The document discusses the use of procurement analytics. It begins by explaining what procurement analytics is and its applications in areas like vendor evaluation, spend analytics, demand forecasting, and contract management. It then discusses why analytics is important for procurement given the large amount of data being generated. The document also summarizes a study that showed analytical tools can improve forecasting accuracy and decision making over intuitive methods. It concludes by providing recommendations for how organizations can implement procurement analytics by addressing challenges related to skills, processes, systems, data, and culture.
IBM Healthcare Business Analytics solutions including Cognos, TM1 and SPSS. How healthcare challenges are met and costs are optimized through the use of Data Visualizations, Performance Management, and Predictive Analytics.
Business analytics in healthcare & life scienceSanjay Choubey
Business analytics for Healthcare, Life Science businesses. Trends, Issues, Challenges, process & steps, Business drivers, Market & compliance, Big data and approach to overcome
Strategic Consulting Partners in Life Science for a High Performing Paperless Lab
- Do you need to increase productivity and effectiveness of your lab operations?
- Do you want to reduce time to market and boost collaboration ?
- Are you concerned about data quality / data integrity issues?
- Do you need to replace your lab informatics systems and feel overwhelmed by the complexity of the market?
- Do you need to align consistent processes throughout the organisation?
With our success-proven seven-step program we have realized up to 30% efficiency improvements for major European Life Science companies lab operations.
We are lab-experienced chemists with a solid background in business administration and management, a passion for sustainable process improvements and in-depth knowledge of lab informatics. Originating from the renowned "Vialis paperless lab solutions" team, PEPR-Consulting continues its tradition of successful strategic management consulting for the life science industry.
Our strengths are the deep understanding of your lab processes, current GxP requirements and a broad and independent knowledge of lab informatics solutions available today and the trends shaping the future lab environment.
The purpose of this presentation is providing an overview of the main approaches in using big data: data focus vs. business analytics focus. The following topics will be covered:
- Why getting data should not be a starting point in business analytics, and why more data not always result in more accurate predictions
- The simulation analytics methodology in comparison to machine learning and data science approach
- Examples of two business cases:
(i) Healthcare: Pediatric Triage in a Severe Pandemic-Maximizing Population Survival by Establishing Admission Thresholds
(ii) Banking & Finance: Analysis of the staffing and utilization of a team of mutual fund analysts for timely producing ‘buy-sell’ reports
IBM Healthcare Business Analytics solutions including Cognos, TM1 and SPSS. How healthcare challenges are met and costs are optimized through the use of Data Visualizations, Performance Management, and Predictive Analytics.
Business analytics in healthcare & life scienceSanjay Choubey
Business analytics for Healthcare, Life Science businesses. Trends, Issues, Challenges, process & steps, Business drivers, Market & compliance, Big data and approach to overcome
Strategic Consulting Partners in Life Science for a High Performing Paperless Lab
- Do you need to increase productivity and effectiveness of your lab operations?
- Do you want to reduce time to market and boost collaboration ?
- Are you concerned about data quality / data integrity issues?
- Do you need to replace your lab informatics systems and feel overwhelmed by the complexity of the market?
- Do you need to align consistent processes throughout the organisation?
With our success-proven seven-step program we have realized up to 30% efficiency improvements for major European Life Science companies lab operations.
We are lab-experienced chemists with a solid background in business administration and management, a passion for sustainable process improvements and in-depth knowledge of lab informatics. Originating from the renowned "Vialis paperless lab solutions" team, PEPR-Consulting continues its tradition of successful strategic management consulting for the life science industry.
Our strengths are the deep understanding of your lab processes, current GxP requirements and a broad and independent knowledge of lab informatics solutions available today and the trends shaping the future lab environment.
The purpose of this presentation is providing an overview of the main approaches in using big data: data focus vs. business analytics focus. The following topics will be covered:
- Why getting data should not be a starting point in business analytics, and why more data not always result in more accurate predictions
- The simulation analytics methodology in comparison to machine learning and data science approach
- Examples of two business cases:
(i) Healthcare: Pediatric Triage in a Severe Pandemic-Maximizing Population Survival by Establishing Admission Thresholds
(ii) Banking & Finance: Analysis of the staffing and utilization of a team of mutual fund analysts for timely producing ‘buy-sell’ reports
Predictive project analytics: Will your project be successful?Deloitte Canada
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
Predictive Analytics: The Next Wave in Business IntelligencePerficient, Inc.
