Business model navigator - 55 business model patterns
This presentation is adapted and based on working Paper “The St.Gallen Business Model Navigator” by Oliver Gassmann, Karolin Frankenberger, Michaela Csik
One of the most important factors to an organization’s success is its ability to extract actionable information from its data. However, the exponential growth of available data has put numerous operational pressures on IT and storage administrators to effectively ingest, transfer, process, store, backup, and archive. AWS offers numerous data transfer and storage services and solutions that can scale with your data growth and help meet security and compliance requirements. Attend this session to learn how to use AWS storage services to manage the entire lifecycle of your data, from ingestion to archive.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
DAMA DMBoK 2.0 keynote presentation at DAMA Australia November 2013.
Overview of DMBOK, what's different in 2.0, and how the DMBOK co-exists and successfully interoperates with other frameworks such as TOGAF and COBIT
Updated with revised DMBoK 2 release date
chris.bradley@dmadvisors.co.uk
Customer-Centric Data Management for Better Customer ExperiencesInformatica
With consumer and business buyer expectations growing exponentially, more businesses are competing on the basis of customer experience. But executing preferred customer experiences requires data about who your customers are today and what will they likely need in the future. Every business can benefit from an AI-powered master data management platform to supply this information to line-of-business owners so they can execute great experiences at scale. This same need is true from an internal business process perspective as well. For example, many businesses require better data management practices to deliver preferred employee experiences. Informatica provides an MDM platform to solve for these examples and more.
This is the pricing model innovation canvas to help innovators and marketers make better decisions with pricing model design. The work is based on many years of research and testing. It fits in well with the Strategyzer business model canvas and the Lean Canvas. It is very adapted for new business models in digital and data-driven offers.
Business model navigator - 55 business model patterns
This presentation is adapted and based on working Paper “The St.Gallen Business Model Navigator” by Oliver Gassmann, Karolin Frankenberger, Michaela Csik
One of the most important factors to an organization’s success is its ability to extract actionable information from its data. However, the exponential growth of available data has put numerous operational pressures on IT and storage administrators to effectively ingest, transfer, process, store, backup, and archive. AWS offers numerous data transfer and storage services and solutions that can scale with your data growth and help meet security and compliance requirements. Attend this session to learn how to use AWS storage services to manage the entire lifecycle of your data, from ingestion to archive.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
DAMA DMBoK 2.0 keynote presentation at DAMA Australia November 2013.
Overview of DMBOK, what's different in 2.0, and how the DMBOK co-exists and successfully interoperates with other frameworks such as TOGAF and COBIT
Updated with revised DMBoK 2 release date
chris.bradley@dmadvisors.co.uk
Customer-Centric Data Management for Better Customer ExperiencesInformatica
With consumer and business buyer expectations growing exponentially, more businesses are competing on the basis of customer experience. But executing preferred customer experiences requires data about who your customers are today and what will they likely need in the future. Every business can benefit from an AI-powered master data management platform to supply this information to line-of-business owners so they can execute great experiences at scale. This same need is true from an internal business process perspective as well. For example, many businesses require better data management practices to deliver preferred employee experiences. Informatica provides an MDM platform to solve for these examples and more.
This is the pricing model innovation canvas to help innovators and marketers make better decisions with pricing model design. The work is based on many years of research and testing. It fits in well with the Strategyzer business model canvas and the Lean Canvas. It is very adapted for new business models in digital and data-driven offers.
Data Quality Management: Cleaner Data, Better Reportingaccenture
In this new Accenture Finance & Risk presentation we explore a process to investigate, prioritize and resolve data quality issues, key to creating a more efficient and accurate reporting environment. View our presentation to learn more.
For more on regulatory reporting, see presentation on Financial Reporting Robotics: http://bit.ly/2qaLK9y
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
How to Build the Data Mesh Foundation: A Principled Approach | Zhamak Dehghan...HostedbyConfluent
Organizations have been chasing the dream of data democratization, unlocking and accessing data at scale to serve their customers and business, for over a half a century from early days of data warehousing. They have been trying to reach this dream through multiple generations of architectures, such as data warehouse and data lake, through a cambrian explosion of tools and a large amount of investments to build their next data platform. Despite the intention and the investments the results have been middling.
In this keynote, Zhamak shares her observations on the failure modes of a centralized paradigm of a data lake, and its predecessor data warehouse.
She introduces Data Mesh, a paradigm shift in big data management that draws from modern distributed architecture: considering domains as the first class concern, applying self-sovereignty to distribute the ownership of data, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
This talk introduces the principles underpinning data mesh and Zhamak's recent learnings in creating a path to bring data mesh to life in your organization.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
It’s been three years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. But what is the state of Data Governance today?
How has it evolved? What’s its role now? Building on prior research, erwin by Quest and ESG have partnered on a new study about what’s driving the practice of Data Governance, program maturity and current challenges. It also examines the connections to data operations and data protection, which is interesting given the fact that improving data security is now the No. 1 driver of Data Governance, according to this year’s survey respondents.
So please join us for this webinar to learn about the:
Other primary drivers for enterprise Data Governance programs
Most common bottlenecks to program maturity and sustainability
Advantages of aligning Data Governance with the other data disciplines
In a post-COVID world, data has the power to be even more transformative, and 84% of business and technology professionals say it represents the best opportunity to develop a competitive advantage during the next 12 to 24 months. Let’s make sure your organization has the intelligence it needs about both data and data systems to empower stakeholders in the front and back office to do what they need to do.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
Serhii Kholodniuk: What you need to know, before migrating data platform to G...Lviv Startup Club
Serhii Kholodniuk: What you need to know, before migrating data platform to GCP (Google cloud platform)
AI & BigData Online Day 2022
Website: https://aiconf.com.ua
Youtube: https://www.youtube.com/startuplviv
FB: https://www.facebook.com/aiconf
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Best Practices in DataOps: How to Create Agile, Automated Data PipelinesEric Kavanagh
Synthesis Webcast with Eric Kavanagh and Tamr
DataOps is an emerging set of practices, processes, and technologies for building and automating data pipelines to meet business needs quickly. As these pipelines become more complex and development teams grow in size, organizations need better collaboration and development processes to govern the flow of data and code from one step of the data lifecycle to the next – from data ingestion and transformation to analysis and reporting.
DataOps is not something that can be implemented all at once or in a short period of time. DataOps is a journey that requires a cultural shift. DataOps teams continuously search for new ways to cut waste, streamline steps, automate processes, increase output, and get it right the first time. The goal is to increase agility and cycle times, while reducing data defects, giving developers and business users greater confidence in data analytic output.
This webcast examines how organizations adopt DataOps practices in the field. It will review results of an Eckerson Group survey that sheds light on the rate and scope of DataOps adoption. It will also describe case studies of organizations that have successfully implemented DataOps practices, the challenges they have encountered and benefits they’ve received.
