This document summarizes a presentation on sales and marketing analytics given by Keyrus consultants. It discusses Keyrus' stepwise approach to analytics projects, from understanding business issues to modeling data to deploying insights.
The presentation uses a case study of an online retailer called Solidstore.com to illustrate challenges in understanding customer behavior and sales trends. Keyrus' approach helped Solidstore analyze sales by country and customer value segments, identify reasons for decreasing sales and customer loss, and realize more potential from its online channel.
The document outlines Keyrus' analytics methodology, including formulating business cases, assessing available data, modeling customer behavior, gaining additional insights, and enriching data sources to continuously monitor metrics and insights.
BI congres 2016-3: Insurance comparison engine - Miloud Belkacem - Business &...BICC Thomas More
9de BI congres van het BICC-Thomas More: 24 maart 2016
Data analytical platform, new generation. In this presentation Miloud Belkacem shows you how to structure your infrastructure and data sources so they can be available not to just data analysts, but also to the whole organization. It’s an insight into a modern data analytical platform.
BI congres 2016-4: Hoe groei je als organisatie in analytische maturiteit? - ...BICC Thomas More
9de BI congres van het BICC-Thomas More: 24 maart 2016
Waar traditionele BI voornamelijk beschrijft van WAT er gebeurd is, kunnen we met Self-Service BI een stapje verder gaan en een eerste verklaring geven WAAROM iets zich voordoet. Als we echter tot de wortel willen geraken, moeten we gebruik maken van Analytics.
LoQutus helps organisations to innovate with analytics and to get insights with data visualisation. We also build large scale data layers to enable interaction with core data, and develop data-driven applications to deliver the insights our customers need. During this session we’ll share what we have learned along the way. We’ll show you our framework for self-service analytics & insights, and some successful case studies.
Dcaf transformation & kg adoption 2022 -alan morrisonAlan Morrison
A keynote presentation on knowledge graph adoption trends and how to do digital transformation differently.
Delivered at the Enterprise Data Transformation & Knowledge Graph Adoption
A Semantic Arts DCAF Event
February 28, 2022
Optimize your cloud strategy for machine learning and analyticsCloudera, Inc.
Join industry superstars Mike Olson (Cloudera CSO and co-founder) and Jim Curtis (451 Research senior analyst) as they outline the best practices for cloud-based machine learning and analytics in this “can’t miss” webinar.
Hot topics include:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Success stories, cautionary tales, and lessons learned
James will share 451 Research findings and offer insights learned from surveying both the vendor landscape and enterprise practitioners.
.
Mike will regale you with his vision for the future of multi-disciplinary machine learning and analytics in hybrid- and multi-cloud environments
3 things to learn:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Enabling a Culture of Self-Service AnalyticsPrecisely
As enterprises strive to create a more data-driven culture, they want to put more data in the hands of more users across the organization. However, not all enterprise data is easy to access and understand, and most decision-makers lack the expertise to evaluate the quality of the data they’re using to know if it’s fit for purpose.
To enable a culture of self-service analytics companies must get all of their data into one place where it can be accessed – like an enterprise data marketplace – and ensure its quality so it can be trusted by the data consumers.
View this webinar on-demand to hear from Matt Aslett, Research VP, Data, AI and Analytics, 451 Research and Jennifer Cheplick, Senior Director, Syncsort about how a combination of technology and cultural change can enable enterprises to provide a foundation of data integration and data quality that arms your organization’s data consumers with the functionality to enable self-service analytics.
Accelerating Fast Data Strategy with Data VirtualizationDenodo
"Information from the past won't support the insights of the future - businesses need real-time data," said Forrester Analyst Noel Yuhanna. In this presentation, he explains the challenges of latent data faced by business users, the need to accelerate fast data strategy using data virtualization, and the implications of such strategy.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/a2xNyZ.
BI congres 2016-3: Insurance comparison engine - Miloud Belkacem - Business &...BICC Thomas More
9de BI congres van het BICC-Thomas More: 24 maart 2016
Data analytical platform, new generation. In this presentation Miloud Belkacem shows you how to structure your infrastructure and data sources so they can be available not to just data analysts, but also to the whole organization. It’s an insight into a modern data analytical platform.
