such as reference data, LEI and counterparty data, and capital
positions data). None of these data types are immune to quality problems, as
research from Informatica has shown that business users can spend up to 30-50%
of their time fixing data quality errors in the reports they draw from their business
applications. The impact that has on efficiency is substantial.
Facing Down the Business
Cost of Data Quality
Data consistency is the top concern, followed by data
accuracy – meanwhile most businesses have eliminated
duplicate records
Which of the following data quality characteristics does your organization most struggle with?
slide 9
Which of the following data quality characteristics does your organization most
struggle with?
36% Consistency – Is the data available being
defined differently?
28% Accuracy – Is the data correct?
18% Integrity – Is all the data there and referenced?
10% Completeness – Is data missing?
7% Conformity – Is the data in a standard format?
1% Duplicates – Are records repeated?
Data quality struggles based
on biggest data concern
Integrity
Consistency
Conformity
Completeness
Accuracy
Risk management
Regulatory compliance
Sales
Marketing
Customer Service
Finance
23%
20%
45%
35%
27%
26%
24%
9%
13%
27%
35%
31%
14%
13%
14%
13%
10%
10%
7% 9%
14%
17%
18%
20%
15% 11%
What types of data are most important to your organization’s success?
Reference
Customer
Products
Counterparties
Vendor
Employee
64%
63%
53%
50%
27%
5%
Reference and customer
data are the most vital to
organizational success
A Deeper Look
7. 7 Modernizing Data Quality & Governance
Poor quality data can lead to real business costs. For example, low-quality sales and
marketing data can impact marketers’ understanding of a customer’s relationship with
their firm, undermining their ability to push relevant products and ultimately lowering
marketing efficacy. For data types associated with risk and compliance, the stakes
are even higher. In those cases, errors on regulatory reports can lead to unnecessary
audits, while incorrect counterparty and credit risk assessments can end in higher
capital reserve requirements. Given the business costs associated with poor-quality
sales and compliance data, it should come as not surprise that reference and
customer data were cited as the data types most crucial to organizational success.
This all underlines the reality that data quality is not an end in and of itself, but rather
it has a very real impact on business performance.
While data touches almost every business function, from sales to finance,
respondents in this study reported that regulatory compliance and risk management
were the top two business functions impacted by poor data quality. Because
regulatory concerns tend to be the most important drivers of data quality, many
financial institutions see data governance as a “must-do,” rather than a ROI-
boosting activity. Although many financial institutions can identify the business
functions affected by data quality, the vast majority are unable to put a number to
that impact. In fact, only 7% of the organizations surveyed can quantify the real
cost of their data quality issues. Clearly, many financial institutions must take steps
to better understand the depth of their data quality issues.
Are you able to quantify the cost to your business of your existing data quality issues?
slide 10
Are you able to quantify the cost to your business of your existing data quality issues?
8% Yes
51% No
41% Not Sure
Please rank the business functions most impacted by poor data quality
Regulatory
Compliance
Risk
Management
Finance
Customer Service
Marketing
Sales
slide 11
Please rank the business functions most impacted by poor data quality
73%
68%
40%
34%
29%
23%
The perceived importance
of addressing data quality
issues based on the business
functions impacted
Risk management
Regulatory compliance
Sales
Marketing
Customer Service
Finance
Very
important
Somewhat
important
26%
21%
28%
25%
10%
6%
11%
12%
12%
15%
13%
21%
Data quality has the greatest importance for regulatory
compliance and risk management
Only 8% of respondents
can quantify the cost to
their business brought
on by data quality
issues
A Deeper Look
8. 8 Modernizing Data Quality & Governance
In order to ensure that their data is high quality, financial institutions have made
significant investments in establishing a formal data governance program and
organization to define the policies, procedures, and roles that allow them to effectively
manage the availability, quality, and consistency of their information assets. Financial
institutions now recognize data governance as a strategic priority that helps them to
server larger business goals, including the support of risk and compliance activities
and the improvement of data-driven business intelligence. However, a best-in-class
data governance program can be transformational, requiring better cross-functional
collaboration and alignment, modified data flow policies, and the deployment of
enabling technologies that can synthesize, manage, and monitor the disparate data
sets housed across a business. In short, data governance not only safeguards sensitive
information and helps satisfy regulatory guidelines, but it also enables financial
institutions to proactively identify and cultivate new business opportunities.
Any data governance program must be monitored and measured, and there
is a wide array of performance indicators that financial institutions can use to
understand how successful their governance programs are. Those measurements
range from hard metrics (such as cost reduction) to softer metrics (including
organizational communication). Based on the present research, the most effective
measurement has been organizational effectiveness, which reflects the overall
business outcomes and efficiencies created through data governance. Risk
reduction and compliance are also top priorities and given that they are such
sensitive issues, they are often the primary drivers behind governance.
Data Governance –
Maturity and Measurement
Organizational effectiveness, reduced risk, and
compliance are the most common measures of the
effectiveness of a data governance program
What is the most effective measurement of the success of your data governance program?
Organizational
Effectiveness
Reduced
Risk
Compliance
Cost reduction
Improved
Audit Results
Better IT
Solution Delivery
Organizational
Communication
Customer
Understanding
62%
60%
53%
41%
35%
30%
24%
12%
The top 3 measurements
remained the same as in
2015
9. 9 Modernizing Data Quality & Governance
As with any governance plan, the institutions that took part in this study vary in the
maturity of their data governance programs. While only 5% of respondents have no
data governance framework in place, more than a third – 37% – are still developing
their policies, processes, and roles. On the other end of the maturity spectrum, 31%
of respondents have solidified enterprise-wide adoption of their data governance
programs. Unsurprisingly, those respondents with formalized data governance
programs generally reported that their data was higher quality than most other
groups. However, those organizations with no data governance systems in place
reported the highest level of confidence in their data quality among all groups.
