BvDEP provides databases and software for transfer pricing analyses. Its TP Catalyst software streamlines the benchmarking process, reducing time and costs. It allows users to search company data, select comparable companies, apply adjustments, and generate professional reports. BvDEP databases contain financial and supplementary company information from public and private sources worldwide, in standardized formats for benchmarking. Users can perform analyses individually or using the TP Catalyst software for an integrated benchmarking solution.
Transfer Pricing Database and Software Catalystdumouche
Bureau van Dijk Electronic Publishing provides data and software solutions for transfer pricing analyses. Its TP Catalyst software streamlines the transfer pricing analysis process and reduces time and costs. TP Catalyst allows users to identify comparable companies, select profit level indicators, apply adjustments, and generate professional transfer pricing reports. The software guides users step-by-step through the analysis and documentation process.
This document discusses Bureau van Dijk Electronic Publishing (BvDEP) and its products and services for transfer pricing analysis. BvDEP provides databases containing financial and company information from public and private companies around the world. This data can be used to benchmark companies and analyze transfer pricing. BvDEP also offers TP Catalyst software, which streamlines the transfer pricing analysis process by automating tasks like identifying comparable companies and generating reports. TP Catalyst guides users step-by-step through profit-based analysis and documentation.
Data Science at Roche: From Exploration to Productionization - Frank BlockRising Media Ltd.
The document summarizes the data science process at Roche Diagnostics from initial ideas through productionization. It discusses how the data science team works end-to-end from initial proofs-of-value (POVs) through several selection gates to deploy models into production. Examples are provided of how data insights led to identifying issues in production processes and developing predictive models for applications like sensor image processing, case classification, and advanced service analytics. Key lessons highlighted include the importance of business proximity, developing business literacy, and focusing on innovative ideas that maximize impact to successfully transition data science projects to production.
Modern Business Intelligence - Design and ImplementationsDavid J Rosenthal
During the first two “waves” of business intelligence, IT professionals and business analysts were the keepers of BI. They made BI accessible and consumable for end users.
While this approach still applies to complex business intelligence needs, today there is a new “wave.” This third wave of BI makes BI available to every kind of user.
As customer strive to take advantage of the digital transformation that is occurring in virtually every industry, they need to re-evaluate how they engage with their customers/prospects, how they transform their products and operations, and how they empower and understand their employees.
In today’s world, doing each of these things is more and more reliant upon data…traditionally, everything you knew about your customers and prospects was available in your business application systems and in the heads of employees. You learned almost everything about your products BEFORE they left your warehouse. Employees used technology more to enter data than to learn from it.
In the data-driven world we live in today, leveraging intelligent insights from data across customers, products, and employees is critical to be able to stay competitive and keep up with or lead digital transformation in any industry. And this isn’t just a customer’s typical business application data – it’s also about augmenting the customer’s data with additional data (e.g. search, employee behavioral data, sentiment data, benchmark data, etc..) – and applying the right intelligence to drive meaningful insights.
AMI Software provides intelligence solutions that collect data from various sources and analyzes it using natural language processing and visualization tools. It is a subsidiary of Bertin IT, which is itself a subsidiary of Bertin Technologies (CNIM). AMI Software has offices in the UK and Morocco and partners in Tunisia and the UAE. Its solutions include intelligence, speech analytics, and cyber security products. It works on projects involving market and competitive intelligence for over 150 clients internationally.
1. The document discusses how PRO helps companies optimize performance through three ways: optimizing current business practices, innovating products/services through new technologies, and accelerating software development.
2. PRO takes a three-phased approach of analyzing needs, mobilizing resources, and delivering step-change results through initiatives like process best practices, emerging technologies, and DevOps methodologies.
3. Decision modeling, cost/revenue models, and other tools are used to evaluate opportunities and scenarios to improve business performance.
The document summarizes PRESTO Audit Manager, a quality control software. It allows companies to embed quality control into their organization in just 30 minutes setup. The software features include customizable checklists, 360-degree reporting, a dashboard to view all audits, pre-made checklists for various standards, and corrective action tracking. It also includes tools to teach 5S principles and link KPIs to goals and metrics for continuous improvement. PRESTO is designed to transform internal auditing from an administrative task to a competitive advantage.
This document provides an overview and agenda for a one-day training on building dashboards in Power BI. It outlines prerequisites including required software and accounts. The training will take place both in the morning and afternoon, covering topics such as accessing and preparing data, data modeling, visualizations, publishing reports, and building and sharing dashboards. Hands-on labs are included for each major topic.