BI congres 2016-4: Hoe groei je als organisatie in analytische maturiteit? - ...BICC Thomas More
9de BI congres van het BICC-Thomas More: 24 maart 2016
Waar traditionele BI voornamelijk beschrijft van WAT er gebeurd is, kunnen we met Self-Service BI een stapje verder gaan en een eerste verklaring geven WAAROM iets zich voordoet. Als we echter tot de wortel willen geraken, moeten we gebruik maken van Analytics.
LoQutus helps organisations to innovate with analytics and to get insights with data visualisation. We also build large scale data layers to enable interaction with core data, and develop data-driven applications to deliver the insights our customers need. During this session we’ll share what we have learned along the way. We’ll show you our framework for self-service analytics & insights, and some successful case studies.
Dcaf transformation & kg adoption 2022 -alan morrisonAlan Morrison
A keynote presentation on knowledge graph adoption trends and how to do digital transformation differently.
Delivered at the Enterprise Data Transformation & Knowledge Graph Adoption
A Semantic Arts DCAF Event
February 28, 2022
Optimize your cloud strategy for machine learning and analyticsCloudera, Inc.
Join industry superstars Mike Olson (Cloudera CSO and co-founder) and Jim Curtis (451 Research senior analyst) as they outline the best practices for cloud-based machine learning and analytics in this “can’t miss” webinar.
Hot topics include:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Success stories, cautionary tales, and lessons learned
James will share 451 Research findings and offer insights learned from surveying both the vendor landscape and enterprise practitioners.
.
Mike will regale you with his vision for the future of multi-disciplinary machine learning and analytics in hybrid- and multi-cloud environments
3 things to learn:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Enabling a Culture of Self-Service AnalyticsPrecisely
As enterprises strive to create a more data-driven culture, they want to put more data in the hands of more users across the organization. However, not all enterprise data is easy to access and understand, and most decision-makers lack the expertise to evaluate the quality of the data they’re using to know if it’s fit for purpose.
To enable a culture of self-service analytics companies must get all of their data into one place where it can be accessed – like an enterprise data marketplace – and ensure its quality so it can be trusted by the data consumers.
View this webinar on-demand to hear from Matt Aslett, Research VP, Data, AI and Analytics, 451 Research and Jennifer Cheplick, Senior Director, Syncsort about how a combination of technology and cultural change can enable enterprises to provide a foundation of data integration and data quality that arms your organization’s data consumers with the functionality to enable self-service analytics.
Accelerating Fast Data Strategy with Data VirtualizationDenodo
"Information from the past won't support the insights of the future - businesses need real-time data," said Forrester Analyst Noel Yuhanna. In this presentation, he explains the challenges of latent data faced by business users, the need to accelerate fast data strategy using data virtualization, and the implications of such strategy.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/a2xNyZ.
Data centric business and knowledge graph trendsAlan Morrison
The deck for my kickoff keynote at the Data-Centric Architecture Forum, February 3, 2020. Includes related data, content, and architecture definitions and fundamental explanations, knowledge graph trends, market outlook, transformation case studies and benefits of large-scale, cross-boundary integration/interoperation.
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Big Data LDN 2018: THE NEXT WAVE: DATA, AI AND ANALYTICS IN 2019 AND BEYONDMatt Stubbs
Date: 14th November 2018
Location: Keynote Theatre
Time: 13:10 - 13:40
Speaker: Matt Aslett
Organisation: 451 Research
About: As 2018 draws to a close, Matt Aslett, Research VP, 451 Research looks ahead to 2019 and the key trends the research company’s Data, AI and Analytics team is anticipating for the year ahead, including the continued rise of DataOps; the increased importance of data science operationalisation; mainstream adoption of AI and machine learning; data platforms evolution; and the confluence of distributed database and blockchain technology in supporting the move towards planetary-scale data processing and analytics.
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATIONMatt Stubbs
Date: 14th November 2018
Location: Keynote Theatre
Time: 13:50 - 14:20
Speaker: Becky Smith
Organisation: Denodo
About: How many users inside and outside of your organization access your organization’s data? Dozens? Hundreds is probably more like it, each with their own structure and content requirements as well as different access rights. As a result, many organizations have witnessed the formation of “data delivery mills,” in various shapes and sizes. How does one create order and reliability in this world of chaotic data streams? Quite easily, if it’s done with data virtualization.