Tune into our webcast to learn:
- User perceptions of DataOps
- The rate of DataOps adoption by industry and other demographic variables
- DataOps adoption by technique and component (i.e., agile, test automation, orchestration, continuous development/continuous integration)
- Key challenges organizations face with DataOps
- Key benefits organizations experience with DataOps
- Best practices in doing DataOps
- Case studies and anecdotes of DataOps at companies
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Scaling and Modernizing Data Platform with DatabricksDatabricks
Today a Data Platform is expected to process and analyze a multitude of sources spanning batch files, streaming sources, backend databases, REST APIs, and more. There is clearly a need for standardizing the platform that scales and be flexible letting data engineers and data scientists focus on the business problems rather than managing the infrastructure and backend services. Another key aspect of the platform is multi-tenancy to isolate the workloads and able to track cost usage per tenant.
In this talk, Richa Singhal and Esha Shah will cover how to build a scalable Data Platform using Databricks and deploy your data pipelines effectively while managing the costs. The following topics will be covered:
Key tenets of a Data Platform
Setup multistage environment on Databricks
Build data pipelines locally and test on Databricks cluster
CI/CD for data pipelines with Databricks
Orchestrating pipelines using Apache Airflow – Change Data Capture using Databricks Delta
Leveraging Databricks Notebooks for Analytics and Data Science teams
The Best Startup Investor Pitch Deck & How to Present to Angels & Venture Cap...J. Skyler Fernandes
Take the online video course on Udemy:
https://www.udemy.com/course/the-best-startup-investor-pitch-deck/?referralCode=A5ED0FBD65120A93A16E
3.5+hrs of video content, walking step by step each part of the pitch, with personal VC stories, examples, and advice.
The "Best" Startup Investor Pitch Deck is an aggregation of some of the best pitch decks and wisdom from some of the top angels, VCs, and entrepreneurs including my own person insight/experience. The slide deck includes a template for entrepreneurs to use to present to investors, with details on what should be addressed on each slide. There are also additional slides on how best to pitch to investors effectively, how to design and format slides, and what to do before the pitch.
A successful data governance capability requires a strategy to align regulatory drivers and technology enhancement initiatives with business needs and objectives, taking into account the organizational, technological and cultural changes that will need to take place.
Navigating the Build vs. Buy Decision for Your Finance Technology NeedsGotransverse
Something that vexes every finance and accounting organization is the challenge of managing your internal technology portfolio to meet the changing needs of your constantly changing company. Should you continue to patch that old system or buy something new? Should you use point solutions or broad-based platforms? How much is the right amount to invest and which direction has the highest ROI. Well, as it happens, the answer is, “it depends”. This event will focus on how to perform a build vs. buy analysis for finance and accounting automation projects covering these very issues, helping you form a coherent technology strategy, along with tactics for execution.
Data Quality Management: Cleaner Data, Better Reportingaccenture
In this new Accenture Finance & Risk presentation we explore a process to investigate, prioritize and resolve data quality issues, key to creating a more efficient and accurate reporting environment. View our presentation to learn more.
For more on regulatory reporting, see presentation on Financial Reporting Robotics: http://bit.ly/2qaLK9y
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
How to Build the Data Mesh Foundation: A Principled Approach | Zhamak Dehghan...HostedbyConfluent
Organizations have been chasing the dream of data democratization, unlocking and accessing data at scale to serve their customers and business, for over a half a century from early days of data warehousing. They have been trying to reach this dream through multiple generations of architectures, such as data warehouse and data lake, through a cambrian explosion of tools and a large amount of investments to build their next data platform. Despite the intention and the investments the results have been middling.
In this keynote, Zhamak shares her observations on the failure modes of a centralized paradigm of a data lake, and its predecessor data warehouse.
She introduces Data Mesh, a paradigm shift in big data management that draws from modern distributed architecture: considering domains as the first class concern, applying self-sovereignty to distribute the ownership of data, applying platform thinking to create self-serve data infrastructure, and treating data as a product.
This talk introduces the principles underpinning data mesh and Zhamak's recent learnings in creating a path to bring data mesh to life in your organization.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
It’s been three years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. But what is the state of Data Governance today?
How has it evolved? What’s its role now? Building on prior research, erwin by Quest and ESG have partnered on a new study about what’s driving the practice of Data Governance, program maturity and current challenges. It also examines the connections to data operations and data protection, which is interesting given the fact that improving data security is now the No. 1 driver of Data Governance, according to this year’s survey respondents.
So please join us for this webinar to learn about the:
Other primary drivers for enterprise Data Governance programs
Most common bottlenecks to program maturity and sustainability
Advantages of aligning Data Governance with the other data disciplines
In a post-COVID world, data has the power to be even more transformative, and 84% of business and technology professionals say it represents the best opportunity to develop a competitive advantage during the next 12 to 24 months. Let’s make sure your organization has the intelligence it needs about both data and data systems to empower stakeholders in the front and back office to do what they need to do.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
Serhii Kholodniuk: What you need to know, before migrating data platform to G...Lviv Startup Club
Serhii Kholodniuk: What you need to know, before migrating data platform to GCP (Google cloud platform)
AI & BigData Online Day 2022
Website: https://aiconf.com.ua
Youtube: https://www.youtube.com/startuplviv
FB: https://www.facebook.com/aiconf
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Best Practices in DataOps: How to Create Agile, Automated Data PipelinesEric Kavanagh
Synthesis Webcast with Eric Kavanagh and Tamr
DataOps is an emerging set of practices, processes, and technologies for building and automating data pipelines to meet business needs quickly. As these pipelines become more complex and development teams grow in size, organizations need better collaboration and development processes to govern the flow of data and code from one step of the data lifecycle to the next – from data ingestion and transformation to analysis and reporting.
DataOps is not something that can be implemented all at once or in a short period of time. DataOps is a journey that requires a cultural shift. DataOps teams continuously search for new ways to cut waste, streamline steps, automate processes, increase output, and get it right the first time. The goal is to increase agility and cycle times, while reducing data defects, giving developers and business users greater confidence in data analytic output.
This webcast examines how organizations adopt DataOps practices in the field. It will review results of an Eckerson Group survey that sheds light on the rate and scope of DataOps adoption. It will also describe case studies of organizations that have successfully implemented DataOps practices, the challenges they have encountered and benefits they’ve received.
Tune into our webcast to learn:
- User perceptions of DataOps
- The rate of DataOps adoption by industry and other demographic variables
- DataOps adoption by technique and component (i.e., agile, test automation, orchestration, continuous development/continuous integration)
- Key challenges organizations face with DataOps
- Key benefits organizations experience with DataOps
- Best practices in doing DataOps
- Case studies and anecdotes of DataOps at companies
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Scaling and Modernizing Data Platform with DatabricksDatabricks
Today a Data Platform is expected to process and analyze a multitude of sources spanning batch files, streaming sources, backend databases, REST APIs, and more. There is clearly a need for standardizing the platform that scales and be flexible letting data engineers and data scientists focus on the business problems rather than managing the infrastructure and backend services. Another key aspect of the platform is multi-tenancy to isolate the workloads and able to track cost usage per tenant.
