How can you analyze data in fragile and conflict affected states? What happens if you ignore the analytics and move on gut feeling? Read more about the three key steps for better data analytics in difficult places.
5 things that keep comptrollers up at nightWorkiva
Get ahead of future challenges in your CAFR process. These are the top 5 concerns state comptrollers have about the future of CAFR reporting. To find out more, visit www.workiva.com/industries/government.
This document discusses how companies can make advanced analytics work for them. It notes that companies using big data and analytics show 5-6% higher productivity. However, some companies struggle because they don't understand their existing data, programs are too complex, or insights aren't actionable. The document recommends that companies identify relevant data sources, build predictive analytics models, and transform their organization to make better decisions based on data and models. Managers must develop business-focused tools and exploit big data capabilities.
An overview of how top performing businesses strategically embrace analytics, and analytical sophistication as a competitive differentiator over their underperforming peers
Making Advanced Analytics Work for You by Dominic Barton and David Court MohitGupta714
The document discusses how advanced analytics can provide competitive advantages but many companies are unsure how to implement them effectively. It identifies that fully exploiting analytics requires the ability to identify and manage multiple data sources, build advanced analytics models, and adapt the organization. Companies must have a clear strategy for using data to compete and the right technology architecture. Managers need to focus on sourcing data, building models, transforming culture with flexibility and promoting creativity around new data sources.
Harnessing the power of predictive analyticsTCM infosys
The document summarizes a report from Deloitte on the state of analytics in businesses. It finds that while many businesses recognize the importance of analytics, they face barriers like poor data management and a lack of analytics expertise. To overcome these barriers, the report recommends that companies start with small, impactful analytics projects to gain executive support. It also suggests that companies acquire needed expertise now, tie analytics to decision making, apply analytics to marketing and customers, create an analytics structure, and develop a long-term analytics strategy to fully realize the benefits of predictive analytics. With proper planning, organizations that evolve their use of analytics will have a competitive advantage in the future.
The document discusses advanced analytics and its growing importance for businesses. It notes that advanced analytics uses sophisticated techniques like machine learning and data mining to generate deeper insights from data. While big data and analytics are becoming increasingly important, many companies are unsure how to implement them effectively. The document recommends targeted efforts to work with data and build models, rather than massive overhauls, to maintain flexibility as technologies evolve. It also acknowledges challenges like risk of failure and need for expertise in interpreting probabilistic results.
5 things that keep comptrollers up at nightWorkiva
Get ahead of future challenges in your CAFR process. These are the top 5 concerns state comptrollers have about the future of CAFR reporting. To find out more, visit www.workiva.com/industries/government.
This document discusses how companies can make advanced analytics work for them. It notes that companies using big data and analytics show 5-6% higher productivity. However, some companies struggle because they don't understand their existing data, programs are too complex, or insights aren't actionable. The document recommends that companies identify relevant data sources, build predictive analytics models, and transform their organization to make better decisions based on data and models. Managers must develop business-focused tools and exploit big data capabilities.
An overview of how top performing businesses strategically embrace analytics, and analytical sophistication as a competitive differentiator over their underperforming peers
Making Advanced Analytics Work for You by Dominic Barton and David Court MohitGupta714
The document discusses how advanced analytics can provide competitive advantages but many companies are unsure how to implement them effectively. It identifies that fully exploiting analytics requires the ability to identify and manage multiple data sources, build advanced analytics models, and adapt the organization. Companies must have a clear strategy for using data to compete and the right technology architecture. Managers need to focus on sourcing data, building models, transforming culture with flexibility and promoting creativity around new data sources.
Harnessing the power of predictive analyticsTCM infosys
The document summarizes a report from Deloitte on the state of analytics in businesses. It finds that while many businesses recognize the importance of analytics, they face barriers like poor data management and a lack of analytics expertise. To overcome these barriers, the report recommends that companies start with small, impactful analytics projects to gain executive support. It also suggests that companies acquire needed expertise now, tie analytics to decision making, apply analytics to marketing and customers, create an analytics structure, and develop a long-term analytics strategy to fully realize the benefits of predictive analytics. With proper planning, organizations that evolve their use of analytics will have a competitive advantage in the future.