According to Gartner, "through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration.” Data virtualization enables organizations to gain data insights from multiple, distributed data sources without the time-consuming processes of data extraction and loading. This allows for faster insights and fact-based decisions, which help business realize value sooner.
Join us to find out more about:
• What data virtualization actually means and how it differs from traditional data integration approaches.
• How you can connect and combine all your data in real-time, without compromising on scalability, security or governance.
• The benefits of data virtualization and its most important use cases.
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.
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
Artificial Intelligence beyond the hype: Local (Belgian) Machine Learning suc...Patrick Van Renterghem
Presentation on "AI beyond the hype: Local (Belgian) Machine Learning success stories" by Peter Depypere (element61), at the BI & Data Analytics Summit on June 13th, 2019 in Diegem (Belgium)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) Dataiku
As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve this problem.
As Data Manager, you know the challenges ahead:
- Multitudes of technology choices to make
- Building a team and solving the skill-set disconnect
- Data can be deceiving...
- Figuring out what the successful data product must be
Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising, and gaming industries, holding various data or CTO roles. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains encountered by data teams all around.
To view recording of this webinar please use the below URL:
http://wso2.com/library/webinars/2016/08/analytics-as-your-business-edge/
Data is the new oil! For most enterprises, data is the oil you’ve been sitting on without realizing its value. You can gain many useful insights from data that lead to new and better products and operations, enables new user experiences, allows better understanding of customers, makes interactions seamless and enables new pay per use business models and dynamic pricing models. Furthermore, data itself can be monetized. Enterprises can broker interactions between end users as done in digital advertising or sell insights to third parties in anonymized forms. Just like in Google and Facebook, data can be a primary asset that organizations collect as part of their operations.
ADV Slides: Data Pipelines in the Enterprise and ComparisonDATAVERSITY
Despite the many, varied, and legitimate data platforms that exist today, data seldom lands once in its perfect spot for the long haul of usage. Data is continually on the move in an enterprise into new platforms, new applications, new algorithms, and new users. The need for data integration in the enterprise is at an all-time high.
Solutions that meet these criteria are often called data pipelines. These are designed to be used by business users, in addition to technology specialists, for rapid turnaround and agile needs. The field is often referred to as self-service data integration.
Although the stepwise Extraction-Transformation-Loading (ETL) remains a valid approach to integration, ELT, which uses the power of the database processes for transformation, is usually the preferred approach. The approach can often be schema-less and is frequently supported by the fast Apache Spark back-end engine, or something similar.
In this session, we look at the major data pipeline platforms. Data pipelines are well worth exploring for any enterprise data integration need, especially where your source and target are supported, and transformations are not required in the pipeline.
Slides: How AI Makes Analytics More HumanDATAVERSITY
People think AI makes analytics less human, replacing human decision making. But the truth is, AI actually makes analytics more human. Augmented analytics are helping organizations finally break through the low levels of adoption and limitations typical of 2nd generation visualization tools.
Most business problems cannot be solved purely by algorithms or machine learning — they require human interaction and perspective. Uniting precedent-based machine learning systems with natural human intuition and curiosity is the foundation of 3rd generation BI and democratizing data across an enterprise.
It is a natural flow to enhance your data eco-system by deploying a platform with augmented intelligence to work alongside users in the pursuit of surfacing new insights, automating tasks, and supporting natural language interaction. All work as accelerators for achieving active intelligence and Data Literacy.
BI congres 2016-2: Diving into weblog data with SAS on Hadoop - Lisa Truyers...BICC Thomas More
9de BI congres van het BICC-Thomas More: 24 maart 2016
De hoeveelheid data die via weblogs verzameld wordt, neemt steeds meer toe. Lisa Truyers zet aan de hand van een praktische case uiteen hoe Keyrus hiermee aan de slag ging
BI congres 2014-6: Opleiding Informatiemanagement - Hans Tubbax - Thomas MoreBICC Thomas More
Nieuwe opleiding Informatiemanagement aan Thomas More
In onze economie is data van groot strategisch belang. Bedrijven worden overspoeld door gegevens en moeten steeds sneller onderbouwde beslissingen nemen. Hoe selecteren ze uit de grote hoeveelheid beschikbare data de interessante informatie? Wat met de privacy en de beveiliging ervan? Data correct analyseren, efficiënt en veilig beheren en helder rapporteren wordt steeds belangrijker!