Transfer Pricing Database and Software Catalystdumouche
Bureau van Dijk Electronic Publishing provides data and software solutions for transfer pricing analyses. Its TP Catalyst software streamlines the transfer pricing analysis process and reduces time and costs. TP Catalyst allows users to identify comparable companies, select profit level indicators, apply adjustments, and generate professional transfer pricing reports. The software guides users step-by-step through the analysis and documentation process.
This document discusses Bureau van Dijk Electronic Publishing (BvDEP) and its products and services for transfer pricing analysis. BvDEP provides databases containing financial and company information from public and private companies around the world. This data can be used to benchmark companies and analyze transfer pricing. BvDEP also offers TP Catalyst software, which streamlines the transfer pricing analysis process by automating tasks like identifying comparable companies and generating reports. TP Catalyst guides users step-by-step through profit-based analysis and documentation.
Data Science at Roche: From Exploration to Productionization - Frank BlockRising Media Ltd.
The document summarizes the data science process at Roche Diagnostics from initial ideas through productionization. It discusses how the data science team works end-to-end from initial proofs-of-value (POVs) through several selection gates to deploy models into production. Examples are provided of how data insights led to identifying issues in production processes and developing predictive models for applications like sensor image processing, case classification, and advanced service analytics. Key lessons highlighted include the importance of business proximity, developing business literacy, and focusing on innovative ideas that maximize impact to successfully transition data science projects to production.
Modern Business Intelligence - Design and ImplementationsDavid J Rosenthal
During the first two “waves” of business intelligence, IT professionals and business analysts were the keepers of BI. They made BI accessible and consumable for end users.
While this approach still applies to complex business intelligence needs, today there is a new “wave.” This third wave of BI makes BI available to every kind of user.
As customer strive to take advantage of the digital transformation that is occurring in virtually every industry, they need to re-evaluate how they engage with their customers/prospects, how they transform their products and operations, and how they empower and understand their employees.
In today’s world, doing each of these things is more and more reliant upon data…traditionally, everything you knew about your customers and prospects was available in your business application systems and in the heads of employees. You learned almost everything about your products BEFORE they left your warehouse. Employees used technology more to enter data than to learn from it.
In the data-driven world we live in today, leveraging intelligent insights from data across customers, products, and employees is critical to be able to stay competitive and keep up with or lead digital transformation in any industry. And this isn’t just a customer’s typical business application data – it’s also about augmenting the customer’s data with additional data (e.g. search, employee behavioral data, sentiment data, benchmark data, etc..) – and applying the right intelligence to drive meaningful insights.
AMI Software provides intelligence solutions that collect data from various sources and analyzes it using natural language processing and visualization tools. It is a subsidiary of Bertin IT, which is itself a subsidiary of Bertin Technologies (CNIM). AMI Software has offices in the UK and Morocco and partners in Tunisia and the UAE. Its solutions include intelligence, speech analytics, and cyber security products. It works on projects involving market and competitive intelligence for over 150 clients internationally.
1. The document discusses how PRO helps companies optimize performance through three ways: optimizing current business practices, innovating products/services through new technologies, and accelerating software development.
2. PRO takes a three-phased approach of analyzing needs, mobilizing resources, and delivering step-change results through initiatives like process best practices, emerging technologies, and DevOps methodologies.
3. Decision modeling, cost/revenue models, and other tools are used to evaluate opportunities and scenarios to improve business performance.
The document summarizes PRESTO Audit Manager, a quality control software. It allows companies to embed quality control into their organization in just 30 minutes setup. The software features include customizable checklists, 360-degree reporting, a dashboard to view all audits, pre-made checklists for various standards, and corrective action tracking. It also includes tools to teach 5S principles and link KPIs to goals and metrics for continuous improvement. PRESTO is designed to transform internal auditing from an administrative task to a competitive advantage.
This document provides an overview and agenda for a one-day training on building dashboards in Power BI. It outlines prerequisites including required software and accounts. The training will take place both in the morning and afternoon, covering topics such as accessing and preparing data, data modeling, visualizations, publishing reports, and building and sharing dashboards. Hands-on labs are included for each major topic.
How to Turn Raw Data into Product Revenue by Retrofit PMProduct School
Most companies have a goldmine of data, yet lack the ability to know what to do with it. In this talk, Monica shared perspective on how to evaluate data, package it, and turn it in to additional revenue streams.
