This document discusses advanced data visualization (ADV) and provides strategies for implementing effective ADV solutions. It outlines seven primary capabilities of ADV solutions, including dynamic data, visual interfaces, multidimensional analysis, and proactive alerts. It also describes methodologies like storyboarding and prototyping to enable ADV. Key benefits of ADV include improved operational efficiency, faster insight from data, and enabling users to create their own visualizations.
Operational Analytics: Best Software For Sourcing Actionable Insights 2013Newton Day Uploads
Actionable Insights are those views of data that cause managers to ask new questions about how processes work and take action. They differ from traditional key performance measures and daily operating reports that focus on delivering a picture of progress against a strategic objective, operating budget or forecast. What software is best for your business to source these game-changing perspectives of your enterprise?
Business Data Analytics Powerpoint Presentation SlidesSlideTeam
Enthrall your audience with this Business Data Analytics Powerpoint Presentation Slides. Increase your presentation threshold by deploying this well crafted template. It acts as a great communication tool due to its well researched content. It also contains stylized icons, graphics, visuals etc, which make it an immediate attention grabber. Comprising twenty nine slides, this complete deck is all you need to get noticed. All the slides and their content can be altered to suit your unique business setting. Not only that, other components and graphics can also be modified to add personal touches to this prefabricated set. https://bit.ly/3d4gdzY
Highlights from three different speakers on the actual use of dashboards for decisionmaking.
MEASURE Evaluation shares the results of a landscape analysis looking for specific examples of dashboards prompting action. BroadReach shares an example of how their Vantage platform is making HIV data accessible in South Africa. JSI shares an example of low-tech but high-impact dashboard development and coaching that has transformed districts in Zimbabwe.
This will explain you what is data visualization,why we need it,what are the technologies in it ,tools available for it and it ends up with how can we get the excellence in visualization
Operational Analytics: Best Software For Sourcing Actionable Insights 2013Newton Day Uploads
Actionable Insights are those views of data that cause managers to ask new questions about how processes work and take action. They differ from traditional key performance measures and daily operating reports that focus on delivering a picture of progress against a strategic objective, operating budget or forecast. What software is best for your business to source these game-changing perspectives of your enterprise?
Business Data Analytics Powerpoint Presentation SlidesSlideTeam
Enthrall your audience with this Business Data Analytics Powerpoint Presentation Slides. Increase your presentation threshold by deploying this well crafted template. It acts as a great communication tool due to its well researched content. It also contains stylized icons, graphics, visuals etc, which make it an immediate attention grabber. Comprising twenty nine slides, this complete deck is all you need to get noticed. All the slides and their content can be altered to suit your unique business setting. Not only that, other components and graphics can also be modified to add personal touches to this prefabricated set. https://bit.ly/3d4gdzY
Highlights from three different speakers on the actual use of dashboards for decisionmaking.
MEASURE Evaluation shares the results of a landscape analysis looking for specific examples of dashboards prompting action. BroadReach shares an example of how their Vantage platform is making HIV data accessible in South Africa. JSI shares an example of low-tech but high-impact dashboard development and coaching that has transformed districts in Zimbabwe.
This will explain you what is data visualization,why we need it,what are the technologies in it ,tools available for it and it ends up with how can we get the excellence in visualization
System Dynamics, Analytics & Big Data (16th Conference of the UK Chapter of t...Michael Mortenson
This talk investigates the relationship between system dynamics, analytics and big data. Drawing on both a historical analysis and text analytics, similarities and differences are identified, and some suggestions on how future research may provide value for the System Dynamics community.
Data visualization is a technique that converts complex data into simple, crisp and strikingly interactive images that present the required information instead of long and boring texts. These visual objects include infographic, dials and gauges, geographic, maps, detailed bar, sparklines, heat maps, pie, fever charts etc.
Data Science Salon: Adopting Machine Learning to Drive Revenue and Market ShareFormulatedby
The race is on to gain strategic and proprietary insights into changes in customer preferences before your competitors. This workshop will cover how and why machine learning is the tool for marketers to drive revenue and increase market share. The adoption of machine learning does not happen overnight. We will discuss the Five Es of machine learning maturity – Educating, Exploring, Engaging, Executing and Expanding. Hear real-world examples of using machine learning to accelerate revenue, identify new customers and introduce new products based on machine learning capabilities.
Next DSS MIA Event - https://datascience.salon/miami/
Dialogue Tool for Value Creation in Digital Transformation: Roadmapping for...Naoshi Uchihira
With the rapid spread of digital technologies into industry and society, the collaboration between humans and machines (artificial intelligence and ma-chine learning) becomes an important issue, but it is not clear what kind of value can be created by the collaboration between humans and machines. Roadmapping is effective as a dialogue tool for clarifying the value among stakeholders. However, the traditional roadmapping methods are insufficient since collaboration between humans and machines is a socio-technical system and evolves together while influencing each other. This paper proposes the new co-evolutionary technology roadmapping method and reports the results of the roadmapping workshop for machine learning applications.
Data analytics presentation- Management career institute PoojaPatidar11
1. The basic definition of Data, Analytics, and Data Analytics
2. Definition: Data: Data is a set of values of qualitative or quantitative variables. It is information in the raw or unorganized form. It may be a fact, figure, characters, symbols etc
Analytics: Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making.
Data Analytics: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.
3.Types of analytics: Predictive Analytics (What could happen?)
Prescriptive Analytics (What should we do)
Descriptive Analytics (What has happened?)