In this talk, Richa Singhal and Esha Shah will cover how to build a scalable Data Platform using Databricks and deploy your data pipelines effectively while managing the costs. The following topics will be covered:
Key tenets of a Data Platform
Setup multistage environment on Databricks
Build data pipelines locally and test on Databricks cluster
CI/CD for data pipelines with Databricks
Orchestrating pipelines using Apache Airflow – Change Data Capture using Databricks Delta
Leveraging Databricks Notebooks for Analytics and Data Science teams
The Best Startup Investor Pitch Deck & How to Present to Angels & Venture Cap...J. Skyler Fernandes
Take the online video course on Udemy:
https://www.udemy.com/course/the-best-startup-investor-pitch-deck/?referralCode=A5ED0FBD65120A93A16E
3.5+hrs of video content, walking step by step each part of the pitch, with personal VC stories, examples, and advice.
The "Best" Startup Investor Pitch Deck is an aggregation of some of the best pitch decks and wisdom from some of the top angels, VCs, and entrepreneurs including my own person insight/experience. The slide deck includes a template for entrepreneurs to use to present to investors, with details on what should be addressed on each slide. There are also additional slides on how best to pitch to investors effectively, how to design and format slides, and what to do before the pitch.
A successful data governance capability requires a strategy to align regulatory drivers and technology enhancement initiatives with business needs and objectives, taking into account the organizational, technological and cultural changes that will need to take place.
Navigating the Build vs. Buy Decision for Your Finance Technology NeedsGotransverse
Something that vexes every finance and accounting organization is the challenge of managing your internal technology portfolio to meet the changing needs of your constantly changing company. Should you continue to patch that old system or buy something new? Should you use point solutions or broad-based platforms? How much is the right amount to invest and which direction has the highest ROI. Well, as it happens, the answer is, “it depends”. This event will focus on how to perform a build vs. buy analysis for finance and accounting automation projects covering these very issues, helping you form a coherent technology strategy, along with tactics for execution.
Strategic Management models and diagrams for professional business presentation.
More downloadable business diagrams on
http://www.drawpack.com
your visual business knowledge
Build vs. Buy: A New Look at the Classic IT DilemmaZuora, Inc.
See how CIOs can deliver the agile pricing, customer acquisition and revenue forecasting capabilities your enterprise needs to test new business models and successfully compete in a world of continuous disruption.
Build vs. Buy: The Cio's Dilemma (Centrastage)Zuora, Inc.
When a forward-thinking company such as Centrastage leads the way in cloud-based remote device management, it is unsurprising that their customers were demanding equally forward-thinking payment methods. As an award winning IT company, it was important that they get their own internal technology right, which is why they made the decision to partner with Zuora, rather than build in house, when making this important transition to a subscription model.
Most organizations underestimate the cost and lost opportunities when they embark on a strategy to build applications rather look at COTS or SAAS solutions. They fail to take into account the other significant hidden costs and impact a decision to build has on the business. The attached presentation of mine attempts to highlight the benefits of COTS and SAAS offerings.
Competing with Software: It Takes a Platform -- Devops @ EMC Worldcornelia davis
Presentation at Devops @ EMC World event, 3 May 2015
In Mark Andreessen’s 2010 piece for the Wall Street Journal, in which he declared “Software is Eating the World,” he talked about well established, large enterprises loosing footing to small, nimble startup companies who are far better at bringing software to their consumers. In fact, it’s not as much that these upstarts are better at meeting customer demands, rather they are the cause of the increased expectations, providing consumers with things they didn’t even know they wanted. What are the factors behind their success? New development and operational approaches including extreme agile & test driven development, continuous delivery and devops practices all play a significant role, and while a part of the difference is cultural, tools matter. In this session we’ll look at why a software-driven enterprise needs platform. Google has one. Facebook has one. Netflix has one. Your enterprise needs one.
When assessing the possibility to in- or outsource often matrixes are used. The two axes of a matrix are however hardly sufficient to capture the complexity of a sourcing decision. More effective are so called sourcing decision trees. This presentations outlines one of them.
Data Visualization Best Practice Webinar presentation slidesYellowfin
Business Intelligence (BI) investment is booming. And, the amount of data available for reporting and analysis is skyrocketing.
So, it’s never been harder, or more important, to quickly uncover and communicate the actionable insights within your data. But, how do you separate the gold from the guff, and deliver value from your BI deployment?
View Yellowfin's Data Visualization Best Practices presentation and learn how to choose, design and deliver the best visualizations to effectively communicate the significance of your metrics and trends to your BI users.
And, watch the on-demand version of Yellowfin's Data Visualization Best Practices Webinar here: http://www.yellowfinbi.com/YFCommunityNews-Data-Visualization-Best-Practices-Webinar-Recording-and-slides-230726
Discover how to:
•Absorb more information quickly
•Uncover new relationships, patterns and business opportunities
•Identify and act on emerging trends fast
•Empower more people to make smarter decisions
•Unlock the value of your data and BI deployment
The Art and Science of Sales Forecasting: A Webinar for Sales Managers and Co...Birst
Overview
Sales forecasting is a science and an art. It is the combination of information and metrics, intuition and best practices. However, sales forecasting is most commonly associated to the standard grading methodology of the particular customer relationship system that is being used (Salesforce.com, Oracle, Microsoft, etc.). In reality, how do key sales leaders become high performing accurate sales forecasters? In addition, how do companies effectively utilize sales forecasting information to increase overall organizational performance?
Here’s what we’ll discuss in this session:
State-of-the-art forecasting strategies, best practices, and key metrics
The interconnection between product complexity, company lifecycle stage, and accurate forecasting
Mitigating downside risk and triangulation strategies to determine the truth
Deal inspection and vetting sales rep forecasts
The different types of sales forecasters; exaggerators, sandbaggers, and Heavy Hitters
The difference between snapshot, intra-department, and inter-department sales forecasting
The five essential steps to building a data productBirst
Building a data-driven product is scary business. You need to get the right platform both for today’s needs and for tomorrow’s possibilities – and then, you need to go beyond the technical to build a go-to-market plan that will set you up for success. Learn the five keys to building a great analytical product from someone who has done it before — and failed! Hear Kevin Smith speak about the mistakes he’s made building data products and how you can benefit from his lessons learned.
The Truth About Cross-Channel Attribution... and Why it Does Not Have to be ...Birst
In a world where the customer is perpetually connected and purchase paths are increasingly complex, cross-channel attribution measurement promises to accurately measure intertwined marketing programs, helping marketers connect with their customers in a contextually relevant way.
Yet, companies struggle to identify the right metrics and technologies needed to help measure these complex marketing exposures. As a result, marketing departments are left scrambling to analyze performance data across multiple sources, such as email tactics, display ads, direct mail, and more.