Main takeaways:
- Identify use cases for data.
- Turn those use cases in to product offerings.
- Create a pricing model & collect revenue.
Timothy Ho from Deloitte discusses seven technologies driving change in finance: cloud, robotics, cognitive computing, in-memory computing, blockchain, advanced analytics, and core modernization. The roles of finance are changing from operator and steward to also include strategist and catalyst. Automation in finance can range from basic monitoring to fully automated processes. Robotic process automation can perform repetitive tasks across systems. Risk analytics uses predictive modeling to identify risks. Cognitive technologies like natural language generation can automate decision support. Getting started requires establishing a digital leadership team and piloting solutions quickly.
THE GOOD, THE BAD, THE DATA - Artificial Intelligence and Robotic Process Aut...Ken O'Connor
Ronan Fitzpatrick, Director of Digital at PWC shares insights from PWC research on AI and Robotic Process Automation. Ronan explains that insight and trust in your data is pivotal to the successful use of Artificial Intelligence and Robotics.
THE GOOD, THE BAD, THE DATA - Artificial Intelligence and Robotic Process Aut...DAMA Ireland
Ronan Fitzpatrick, Director of Digital at PWC shares insights from PWC research on AI and Robotic Process Automation. Ronan explains that insight and trust in your data is pivotal to the successful use of Artificial Intelligence and Robotics.
Unifying feature management with experiments - Server Side Webinar (1).pdfVWO
What’s common across companies like Netflix, Airbnb, Amazon, and Google? - Their ability to continuously experiment and roll out enhancements quickly across all aspects of their business. Today, every business is fundamentally a technology business. To stay ahead of the competition, you must iterate quickly with experiments across configurations in search algorithms, navigation, checkout flows, discounts, and security settings without breaking existing experience. That’s precisely what unifying experimentation with feature management enables for you.
Join Thejas Sridhar, Manager of Product Marketing, to explore the power of unifying A/B testing and feature management for continuous development.
Society is a consulting firm with 250 employees across 13 states that provides analytics, technology, and operations services. It helps clients compete on customer experience and delivers services including digital analytics, data platforms, data science, and flexible engagements. Society works with major brands in tech, media, retail, and other industries.
This newsletter provides an update on the growth of the LinkedIn CFD professional group and discusses several ongoing initiatives. It announces that the LinkedIn group now has over 500 members. It also informs readers that LinkedIn will soon enable homepage features for professional groups. Additionally, it describes an opportunity for CFD experts to collaborate with the U.S. FDA on standardizing CFD techniques for evaluating medical devices, and provides details on participating in their round robin study.
40 ° advises and supports companies and institutions to generate real added value from data and to generate data-driven innovations and new business models. We help to reinvent your business with data. 40 ° is the expert for data driven business transformation
Big Data and Big Ideas: Quantitative Modeling in UX Research - T.S. BalajiUXPA International
This presentation will bring big data into the context of UX research by describing how big data can inform usability in three ways, focusing primarily on strategy and quantitative models. A case study involving field research will be explained and the audience will act as the UX team to help build the model at each stage to better understand the theory and final product that resulted. Quantitative models help make product research more interpretable by developing testable, causal relationships between product features and business outcomes (e.g., feel of product and product satisfaction), going beyond descriptive statistics for each feature and attribute. In this way, stakeholders know not just what features are performing or underperforming, but whether those are impacting the overall performance of the product on key outcomes.
UXPA 2015 Big Data & Big Ideas: The Changing Landscape of UX ResearchTS Balaji
The document discusses the changing landscape of UX research and how a UX team at Cox utilizes research and analytics in their design process. The team combines research and analytics functions to generate insights to inform design. They use an iterative design process involving research, prototyping, and analytics to create solutions that meet customer needs and business goals. The presentation outlines their approach to research, analytics, design, and the roles of various team members.
The Business Conundrum Facing Manufacturers
Manufacturing companies have traditionally
had an on-again-off-again relationship with
technology. However, the paradigm shift driven
by global manufacturing and distribution,
combined with rapid digital innovation, is
changing this equation.