In de snel veranderende wereld van Big Data veranderen jobs voortdurend van naam, maar met een diploma van Informatiemanagement op zak ben je een gegeerde professional! Bedrijven zijn immers druk op zoek naar specialisten die uit grote hoeveelheden data winst kunnen genereren.
Data centric business and knowledge graph trendsAlan Morrison
The deck for my kickoff keynote at the Data-Centric Architecture Forum, February 3, 2020. Includes related data, content, and architecture definitions and fundamental explanations, knowledge graph trends, market outlook, transformation case studies and benefits of large-scale, cross-boundary integration/interoperation.
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Big Data LDN 2018: THE NEXT WAVE: DATA, AI AND ANALYTICS IN 2019 AND BEYONDMatt Stubbs
Date: 14th November 2018
Location: Keynote Theatre
Time: 13:10 - 13:40
Speaker: Matt Aslett
Organisation: 451 Research
About: As 2018 draws to a close, Matt Aslett, Research VP, 451 Research looks ahead to 2019 and the key trends the research company’s Data, AI and Analytics team is anticipating for the year ahead, including the continued rise of DataOps; the increased importance of data science operationalisation; mainstream adoption of AI and machine learning; data platforms evolution; and the confluence of distributed database and blockchain technology in supporting the move towards planetary-scale data processing and analytics.
Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATIONMatt Stubbs
Date: 14th November 2018
Location: Keynote Theatre
Time: 13:50 - 14:20
Speaker: Becky Smith
Organisation: Denodo
About: How many users inside and outside of your organization access your organization’s data? Dozens? Hundreds is probably more like it, each with their own structure and content requirements as well as different access rights. As a result, many organizations have witnessed the formation of “data delivery mills,” in various shapes and sizes. How does one create order and reliability in this world of chaotic data streams? Quite easily, if it’s done with data virtualization.
According to Gartner, "through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration.” Data virtualization enables organizations to gain data insights from multiple, distributed data sources without the time-consuming processes of data extraction and loading. This allows for faster insights and fact-based decisions, which help business realize value sooner.
Join us to find out more about:
• What data virtualization actually means and how it differs from traditional data integration approaches.
• How you can connect and combine all your data in real-time, without compromising on scalability, security or governance.
• The benefits of data virtualization and its most important use cases.
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.
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
Artificial Intelligence beyond the hype: Local (Belgian) Machine Learning suc...Patrick Van Renterghem
Presentation on "AI beyond the hype: Local (Belgian) Machine Learning success stories" by Peter Depypere (element61), at the BI & Data Analytics Summit on June 13th, 2019 in Diegem (Belgium)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) Dataiku
As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve this problem.
As Data Manager, you know the challenges ahead:
- Multitudes of technology choices to make
- Building a team and solving the skill-set disconnect
- Data can be deceiving...
- Figuring out what the successful data product must be
Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising, and gaming industries, holding various data or CTO roles. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains encountered by data teams all around.
To view recording of this webinar please use the below URL:
http://wso2.com/library/webinars/2016/08/analytics-as-your-business-edge/
Data is the new oil! For most enterprises, data is the oil you’ve been sitting on without realizing its value. You can gain many useful insights from data that lead to new and better products and operations, enables new user experiences, allows better understanding of customers, makes interactions seamless and enables new pay per use business models and dynamic pricing models. Furthermore, data itself can be monetized. Enterprises can broker interactions between end users as done in digital advertising or sell insights to third parties in anonymized forms. Just like in Google and Facebook, data can be a primary asset that organizations collect as part of their operations.
ADV Slides: Data Pipelines in the Enterprise and ComparisonDATAVERSITY
Despite the many, varied, and legitimate data platforms that exist today, data seldom lands once in its perfect spot for the long haul of usage. Data is continually on the move in an enterprise into new platforms, new applications, new algorithms, and new users. The need for data integration in the enterprise is at an all-time high.