In this webinar, our guest speaker Tina Moffett, an analyst from Forrester Research, will help you interpret the tricky landscape of attribution analysis. Tina will:
· Share the latest trends in marketing measurement and technology.
· Illustrate the challenges and risks inherent in cross-channel attribution measurement – and how to overcome them.
· Outline the core technology capabilities that will help you evaluate marketing analytics and attribution technology.
You’ll also see a demo of Birst and our capabilities around multi-touch attribution.
SPSNYC2019 - What is Common Data Model and how to use it?Nicolas Georgeault
Are you using PowerApps? Not yet or maybe just the Canvas option? All you need to know about the CDS Database, the way to deploy it and the way to use it to modernize your business applications using both Canvas and Model-Driven Apps.
Creating a Single Source of Truth: Leverage all of your data with powerful an...Looker
With a centralized data store, the entire spectrum of analytics is at your fingertips. Using Looker & Segment, you can collect, store and analyze everything from click-stream and event data to transactional and behavioral data in your data warehouse.
Some of the topics this webinar will include:
-The advantages of a centralized data warehouse with Segment Warehouses
-Creating a data model to get your company on the same page with Looker Blocks
-Putting it all together: Best practices for making your data accessible to your end users
A practical guide for startups to drive growth and innovation.
Denver Startup Week Product Track presentation by Argie Angeleas, Taylor Names, Matt Reynolds
What Am I Buying? Understanding Website Cost and TechnologyIan Mariano
Your website is a crucial part of your brand experience, and the decisions you make can make or break its effectiveness. Technology decisions are key to a successful site—having insight into the technology and its associated costs can help you make choices that benefit your site and your organization.
Azure for AWS & GCP Pros: Which Azure services to use?Daniel Zivkovic
Learn how to choose which #Azure services to use so that you can start "Jumping Clouds" with confidence :) Watch the recording at https://youtu.be/34U1hUJmCUc and for more forward-looking #Software #Developerment topics, join http://ServerlessToronto.org User Group
LINKS FROM THE MEETUP & CHAT
https://www.askyourdeveloper.com/
http://youtube.serverlesstoronto.org
https://youtu.be/Ivcndg9pTpk?t=1390
https://www.meetup.com/Serverless-Toronto/events/276721419/
https://www.meetup.com/Serverless-Toronto/events/275256767/
https://www.meetup.com/Serverless-Toronto/events/276752609/
https://developerweeklypodcast.com/
https://channel9.msdn.com/Shows/Azure-Friday
https://www.pluralsight.com/paths/microsoft-azure-compute-for-developers
https://azureoverview.com/
https://build5nines.com/
https://azure.microsoft.com/en-us/updates/
https://azure.microsoft.com/en-us/blog/
https://docs.microsoft.com/en-us/azure/architecture/
https://www.mssqltips.com/sqlservertip/5144/sql-server-temporal-tables-vs-change-data-capture-vs-change-tracking--part-3/
https://azure.microsoft.com/en-us/pricing/details/synapse-analytics/
https://www.manning.com/books/azure-data-engineering
https://www.manning.com/books/azure-storage-streaming-and-batch-analytics
https://docs.microsoft.com/en-us/azure/azure-functions/durable/durable-functions-overview?tabs=csharp
https://cloudevents.io/
https://docs.microsoft.com/en-us/azure/architecture/patterns/
https://www.linkedin.com/pulse/you-asking-your-team-design-perfect-solution-daniel-zivkovic/
https://youtu.be/GBTdnfD6s5Q
https://www.linkedin.com/company/serverless-toronto/
Designing Outcomes For Usability Nycupa Hurst FinalWIKOLO
MarkoHurst.com :: My topic of discussion at the Feb 17 2009 NYC UPA.
Even as the pace of society, business, and the Internet continue to increase, many budgets and time lines continue to decrease. To compound this issue, there is a serious disconnect between business goals, user goals, and what visitors actually do on your site. UX practitioners need a simple and efficient way to reconcile these diverse needs while taking action on their data. Join us to learn about a new method for incorporating quantitative data such as web analytics and business intelligence into your qualitative user experience deliverables: personas, wireframes, and more. This presentation will include discussions of online business models, feedback loops for ensuring cross-discipline collaboration, and ongoing revisions.
Data Visualization and the Art of Self-RelianceInside Analysis
The Briefing Room with Robin Bloor and Tableau Software
Live Webcast April 23, 2013
Classic author Ralph Waldo Emerson inspired a generation with his essays on self-reliance. The wisdom of his message still rings true today, and provides a vision for the modern information worker: trust in yourself and your ideas. Then, pull together the right data, and the right visualization of that data, to tell a compelling story. Rely on your instincts, and use information assets to your advantage.
Check out the slides from this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor provide a roadmap for enabling self-reliant knowledge workers. He'll be briefed by Suzanne Hoffman of Tableau Software, who will demonstrate how the latest version of Tableau's data visualization platform can empower information professionals from all segments of the enterprise. She'll demonstrate how anyone from a casual business user to a Data Scientist can quickly create, modify and share visualizations.
Visit: http://www.insideanalysis.com
Atlassian builds tools for all teams... including ourselves! There's no right or wrong way to use our tools, but we've developed some best practices that a lot of our teams have adopted.
In this session you will learn how an Atlassian developer uses JIRA, Confluence, HipChat, BitBucket, and Bamboo to plan, build, test, and continuously deploy HipChat. You will also learn some tips and tricks for using the Atlassian toolset to take a project from a concept to a released application.
David Cruz, Senior Software Developer - HipChat Desktop, Atlassian
Analytics is more than "slap on the google analytics tag and we're done". Any good Digital project starts out with a good set of Goals & Objectives...but when was the last time that you measured the result of those goals & objectives? Lean Analytics is about integrating the analytics in the whole process...from the start. In a LEAN way
Shared at "Data-Driven Design for User Experience" with Le Wagon Tokyo, 25 Aug
https://www.meetup.com/ja-JP/Le-Wagon-Tokyo-Coding-Station/events/280067831/
In UX design, data means the voice of users (customers) and actionable insights that are beyond just numbers. Hearing these voices through user research and usage analytics is a critical process of building a human-centric design. Based on data-driven design, UX designers, product managers, and even senior management can listen to the inner voice of users and extrapolate those to discover a user journey for clear call-to-action and unwavering customer loyalty.
At this webinar, our guest speaker Emi Kwon, UX Design Director at Metlife, will walk you through the basics of data-driven design as well as share some tips and tricks for making data-driven design your value proposition as a product manager/ UX specialist.
Agenda:
✔️ Data ecosystem — Data lake, data warehouse…what does it mean for UX?
✔️ Small data and big data — the opportunities and pitfalls
✔️ Research method basics — qualitative, quantitative or triangulated
✔️ Usage analytics and A/B testing
✔️ What about COVID-19 and remote usability testing?