The document discusses advanced analytics and its growing importance for businesses. It notes that advanced analytics uses sophisticated techniques like machine learning and data mining to generate deeper insights from data. While big data and analytics are becoming increasingly important, many companies are unsure how to implement them effectively. The document recommends targeted efforts to work with data and build models, rather than massive overhauls, to maintain flexibility as technologies evolve. It also acknowledges challenges like risk of failure and need for expertise in interpreting probabilistic results.
Identifying Costs of Shadow Finance in FP&ASteve Potter
Are there incidences of Shadow Finance in your business? If there are, what impact are they having on your business? It is estimated that finance operations should cost 1% of revenue - is that the case for your business?
This document discusses how companies can make advanced analytics work for them. It notes that while big data is attracting investment, most companies are unsure how to implement it. It recommends that companies 1) choose the right data sources, 2) build models that predict and optimize business outcomes, and 3) transform their capabilities to develop analytics that managers understand and can use daily. The key is aligning analytics with business goals and processes rather than just focusing on data itself.
This document discusses how Chief Data Officers can show value within their organizations. It begins by listing common questions executives may ask about operational inefficiencies and issues. The document then argues that data problems are not necessarily the fault of the CDO. It discusses how CDOs can strengthen the case for data management by improving data quality, reducing costs and risks, and meeting regulatory demands. The document outlines how CDOs can develop an enterprise data management strategy and mission, and manage organizational change to make data a driver of the business. It envisions how companies can develop a "digital twin" and become truly data-driven and connected.
According to research, companies that effectively use big data and analytics show 5-6% higher productivity and profitability than their peers. To do this requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models improve decisions. The document provides recommendations for choosing usable data sources, establishing supportive IT infrastructure, building models to optimize business outcomes, and transforming company capabilities to develop and utilize analytics. Overall, developing capabilities around big data may become a key competitive advantage.
The document discusses how finance analytics can help organizations by reducing risk and instilling confidence in decision making through gaining control over analytical processes. It describes how modernizing financial processes and putting core finance data in a centralized system can free the finance function from inefficiencies and allow it to focus on value-added analysis. Implementing finance analytics solutions can increase finance efficiency, enable more effective business partnering, and support better risk analysis and decision making.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
This document discusses how companies can make advanced analytics work for them. It identifies three key capabilities: 1) Choosing the right data, including both internal and external sources, and asking how available data can help key decisions. 2) Building predictive models that optimize outcomes simply, focusing on the least complex model that improves performance. 3) Transforming company capabilities by embedding analytics in tools for front-line use and making analytics central to daily operations.
Go Figure: Data Processing Is Needed but Analytical Insight Is the Real ValueStatPro Group
The document discusses the changing role of middle offices in asset management firms. Middle offices can no longer just process numbers and must provide strategic insights through data analysis. To do this effectively, middle offices need clean and accurate data as well as strong analytical capabilities. Smaller boutique asset management firms are well-positioned to transform their middle offices due to their flexibility and agility. Embracing new technologies, they can create a single, clean data set and provide the accurate analytics and tracking needed to inform strategic decision-making.
Marketers struggle to effectively analyze and apply big data. While big data can be used for customer optimization, engagement, and retention, most organizations rely too heavily on intuition over data-driven insights. Additionally, many marketers struggle with statistics and can become distracted by excessive data without applying critical thinking. To improve, marketers should focus on goals and filter irrelevant data, ask strategic questions, and maintain a narrow focus on higher-level objectives rather than getting lost in data details.
The big-data explosion is driving a shift away from gut-based decision making and marketing in particular is feeling the pressure to embrace new data driven capabilities.
Under Pressure - The state of middle offices in smaller asset management firmsStatPro Group
Smaller asset management firms face growing complexities in their middle offices due to increased trading volumes, regulations, and data requirements. While this presents challenges, it also provides an opportunity for smaller firms to take advantage of new technologies and implement more nimble, flexible solutions compared to larger firms hampered by legacy systems. The middle office is crucial for smaller firms to perform well and add strategic value through enhanced analytics that inform business strategy. New technologies allow data to be efficiently processed and transformed into a valuable asset for competitive advantage.