4.Why Data analytics? Data Analytics is needed in Business to Consumer applications (B2C)
5.The process of Data analytics: Data requirements,
Data collection, Data processing, Data cleaning, Exploratory data analysis,
Modeling and algorithms, Data product, Communication
6.The scope of Data Analytics: Bright future of data analytics, many professionals and students are interested in a career in data analytics.
7.Importance of data analytics:1. Predict customer trends and behaviors
Analyze,
2 interpret and deliver data in meaningful ways
3.Increase business productivity
4.Drive effective decision-making
8.why become a data analyst? talented gaps of skill candidates, good salaries for freshers, great future growth path
9. What recruiters look for in applicants: Problem-Solving Skills, Analytical Mind, Maths and Statistic Skills, Communication (both oral and written), Teamwork Abilities
10. Skill is required for Data analytics?
1.) Analytical Skills
2.) Numeracy Skills
3.) Technical and Computer Skills
4.) Attention to Details
5.) Business Skills
6.) Communication Skills
11. Data analytics tools
1.SAS: SAS (Statistical Analysis System) is a software suite developed by SAS Institute. sas language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis. The language has the ability to read data from databases and common spreadsheets.
2. R: R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.
3.PYTHON: Python is a popular programming language Python is a powerful, flexible, open-sources language that is easy to use,
and has a powerful library for data manipulation and analysis.
4.TABLEAU: Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.
Small and medium enterprise business solutions using data visualizationjournalBEEI
The small and medium enterprise (SME) companies optimize performance using different automated systems to highlight the operations concerns. However, lack of efficient visualization in reporting results in slow feedbacks, difficulties in extracting root cause, and minimal corrective actions. To complicate matters, the data heterogeneity has intensely increased, and it is produced in a fast manner making it unmanageable if the traditional methods of analytics are applied. Hence, we propose the use of a dashboard that can summarize the operational events using real-time data based on the data visualization approach. This proposed solution summarizes the raw data, which allows the user to make informed decisions that can give a positive impact on business performance. An interactive intelligent dashboard for SME (iid-SME) is developed to tackle issues such as measurement of cases completed, the duration of time needed to solve a case, the individual performance of handling cases and other tasks as a proof of concept. From the result, the implementation of the iid-SME approach simplifies the conveyance of the message and helps the SME personnel to make decisions. With the positive feedback obtained, it is envisaged that such a solution can be further employed for SME improvement for better profit and decision making.
Data Science Salon: Building smart AI: How Deep Learning Can Get You Into Dee...Formulatedby
Presented by Michael Housman Chief Data Scientist at RapportBoost.AI
Next DSS NYC Event 👉 https://datascience.salon/newyork/
Next DSS LA Event 👉 https://datascience.salon/la/
Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
Though the term machine learning has become very visible in
the popular press over the past few years—making it appear to be the newest shiny object—the technology has actually been
in use for decades. In fact, machine learning algorithms such as decision trees are already in use by many organizations for predictive analytics.
Compliance implications of crossing the $10 billion asset thresholdGrant Thornton LLP
Since the passage of the Dodd-Frank Act, small regional banks have been forced to rethink their growth strategies as they inch closer to the $10 billion assets threshold. Here’s guidance on navigating the new regulatory field.
2014 Acquisition Policy Survey - A Closing Window: Are we missing the opportu...Grant Thornton LLP
For over a decade, our firm and the Professional Services Council (PSC) have conducted a biennial Acquisition Policy Survey capturing opinions & insights of federal government acquisition leaders on the current state of the profession, noteworthy trends, and future challenges/opportunities. This report covers the survey findings in 5 areas: Budget Uncertainty, Workforce, Access to Innovation, Communications & Collaboration, and Oversight & Compliance.
Crap! It doesn't look quite right, or, how I learned to stop worrying and set...Lyza Gardner
From Over the Air, 2011, Bletchley Park, UK.
The mobile web—or whatever we want to call it—is still in its Wild West phase, crazy, chaotic and exciting. Right now, being successful on the web is getting more complicated, to the point that it can feel impossible to succeed.
We can’t fix everything right now, but by
thinking in a future-friendly manner and
relinquishing control we never had in the first place, we can help shape the future of the web.
Sukcesja jako kluczowy moment w rozwoju firmyGrant Thornton
Dlaczego tylko 30% prywatnych przedsiębiorstw jest w stanie przetrwać do drugiego pokolenia, a tylko 12% do trzeciego? Jak długo zamierzasz pozostać aktywny w swoim biznesie? Jak stabilny jest model rozwoju twojego przedsiębiorstwa? Jak wytypować menedżerów zdolnych do stymulowania dalszego rozwoju firmy w przyszłości? Jak te decyzje wpłyną na kluczowych pracowników firmy, twoją rodzinę? Kiedy należy zacząć myśleć o sukcesji? Co Ciebie powstrzymuje od rozpoczęcia planowania sukcesji?
System Dynamics, Analytics & Big Data (16th Conference of the UK Chapter of t...Michael Mortenson
This talk investigates the relationship between system dynamics, analytics and big data. Drawing on both a historical analysis and text analytics, similarities and differences are identified, and some suggestions on how future research may provide value for the System Dynamics community.
Data visualization is a technique that converts complex data into simple, crisp and strikingly interactive images that present the required information instead of long and boring texts. These visual objects include infographic, dials and gauges, geographic, maps, detailed bar, sparklines, heat maps, pie, fever charts etc.
Data Science Salon: Adopting Machine Learning to Drive Revenue and Market ShareFormulatedby
The race is on to gain strategic and proprietary insights into changes in customer preferences before your competitors. This workshop will cover how and why machine learning is the tool for marketers to drive revenue and increase market share. The adoption of machine learning does not happen overnight. We will discuss the Five Es of machine learning maturity – Educating, Exploring, Engaging, Executing and Expanding. Hear real-world examples of using machine learning to accelerate revenue, identify new customers and introduce new products based on machine learning capabilities.