#NoEstimates - Stop lying to yourself and your customers, and stop estimatinggerardbeckerleg
After his successful session last year on Agile Scrum, our resident Scrum White Robe Gerard Beckerleg is at it again, except this time he's taking on one of the most divisive topics in software development: Estimation.
In this video recorded at the Sydney SSW offices, Gerard Beckerleg takes a dive into the depths of this controversial topic and extracts the most interesting ideas and raises some very difficult questions about the big white elephant in the room that is Software Estimation.
After examining the pros and cons of estimation Gerard lays the blueprint for a better way to help you and your clients get what they are really looking for.
Optimizing Innovation: Modular Toolchains that Enable Digital TransformationsDevOps.com
Lean practices for software delivery are critical to digital transformation and innovation, and the failure to execute on them opens the door to disruption. Software investment and staffing decisions are made anecdotally, using static and stale slivers of data. But what if we could take an fMRI (Functional Magnetic Resonance Imaging) of the organization and see the flow of business value in real-time? See evidence of bottlenecks and use them to prioritize IT investment? Join us as we introduce the concept of Value Stream Networks and explain how to create a modular framework enabling end-to-end business value flow, at any scale.
Optimizing Innovation- Modular Toolchains that Enable Digital TransformationsTasktop
Lean practices for software delivery are critical to digital transformation and innovation, and the failure to execute on them opens the door to disruption. Software investment and staffing decisions are made anecdotally, using static and stale slivers of data. But what if we could take an fMRI (Functional Magnetic Resonance Imaging) of the organization and see the flow of business value in real-time? See evidence of bottlenecks and use them to prioritize IT investment? Join us as we introduce the concept of Value Stream Networks and explain how to create a modular framework enabling end-to-end business value flow, at any scale.
Advance Data Visualization and Storytelling Virtual WorkshopCCG
Join CCG and Microsoft for a virtual workshop, hosted by Senior BI Architect, Martin Rivera, taking you through a journey of advanced data visualization and storytelling.
SPS Utah 2016 - Unlock your big data with analytics and BI on Office 365Brian Culver
SharePoint Saturday Utah has begun with a great crowd. I presented my session "Unlock your Big Data with Analytics and BI on Office 365" which is a Level 200 class. In my session I discuss how companies have huge amounts of data waiting to be explored. With Azure HDInsights (Microsoft's Hadoop cluster solution in partnership with Nortonworks) you can realize the value of your data. With Microsoft Excel 2013 and Office 365, you have a complete platform for BI solutions and services. PowerPivot, Power View, Power Query, Power Map and Power BI Sites empowers users analyze and make decisions using structured and unstructured data.
Attendee Takeaways:
1. Learn to setup and configure HDInsights on Microsoft Azure.
2. Understand how to use Excel for BI capabilities.
3. Build a BI Dashboard in Office365.
Driving Customer Loyalty with Azure Machine LearningCCG
Learn how you can leverage the elastic, on-demand processing power of Microsoft Azure to create faster, more applicable analytics by viewing this informative webinar. Data Scientist and Author, Ahmed Sherif, demonstrates key analytic use cases that can be spun up quickly with minimal effort and maximum return on investment. To watch the full recording of this webinar, visit http://ccgbi.com/resources/webinars/driving-customer-loyalty-with-AML
Smarter Analytics: Supporting the Enterprise with AutomationInside Analysis
The Briefing Room with Barry Devlin and WhereScape
Live Webcast on June 10, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=5230c31ab287778c73b56002bc2c51a
The data warehouse is intended to support analysis by making the right data available to the right people in a timely fashion. But conditions change all the time, and when data doesn’t keep up with the business, analysts quickly turn to workarounds. This leads to ungoverned and largely un-managed side projects, which trade short-term wins for long-term trouble. One way to keep everyone happy is by creating an integrated environment that pulls data from all sources, and is capable of automating both the model development and delivery of analyst-ready data.
Register for this episode of The Briefing Room to hear data warehousing pioneer and Analyst Barry Devlin as he explains the critical components of a successful data warehouse environment, and how traditional approaches must be augmented to keep up with the times. He’ll be briefed by WhereScape CEO Michael Whitehead, who will showcase his company’s data warehousing automation solutions. He’ll discuss how a fast, well-managed and automated infrastructure is the key to empowering faster, smarter, repeatable decision making.
Visit InsideAnlaysis.com for more information.
Similar to Birst Webinar Slides: "Build vs. Buy - Making the Right Choice for a Great Data Product" (20)
Birst 5X: Turn Information Consumers into Information Producers – Connected o...Birst
Join us as we introduce Birst 5X and welcome our featured speaker, Mike Bozek, VP of Business Line Management for Cancer Care Solutions at Elekta who will describe how this innovative human care company uses business intelligence to make a difference in people’s lives.
Traditional BI and analytics solutions offer fragmented experiences for dashboards, discovery and mobile that target distinct audiences: dashboards for information “consumers” and discovery for information “producers”. This approach locks people into rigid user roles that don’t reflect how the modern business person works with data.
Birst 5X delivers an Adaptive User Experience designed to support how people interact with data, enabling them to seamlessly transition between dashboards, discovery and mobile, connected or disconnected, and turning every information consumer into an information producer. In this webinar, you will:
Find out how Birst 5X breaks down the wall between dashboards and visual discovery
Learn how its enhanced mobile experience supports disconnected analysis to deliver insights anywhere
Understand why interoperability is essential to adapt to heterogeneous analytics environments
Learn how Elekta helps healthcare providers make more informed decisions around the quality of care and business practices
Shhh… Insider Secrets of How One Company is Meeting its Revenue Goals with 95% Confidence
You’re about to walk into the weekly management team meeting. In your hand is your sales forecast for the quarter, which was created based on data rolled up from your individual sales reps. Are you entering this meeting with confidence?
Most sales leaders would be cringing.
But not here!
In this webinar, Adam Sold, Jive’s VP of Sales Operations, will discuss how analytics have enabled them to:
- Forecast bookings and billing with accuracy and a mere 5% margin of error
- Commit to numbers two weeks ahead of the quarter, rather than two days left in the quarter
- Establish the ideal profiles for deals and reps
- Maintain optimal field and quota coverage
- Translate sales data to company insights
According to Adam, the “biggest advantage of analytics for Sales has been the ability to get predictive insights well ahead of time and allow for timely course correction”
Attend this webinar and learn how your company can experience similar results!
When Salesforce Isn’t Enough: Using Birst to Accelerate Your Business and Und...Birst
Organizations rely on solutions like Salesforce to run day-to-day operations and keep track of the massive amounts of data generated by daily customer interactions. As their business grows and their data analysis requirements evolve, these companies often find they need more robust reporting capabilities than what Salesforce offers out-of-the-box.
Join industry analyst James Haight from Blue Hill Research as he presents his new research paper, “Using Birst to Increase Efficiency and Customer Insight in Salesforce,” and describes how companies are turning to business intelligence solutions like Birst to help decision-makers glean greater insight from Salesforce data and deliver increased value to customers.