Manufacturing companies have traditionally
had an on-again-off-again relationship with
technology. However, the paradigm shift driven
by global manufacturing and distribution,
combined with rapid digital innovation, is
changing this equation. Deloitte’s 2016 MHI
survey reveals that 83% of manufacturing
organizations believe investing in key digital
technologies such as IoT, robotics, Big
Data, cloud computing, etc. will be key to
competitive advantage in the near future.1
The document discusses how a digital operations center (DOC) can help manufacturers transform their operations through digital technologies. A DOC integrates data from across a manufacturer's business functions to provide insights. It describes the typical technology stack of a DOC and how it can enable capabilities like monitoring, control, optimization, and autonomy of products. The document outlines eight steps to successfully build a DOC and provides a use case example of how a DOC could benefit different areas of a motor manufacturer's business such as sales, product development, supply chain management, and aftermarket support.
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
How automation, AI, and dashboarding can help scale experimentation programs
If you are running 10+ experiments monthly across multiple teams, you know how important efficiency is. Moreover, available resources may not scale uniformly, making automation increasingly necessary to streamline processes. The challenge intensifies with execution quality when coordinating programs across diverse teams.
While having clear standards and disciplined workflows is beneficial, integrating automation, leveraging AI, and utilizing dashboards significantly enhances any experimentation program. This not only addresses scalability issues but also provides stakeholders with more time to focus on the core aspects of their work.
This webinar will be your script to plan and build automation, leverage AI and effective dashboarding to scale experimentation programs. Sign up now!
VWO - Mark de Winter - Run more experiments with fewer resources.pdfVWO
How automation, AI, and dashboarding can help scale experimentation programs
If you are running 10+ experiments monthly across multiple teams, you know how important efficiency is. Moreover, available resources may not scale uniformly, making automation increasingly necessary to streamline processes. The challenge intensifies with execution quality when coordinating programs across diverse teams.
While having clear standards and disciplined workflows is beneficial, integrating automation, leveraging AI, and utilizing dashboards significantly enhances any experimentation program. This not only addresses scalability issues but also provides stakeholders with more time to focus on the core aspects of their work.
This webinar will be your script to plan and build automation, leverage AI and effective dashboarding to scale experimentation programs. Sign up now!
Benchmarking for Big Data Applications with the DataBench Framework, Arne Ber...DataBench
The document discusses benchmarking for big data applications using the DataBench framework. It provides an overview of business and technical benchmarking, describes how DataBench links the two through its workflow and toolbox, and outlines some early results from DataBench's business user survey. It also discusses identifying relevant benchmarks based on the BDVA reference model and introduces some benchmarks that could be integrated into DataBench's toolbox, including HiBench, SparkBench, and YCSB.
The document discusses the role of information technology in supply chain management. It describes how IT solutions like email, EDI, and ERP can help achieve better inventory control, utilization of manpower, and tracking of inventory levels across organizations in the supply chain. The automobile industry in India is highlighted as an example where internet is being used to interlink suppliers, manufacturers, wholesalers and retailers to reduce costs and lead times. Both the benefits and limitations of IT applications in supply chain management are outlined.
The document discusses IBM's AI tools and capabilities. It summarizes IBM's suite of AI products including Watson Studio, Watson Machine Learning, Watson OpenScale, and the Watson Knowledge Catalog which help with data preparation, building and training models, deploying and managing models, and ensuring trusted AI. It also discusses IBM's strategy around automating the AI lifecycle through capabilities like transfer learning, neural network search, and AutoAI experiments.
The document provides an overview of the GoodData analytics platform. It discusses how the platform aims to democratize analytics and empower more business users, beyond just analysts. The platform is designed to distribute analytics to business networks to drive revenue, efficiency and other benefits. It achieves this through its distribution, analytics and insights services which allow customers to define, distribute and improve analytic products for their networks.
How to Turn Raw Data into Product Revenue by Retrofit PMProduct School
Most companies have a goldmine of data, yet lack the ability to know what to do with it. In this talk, Monica shared perspective on how to evaluate data, package it, and turn it in to additional revenue streams.
Main takeaways:
- Identify use cases for data.
- Turn those use cases in to product offerings.
- Create a pricing model & collect revenue.
Timothy Ho from Deloitte discusses seven technologies driving change in finance: cloud, robotics, cognitive computing, in-memory computing, blockchain, advanced analytics, and core modernization. The roles of finance are changing from operator and steward to also include strategist and catalyst. Automation in finance can range from basic monitoring to fully automated processes. Robotic process automation can perform repetitive tasks across systems. Risk analytics uses predictive modeling to identify risks. Cognitive technologies like natural language generation can automate decision support. Getting started requires establishing a digital leadership team and piloting solutions quickly.