Next DSS MIA Event - https://datascience.salon/miami/
Dialogue Tool for Value Creation in Digital Transformation: Roadmapping for...Naoshi Uchihira
With the rapid spread of digital technologies into industry and society, the collaboration between humans and machines (artificial intelligence and ma-chine learning) becomes an important issue, but it is not clear what kind of value can be created by the collaboration between humans and machines. Roadmapping is effective as a dialogue tool for clarifying the value among stakeholders. However, the traditional roadmapping methods are insufficient since collaboration between humans and machines is a socio-technical system and evolves together while influencing each other. This paper proposes the new co-evolutionary technology roadmapping method and reports the results of the roadmapping workshop for machine learning applications.
Data analytics presentation- Management career institute PoojaPatidar11
1. The basic definition of Data, Analytics, and Data Analytics
2. Definition: Data: Data is a set of values of qualitative or quantitative variables. It is information in the raw or unorganized form. It may be a fact, figure, characters, symbols etc
Analytics: Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making.
Data Analytics: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.
3.Types of analytics: Predictive Analytics (What could happen?)
Prescriptive Analytics (What should we do)
Descriptive Analytics (What has happened?)
4.Why Data analytics? Data Analytics is needed in Business to Consumer applications (B2C)
5.The process of Data analytics: Data requirements,
Data collection, Data processing, Data cleaning, Exploratory data analysis,
Modeling and algorithms, Data product, Communication
6.The scope of Data Analytics: Bright future of data analytics, many professionals and students are interested in a career in data analytics.
7.Importance of data analytics:1. Predict customer trends and behaviors
Analyze,
2 interpret and deliver data in meaningful ways
3.Increase business productivity
4.Drive effective decision-making
8.why become a data analyst? talented gaps of skill candidates, good salaries for freshers, great future growth path
9. What recruiters look for in applicants: Problem-Solving Skills, Analytical Mind, Maths and Statistic Skills, Communication (both oral and written), Teamwork Abilities
10. Skill is required for Data analytics?
1.) Analytical Skills
2.) Numeracy Skills
3.) Technical and Computer Skills
4.) Attention to Details
5.) Business Skills
6.) Communication Skills
11. Data analytics tools
1.SAS: SAS (Statistical Analysis System) is a software suite developed by SAS Institute. sas language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis. The language has the ability to read data from databases and common spreadsheets.
2. R: R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.
3.PYTHON: Python is a popular programming language Python is a powerful, flexible, open-sources language that is easy to use,
and has a powerful library for data manipulation and analysis.
4.TABLEAU: Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.
Small and medium enterprise business solutions using data visualizationjournalBEEI
The small and medium enterprise (SME) companies optimize performance using different automated systems to highlight the operations concerns. However, lack of efficient visualization in reporting results in slow feedbacks, difficulties in extracting root cause, and minimal corrective actions. To complicate matters, the data heterogeneity has intensely increased, and it is produced in a fast manner making it unmanageable if the traditional methods of analytics are applied. Hence, we propose the use of a dashboard that can summarize the operational events using real-time data based on the data visualization approach. This proposed solution summarizes the raw data, which allows the user to make informed decisions that can give a positive impact on business performance. An interactive intelligent dashboard for SME (iid-SME) is developed to tackle issues such as measurement of cases completed, the duration of time needed to solve a case, the individual performance of handling cases and other tasks as a proof of concept. From the result, the implementation of the iid-SME approach simplifies the conveyance of the message and helps the SME personnel to make decisions. With the positive feedback obtained, it is envisaged that such a solution can be further employed for SME improvement for better profit and decision making.
Data Science Salon: Building smart AI: How Deep Learning Can Get You Into Dee...Formulatedby
Presented by Michael Housman Chief Data Scientist at RapportBoost.AI
Next DSS NYC Event 👉 https://datascience.salon/newyork/
Next DSS LA Event 👉 https://datascience.salon/la/
Recent advances in deep learning have fueled tremendous excitement about the potential for artificial intelligence to solve countless problems. But there are some perils and pitfalls endemic to these new techniques, particularly because they ignore two essential components of the scientific method: (1) understanding the how; and (2) explaining the why. Dr. Michael Housman offers up a two specific examples from his own career as a data scientist to show how a naive application of deep learning algorithms can lead data scientists to the wrong conclusion and offers up some guidance for avoiding these mistakes.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
Though the term machine learning has become very visible in
the popular press over the past few years—making it appear to be the newest shiny object—the technology has actually been
in use for decades. In fact, machine learning algorithms such as decision trees are already in use by many organizations for predictive analytics.
Compliance implications of crossing the $10 billion asset thresholdGrant Thornton LLP
Since the passage of the Dodd-Frank Act, small regional banks have been forced to rethink their growth strategies as they inch closer to the $10 billion assets threshold. Here’s guidance on navigating the new regulatory field.
2014 Acquisition Policy Survey - A Closing Window: Are we missing the opportu...Grant Thornton LLP
For over a decade, our firm and the Professional Services Council (PSC) have conducted a biennial Acquisition Policy Survey capturing opinions & insights of federal government acquisition leaders on the current state of the profession, noteworthy trends, and future challenges/opportunities. This report covers the survey findings in 5 areas: Budget Uncertainty, Workforce, Access to Innovation, Communications & Collaboration, and Oversight & Compliance.
Crap! It doesn't look quite right, or, how I learned to stop worrying and set...Lyza Gardner
From Over the Air, 2011, Bletchley Park, UK.