In this webinar, you will learn:
How a leading health insurance provider recognized it reached the upper limits of Salesforce reporting
The factors this organization considered when choosing a business intelligence solution
How this company transformed its business operations with greater efficiency and deeper customer insight.
How Best-in-Class Sales Leaders Create Better Forecasts and Increase RevenueBirst
Let’s face it, a new or improved CRM won’t help most enterprises forecast more accurately, or even close more deals. Today, maximizing sales requires both art and science, and this need is driving a need for consumer friendly production analytics across the Sales organization in conjunction with serving the data starved Sales Operations function.
Join Aberdeen's Sales Effectiveness Analyst Peter Ostrow in this free webinar and learn how the most progressive companies are creating better forecasts and more predictable revenue.
Register today and learn:
The pressures facing sales leaders related to forecasting
The actions taken to improve sales forecasting with data analytics
The capabilities and technologies used by Best-in-Class companies to leverage data
How to improve your own sales forecasts through analytics
The Analytic Trifecta: Abstraction, the Cloud, and VisualizationBirst
Twenty-first century pharma and biotech organizations are rapidly transforming into data-driven companies. This transformation is critical, future success and discoveries hinge on the ability to quickly and intuitively leverage, analyze, and take action on its data.
In this webinar Lindy Ryan, Research Director at Radiant Advisors, will share her research on how companies successfully manage this transformation by embracing a data unification strategy that’s built on cloud technologies.
Join us and learn how life sciences companies use cloud technology to:
Create a flexible infrastructure with the ability to agilely and quickly unify multiple data sources
TProvide a framework that enables business user agile data access while addressing governance and compliance challenges
Balance the need for data democratization while maintaining proper IT oversight and stewardship
Finally, you don’t have to choose between aging legacy BI or limited data discovery tools because Birst is now available on SAP HANA. The combination of Birst’s agile Business Intelligence and the lightning fast performance of HANA enables you to analyze more data, more quickly than ever before leading to new insights on how to improve the performance of your organization.
Boost Your Analytics Acumen: Learn Where BI is Headed from the Wisdom of the ...Birst
Want to know where business intelligence and analytics are heading in 2014? Want to understand what BI technologies are having the most business impact—and which are not? Want to know which traits successful organizations exhibit in their analytic initiatives—and why?
Learn from your peers as the “godfather” of BI research and eminent analyst Howard Dresner shares the results of his latest Wisdom of the Crowds 2014 Report. Hot off the presses, the report surveys over 1,300 global BI and details the latest trends, success measures and best practices for deploying and using analytics.
In this webinar, you will learn about:
Which analytic technology trends matter most and which don’t
When organizations’ analytic strategies prove successful and not
Which vendors to watch and why
Joining Howard is Birst to share their survey assessment and demonstrate its enterprise-caliber Cloud BI platform. Learn where BI is headed.
Webinar: Get Embedded Marketing Analytics in your CRMBirst
Discussion around the true power of your CRM and Marketing Automation Systems to move past standard reports on lead conversion rates and drive strategic marketing growth. Accompanying Mark will be Birst, sharing its cloud approach to marketing analytics.
Learn how to relate Programs to Channels to Sales Accepted Leads criteria and Opportunities
Get compelling and interactive Visualizations that you can embed in your CRM system and drill-down to the source data
Be able to clearly demonstrate the Marketing Influence on the Pipeline and Opportunities
Enable your Sales colleagues to make informed decisions on the effectiveness and success of Programs and Campaigns
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Company Valuation webinar series - Tuesday, 4 June 2024FelixPerez547899
This session provided an update as to the latest valuation data in the UK and then delved into a discussion on the upcoming election and the impacts on valuation. We finished, as always with a Q&A
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
Premium MEAN Stack Development Solutions for Modern BusinessesSynapseIndia
Stay ahead of the curve with our premium MEAN Stack Development Solutions. Our expert developers utilize MongoDB, Express.js, AngularJS, and Node.js to create modern and responsive web applications. Trust us for cutting-edge solutions that drive your business growth and success.
Know more: https://www.synapseindia.com/technology/mean-stack-development-company.html
The key differences between the MDR and IVDR in the EUAllensmith572606
In the European Union (EU), two significant regulations have been introduced to enhance the safety and effectiveness of medical devices – the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR).
https://mavenprofserv.com/comparison-and-highlighting-of-the-key-differences-between-the-mdr-and-ivdr-in-the-eu/
Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
This Digital Transformation and IT Strategy Toolkit was created by ex-McKinsey, Deloitte and BCG Management Consultants, after more than 5,000 hours of work. It is considered the world's best & most comprehensive Digital Transformation and IT Strategy Toolkit. It includes all the Frameworks, Best Practices & Templates required to successfully undertake the Digital Transformation of your organization and define a robust IT Strategy.
Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
This PowerPoint presentation is only a small preview of our Toolkits. For more details, visit www.domontconsulting.com
B2B payments are rapidly changing. Find out the 5 key questions you need to be asking yourself to be sure you are mastering B2B payments today. Learn more at www.BlueSnap.com.
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
Kseniya Leshchenko: Shared development support service model as the way to ma...Lviv Startup Club
Kseniya Leshchenko: Shared development support service model as the way to make small projects with small budgets profitable for the company (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
8. ‹#›
We had resources.
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me! + +
14. ‹#›
Time has changed the analytics game
It’s on
the
web?
NICE!
1990
It’s only
30 days
old?
NICE!
1995
I can sort
by
column
headers?
NICE!
2000
A chart?
In color?
NICE!
2005
Real-time
data?
NICE!
2010
I can’t drag this
chart to a new
location, apply
filters and have it
notify me when it
exceeds the
targets I
uploaded?
FAIL.
2015
15. ‹#›
Because table stakes & delighters aren’t
static
Table Stakes
• Expected
• Can’t compete here
• Your competition
has them
• Can’t charge for this
• Increases over time
Delighters
• Unexpected
• The place to
compete
• Useful for
differentiation
• Can charge
• Transition to table
stakes over time
18. ‹#›
We can build it for less!
• Pay for Highcharts
• Cost to build ETL
• Cost to build SSO
• Cost to build pages
Build
it
year 1 year 2 year 3
• Possibly buy
more storage
• Possibly buy
more bandwidth
maybe $150K? +20K?
• Possibly buy
more storage
• Possibly buy
more bandwidth
+20K?
Our cost to build = $190,000
over next 3 years
19. ‹#›
Buy
it
Buying is expensive!
year 1 year 2 year 3
$250K
• Pay platform fee
• Pay for
implementation
• Pay for training
• Pay platform fee• Pay platform fee
$100K $100K
Our cost to buy = $350,000 over
next 3 years
20. ‹#›
Our cost to buy = millions and
millions over an infinite timeframe
Buy
it
Buying is, like, SUPER expensive!
year 1 year 2 year 3
$250K
• Pay platform fee
• Pay for
implementation
• Pay for training
• Pay platform fee• Pay platform fee
$100K $100K
infinity
$100K
times infinity
21. ‹#›
• Pay for Highcharts
• Cost to build ETL
• Cost to build SSO
• Cost to build pages
Build
it
Buy
it
• Possibly buy
more storage
• Possibly buy
more bandwidth
year 1 year 2 year 3
• Possibly buy
more storage
• Possibly buy
more bandwidth
• Pay platform fee
• Pay for
implementation
• Pay for training
• Pay platform fee• Pay platform fee
$190K
(but
probably
even less)
Infinite
money
Clearly, we should build it!