We discuss how Predictive Analytics enables decision makers to predict future events and proactively act on that insight to drive better business outcomes and deliver the insight needed to answer key business questions:
- How to reduce churn and retain the most loyal customers to maximize profitability (predict which customers are most likely to leave and which are most loyal)
- How to detect and ultimately prevent fraudulent activity
- Which factors are most likely to drive customers to choose my product over the competitor’s?
- How to integrate Predictive Analytics with an existing Business Intelligence platform
Presenter Tom Lennon is Director of Perficient's National Business Intelligence Competency Center.
This presentation was part of the talk delivered by T Ashok Founder & CEO STAG Software at the HSTC 2013: "Think Testing" Conference on Nov 21 & 22 at Hyderabad.
By Leonard Oruko and Howard Elliott.
Presented at the ASTI-FARA conference Agricultural R&D: Investing in Africa's Future: Analyzing Trends, Challenges, and Opportunities - Accra, Ghana on December 5-7, 2011. http://www.asti.cgiar.org/2011conf
PoT - probeer de mogelijkheden van datamining zelf uit 30-10-2014Daniel Westzaan
IBM Proof of Technology
Probeer de Mogelijkheden van Datamining zelf uit
30-10-2014 Amsterdam, IBM Client Center
Presentatie van Laila Fettah & Robin van Tilburg
Better decisions through analytics in healthcare industry. Our journey so farSAS Asia Pacific
Visit http://www.sas.com/baexchange
Better decisions through analytics in healthcare industry. Our journey so far… presented by Michael Wong, Chief Financial Officer, Penang Adventist Hospital
Data Analytics and the Small Audit Department: How to Implement for Big GainsCaseWare IDEA
Listen to playback of this webinar: https://www.casewareanalytics.com/webinars/data-analytics-and-small-audit-department-how-implement-big-gains
Most internal auditors recognize the need for data analytics and the improved coverage it offers. But did you know that even small audit teams can effectively leverage data analytics in their audit programs?
It is time to get through the excuses and join our experts as they as they debunk the myth that only large audit teams can use data analytics. This webinar discusses how small audit firms can start with an analytics program; how to leverage analytic techniques along with critical thinking at various phases of the audit process, including risk assessment, macro level audit planning and micro-level audit planning; and finally a methodical plan on how small teams can grow their data analytics program to increase their effectiveness and confidence in the internal audit process.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
"From Insights to Production with Big Data Analytics", Eliano Marques, Senior...Dataconomy Media
"From Insights to Production with Big Data Analytics", Eliano Marques, Senior Data Scientist at ThinkBig, a Teradata Company
YouTube Link: https://www.youtube.com/watch?v=caTyh1KflsI
Watch more from Data Natives 2015 here: http://bit.ly/1OVkK2J
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS
About the author:
Eliano is an analytics professional that combines Data Science skills with leadership, vision, creativity, project management, team building/management acquired through academia, personal research, leading internal Data Science/Advanced Modelling teams and providing consulting services to customers in several industries (including manufacturing, utilities, telcos, financial services, hospitality, sports).
This presentation given by Think Big's senior data scientist Eliano Marques at Digital Natives conference in Berlin, Germany (November 2015), details how to go from experimentation to productionization for a predictive maintenance use case.
Predictive project analytics: Will your project be successful?Deloitte Canada
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
Predictive Analytics: The Next Wave in Business IntelligencePerficient, Inc.
We discuss how Predictive Analytics enables decision makers to predict future events and proactively act on that insight to drive better business outcomes and deliver the insight needed to answer key business questions:
- How to reduce churn and retain the most loyal customers to maximize profitability (predict which customers are most likely to leave and which are most loyal)
- How to detect and ultimately prevent fraudulent activity
- Which factors are most likely to drive customers to choose my product over the competitor’s?