The mobile web—or whatever we want to call it—is still in its Wild West phase, crazy, chaotic and exciting. Right now, being successful on the web is getting more complicated, to the point that it can feel impossible to succeed.
We can’t fix everything right now, but by
thinking in a future-friendly manner and
relinquishing control we never had in the first place, we can help shape the future of the web.
Sukcesja jako kluczowy moment w rozwoju firmyGrant Thornton
Dlaczego tylko 30% prywatnych przedsiębiorstw jest w stanie przetrwać do drugiego pokolenia, a tylko 12% do trzeciego? Jak długo zamierzasz pozostać aktywny w swoim biznesie? Jak stabilny jest model rozwoju twojego przedsiębiorstwa? Jak wytypować menedżerów zdolnych do stymulowania dalszego rozwoju firmy w przyszłości? Jak te decyzje wpłyną na kluczowych pracowników firmy, twoją rodzinę? Kiedy należy zacząć myśleć o sukcesji? Co Ciebie powstrzymuje od rozpoczęcia planowania sukcesji?
Succession planning, regardless of the age of owners or management, is not an event, but an ongoing process that needs to begin now. Find out what are the are critical decisions that need to be addressed (but not necessarily resolved today)
Na odpowiednio przeprowadzoną sukcesję ma wpływ długotrwały proces planowania, jak i odpowiednie przygotowanie sukcesora. Eksperci z Grant Thornton pomogą podjąć odpowiednią decyzję w sprawie przyszłości firmy, jak i zrealizować cały proces sukcesji.
The right sales channel mix leads to increased profits Grant Thornton LLP
Selling directly to consumers is not only the preferred sales strategy, but also the most profitable, according to Grant Thornton's Strategic Source and Sell: Channel diversity survey. This infographic highlights top findings from the survey.
- See more at: http://gt-us.co/1x3mBLU
The 2015 survey uncovers the latest issues organizations are facing as they respond to risks, assess the effectiveness of their risk mitigation activities and gain a deeper understanding of what they are doing to address cybersecurity.
Research shows only 30% of organizations see their change management as successful. Here are 3 key areas to focus on to enable change.
Learn more - http://gt-us.co/1aDc2t1
The net investment income (NII) applies a 3.8% Medicare tax on all NII once an individual’s adjusted gross income passes certain thresholds, but the IRS allows deductions to reduce it.
Our Year-end tax guide includes more easy-to-use information. See more at: http://gt-us.co/1tktvfy
Our 2015 Financial Executive Compensation Survey with the Financial Executives Research Foundation—This survey examines the growth in executive salary both in the public and private sectors as well gives an exclusive look into the salaries of financial executives across the US.
For digital media companies, effective cybersecurity programs a mustGrant Thornton LLP
In digital media trust is everything, without it your business model doesn’t work. Cybersecurity can be a key component, ensuring the integrity of your services. Check out this brief guide to securing your data.
Recommendations for supporting population health include motivational, organizational and personnel components most important to creating and sustaining a collaborative partnership.
Compliance program requirements for the Volcker Rule of the Dodd-Frank ActGrant Thornton LLP
Regulatory requirements for both enhanced and standardized compliance programs as stipulated by the Volcker Rule of the Dodd-Frank Act.
Watch our webcast to learn more: http://gt-us.co/1FOw3XF
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
Unifying the big data analytics stack by enabling ETL, OLAP, visualization, and collaboration via a single interface. Get an End To End implementation of The Modern Analytics Architecture.
"Making Data Actionable" by Budiman Rusly (KMK Online)Tech in Asia ID
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
[DSC Europe 22] The Making of a Data Organization - Denys HolovatyiDataScienceConferenc1
Data teams often struggle to deliver value. KPIs, data pipelines, or ML driven predictions aren't inherently useful - unless the data team enables the business to use them. Having worked on 37 data projects over the past 5 years, with total client revenue clocking at about $350B, I started noticing simple success factors - and summarized those in the Operating Model Canvas & the Value Delivery Process. With those, I branched out into what I call data organization consulting and help clients build their data teams for success, the one you see not only on paper but also in your P&L. In this talk, I'll share some insight with you.
Documentation Workbook Series. Step 3 Presenting Information (Visual Document...Adrienne Bellehumeur
This booklet is part of Step 3 Presenting of the five-step documentation process (Step 1 – Capturing Information, Step 2 – Structuring Information, Step 3 – Presenting Information, Step 4 –Communicating Information, Step 5 – Storing and Maintaining Information). This booklet provides some basic tips, techniques, approaches and exercises for understanding and practicing how to produce high quality visuals in your documentation.
Visual Analytics combines human intuition and data science to derive knowledge from the data in a very efficient, effective and easy way. Visual Analytics empowers your people to interact with the data and generate new insights.
Data pipelines are the heart and soul of data science. Are you a beginner looking to understand data pipelines? A glimpse into what they are and how they work.
How Can Business Analytics Dashboard Help Data Analysts.pdfGrow
The Business analytics dashboard provides a centralized platform that empowers data analysts to explore, analyze, and visualize data more efficiently and intuitively, ultimately driving better decision-making. You can discover how these intuitive Business Intelligence dashboard tools enhance every aspect of data analysis and streamline a data analyst's job. Revolutionize how data analysts function and make the data analysis journey successful by visiting Grow.com.
In this document, the five disruptive trends shaping the corporate IT landscape today are layed out. Out of the five, Big Data has the biggest potential to generate new sustainable competitive advantages. But the benefits will remain out of reach of many organizations as they struggle to adopt the technology, develop new capabilities, and manage the cultural change associated with the use of big data. This document offers a pragmatic approach to generating business value.