Solutions that meet these criteria are often called data pipelines. These are designed to be used by business users, in addition to technology specialists, for rapid turnaround and agile needs. The field is often referred to as self-service data integration.
Although the stepwise Extraction-Transformation-Loading (ETL) remains a valid approach to integration, ELT, which uses the power of the database processes for transformation, is usually the preferred approach. The approach can often be schema-less and is frequently supported by the fast Apache Spark back-end engine, or something similar.
In this session, we look at the major data pipeline platforms. Data pipelines are well worth exploring for any enterprise data integration need, especially where your source and target are supported, and transformations are not required in the pipeline.
Slides: How AI Makes Analytics More HumanDATAVERSITY
People think AI makes analytics less human, replacing human decision making. But the truth is, AI actually makes analytics more human. Augmented analytics are helping organizations finally break through the low levels of adoption and limitations typical of 2nd generation visualization tools.
Most business problems cannot be solved purely by algorithms or machine learning — they require human interaction and perspective. Uniting precedent-based machine learning systems with natural human intuition and curiosity is the foundation of 3rd generation BI and democratizing data across an enterprise.
It is a natural flow to enhance your data eco-system by deploying a platform with augmented intelligence to work alongside users in the pursuit of surfacing new insights, automating tasks, and supporting natural language interaction. All work as accelerators for achieving active intelligence and Data Literacy.
BI congres 2016-2: Diving into weblog data with SAS on Hadoop - Lisa Truyers...BICC Thomas More
9de BI congres van het BICC-Thomas More: 24 maart 2016
De hoeveelheid data die via weblogs verzameld wordt, neemt steeds meer toe. Lisa Truyers zet aan de hand van een praktische case uiteen hoe Keyrus hiermee aan de slag ging
BI congres 2014-6: Opleiding Informatiemanagement - Hans Tubbax - Thomas MoreBICC Thomas More
Nieuwe opleiding Informatiemanagement aan Thomas More
In onze economie is data van groot strategisch belang. Bedrijven worden overspoeld door gegevens en moeten steeds sneller onderbouwde beslissingen nemen. Hoe selecteren ze uit de grote hoeveelheid beschikbare data de interessante informatie? Wat met de privacy en de beveiliging ervan? Data correct analyseren, efficiënt en veilig beheren en helder rapporteren wordt steeds belangrijker!
In de snel veranderende wereld van Big Data veranderen jobs voortdurend van naam, maar met een diploma van Informatiemanagement op zak ben je een gegeerde professional! Bedrijven zijn immers druk op zoek naar specialisten die uit grote hoeveelheden data winst kunnen genereren.
BI congres 2014-4: thinking out of the box - Jos Cools - CrosspointBICC Thomas More
7de BI congres van het BICC-Thomas More: 3 april 2014
Business Analytics @ Immoweb
In 2013 heeft Immoweb gekozen voor SAS Visual Analytics om een solide basis uit te bouwen om de besluitvorming te ondersteunen.
Tijdens deze sessie worden een aantal uitdagingen onder de loep genomen rond rapportering, analytics en forecasting.
Met deze praktijk case krijg je inzicht in hoe je een analytics en rapporterings omgeving kan opzetten als beleidsondersteuning.
BI congres 2014-5: from BI to big data - Jan Aertsen - PentahoBICC Thomas More
7de BI congres van het BICC-Thomas More: 3 april 2014
Reisverslag van Business Intelligence naar Big Data
De reisbranche is sterk in beweging. Deze presentatie zal een reis door klassieke en moderne BI bestemmingen zijn, toont een serie snapshots van verschillende use cases in de reisbranche. Tijdens de sessie benadrukken we de capaciteit en flexibiliteit die een BI-tool nodig heeft om u te begeleiden op uw reis van klassieke BI-implementaties naar de moderne big data uitdagingen .
BI congres 2014-3: facts not opinions - Tobias Temmink - TeradataBICC Thomas More
7de BI congres van het BICC-Thomas More: 3 april 2014
Geen meningen maar Feiten!