The document discusses how companies can make advanced analytics work for them by overcoming problems and fully exploiting data. It recommends that companies 1) identify multiple data sources and manage the data, 2) build advanced analytics models, and 3) transform their organization. It also stresses having a clear data strategy and the right technology. Managers are advised to develop business analytics, embed tools for front lines, and build big data capabilities.
The document discusses why businesses should not be intimidated by data governance and provides a framework for implementing an effective data governance program. It notes that proper data governance starts with strong policies and oversight to ensure consistent and proper handling of data. The framework outlines a top-down approach with three phases - plan, design, and execute. Businesses are advised to start small by focusing on a single department or initiative and then expand data governance practices over time for maximum buy-in and success. Effective data governance provides many benefits including improved decision making, risk management, and the ability to treat information as a valuable business asset.
In it Together: why “collaboration” is now an essential skillset for asset ma...StatPro Group
Traditionally, asset management teams have worked in silos. But with asset classes and the data becoming more complex, greater collaboration is now needed. Find out why.
A survey found that 43% of organizations had recently killed an IT project. The top 5 reasons for terminating projects were: 1) Business needs changed (30%), 2) Did not deliver as promised (23%), 3) Project was no longer a priority (14%), 4) Project exceeded budget (13%), and 5) Project did not support business strategy (7%). While these reasons indicate failed projects, some terminations could be justified if the business environment or requirements changed legitimately. Determining a project's success requires examining its full context and delivered business value.
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.
Cyber fraud and Security - What risks does family office's face intoday's wo...Kannan Subbiah
Presented at the Private Wealth Management Summit 2017 held at Mumbai, India.
Security has to be considered as the foundation on which one can build a business. Gone are the days when we can build a perimeter, sit back and feel secure. In today’s digital environment we partner with others, we outsource, we have alliances, we let our customers into our systems and as we extend our networks.
In the digital economy, effective cyber security can mean the difference between a business’s success and its failure.
Achieving Better Outcomes with Data Driven Decision-MakingDelaney Turner
What are the aspects of Smarter Government? Learn how Business Analytics solutions from IBM help government agencies improve outcomes and optimize results.
CRITICAL SUCCESS FACTORS IN IMPLEMENTING INFORMATION GOVERNANCE (IG)Irmbulldog
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.
This document discusses how companies can make advanced analytics work for them by leveraging big data. It provides three key insights:
1) Companies should identify a clear strategy for how they will use data analytics to compete, deploy the right technology, and take an integrated approach to data, models, and organizational transformation.
2) Companies need to choose the right data sources to solve business problems, get necessary IT support to analyze data, and build models that predict and optimize outcomes.
3) Companies must transform their capabilities by developing business-relevant analytics tools for managers, embedding analytics in frontline tools, and developing skills to exploit big data.
This white paper discusses nine common mistakes that lead to failed ERP system implementations in the public sector. The mistakes include: assuming there is natural support for the project; focusing on technology over people issues; not properly preparing by documenting current processes; trying to implement everything at once instead of in phases; providing minimal user support; underestimating resource requirements; overestimating how many "best practices" can be adopted; taking a narrow view of the project instead of considering external factors; and allowing deadlines to slip. The paper provides strategies for avoiding each mistake and successfully implementing an ERP system.
We conducted a groundbreaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Find out:
Why nearly a third of IT Directors feel their organisation uses data poorly
What the hybrid data manager of the future will look like
Why understanding customer behaviour remains the holy grail for so many
We conducted a ground-breaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Identifying Costs of Shadow Finance in FP&ASteve Potter
Are there incidences of Shadow Finance in your business? If there are, what impact are they having on your business? It is estimated that finance operations should cost 1% of revenue - is that the case for your business?
This document discusses how companies can make advanced analytics work for them. It notes that while big data is attracting investment, most companies are unsure how to implement it. It recommends that companies 1) choose the right data sources, 2) build models that predict and optimize business outcomes, and 3) transform their capabilities to develop analytics that managers understand and can use daily. The key is aligning analytics with business goals and processes rather than just focusing on data itself.