THE GOOD, THE BAD, THE DATA - Artificial Intelligence and Robotic Process Aut...Ken O'Connor
Ronan Fitzpatrick, Director of Digital at PWC shares insights from PWC research on AI and Robotic Process Automation. Ronan explains that insight and trust in your data is pivotal to the successful use of Artificial Intelligence and Robotics.
THE GOOD, THE BAD, THE DATA - Artificial Intelligence and Robotic Process Aut...DAMA Ireland
Ronan Fitzpatrick, Director of Digital at PWC shares insights from PWC research on AI and Robotic Process Automation. Ronan explains that insight and trust in your data is pivotal to the successful use of Artificial Intelligence and Robotics.
Unifying feature management with experiments - Server Side Webinar (1).pdfVWO
What’s common across companies like Netflix, Airbnb, Amazon, and Google? - Their ability to continuously experiment and roll out enhancements quickly across all aspects of their business. Today, every business is fundamentally a technology business. To stay ahead of the competition, you must iterate quickly with experiments across configurations in search algorithms, navigation, checkout flows, discounts, and security settings without breaking existing experience. That’s precisely what unifying experimentation with feature management enables for you.
Join Thejas Sridhar, Manager of Product Marketing, to explore the power of unifying A/B testing and feature management for continuous development.
Society is a consulting firm with 250 employees across 13 states that provides analytics, technology, and operations services. It helps clients compete on customer experience and delivers services including digital analytics, data platforms, data science, and flexible engagements. Society works with major brands in tech, media, retail, and other industries.
This newsletter provides an update on the growth of the LinkedIn CFD professional group and discusses several ongoing initiatives. It announces that the LinkedIn group now has over 500 members. It also informs readers that LinkedIn will soon enable homepage features for professional groups. Additionally, it describes an opportunity for CFD experts to collaborate with the U.S. FDA on standardizing CFD techniques for evaluating medical devices, and provides details on participating in their round robin study.
40 ° advises and supports companies and institutions to generate real added value from data and to generate data-driven innovations and new business models. We help to reinvent your business with data. 40 ° is the expert for data driven business transformation
Big Data and Big Ideas: Quantitative Modeling in UX Research - T.S. BalajiUXPA International
This presentation will bring big data into the context of UX research by describing how big data can inform usability in three ways, focusing primarily on strategy and quantitative models. A case study involving field research will be explained and the audience will act as the UX team to help build the model at each stage to better understand the theory and final product that resulted. Quantitative models help make product research more interpretable by developing testable, causal relationships between product features and business outcomes (e.g., feel of product and product satisfaction), going beyond descriptive statistics for each feature and attribute. In this way, stakeholders know not just what features are performing or underperforming, but whether those are impacting the overall performance of the product on key outcomes.
UXPA 2015 Big Data & Big Ideas: The Changing Landscape of UX ResearchTS Balaji
The document discusses the changing landscape of UX research and how a UX team at Cox utilizes research and analytics in their design process. The team combines research and analytics functions to generate insights to inform design. They use an iterative design process involving research, prototyping, and analytics to create solutions that meet customer needs and business goals. The presentation outlines their approach to research, analytics, design, and the roles of various team members.
The Business Conundrum Facing Manufacturers
Manufacturing companies have traditionally
had an on-again-off-again relationship with
technology. However, the paradigm shift driven
by global manufacturing and distribution,
combined with rapid digital innovation, is
changing this equation.
Manufacturing companies have traditionally
had an on-again-off-again relationship with
technology. However, the paradigm shift driven
by global manufacturing and distribution,
combined with rapid digital innovation, is
changing this equation. Deloitte’s 2016 MHI
survey reveals that 83% of manufacturing
organizations believe investing in key digital
technologies such as IoT, robotics, Big
Data, cloud computing, etc. will be key to
competitive advantage in the near future.1
The document discusses how a digital operations center (DOC) can help manufacturers transform their operations through digital technologies. A DOC integrates data from across a manufacturer's business functions to provide insights. It describes the typical technology stack of a DOC and how it can enable capabilities like monitoring, control, optimization, and autonomy of products. The document outlines eight steps to successfully build a DOC and provides a use case example of how a DOC could benefit different areas of a motor manufacturer's business such as sales, product development, supply chain management, and aftermarket support.