Big Data heeft de wereld van BI en Analytics veranderd. Of toch niet? Wat is nog altijd hetzelfde en wat is er veranderd? Wat is er vandaag voor nodig om een volledig data gedreven organisatie te worden? Ik zal laten zien hoe bedrijven als Netflix, Full Tilt Poker, en Wells Fargo nieuwe en bestaande technologien gebruiken om hun bedrijven te draaien en te verbeteren.
Spinning Data Into Gold: How to “weaponize” data to de-risk and grow your bus...Max Connect Marketing
Most businesses now face the same difficult questions - how to cut costs without killing the business, how to make marketing dollars go further, and how to find growth opportunities while most other businesses are shrinking. Analytics expert, Tim Kapp, will talk about how companies of every size should be “weaponizing” data to de-risk and grow their business during a downturn. We’ll cover customer retention, improved campaign targeting, cost-cutting through process mining, and how to build data expertise without breaking the bank.
Modern Services Marketing Session at TSIA/TSW 2017 San DiegoFred Isbell
Digital transformation and innovations including cloud, social media, and Big Data/analytics have redefined services marketing -- no one debates that. The modern services marketer must both combine art and science to meet changing needs of the services marketplace, including digital technologies, thought leadership and storytelling, and analytics for key insights. We addressed the skills of a successful modern services marketer, technology as a key enabler to transformation and innovation, and address key best practices in this session given at the TSIA Technology Services World (TSW) 2017 event in San Diego CA.
2015 Outlook for Marketing Performance Measurement by BECKONAmanda Roberts
Join Kevin Dodson, Beckon VP of Data strategy for a look at the 2015 Outlook for Marketing Performance Measurement. In this 30-minute webcast, we’ll cover what’s in store for 2015 and the critical measurement capabilities you need to master.
Power to the People: A Stack to Empower Every User to Make Data-Driven DecisionsLooker
Infectious Media runs on data. But, as an ad-tech company that records hundreds of thousands of web events per second, they have have to deal with data at a scale not seen by most companies. You can not make decisions with data when people need to write manual SQL only for queries take 10-20 minutes to return. Infectious Media made the switch to Google BigQuery and Looker and now every member of every team can get the data they need in seconds.
Infectious Media shares:
- Why they chose their current stack
- Why faster data means happier customers
- Advantages and practical implications of storing and processing that much data
Check out the recording at https://info.looker.com/h/i/308848878-power-to-the-people-a-stack-to-empower-every-user-to-make-data-driven-decisions
Big Data, Big Thinking: Untapped OpportunitiesSAP Technology
In this webinar factsheet, SAP’s Rohit Nagarajan and Suni Verma from Ernst & Young explore Big Data in India, adoption patterns across the globe, and how you can embark on your own Big Data journey.
The Analytics COE positioning your business analytics program for successKiran Garimella
You should consider the following three aspects of your Business Analytics Program:
* The Business (not just data science, big data, and technology)
* Analytics as the DNA of the company (and not just a competency of an elite few)
* A Programmatic approach that sustainable for the life of the company (and not just a one-time project or initiative)
What role do classical statistics, Bayesian statistics, judgment under uncertainty, heuristics, biases, categorical data analysis, etc., play in such a program?
A COE (Center of Excellence) framework seeks to address these aspects and ensure the company can progress on all fronts.
Digital-Warriors-Marketing Roadmap with Big Data AnalyticsJaysonBowden
When the data speaks, why should you listen?
Big data has become a buzzword among marketers all over the world and I am frankly sick of all the buzz without concrete value. In this context, my goal is to make Big Data real and tangible for marketers so you can realize the disruptive shift that is upon us. Every marketer is familiar with the 4 P’s of marketing (Product, Price, Place, and Promotion) so we will start our discussion in a way that begins to extend these concepts. I like to describe these as ‘The New 4 of P’s of Marketing with Big Data: Personalization, Performance, Prediction, and Privacy’
Seeing Redshift: How Amazon Changed Data Warehousing ForeverInside Analysis
The Briefing Room with Claudia Imhoff and Birst
Live Webcast April 9, 2013
What a difference a day can make! When Amazon announced their new RedShift offering – a data warehouse in the cloud – the entire industry of information management changed. The most notable disruption? Price. At a whopping $1,000 per year for a terabyte, RedShift achieved a price-point improvement that amounts to at least two orders of magnitude, if not three when compared to its top-tier competitors. But pricing is just one change; there's also the entire process by which data warehousing is done.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Claudia Imhoff explain why a new cloud-based reality for data warehousing significantly changes the game for business intelligence and analytics. She'll be briefed by Brad Peters of Birst who will tout his company's BI solution, which has been specifically architected for cloud-based hosting. Peters will discuss several key intricacies of doing BI in the cloud, including the unique provisioning, loading and modeling requirements. Founded in 2004, Birst has nearly a decade of doing cloud-based BI and Analytics.