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
GT Events and Program Guide is a look ahead at the latest knowledge and insights available from Grant Thornton LLP. It includes a collection of our research, thought leadership and a schedule of upcoming webcasts and events.
GT Events and Program Guide is a look ahead at the latest knowledge and insights available from Grant Thornton LLP. It includes a collection of our research, thought leadership and a schedule of upcoming webcasts and events.
GT Events and Program Guide is a look ahead at the latest knowledge and insights available from Grant Thornton LLP. It includes a collection of our research, thought leadership and a schedule of upcoming webcasts and events.
GT Events & Program Guide: ForwardThinking October/November 2017Grant Thornton LLP
ForwardThinking is a look ahead at the latest knowledge and insights available from Grant Thornton LLP. It includes a collection of our research, thought leadership and a schedule of upcoming webcasts and events.
Real Estate Industry Success: Build, Transform and Protect Value into 2020Grant Thornton LLP
REITS are finding that while online shopping is active, their real estate holdings — stores and malls — continue to draw actual shoppers. Most sales still take place in brick-and-mortar, with technology shaping retail and real estate success.
Asset Management Industry Success: Build, Transform and Protect Value into 2020Grant Thornton LLP
Though hedge fund volume has doubled in the past five years, fees are pressured down; responsive strategies to replace fee dependency include expansion — M&A, joint ventures and alliances.
Technology Industry Success: Build, Transform and Protect Value into 2020Grant Thornton LLP
Technology leaders are making bold decisions and reinventing their company, exploiting innovative technologies, sharpening a competitive edge, investing significantly in R&D, embracing a new business model and taking a more strategic view of risk.
Banking Industry Success: Build, Transform and Protect Value into 2020Grant Thornton LLP
Banking leaders say their focus on customer service will double between now and 2020, becoming their No. 1 priority in an increasingly competitive environment.
GT Events & Program Guide: ForwardThinking August/September 2017Grant Thornton LLP
ForwardThinking is a look ahead at the latest knowledge and insights available from Grant Thornton LLP. It includes a collection of our research, thought leadership and a schedule of upcoming webcasts and events.
Why prepare now? 5 things that smart businesses are doing TODAY to prepare fo...Grant Thornton LLP
Tax reform is top of mind for many of today’s businesses as they struggle to understand what it might mean to them, and what they should be doing to prepare. While it may be easy to be paralyzed by the uncertainty of the legislative process, a “wait-and-see” approach is a mistake. The prospect of tax reform creates tremendous new tax planning opportunities, and many of these are effective only if done before tax reform is enacted. No company should be making long-term business decisions without understanding how tax reform could affect the economic impact. Learn the five steps your business can take now to prepare for tax reform.
ForwardThinking is a look ahead at the latest knowledge and insights available from Grant Thornton LLP. It includes a collection of our research, thought leadership and a schedule of upcoming webcasts and events.
The Future of Growth and Industries Webcast Series: Trends to watch for 2020Grant Thornton LLP
An analysis of future challenges across industry based on recent research. The presentation features technology disruption and internationalism as key themes.
ForwardThinking is a look ahead at the latest knowledge and insights available from Grant Thornton LLP. It includes a collection of our research, thought leadership and a schedule of upcoming webcasts and events.
The Future of Industry: Sector Convergence & 2017 OutlookGrant Thornton LLP
What is the future of industries? How should we respond to the opportunities and challenges presented by this disruption? Every industry is being disrupted by fast-paced change on many fronts. In this deck, Grant Thornton industry leaders explore cross-industry issues and potential solutions to support your business in this ever-changing world.
ForwardThinking is a look ahead at the latest knowledge and insights available from Grant Thornton LLP. It includes a collection of our research, thought leadership and a schedule of upcoming webcasts and events.
DOL fiduciary rule: How it affects the insurance industry Grant Thornton LLP
We explore how the Department of Labor's final rule expanding the definition of fiduciary investment advice for advisers to retirement plans, participants and beneficiaries will affect the insurance industry.
Tightening pressure transforms the landscape: The state of asset managementGrant Thornton LLP
After years of growth, asset managers face a number of challenges. Here, we examine these challenges and provide insight into the state of the asset management industry.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
2. Contents
1 Introduction
2 Defining the problem
3 Methodologies to enable ADV
6 Functional capabilities
8 ADV gallery – Top 10 visualizations
14 Technical platform capabilities
15 Benefits realized
16 Risks and lessons learned
3. Solving the data visualization dilemma
1
Our brains are wired to love information, but when it comes
to handling data, we quickly develop headaches. Advanced
data visualization (ADV) is a rapidly emerging concept that is
becoming pervasive in business and society. ADV has a lofty
goal of transforming data into information. Merely noting how
annual reports have changed over the past 10 years — with data
displayed prominently in graphical formats — shows the impact
of ADV. Three converging trends have brought data visualization
to the forefront as a value driver. First is the prominence of big
data as table stakes in any organization. The second has been
the democratization of visualization tools, which allows access
to users who do not have advanced technical skills to build
visualizations. Finally, the pervasiveness of infographics in our
daily lives has increased expectations for visual representations.
Introduction
4. Solving the data visualization dilemma
2
“[Data] scientists will need visualization
experts the way writers need editors.”
— Harvard Business Review, Visualizing Data, April 2013
Defining the problem
Despite solving for the fundamental capabilities of big data and
providing easy-to-use tools for visualization, organizations are
still struggling with the basics: graduating from static reporting
to interactive, online presentation tools. The data visualization
discipline needs to be seen as an analytic process, not a reporting
outcome. This is the first barrier to overcome on the business
intelligence (BI) maturity model.