22. ‹#›
What we all think we need to do…
Buy charting package
Build ETL
Build Charts
Build Dashboards
Connect via SSO
23. ‹#›
In reality, there’s a bit more.
Buy charting package
Build data load
Build Charts
Build Dashboards
Theming
Aggregate data
Build roll-ups
User permissioning
Admin pages
Multi-tenancy
Connect via SSO Filters
DimensionsTarget setting
Target setting
Transformations
UI controls
Drill down
Drill Across
QA
25. ‹#›
Cost to build
analytics
Cost to
support
analytics
Cost of NOT
working on your
core
application
The cost of missed core product
value
26. ‹#›
Your most talented people should
work on unsolved problems.
50% of companies base their decision to build on the fact that they
have the necessary talent to build analytics
From Wayne Eckerson, “Embedded BI: Putting Reporting and Analysis Everywhere”, TechTarget, December, 2014.
27. ‹#›
Where can we add the most
differentiating value?
Ask
Core Product Analytical Platform
• Do we have all the features we
need to solve the customers’
needs?
• Could we build features that
differentiate us from the
competition?
• Could we build functionality that
would be hard to copy?
• Is analytics where we want to
compete?
• Do we need to build the
infrastructure in order to
achieve this?
• Can we build BI functionality
that is differentiating?
28. ‹#›
Can you build what you need down the road?
Category Types of Analytics Questions Answered
Prescriptive
• Optimization
• Randomized testing
• What’s the best that can happen?
• What happens if we try this?
Predictive
• Predictive modeling/forecasting
• Statistical modeling
• What will happen next?
• What is making this happen?
Diagnostic
• Data exploration
• Intuitive visuals
• Why did this happen?
• What insights can I gain?
Descriptive
• Alerts
• Query/drill-down
• Ad hoc reports/scorecards
• Standard reports
• What actions are needed?
• What is the problem?
• How many, often, where?
• What happened?
SOURCE:
Disambiguating Analytics, July 2, 2013, Sanjeev Kumar,
International Institute for Analytics
SOURCE:
Magic Quadrant for Business Intelligence and Analytics Platforms, February 5, 2013,
Analyst(s): Kurt Schlegel, Rita L. Sallam, Daniel Yuen, Joao Tapadinhas
Capability
Easy (er) to build
Hard to build
Much harder to build
YOU have to build this
30. ‹#›
Two ways to compete on analytics
Differentiate
(we’re the leaders!)
Neutralize
(we’ve got BI too!)
Core Value Key Metric Main Challenge
Separation Unmatchable How far?
Comparability Good enough How fast?
Framework adapted from Reaching Escape Velocity, Geoffrey Moore, 2012
31. ‹#›
Two ways to compete on analytics
Differentiate
(we’re the leaders!)
Neutralize
(we’ve got BI too!)
Core Value Key Metric Main Challenge
Separation Unmatchable How far?
Comparability Good enough How fast?
Framework adapted from Reaching Escape Velocity, Geoffrey Moore, 2012
Can you build fast
enough to
differentiate?
33. ‹#›
Two ways to compete on analytics
Differentiate
(we’re the leaders!)
Neutralize
(we’ve got BI too!)
Core Value Key Metric Main Challenge
Separation Unmatchable How far?
Comparability Good enough How fast?
Framework adapted from Reaching Escape Velocity, Geoffrey Moore, 2012
Are you willing to cede
your development
roadmap to the
competition?
36. ‹#›
It’s an equation, not a single number
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
Cost Side Strategy Side
37. ‹#›
It’s an equation, not a single number
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
What’s the
TCO for
purchasing
analytics
What’s the
real cost to
build
What aren’t we
doing if we build
and how
important is it?
Will we be able
keep up the
development pace
for the foreseeable
future?
38. ‹#›
1 The cost to buy embedded analytics
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
What’s the
TCO for
purchasing
analytics
What’s the
real cost to
build
What aren’t we
doing if we build
and how
important is it?
Will we be able
keep up the
development pace
for the foreseeable
future?
39. ‹#›
2 The real cost to build
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
What’s the
TCO for
purchasing
analytics
What’s the
real cost to
build
What aren’t we
doing if we build
and how
important is it?
Will we be able
keep up the
development pace
for the foreseeable
future?
40. ‹#›
Capture all of the true costs
Task Type Task Title Description
Licensing Buy the software to make the visuals
Purchase of the software to make the charts + maintenance & support for Hi Charts (10 developer
license) -- this ONLY includes production
ETL Build connector to data source Create processes which will connect the charting software to the data source(s)
ETL Perform transformations Transform the data into an analytic ready state for charting
Data Modeling Create data aggregations Perform the roll-ups of data so that you can compare to previous yrs , qtrs, etc.
UI Create dashboard page Create the page which will contain your analytics
QA Perform QA Inspect the analytics and all calculations for accuracy
UI Create dimensions Create the dimensions by which measurement can be examined
Data Modeling Create filters Create the filtering element to include/exclude data by dimension
Data Modeling Build drill-down/across paths Link analytics together so that users can drill down and across to explore causes
Security Build multi-tenancy model Develop model to ensure that customers can't see each other's data
Security Build security model Develop model to ensure that users see only the data they are allowed to see
Data Create data model for targets Build a model to store targets for the metrics
UI Build UI for target setting Create an interface to allow for the setting of targets by metric
UI Build UI for alerts Create the interface for setting alers and notifications for user self-service
Data Modeling Create visualizations Build the visualizations to display the data such as bar charts, line charts, infographics, etc.
Data Modeling Create reports
Build the pixel perfect reports that use the metrics and dimensions to display the data in a tabluar
format with rollups, sub-groups, totals, etc.