This document discusses how Chief Data Officers can show value within their organizations. It begins by listing common questions executives may ask about operational inefficiencies and issues. The document then argues that data problems are not necessarily the fault of the CDO. It discusses how CDOs can strengthen the case for data management by improving data quality, reducing costs and risks, and meeting regulatory demands. The document outlines how CDOs can develop an enterprise data management strategy and mission, and manage organizational change to make data a driver of the business. It envisions how companies can develop a "digital twin" and become truly data-driven and connected.
According to research, companies that effectively use big data and analytics show 5-6% higher productivity and profitability than their peers. To do this requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models improve decisions. The document provides recommendations for choosing usable data sources, establishing supportive IT infrastructure, building models to optimize business outcomes, and transforming company capabilities to develop and utilize analytics. Overall, developing capabilities around big data may become a key competitive advantage.
The document discusses how finance analytics can help organizations by reducing risk and instilling confidence in decision making through gaining control over analytical processes. It describes how modernizing financial processes and putting core finance data in a centralized system can free the finance function from inefficiencies and allow it to focus on value-added analysis. Implementing finance analytics solutions can increase finance efficiency, enable more effective business partnering, and support better risk analysis and decision making.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
This document discusses how companies can make advanced analytics work for them. It identifies three key capabilities: 1) Choosing the right data, including both internal and external sources, and asking how available data can help key decisions. 2) Building predictive models that optimize outcomes simply, focusing on the least complex model that improves performance. 3) Transforming company capabilities by embedding analytics in tools for front-line use and making analytics central to daily operations.
Go Figure: Data Processing Is Needed but Analytical Insight Is the Real ValueStatPro Group
The document discusses the changing role of middle offices in asset management firms. Middle offices can no longer just process numbers and must provide strategic insights through data analysis. To do this effectively, middle offices need clean and accurate data as well as strong analytical capabilities. Smaller boutique asset management firms are well-positioned to transform their middle offices due to their flexibility and agility. Embracing new technologies, they can create a single, clean data set and provide the accurate analytics and tracking needed to inform strategic decision-making.
Marketers struggle to effectively analyze and apply big data. While big data can be used for customer optimization, engagement, and retention, most organizations rely too heavily on intuition over data-driven insights. Additionally, many marketers struggle with statistics and can become distracted by excessive data without applying critical thinking. To improve, marketers should focus on goals and filter irrelevant data, ask strategic questions, and maintain a narrow focus on higher-level objectives rather than getting lost in data details.
The big-data explosion is driving a shift away from gut-based decision making and marketing in particular is feeling the pressure to embrace new data driven capabilities.
Under Pressure - The state of middle offices in smaller asset management firmsStatPro Group
Smaller asset management firms face growing complexities in their middle offices due to increased trading volumes, regulations, and data requirements. While this presents challenges, it also provides an opportunity for smaller firms to take advantage of new technologies and implement more nimble, flexible solutions compared to larger firms hampered by legacy systems. The middle office is crucial for smaller firms to perform well and add strategic value through enhanced analytics that inform business strategy. New technologies allow data to be efficiently processed and transformed into a valuable asset for competitive advantage.
The document discusses how companies can make advanced analytics work for them by overcoming problems and fully exploiting data. It recommends that companies 1) identify multiple data sources and manage the data, 2) build advanced analytics models, and 3) transform their organization. It also stresses having a clear data strategy and the right technology. Managers are advised to develop business analytics, embed tools for front lines, and build big data capabilities.
The document discusses why businesses should not be intimidated by data governance and provides a framework for implementing an effective data governance program. It notes that proper data governance starts with strong policies and oversight to ensure consistent and proper handling of data. The framework outlines a top-down approach with three phases - plan, design, and execute. Businesses are advised to start small by focusing on a single department or initiative and then expand data governance practices over time for maximum buy-in and success. Effective data governance provides many benefits including improved decision making, risk management, and the ability to treat information as a valuable business asset.
In it Together: why “collaboration” is now an essential skillset for asset ma...StatPro Group
Traditionally, asset management teams have worked in silos. But with asset classes and the data becoming more complex, greater collaboration is now needed. Find out why.