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
How automation, AI, and dashboarding can help scale experimentation programs
If you are running 10+ experiments monthly across multiple teams, you know how important efficiency is. Moreover, available resources may not scale uniformly, making automation increasingly necessary to streamline processes. The challenge intensifies with execution quality when coordinating programs across diverse teams.
While having clear standards and disciplined workflows is beneficial, integrating automation, leveraging AI, and utilizing dashboards significantly enhances any experimentation program. This not only addresses scalability issues but also provides stakeholders with more time to focus on the core aspects of their work.
This webinar will be your script to plan and build automation, leverage AI and effective dashboarding to scale experimentation programs. Sign up now!
VWO - Mark de Winter - Run more experiments with fewer resources.pdfVWO
How automation, AI, and dashboarding can help scale experimentation programs
If you are running 10+ experiments monthly across multiple teams, you know how important efficiency is. Moreover, available resources may not scale uniformly, making automation increasingly necessary to streamline processes. The challenge intensifies with execution quality when coordinating programs across diverse teams.
While having clear standards and disciplined workflows is beneficial, integrating automation, leveraging AI, and utilizing dashboards significantly enhances any experimentation program. This not only addresses scalability issues but also provides stakeholders with more time to focus on the core aspects of their work.
This webinar will be your script to plan and build automation, leverage AI and effective dashboarding to scale experimentation programs. Sign up now!
Benchmarking for Big Data Applications with the DataBench Framework, Arne Ber...DataBench
The document discusses benchmarking for big data applications using the DataBench framework. It provides an overview of business and technical benchmarking, describes how DataBench links the two through its workflow and toolbox, and outlines some early results from DataBench's business user survey. It also discusses identifying relevant benchmarks based on the BDVA reference model and introduces some benchmarks that could be integrated into DataBench's toolbox, including HiBench, SparkBench, and YCSB.
The document discusses the role of information technology in supply chain management. It describes how IT solutions like email, EDI, and ERP can help achieve better inventory control, utilization of manpower, and tracking of inventory levels across organizations in the supply chain. The automobile industry in India is highlighted as an example where internet is being used to interlink suppliers, manufacturers, wholesalers and retailers to reduce costs and lead times. Both the benefits and limitations of IT applications in supply chain management are outlined.
The document discusses IBM's AI tools and capabilities. It summarizes IBM's suite of AI products including Watson Studio, Watson Machine Learning, Watson OpenScale, and the Watson Knowledge Catalog which help with data preparation, building and training models, deploying and managing models, and ensuring trusted AI. It also discusses IBM's strategy around automating the AI lifecycle through capabilities like transfer learning, neural network search, and AutoAI experiments.
The document provides an overview of the GoodData analytics platform. It discusses how the platform aims to democratize analytics and empower more business users, beyond just analysts. The platform is designed to distribute analytics to business networks to drive revenue, efficiency and other benefits. It achieves this through its distribution, analytics and insights services which allow customers to define, distribute and improve analytic products for their networks.
1. Solutions for
transfer pricing
Data and software for profit-based analysis
for transfer pricing professionals
TP Catalyst
Empowering transfer pricing analysis
2. Definitive company data for transfer pricing analyses
Detailed company information for benchmarking
Bureau van Dijk Electronic Publishing (BvDEP) is recognised as a key participant in the transfer
pricing market and the “provider of choice” for company information for profit-based analyses.
BvDEP publishes various databases containing company information that bring together data from
many specialist and local providers. Covering both listed and unlisted companies, the accounts are
presented in detailed comparable formats. Supplementary information includes activities and trends
to help users accurately generate lists of corresponding companies plus market intelligence and
detailed company structures.
Users can create tabular and graphical analyses to ultimately benchmark rates of return for economic
activities. The databases can be used in isolation, or in conjunction with BvDEP’s TP Catalyst for the
ultimate, streamlined benchmarking solution.
Public and private companies around the world
Companies across Europe in a standardised,
comparable format
Companies across the Asia-Pacific region in a standardised,
comparable format
Listed companies worldwide
3. Tp catalyst
Developed to save your time
TP Catalyst simplifies the execution of profit-based analyses and brings substantial time and cost
savings. Developed in consultation with transfer pricing specialists, it streamlines processes,
eliminates redundant steps, reduces data manipulation and delivers professional results.
TP Catalyst is used in conjunction with BvDEP’s database products, enhancing their searching and data
retrieval capabilities specifically for transfer pricing analyses. It facilitates the manual identification of
“comparables”, then summarises the process and decisions taken to create the profit-based analysis report.