The overarching pain points in achieving data visualization that
are impediments to the goal are threefold.
1. Consumers want to easily recognize patterns in complex
data sets.
2. Companies need to synthesize large amounts into a single
palette. This is the “one-page thinking” principle.
3. The struggle of balancing breadth and depth of a complex data
model turns most users away.
Another primary obstacle to achieving value in ADV is
addressing the convergent skill sets needed: It is rare to find an
expert in programming, design and statistics that can readily
generate ADVs. Combining the right skills in a team seeking to
build data visualizations starts with the ability to ask the right
questions about data and toolsets. As we find answers, it becomes
possible to align and deploy ADV solutions and capabilities.
81% of executives state they highly value
data visualization, yet only 14% say they
interact directly with data visualization
tools and technology.
— Based on research of more than 500 Grant Thornton
Technology Solutions engagements
5. Breadth of solution
Speedtosolution
POC
Analytic Assessment
Approach (A3) methodology
Reporting
mockups
storyboards
Proof of concept (POC) –
Developed and visualized directly in
OBI based on known requirements
and developed data models
Models rapidly develop conceptual
designs and visuals using storyboards
and wire-framing design tools. Used
to validate key analytic paths
Comprehensive subject area
design, including data source
strategies and, measurement
strategy, reporting requirements
3
ADV solutions should contain seven primary capabilities that
address these obstacles (see Figure 1). It is important that both
functional and technical platform capabilities include each of these
components, just as classic BI/data warehousing solutions strive
to address reporting.
Methodologies to enable ADV
We hear two frequent questions across our BI and
analytics projects.
1. How do I know what is possible when it comes to data
visualization? (This deals with the classic conundrum
of knowing what to ask for, and also seeking the “silver
bullet” answer.)
2. How do I get started?
To answer these questions, Grant Thornton has developed
tiered methodologies (see Figure 2) to comprehensively address
initiating data visualization that take into account breadth of
solution and speed to deliver.
7 primary capabilities
1. Dynamic and immediate data
2. Visual interfaces with interactivity
3. Multidimensional analysis
4. Animation/use of motion
5. Personalization to end users
6. Actions/action frameworks
7. Proactive alerts
Figure 1: Fundamentals of ADV solutions
Figure 2: Tiered methodologies
6. Solving the data visualization dilemma
4
Storyboards and mock-ups
Using storyboards and mock-ups, we can rapidly develop
conceptual designs and visuals. Prior to the storyboard
process, we usually conduct a demonstration of BI application
functionality to set the stage. Creating a conceptual design, the
storyboard and mock-up process reduces development time and
rework (see Figure 4). Additional activities include prioritization
of content and reporting requirements, exploration of design
options, and rapid prototyping in the actual storyboard sessions.
Analytic Assessment Approach (A3)
The A3 methodology focuses on defining three key strategies or
inputs (see Figure 3).
1. The measurement strategy that defines key metrics, hierarchies
and calculations, which are important to the business. This is
the precursor to key performance indicators (KPIs).
2. The reporting strategy, which focuses on the current and
future-state delivery mechanisms for reporting.
3. Data strategy that assesses the target data sources, and how
data will be extracted and transformed for analysis.
Key outcomes of the A3 methodology include: analytic
roadmaps; detailed implementation and resource plans; business
case and return on investment calculations; and technology
selection and utilization plans.
Division heat map — portal into detail
using key metrics and indicators
Division financial reporting
(currently published monthly — period agnostic)
Project financial
summery
Project financial
detail
New business/
CRM
AR/cash
management
DFO/cash management summery (initiative/KPI-based)
I.
Measurement
Strategy
III.
Data
Strategy
Inputs Outcomes
II.
Reporting
Strategy
Implimentation Plan
Create Prototype
Tool Utilization
Figure 3: Analytic Assessment Approach
Figure 4: Financial reporting review
7. 5
Prototyping and proof of concepts
A proof of concept (POC) is a data visualization developed
and visualized directly in the BI technology, based on known
requirements and a sample data model. A POC relies on the
storyboard conceptual vision — focusing on a primary subject
area, detailed scenarios and aggregate presentation views.
Often, this process leverages the storyboard and uses it as an
interim landing or navigation page for new users in Oracle
Business Intelligence. POCs are typically detailed, visualized
analytic scenarios based on data models and reporting
requirements.
8. Solving the data visualization dilemma
6
Modes of delivery
The seven standard capabilities of ADV are delivered in three
primary modes.
1. ADV customers engage with the toolset via visual analysis and
discovery. Users interrogate the visualization — interact, drill,
pivot and zoom — to answer questions and pose new analysis.
2. Users engage via a familiar display of snapshot or point-in-
time reporting. The easiest way to relate to this mode is the
classic balanced scorecard report. At the end of the day/
month, the scorecard is the snapshot of reporting at that point
in time, with further information on KPIs, etc.
Functional capabilities
3. Proactive alerts to end users — regardless of device or
toolset, data visualizations can alert end users without the
need to interrogate data visualizations to find answers to a
predetermined question.
These modes of delivery combine with the ADV capabilities to
frame the functional capabilities.