Administrative Build user mangement capabilities
Create the functionality that allow you to add and remove customers and companies from the
analytical functionality
Administrative Build monitoring
Develop the monitoring capabilities so that you can see the total usage by customer (for billing
purposes)
41. ‹#›
And calculate both money & time
Variable Value
Hourly rate $150.00
# of data sources 2
# of visualizations 15
# of reports 2
# of metrics 30
# of dashboards 1
# Dimensions/metric 2
Task Type Task Title Description QuantityHours per Item Total Hours Total Cost for Task
Licensing
Buy the software to make
the visuals
Purchase of the software to make the charts +
maintenance & support for Hi Charts (10 developer
license) -- this ONLY includes production 1 n/a n/a $3,600.00
ETL
Build connector to data
source
Create processes which will connect the charting
software to the data source(s) 2 20 40 $6,000.00
ETL Perform transformations Transform the data into an analytic ready state for charting 30 10 300 $45,000.00
Data Modeling Create data aggregations
Perform the roll-ups of data so that you can compare to
previous yrs , qtrs, etc. 30 10 300 $45,000.00
UI Create dashboard page Create the page which will contain your analytics 1 20 20 $3,000.00
QA Perform QA Inspect the analytics and all calculations for accuracy 30 5 150 $22,500.00
UI Create dimensions
Create the dimensions by which measurement can be
examined 60 5 300 $45,000.00
Create the filtering element to include/exclude data by
The Powered by Birst Buy vs. Build Calculator
* not including the time to manage the project
$226,350
Building your dashboard in-house would cost
at least:
that's 1485 hours or 0.67 FTE years not
working on your core product
How much does it REALLY cost to build dashboards for your product on your own?
Your cost to build using these parameters
Our expected cost to build:
42. ‹#›
3 Opportunity costs & risks of building
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
What’s the
TCO for
purchasing
analytics
What’s the
real cost to
build
What aren’t we
doing if we build
and how
important is it?
Will we be able
keep up the
development pace
for the foreseeable
future?
43. ‹#›
Four parts to this side of the equation
Can we build it FAST
enough?
What ELSE could we
build with the time?
Do we want to KEEP
building it?
Can we build it GOOD
enough?
1 2
3 4
44. ‹#›
Can we build it FAST
enough?
• Do you have the resources to build it?
• Can you build it quickly enough to
meet demand?
• Can you build it fast enough to outpace
the competition?
1
45. ‹#›
Can we build it GOOD
enough?
• Do we have the talent to build this?
• Can we get to the “delighter” functionality in
the near term?
• Will we be able to meet the “table stakes”?
• Do we know what our customers need?
2
46. ‹#›
Do we want to KEEP
building it?
• Will we have the resource to continue to
support this?
• Will we have the resources to continue to
develop this?
• Will we be able to meet one-off requests and
future table stakes?
3
47. ‹#›
What ELSE could we
build with the time?
• Is this as or more important than our core
functionality?
• Are we willing to delay core product
functionality to build (and maintain) analytics?
• Is this the best use of our resources - is this
why customers buy our product?
4
49. ‹#›
Low Risk Medium High Risk
Can we build it fast
enough?
We’ve got a development team
dedicated to analytics, fully-
trained in the entire stack, and
can build quickly.
We have resources, but may
have trouble building quickly
enough to achieve table stakes.
We don’t have the resources/
don’t want to dedicate the
resources to build analytics.
Can we build it good
enough?
Yes — we can build all the
basics plus functionality to
differentiate ourselves from the
competition.
Maybe — we can add some
table stakes, not all. Maybe our
delighters will outweigh the
gaps in functionality.
Nope — we’d have trouble
getting to table stakes.
Do we want to keep
building?
Yes — this is where we will
compete so we’ll devote equal
resources to analytics develop
as our core app.
Maybe — we could add some
functionality over time but it
would secondary in importance
to the core app.
No — we’d prefer to use our
resources on other things.
Could we be doing
other things?
No — analytics are the app for
us. We consider this to be the
core of what we do.
Maybe — analytics are
important and our core app
roadmap is not full.
Yes — we can add more value
by working on our core
application.
The Buy vs. Build Decision Matrix
50. ‹#›
Low Risk
(1 point)
Medium
(3 points)
High Risk
(5 points)
TOTAL
Can we build it fast
enough?
We’ve got a development
team dedicated to analytics,
fully-trained in the entire stack,
and can build quickly.
We have resources, but may
have trouble building quickly
enough to achieve table
stakes.
We don’t have the resources/
don’t want to dedicate the
resources to build analytics. 3
Can we build it good
enough?
Yes — we can build all the
basics plus functionality to
differentiate ourselves from the
competition.
Maybe — we can add some
table stakes, not all. Maybe
our delighters will outweigh
the gaps in functionality.
Nope — we’d have trouble
getting to table stakes.
3
Do we want to keep
building?
Yes — this is where we will
compete so we’ll devote equal
resources to analytics develop
as our core app.
Maybe — we could add some
functionality over time but it
would secondary in
importance to the core app.
No — we’d prefer to use our
resources on other things.
2
Could we be doing
other things?
No — analytics are the app for
us. We consider this to be the
core of what we do.
Maybe — analytics are
important and our core app
roadmap is not full.
Yes — we can add more value
by working on our core
application. 2
GRAND TOTAL
(possible 20 points)
10 points
The Buy vs. Build Decision Matrix
51. ‹#›
Low
(1 point)
Medium
(3 points)
High
(5 points)
Our Rating Importance
(1=low to 3=high)
TOTAL
Can we build
it fast
enough?
We’ve got a development
team dedicated to analytics,
fully-trained in the entire
stack, and can build quickly.
We have resources, but may
have trouble building quickly
enough to achieve table
stakes.
We don’t have the
resources/don’t want to
dedicate the resources to
build analytics.
5 2 10
Can we build
it good
enough?
Yes — we can build all the
basics plus functionality to
differentiate ourselves from
the competition.
Maybe — we can add some
table stakes, not all. Maybe
our delighters will outweigh
the gaps in functionality.
Nope — we’d have
trouble getting to table
stakes. 5 3 15
Do we want
to keep
building?
Yes — this is where we will
compete so we’ll devote
equal resources to analytics
develop as our core app.
Maybe — we could add some
functionality over time but it
would secondary in
importance to the core app.
No — we’d prefer to use
our resources on other
things. 3 2 6
Could we be
doing other
things?
No — analytics are the app
for us. We consider this to
be the core of what we do.
Maybe — analytics are
important and our core app
roadmap is not full.
Yes — we can add more
value by working on our
core application. 2 3 6
GRAND TOTAL
(possible 60 points)
37
points
The Buy vs. Build Decision Matrix
x =
52. ‹#›
The Buy vs. Build Decision Spectrum
Consider building your
own analytics
You likely will be able to
build fast enough and
keep building fast enough
to hold off the
competition
Consider buying your
analytics
It is unlikely you will get
to market fast enough or
be able to stay ahead of
your competition
Consider a combination
strategy
You may be able to build
fast enough and keep
building fast enough to
beat the competition in
select areas
Low Risk High Risk
The red zone
Medium Risk
0 - 20 points 21 - 40 points 41 - 60 points
The yellow zoneThe green zone
53. ‹#›
Weigh the pros & cons to make the
decision that’s right for your situation
Cost Side
May save $53K
Strategy Side
Medium High risk
to build & keep
building
54. ‹#›
In summary: don’t use “internal” criteria
4
Make a balanced decision
The BI bar has been raised
It will take longer & cost more than you expected
You can’t let up on the pace for your strategy
What are you skipping in order to build?3
5
2
1