A survey found that 43% of organizations had recently killed an IT project. The top 5 reasons for terminating projects were: 1) Business needs changed (30%), 2) Did not deliver as promised (23%), 3) Project was no longer a priority (14%), 4) Project exceeded budget (13%), and 5) Project did not support business strategy (7%). While these reasons indicate failed projects, some terminations could be justified if the business environment or requirements changed legitimately. Determining a project's success requires examining its full context and delivered business value.
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.
Cyber fraud and Security - What risks does family office's face intoday's wo...Kannan Subbiah
Presented at the Private Wealth Management Summit 2017 held at Mumbai, India.
Security has to be considered as the foundation on which one can build a business. Gone are the days when we can build a perimeter, sit back and feel secure. In today’s digital environment we partner with others, we outsource, we have alliances, we let our customers into our systems and as we extend our networks.
In the digital economy, effective cyber security can mean the difference between a business’s success and its failure.
Achieving Better Outcomes with Data Driven Decision-MakingDelaney Turner
What are the aspects of Smarter Government? Learn how Business Analytics solutions from IBM help government agencies improve outcomes and optimize results.
CRITICAL SUCCESS FACTORS IN IMPLEMENTING INFORMATION GOVERNANCE (IG)Irmbulldog
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.
This document discusses how companies can make advanced analytics work for them by leveraging big data. It provides three key insights:
1) Companies should identify a clear strategy for how they will use data analytics to compete, deploy the right technology, and take an integrated approach to data, models, and organizational transformation.
2) Companies need to choose the right data sources to solve business problems, get necessary IT support to analyze data, and build models that predict and optimize outcomes.
3) Companies must transform their capabilities by developing business-relevant analytics tools for managers, embedding analytics in frontline tools, and developing skills to exploit big data.
This white paper discusses nine common mistakes that lead to failed ERP system implementations in the public sector. The mistakes include: assuming there is natural support for the project; focusing on technology over people issues; not properly preparing by documenting current processes; trying to implement everything at once instead of in phases; providing minimal user support; underestimating resource requirements; overestimating how many "best practices" can be adopted; taking a narrow view of the project instead of considering external factors; and allowing deadlines to slip. The paper provides strategies for avoiding each mistake and successfully implementing an ERP system.
We conducted a groundbreaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Find out:
Why nearly a third of IT Directors feel their organisation uses data poorly
What the hybrid data manager of the future will look like
Why understanding customer behaviour remains the holy grail for so many
We conducted a ground-breaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
We conducted a survey of the UK's data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
What is Business intelligence
Core Capabilities of Business Intelligence
Elements of Business Intelligence
Why Companies opt for Business Intelligence
Benefits of Business Intelligence
User of Business Intelligence
Reports of Business Intelligence
Business Application in Extended Enterprise
Business Analytics
Golden Rules for Business Intelligence
5 Stages of Business Intelligence
5 Steps to Transform into a Data-Driven Organization - Ganes Kesari - Gramen...Ganes Kesari
This session was presented on May 27th, 2021, in a Webinar organized by Gramener.
https://info.gramener.com/5-steps-to-transform-into-data-driven-organization
Session Details:
Today, organizations struggle to get value from data despite significant investments. Did you know that there's one factor that influences the outcomes of all your data initiatives?
This webinar will highlight how an organization's data maturity influences its performance. It will show how you can assess your data maturity and plan the five steps for data-driven business transformation.
Pain points we would be discussing:
Most organizations stagnate midway in their data journey.
Gartner says that over 87% of organizations in the industry are at lower levels of data maturity (levels 1 and 2 on a scale of 5).
Just doing more data science projects will not improve your capabilities or outcomes. The fact is that the top challenges reported by CDOs fall into five common areas.
This webinar will show what they are and how you can tackle them.
Who should attend
- Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Managers
What Will You Learn?
- What is data science maturity, and why does it matter?
- How do you assess data science maturity and limitations of the assessment?
- How can data science maturity help your organization level up (explained with an example)?
5 Steps To Become A Data-Driven Organization : WebinarGramener
Gramener's Chief Data Scientist and Co-founder Ganes Kesari conducted an interesting webinar that will give you an idea of how to analyze your data maturity and plan the five steps to transforming your business using data.