Figure 5: Graphical relationships
gallery
modes of
delivery
capabilities
relationships
Data
visualization
gallery
Primary
modes of
delivery
Functional
capabilities
Standard
graphical
relationships
Data
visualization
gallery
Primary
modes of
delivery
Functional
capabilities
Standard
graphical
relationships
1. Visual analysis
2. Reactive snapshots
3. Proactive reporting
1. Nominal comparisons
2. Rankings
3. Time series
4. Part-to-whole
5. Deviations
6. Distributions
7. Correlations
1. Dynamic and
immediate data
2. Visual interfaces
with interactivity
3. Multidimensional analysis
4. Animation/use of motion
5. Personalization to end users
6. Actions/action frameworks
7. Proactive alerts
1. Classic waterfall
2. Strategy trees and wheels
3. Geo-spatial/geoprompting
4. Sparkline graphs
5. 80-20 relationships
6. Comparative distributions
7. Scatter cloud
8. Boxplot and whisker
9. Bubble chart
10. Master/detail views
1. Visual analysis
2. Reactive snapshots
3. Proactive reporting
1. Nominal comparisons
2. Rankings
3. Time series
4. Part-to-whole
5. Deviations
6. Distributions
7. Correlations
1. Dynamic and
immediate data
2. Visual interfaces
with interactivity
3. Multidimensional analysis
4. Animation/use of motion
5. Personalization to end users
6. Actions/action frameworks
7. Proactive alerts
1. Classic waterfall
2. Strategy trees and wheels
3. Geo-spatial/geoprompting
4. Sparkline graphs
5. 80-20 relationships
6. Comparative distributions
7. Scatter cloud
8. Boxplot and whisker
9. Bubble chart
10. Master/detail views
Data
visualization
gallery
Primary
modes of
delivery
Functional
capabilities
Standard
graphical
relationships
1b
9. 7
1
Harvard Business Review. Visualizing Data, April 2013.
2
Few, Stephen. “Selecting the Right Graph for Your Message,” Perceptual Edge, Sept. 18, 2004.
Understanding graphical relationships
With functional capabilities defined through general capabilities
of ADV and the modes of delivery, it is also necessary to have a
fundamental understanding of standard graphical relationships.
Data scientists need designers like writers need editors1
.
Understanding the basic tools of graphical relationships and
where they are used is a common cure for writer’s block when it
comes to ADV.
There are seven classic forms of graphical relationships. The
vast majority of quantitative depictions in business settings can
be described as one or a combination of these seven graphical
elements2
. Understanding these fundamentals can drive value in
selecting the right visualization concept.
1. Nominal comparisons are simple comparisons of the
categories and subcategories of one or more components in
any order.
2. Rankings simply list data points in a defined order by
a dimensional value selected — commonly shown in
descending or ascending order.
3. Time series relationships are a sequence of data points that
are ordered in common time buckets and typically plotted for
trending purposes.
4. Part-to-whole comparisons identify how subsets of a data
population relate to the total population value — displaying
ratios to the whole.
5. Deviations provide a comparative analysis of a standard
deviation on a data point for a selected set of dimensions
or values.
6. Distributions describe basic statistical discrete distribution
views of a selected population or data set.
7. Correlations refer to any of a broad grouping of statistical
relationships involving dependence between the
different groups.
10. Solving the data visualization dilemma
8
Below are the top 10 visualizations based on Grant Thornton’s
client projects and initiatives focusing on ADV and executive
analytics3
. Maintaining gallery visualizations are critical to
answering, “What is possible?”
1. Classic waterfall
Waterfall graphics show how an initial value is increased and
decreased by a series of intermediate values. They are favorites
of financial and accounting departments to show contributions
and profitability.
ADV gallery – Top 10 visualizations
3
All gallery screen shots are from Oracle Business Intelligence Enterprise Edition samples.
Figure 6: Classic waterfall
11. 9
2. Strategy trees and wheels
A strategy tree shows an objective and its supporting objectives
and KPIs hierarchically. The contribution wheel consists of a
center circle (or focus node) that represents the starting objective
of the diagram.
3. Geospatial/geoprompting
Geospatial reporting provides comparisons with a map backdrop
or comparison of distances between. Geoprompting provides
heat map alerts for users and prompts them to select areas and
drill to greater detail.
Figure 7: Strategy trees and wheels
Figure 8: Geospatial/geoprompting
12. Solving the data visualization dilemma
10
4. Sparkline graphs
A sparkline is a very small line chart, typically drawn without
axes or coordinates. It presents the general shape of the
variation — typically over time — in some measurement, such
as temperature or stock market price, in a simple and highly
condensed way.
Figure 9: Sparkline graphs
13. 11
5. 80-20 relationships
This report measures how the upper group of a specific
population set contributes in descending order of value. Filters
enable users to set a percentage limit of value for the top group,
and the report renders the corresponding percentage of the
population that makes up that value.
6. Comparative distributions
Comparative distributions are representations of statistical
distributions, by individuals, for a selected population. It allows
users to see how a metric is distributed among different categories.
Figure 10: 80-20 relationships
Figure 11: Comparative distributions
14. Solving the data visualization dilemma
12
7. Scatter cloud
This report provides a graphical summary of a set of data.
Individual values are represented by the position of the point in
the chart space. It displays measures of central median, dispersion
and skewness.
8. Boxplot and whisker
This report displays a boxplot and whisker diagram comparing
the spread of detailed data point values between individuals of a
dimension. It depicts a set of values for each dimension individual
through seven number summaries: smallest observation (bottom);
lower decile (10% mark); lower quartile and upper quartile
(IQR); median and average; upper decile (90% mark); and largest
observation (top).
Figure 12: Scatter cloud
Figure 13: Boxplot and whisker
15. 13
9. Bubble chart
Bubble charts are used in scatter plot scenarios where more
than two variables can be used. Data points are depicted by
the location and size of round data markers (bubbles). Bubble
graphs are used to show correlations among three types of values,
especially when you have a number of data items and you want to
see the general relationships. Bubble charts are useful to segment
populations of data, apply quadrant labels and prompt users for
further investigation.