- How to integrate Predictive Analytics with an existing Business Intelligence platform
Presenter Tom Lennon is Director of Perficient's National Business Intelligence Competency Center.
This presentation was part of the talk delivered by T Ashok Founder & CEO STAG Software at the HSTC 2013: "Think Testing" Conference on Nov 21 & 22 at Hyderabad.
By Leonard Oruko and Howard Elliott.
Presented at the ASTI-FARA conference Agricultural R&D: Investing in Africa's Future: Analyzing Trends, Challenges, and Opportunities - Accra, Ghana on December 5-7, 2011. http://www.asti.cgiar.org/2011conf
PoT - probeer de mogelijkheden van datamining zelf uit 30-10-2014Daniel Westzaan
IBM Proof of Technology
Probeer de Mogelijkheden van Datamining zelf uit
30-10-2014 Amsterdam, IBM Client Center
Presentatie van Laila Fettah & Robin van Tilburg
Better decisions through analytics in healthcare industry. Our journey so farSAS Asia Pacific
Visit http://www.sas.com/baexchange
Better decisions through analytics in healthcare industry. Our journey so far… presented by Michael Wong, Chief Financial Officer, Penang Adventist Hospital
Data Analytics and the Small Audit Department: How to Implement for Big GainsCaseWare IDEA
Listen to playback of this webinar: https://www.casewareanalytics.com/webinars/data-analytics-and-small-audit-department-how-implement-big-gains
Most internal auditors recognize the need for data analytics and the improved coverage it offers. But did you know that even small audit teams can effectively leverage data analytics in their audit programs?
It is time to get through the excuses and join our experts as they as they debunk the myth that only large audit teams can use data analytics. This webinar discusses how small audit firms can start with an analytics program; how to leverage analytic techniques along with critical thinking at various phases of the audit process, including risk assessment, macro level audit planning and micro-level audit planning; and finally a methodical plan on how small teams can grow their data analytics program to increase their effectiveness and confidence in the internal audit process.
SLIDESHARE: www.slideshare.net/CaseWare_Analytics
WEBSITE: www.casewareanalytics.com
BLOG: www.casewareanalytics.com/blog
TWITTER: www.twitter.com/CW_Analytic
"From Insights to Production with Big Data Analytics", Eliano Marques, Senior...Dataconomy Media
"From Insights to Production with Big Data Analytics", Eliano Marques, Senior Data Scientist at ThinkBig, a Teradata Company
YouTube Link: https://www.youtube.com/watch?v=caTyh1KflsI
Watch more from Data Natives 2015 here: http://bit.ly/1OVkK2J
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS
About the author:
Eliano is an analytics professional that combines Data Science skills with leadership, vision, creativity, project management, team building/management acquired through academia, personal research, leading internal Data Science/Advanced Modelling teams and providing consulting services to customers in several industries (including manufacturing, utilities, telcos, financial services, hospitality, sports).
This presentation given by Think Big's senior data scientist Eliano Marques at Digital Natives conference in Berlin, Germany (November 2015), details how to go from experimentation to productionization for a predictive maintenance use case.
The New Self-Service Analytics - Going Beyond the ToolsKatherine Gabriel
In today’s business climate, using data to make quick decisions is a common ask across organizations. To fulfill such asks business users want more, faster, and better access to data and analytic tools. IT wants to balance this need for speed with the responsibility to protect the data assets from security, privacy, and quality risks. A common solution to this scenario is self-service BI or self-service analytics. Chances are you are already using self-service BI in some way, shape, or form or have heard a pitch from an analytic tool vendor!
Self-service BI has been around for several decades and yet business users keep asking for more and more. Has self-service BI failed to deliver on its promise? Is it time to revisit what self-service really means? How can business and IT work together to achieve better decision-making outcomes for their organization?
We cover:
• How to demystify what self-service analytics means
• New trends driving the self-service analytics evolution
• Best practices and lessons learned from real-life examples
• Recommendations for making progress within your organization
Advance your self-service journey.