Who should watch this webinar?
Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Directors, and Managers.
Important points discussed on the webinar:
-The majority of businesses reach a halt in the middle of their data journey.
-According to Gartner, approximately 87% of companies in the business have a poor degree of data maturity (levels 1 and 2 on a scale of 5).
-Adding more data science projects to your portfolio will not boost your talents or results. The truth is that CDOs' primary issues are divided into five categories.
Learnings from this webinar:
-Data Science Maturity. What is it and why is it important?
-How can you determine the maturity of data science and its limitations?
-How does data science maturity (described with an example) assist your business in progressing?
Watch the full webinar on:
https://info.gramener.com/5-steps-to-transform-into-data-driven-organization
To know more about Data Maturity visit:
https://gramener.com/data-maturity/#
Enabling Success With Big Data - Driven Talent AcquisitionDavid Bernstein
Adopting an evidence-based recruitment marketing strategy is not just reserved for large employers. In fact, a targeted sourcing strategy can in some ways have a greater impact on small and mid-size businesses who need to allocate already-limited resources to the areas that will provide the most value. Ultimately, hiring the right candidate means profitability for your business. How can talent acquisition professionals gain the insights their organizations need to make better-informed decisions about their recruitment marketing efforts?
Workforce Analytics-Big Data in Talent Development_2016 05Rob Abbanat
1) The document discusses how workforce analytics uses big data approaches to improve talent management and recruiting. It outlines a 5-step process for implementing workforce analytics: clarifying the problem, determining metrics, gathering data, analyzing the data, and presenting results visually.
2) Most companies are still only reporting workforce analytics data, while few are able to forecast or simulate results. Examples are given of how some companies have used workforce analytics to optimize retention, promotions, and talent acquisition strategies.
3) The meeting discussed how workforce analytics can help move companies from decisions based on hunches to data-driven models, showing clearer links between talent expenditures and organizational performance.
Workforce analytics, also called HR analytics or people analytics is getting much attention lately. And rightly so! Research has shown that companies using data to drive their decisions and actions are more succesfull than others. With (predictive) analytics an accurate view of the future requires predictions based on data rather than personal hunches or speculation.
This document discusses how businesses often approach major IT initiatives in a way that is not business-focused or tied to clear business objectives. It outlines common concerns executives have with IT spending, such as unclear returns on investment and projects not delivering expected results. The document proposes that businesses need an objective, performance-based methodology for developing an IT strategy, making investment decisions, and executing initiatives in a way that relates directly to organizational metrics. It describes resources available to help businesses quickly develop a business-focused approach to critical IT issues.
2013 04 irm mdmdg - jon asprey 4 most asked dg questions v 1 3Taldor Group
This document provides guidance on addressing the four most common questions around implementing an effective data governance program. It recommends starting by understanding business processes and identifying "hidden factories" of data issues. Standards and rules should be defined through dialogue with stakeholders. Engaging the business requires building a business case by quantifying impacts and communicating successes. Demonstrating value involves measuring improvements in key business metrics from risk reduction, cost savings, or increased efficiency.
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.
This document discusses the importance of having accurate key performance indicators (KPIs) and discusses some common issues that can lead to inaccurate KPIs. It notes that smaller companies often manually manipulate spreadsheet data to calculate KPIs, which can introduce errors. It recommends that smaller companies have an independent consultant validate their KPI spreadsheets. The document also discusses the importance of having clean, relevant data for effective business decision making and management reporting. It outlines some best practices for evaluating data quality issues and ensuring data relevance.
This document provides information on data governance and discusses several challenges and approaches to data governance. It discusses that 80% of enterprise data is unstructured and spread across many sources like web data, enterprise applications, emails, and social media. Governing such diverse data assets is a complex long-term journey. It also discusses why data governance is needed, challenges of data governance, and different routes and frameworks to conduct data governance assessments and develop solutions. These include using cases studies, lean six sigma methodology, enterprise data architecture approaches, and linking data governance with machine learning. The document concludes by emphasizing structure of data, experimenting with different assessment and solutioning methods, and leveraging machine learning as a new capability.