TP Catalyst is the ultimate resource to liberate time. It empowers users to efficiently create analyses and to
acquire expertise in approximating “arm’s length” rates of return.
Flexible and technically sophisticated
TP Catalyst has a wizard-led approach to take users step by step through the process of identifying
comparable companies and creating profit-based analyses.
Users can attach documents, record and justify choices and rejections, and tailor the presentation of the
output. The following methods are available:
• Comparable Profits Method
• Transactional Net Margin Method
• Profit Split Method
Users can exclude or include companies based on the availability of specific financial items, their structures,
financial ratios, activities and other criteria. Links provide users with easy access to the companies’ profiles
and websites. A summary of the selection is displayed, and users can annotate and explain each filter. The
potential comparable companies can then be screened to determine their suitability for the analysis.
Users can choose from 15 profit level indicators (PLI) and apply typical asset-intensity or working capital
adjustments to the financial data of selected comparable companies. Once the appropriate PLI has been
selected, and any desired adjustments have been applied, TP Catalyst computes and presents the
unadjusted and adjusted “arm’s length” benchmarks.
Professional reporting
TP Catalyst produces a report which can be presented in
various formats including Microsoft Word, Excel and Adobe
Acrobat. This report encompasses a detailed audit trail of
the steps undertaken to select the comparable companies
and to identify the “arm’s length” benchmark.
It can be attached as an appendix to any transfer pricing
study (whether prepared internally or by an external advisor)
or used as a stand-alone tax memorandum. The report
generation facility can be used within the contexts of (i)
documenting results for transactions that occurred in a
closed fiscal year, (ii) establishing inter-company pricing for
transactions to take place in future years, (iii) determining
the size of potential tax adjustments or the tax provision to be booked, or (iv) identifying routine returns for
routine functions in Profit Split analyses.
4. Tp catalyst
Wizard-led steps lead you through the data selection, analysis and documentation, to create tailored
professional level reports.
1. Determine the parameters of your analysis
Choose the scope of your analysis in terms of regions,
time period and format of the financial data to be used.
2. Select the party to be tested
Use data held on BvDEP’s products, modify it or use
your own information.
3. Define your objectives
Describe your study’s objectives, integrate your
functional and risk profile and annotate your choices.
4. Choose and justify your method
Select your chosen methodology and include the
reasons for your choice.
5. Select the population for your comparison
Enter your selection criteria to determine on what basis
to select your companies, annotate your selection
summary and sort/view an initial list of potentially
comparable companies.
5. Giving you the tools to be your own expert
6. Reject companies and show your reasons
Screen companies using qualitative and quantitative
filters, review companies' business descriptions and
websites for comparability and annotate each rejection
to justify your decisions.
7. Select your PLI
Choose from 15 pre-defined PLI - include your
parameters for adjustment and comment.
8. Tested party against the comparable companies
Review the results of your analysis, including adjusted
and unadjusted results for the selected PLI. Evaluate the
results of the tested party against the results of the
comparable companies selected.
9. Generate your report
View, print, save or export your tailored report with
access to any of your chosen attachments.
6. Belgium United Kingdom France Germany
Avenue Louise 250 10 Northburgh Street 7 Rue Drouot Hanauer Landstrasse 175-179
1050 Brussels London EC1V 0PP 75009 Paris 60314 Frankfurt
tel: 32 2 639 06 06 tel: 44 (0)20 7549 5000 tel: 33 1 53 45 46 00 tel: 49 (69) 963 665 0
fax: 32 2 648 82 30 fax: 44 (0)20 7549 5010 fax: 33 1 53 45 46 28 fax: 49 (69) 963 665 50
brussels@bvdep.com london@bvdep.com paris@bvdep.com frankfurt@bvdep.com
Netherlands 24 Great King Street Spain Switzerland
Amsteldijk 166 Edinburgh EH3 6QN C/Santa Teresa 2-2º Rue Charles-Sturm 20
1079 LH Amsterdam tel: 44 (0)131 200 7110 28004 Madrid 1206 Geneva
tel: 31 (0) 20 5400 100 fax: 44 (0)131 200 7120 tel: 34 91 310 38 04 tel: 41 22 830 05 44
fax: 31 (0) 20 5400 111 edinburgh@bvdep.com fax: 34 91 319 49 67 fax: 41 22 346 11 51
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