How mature is your data governance program?
37% Policies, Processes, and Roles Being Developed
31% Policies, Processes, and Roles Defined – Enterprise
Adoption
27% Policies, Processes, and Roles Defined – In Pilot
Within a Few Departments
5% Nothing In Place
Respondents are fairly evenly distributed across the data
governance maturity spectrum, although it bears noting
that just under a third have achieved enterprise adoption
of their data governance program
Reported data quality
based on data governance
maturity level
1 3 2 4 5
Enterprise
Adoption
Piloting
In development
Nothing
in place
slide 5 Comparison Data
Reported data quality based on data governance maturity level
4%
10%
25%
10% 14%
54%
76%
25%
59%
42%
14%
50%
49% 3%
A Deeper Look
10. 10 Modernizing Data Quality & Governance
Data quality management is an important responsibility, one that touches many
levels of an organization and functions. Over time, most financial institutions have
come to understand that data stewardship cannot be isolated to one department or
another. Rather, it requires intense collaboration between the information technology
professionals who maintain data governance systems and the business leaders
who will eventually use that data to help control risk and unlock new insights. That
collaboration already exists for 83% of respondents in this study, who say that IT and
business leaders work together to manage data quality in their organizations.
However, businesses are not all using the same tools to manage their data. This is
shown by the fact that the number of organizations currently using an off-the-shelf
data quality tool is split. Beyond the implications for data quality, the presence or
absence of such a tool reverberates throughout the organization, shaping its very
structure. In fact, organizations that do not have off-the-shelf data quality tools must
devote a greater percentage of their human resources to data quality management
tasks. That resource allocation can have a detrimental effect on the institution’s
ability to devote personnel to other important projects.
How Data is Managed
slide 16
Who manages data quality issues in your organization today?
slide 17
Does your organization currently own an off-the-shelf data quality tool to manage
data quality?
slide 18
What percentage of your data quality management do human beings conduct? (E.g.
either in IT or line of business
In more than 80% of
organizations, IT and
business leaders both
play a role in managing
data quality
Respondents were split on
ownership of off-the-shelf data
quality management tools
A third of respondents rely on
people for 60-80% of their
data quality management
Who manages data
quality issues in your
organization today?
83% IT and Business (Data Stewards)
13% Business Only
3% IT
1% Not Sure
42% Yes
42% No
14% Don’t
Know
12% 0-20%
21% 20-40%
22% 40-60%
33% 60-80%
12% 80-100%
Does your organization currently own an
off-the-shelf data quality tool to manage
data quality?
What percentage of your data quality
management do human beings conduct?
(E.g. either in IT or line of business)
0-20%
20-40%
40-60%
60-80%
80-100%
The impact of off-the-shelf tools on data quality management responsibilities
Yes
No
Don’t
know
Ownoff-the-shelfdataquality
managementtool?
% of data quality management done by humans
slide 4 Comparison Data
The impact of off-the-shelf tools on data quality management responsibilities
16%
9%
8%
13%
28%
26%
25%
6%
41%
36%
58%
6%
21%
8%
Those businesses without
off-the-shelf data quality
tools tend to have a greater
percentage of their data
quality management
performed by people,
rather than computers.
11. 11 Modernizing Data Quality & Governance
Recommendations
To Improve Data
Quality Management
Identify and quantify
the true business
impact of poor
data quality
Ensure that data
governance policies
are well-defined and
serve the core needs
of the business
Clearly define roles
and responsibilities
Invest in technologies
that support a more
collaborative and
comprehensive
management of
data quality
It begins with
measurement. Financial
services firms must
understand the depth of
their data quality issues –
which business functions
are being impacted and
to what degree – before
they can address the root
causes.
A well-defined and
documented data
governance program is
critical to data quality.
Data governance requires
better cross-functional
collaboration, modified
data flow policies, and the
deployment of enabling
technologies, all of which
must be aligned with data
quality challenges and
organizational goals.
Business users and IT
personnel across the
organization must be
extremely clear about
the delegation of
responsibilities related
to data profiling, rules
management, remediation,
and oversight. Processes
for each of those functions
must also be clearly
defined.
Technologies must evolve
with the needs of the
business. In order for
enterprise-wide data quality
management to truly take
root, the business must first
have the right technologies
in place.
12. 12 Modernizing Data Quality & Governance
Appendices
WBR Digital conducted online surveys of 78 American-based data management
professionals from medium and large banking institutions, insurance companies,
and asset management groups. Survey participants included decision-makers and
executives with responsibility for their firms’ data management, IT architecture, and
data risk and compliance strategies. Responses were collected in March 2016.
Transforming Financial Institutions Through Data Governance“, WBR Digital,
March 2015
Appendix A: Methodology
Appendix B: Related Research
CLICK HERE TO READ NOW
13. 13 Modernizing Data Quality & Governance
Research Partner
A special thank you to our research partner, Informatica, whose vision and
expertise helped make this report possible.
Informatica is a leading independent software provider focused on delivering
transformative innovation for the future of all things data. Organizations around
the world rely on Informatica to realize their information potential and drive
top business imperatives. More than 5,800 enterprises and over 800 financial
institutions including 27 out of the top 30 global banks and 45 out of the top 50
insurance companies depend on Informatica to fully leverage their information
assets to satisfying industry regulations, reduce risk, improve customer success, and
improve business efficiency.
For more information, call +1 650-385-5000 (1-800-653-3871 in the U.S.),
or visit www.informatica.com.
Connect with Informatica at:
14. 14 Modernizing Data Quality & Governance
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