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
This document is a quarterly publication that provides insights for boards and audit committees. It discusses how boards can help organizations embrace data analytics to derive value from big data. It also explores how strengthening internal controls can help tackle corruption risks. Additionally, it highlights an interview discussing the role of nomination committees in selecting directors and evaluating board performance, with a focus on both monetary and non-monetary criteria.
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
United Nations World Oceans Day 2024; June 8th " Awaken new dephts".Christina Parmionova
The program will expand our perspectives and appreciation for our blue planet, build new foundations for our relationship to the ocean, and ignite a wave of action toward necessary change.
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
Monitoring Health for the SDGs - Global Health Statistics 2024 - WHOChristina Parmionova
The 2024 World Health Statistics edition reviews more than 50 health-related indicators from the Sustainable Development Goals and WHO’s Thirteenth General Programme of Work. It also highlights the findings from the Global health estimates 2021, notably the impact of the COVID-19 pandemic on life expectancy and healthy life expectancy.
UN WOD 2024 will take us on a journey of discovery through the ocean's vastness, tapping into the wisdom and expertise of global policy-makers, scientists, managers, thought leaders, and artists to awaken new depths of understanding, compassion, collaboration and commitment for the ocean and all it sustains. The program will expand our perspectives and appreciation for our blue planet, build new foundations for our relationship to the ocean, and ignite a wave of action toward necessary change.
The Antyodaya Saral Haryana Portal is a pioneering initiative by the Government of Haryana aimed at providing citizens with seamless access to a wide range of government services
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
Preliminary findings _OECD field visits to ten regions in the TSI EU mining r...OECDregions
Preliminary findings from OECD field visits for the project: Enhancing EU Mining Regional Ecosystems to Support the Green Transition and Secure Mineral Raw Materials Supply.
3. Data to rely on with
regards to an institution’s
operations, functioning
and performances
Example: Number of civil
servants
Data in fragile and
developing countries
Economic data collection
and statistics
Example: GDP growth
Data collected Data needed
There is a misalignment of data collected vs. data
needed to design and implement institutional
reforms
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4. Management must be able to evaluate its own
capabilities and performances
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- How many payments have
been made this month?
- How many children
graduated?
- What´s the cost of processing a
procurement?
Performance metrics examples: Capability metrics examples:
- What´s the education of my
staff? What's their age?
- How staff have roles and
responsibilities assigned?
- What's the cost of maintaining
the FMIS system?
5. Why do decision making in the blind?
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6. 2. WHAT ARE THE CAUSES AND CONSEQUENCES
OF DATA-POVERTY IN FRAGILE CONTEXTS?
7. Successful data collection for institutions in fragile states
might be costly and require getting one’s hands dirty
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Performance metrics examples:
Poor validity
Unavailable
Unreliable
The data may poorly
represent the problem
The data is often populated
manually, with many errors,
and few options to quality
control
The data is not likely to be
readily available when needed.
Often the data only exist in hard
copies. If digitized, data might
lack of structure
8. Might be possible to only deliver analytics that are
fairly simple
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But you rarely need a fancy
performance dashboard
Questions needing
to be answered are
often simple – and
often information
is lacking:
9. Not engaging with data analysis during planning
can dramatically increase risk of reform failure
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Performance metrics examples:Both an under- and over-estimation of
an organization’s existing capacities and
needs
An issue being wrongly scoped or
overlooked
Solutions addressing issues that either:
(i) do not exist, or (ii) do not have to be
prioritized
11. 3 steps to better decision-making
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Identify data sources
within processes
Improve information
management practices
within organization.
Move from blank
spreadsheet to
management-by-
spreadsheet
Where is information
recorded in processes?
How is it recorded?
What change can be made to
processes to improve
recording and query?
Is it possible to leapfrog?
What questions need
answers?
How to integrate data in
decision-making process?
12. Focus on core data-generation activities:
accounting, record keeping and archiving
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Public institutions
perform
administrative
processes that contain
traditional elements
of accounting, record
keeping and
archiving
Ledgers, payrolls or
registries are
goldmines of
information about an
institution’s
capabilities and
performance