In the past decade, the HR function has undergone a significant transformation. It has evolved from being a support function to a strategic business driver. Modern day HR’s can leverage plethora of data that to manage Employee Engagement. This presentation describes about BRIDGEi2i’s offering on Employee Engagement Analytics and how HR’s can leverage the data eco system to get granular insights for improving Employee Engagement
Making IT Work for Your Business - 4 Key Concepts to Get the Most Out of Your...Audrey Reynolds
Learn key tools, processes and best practices from the Business Analyst toolbox that you can use to make better technology decisions and manage your IT projects effectively.
Intro of Key Features of Soft CAAT Ent Softwarerafeq
This presentation provides a brief overview of SoftCAAT Ent with use cases. SoftCAAT Ent is a data analytics/BI software used by CAs and CXOs for Assurance, Compliance and Fraud Investigations.
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
Week 1 - Information Systems Strategy TriangleBusiness Strateg.docxmelbruce90096
Week 1 - Information Systems Strategy Triangle
Business Strategy Elements
Organizational Strategy Elements
Information Strategy Elements
Impacts between the elements:
Industry Strategy Elements
Industry Organizational Strategy Elements
Industry Strategy Elements
Similarities and differences:
:
Recommended actions and decisions:
Step 1: Create lists of case details that fit each side of the triangle.
Step 2: Then look at each item and think about how that item affects the other sides of the triangle.
Step 3: Take a look at the industry. Make a list of triangle attributes you find. Compare the industry items with the case company items.
Information Strategy
Organizational Strategy
Business Strategy
Zara Case Situation
You are a member of a Zara employee taskforce. The taskforce has been asked to make recommendations on selecting a new point-of-sale device for all of their retail locations. The team has narrowed the choices down to three products. The first product allows for access to the internet for both store use and sending sales transactions reporting, email, customer data collection and lookup, and full inventory functions (in-stock, location, reduction information). The second product has the same functions as the first but with limited in-store only inventory functions (search only). The third product has no inventory functions and access to the internet is limited to sales reporting to corporate. Based on your knowledge of Zara’s business and process management, explain which POS product you would recommend. Support your opinion with the case information.
Step 4: How would evaluate the options? What criteria would you use? How do the triangle sides impact the options?
Step 5: What decisions and actions would you recommend to the case company? What data supports your conclusions? Why should the case company take your advice?
CMBA SuperStar
Panther ID: 007
Information Systems Strategy Triangle
Business Strategy Elements
Organizational Strategy Elements
Information Strategy Elements
Differentiation focuses of Orders-of-magnitude improvements in logistics and services, reducing the cycle time and ensure consistent delivery of quality products and services.
Improve visibility of the service business performance to management, enabling it to provide more effective quality service to customers.
Centralized customer service systems to dispatch service mechanics. OTISLINE customer service centers.
Goal to be a recognized leader in service excellence among all companies, streamlined manufacturing operations.
OTISLINE produces “excess” callback reports for various levels of management.
Information from multiple Otis data sources, rapid response as an important design element.
Institutionalized customer service, standard of work, process flows, and metrics to govern every customer interaction and every internal activity.
Involvement with district manager, regional v.
Retail Decision Analytics: Linking BI with automated executionQuantum Retail
This presentation covers the history of retail business analytics, the challenges retailers face, and checkpoints of what they should be seeking in a BI or analytics tool.
7. Source: Competing on Analytics: The New Science of Winning (Davenport / Harris)
What?
CompetitiveAdvantage
Sophistication of Intelligence
Optimization
Predictive Modeling
Forecasting/extrapolation
Statistical analysis
Alerts
Query/drill down
Ad hoc reports
Standard Reports
“What’s the best that can happen?”
“What will happen next?”
“What if these trends continue?”
“Why is this happening?”
“What actions are needed?”
“What exactly is the problem?”
“How many, how often, where?”
“What happened?”
Predictive
Analytics
Descriptive
Analytics
Analytics Sophistication Levels
WHAT WHY HOW
24. Next Steps
Organisation
Leadership – gain support
BUs – collaborate, share
Skills – develop, hire or share
Culture – fact-based, not just gut-
feel decisions
Process
Embed analytics in processes
WHAT WHY HOW
Systems
Short-term – integrate, consolidate
& fully utilise existing systems
Long-term – invest in an
eprocurement suite and
specialised analytical tools