10. Master/detail views
The master/detail linking allows you to establish a relationship
between two or more views; one view is called the master and will
drive changes in one or more views called detail views. You can
think of a master/detail relationship in a manner similar to what
you do when navigating from one report to another, but you do
not lose sight of the master view.
Figure 14: Bubble chart
Figure 15: Master/detail views
16. Solving the data visualization dilemma
14
Technical platforms need to address many advanced
requirements. We focus on three primary platform capabilities
of note.
Engineered systems
An engineered system simply refers to the “appliance concept”
to deliver the function of BI, analytics and visualizations. Apart
from the classic IT approach to technical platforms that often
considers hardware and software separately, analytic technical
platforms are increasingly thought of as an engineered system
possessing all critical components — software applications,
middleware, integration tools, hardware, etc. Perhaps the most
popular engineered system to date is the Apple iPad. This
solution-in-a-box thinking is a key requirement for ADV
technical platforms.
Technical platform capabilities
In-memory processing
In-memory processing is a fairly simple, yet very powerful,
innovation. Retrieving data from disk storage is the slowest
part of data processing: The more data you need to work with,
the slower the analytics process. The usual way of addressing
this performance issue has been to preprocess data in some way
(cubes, query sets, aggregate tables, etc.). In-memory processing
makes it possible to see the data more actively and at a deeper
level of detail, rather than in predefined high-level views. It allows
data visualizations to be more like natural thoughts.
Advanced interaction via write backs
Interactivity with data visualization is paramount, and often users
of a visualization tool need to provide additional input to alter
or enhance the analysis. From a BI standpoint, this is called a
“write back” and has special complexities and implications. This
goes beyond standard selection of parameters or prompting on
predetermined values or filters. Certain BI tools handle write
backs better than others; however, any ADV technical platform
must address this critical requirement. Our clients most often
use write backs to the underlying data model in what-if analyses,
predictive models and interactive commentaries with the data set.
17. 15
CEOs are demanding faster insight from data on hand, which
provides the platform for most business leaders and analysts. Data
visualization allows data discovery and visual analysis and reduces
time to insight.
As data visualization and BI tools drive interactivity with
underlying data, you can apply the global positioning system
(GPS) analogy. A strong ADV tells us where we are and where
we are going. ADV should enable end users to create their own
visualizations, providing a true democratization of analytics tools.
You can reap these benefits from data visualization efforts, as well
as the broader BI function:
Benefits realized
1. Improved operational efficiency
2. Alignment across organization and functional groups
3. Decreased time to insight
4. Faster response to changes
5. Ability to identify new business opportunities
6. Higher employee and partner productivity
7. Improved compliance with established standards
18. Solving the data visualization dilemma
16
The risks and lessons learned in executing data visualizations
relate back to our three main problem areas: recognizing patterns
in complex data, synthesizing data into a single point of view,
and balancing breadth and depth. The following risks and lessons
learned are common throughout ADV initiatives:
1. Data quality. Do not underestimate the importance of
data quality. Master data management tools cleanse data
at the integration level, and BI tools expose data issues to
be addressed. Data visualizations can mask data issues and
provide users with inaccuracies that will taint the analysis.
2. Content misrepresentation. Taking into account functional
capabilities, it is possible to select inappropriate graphical
representations and modes of delivery for data visualizations.
This can cause a misrepresentation of the data and the
information that the ADV is trying to convey.
3. Biases. Data visualizations can give power to the underlying
biases of the developer, designer or statistician and
contaminate the analysis of the end user.
Risks and lessons learned
4. Cluttered design. With all the functional capabilities for data
visualization, it is possible to take things too far — especially
in a single view. This can turn away the typical end user.
5. Data overload. Exposing too much data, without a logical
progression, or using data that is not absolutely necessary for
the intended purpose of the visualization, will overload the
end user and limit the effectiveness of the tool.
6. Delivery device agnostic. With dozens of potential interface
mechanisms, it is important to design the data visualization
with the intent of being flexible regardless of device — online
browser, laptop, tablet, smartphone, screen projection, etc.
7. Balance flash vs. function. Think simple and modern. Form
must always follow function with ADV, making the purpose
of the analysis the most important. Flashy graphics get “oohs”
and “ahs” initially, but are often abandoned quickly for
something else that works.
Conclusion
As organizations deal with exponentially increasing amounts of data, the patience of end users is decreasing. We see continued
struggles in addressing data visualization and turning data into information. Perhaps the greatest sign of a successful data
visualization or infographic is the degree to which it is used to solve problems. Data visuals must provide opportunities for
comprehension, conveying knowledge and clarity in understanding. Finally, success can be measured in retention, or how well
the visualization imparted meaningful knowledge. Using these fundamental factors for success, we can continuously improve
our data visualizations and techniques.
19. 17
About the author
John Stilwell is a senior manager in Grant Thornton’s Business
Advisory Services practice. He is currently a national lead
in Grant Thornton’s Business Technology Solutions group
with a focus on Oracle Business Intelligence. Stilwell has deep
experience in the area of analytics and business transformation
initiatives. He is a recognized national speaker and thought leader
on the topics of foundation analytics, mobile analytics, scorecard
and strategy management, and multidimensional reporting tools.
Stilwell has more than 15 years of consulting and technology
experience in a range of industries where he has provided clients
with solutions, including analytics, enterprise performance
management, strategic planning and strategic cost reduction.
John Stilwell
Senior Manager
Business Advisory Services
T 913.272.2721
E john.stilwell@us.gt.com