Visit: http://www.insideanalysis.com
Basics of BI and Data Management (Summary).pdfamorshed
Basics of Business Intelligence and Data Management
BI Architecture
How BI works?
DMBOK framework
what is Data literacy
Data quality
Data Governance
what is self-service or modern BI
Power BI Architecture
How Power BI Works
BI Implementation steps
Ana Villegas, Dell - Using Data, Technology and Creativity to Break Through T...Marketing United
Digital marketing is a crucial part of any B2B marketing strategy. Today, B2B marketers are faced with the challenge of breaking through the noise and using programmatic to optimize both content and marketing strategies to reach BDMs and ITDMs. Using cutting edge data technology to fuel unique and creative strategies, Dell is going beyond Digital 101 to become a thought leader in the world of digital B2B marketing.
Building a 360 Degree View of Your Customers on BICSPerficient, Inc.
Why there is a need for Customer 360 and what the proposed cloud based solution is. We cover the stages of strategic marketing and how Oracle BI can help.
Using Machine Learning to Understand and Predict Marketing ROIDATAVERSITY
Marketing is all about attracting, retaining and building profitable relationships with your customers, but how do you know which customers to target, which campaigns to run, and which marketing programs to invest in, to get most return for your dollar?
Join Alteryx and Keyrus as we demonstrate how to combine all relevant marketing, sales and customer data, and perform sophisticated analytics to deepen customer insight and calculate ROI of marketing programs.
You’ll walk away knowing how to:
Segment and profile your customers – take that raw data and translate it into real value
Build a marketing attribution model within Alteryx, creating a personal answer engine for your company.
Leverage R or Python code in an Alteryx workflow so data scientists can collaborate with non-coding stake holders in a code-friendly and code-free environment.
Join Alteryx and Keyrus and get the actionable insights you need to drive marketing ROI analytics, and answer million-dollar questions without spending millions of dollars on standardized solutions.
How to Capitalize on Big Data with Oracle Analytics CloudPerficient, Inc.
The average age of a company listed on the S&P 500 has fallen from almost 60 years old in the 1950s to less than 20 years old today. Innovative companies that are willing to embrace transformative technologies make the list today, while businesses that are hesitant to embrace change risk becoming obsolete.
Innovators use big data solutions as a competitive advantage to increase revenue, reduce cost, and improve cash flow. Turn big data into actionable insights with Oracle Analytics Cloud.
We identified the big data opportunities in front of you and how to take advantage of them:
-Big data and its architecture
-Why a big data strategy is imperative to remaining relevant
-How Oracle Analytics Cloud can help you connect people, places, data, and systems to fundamentally change how you analyze, understand, and act on information
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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
3. Onze missie
Een neutraal en onafhankelijk
platform voor samenwerking met
de bedrijfswereld met als
hoofddoel kennisdeling en
innovatie te stimuleren
16. Agenda
• Sessie 1: Open source, meer dan disruptieve software?
Bart Maertens, Managing Partner, know.bi
• Sessie 2: M&S Analytics: Join the Big Data revolution!
Carl Sablon, Senior Consultant, Keyrus
Peter Poppe, Principal Consultant, Keyrus
• Sessie 3: Hoe het overzicht bewaren?
Jörgen Jacob, Business Unit Manager, Fit IT
• Sessie 4: Een versie van de waarheid: een achterhaald idee?
Tobias Temminck, Teradata, Benelux Technology Officer
• Sessie 5: Nood aan meer strategisch management?
Dries Van Nieuwenhuyse, Onderzoeker, BICC Thomas More
17. Before lift-off
• Mogelijkheid tot korte interactie (vragen) na elke sessie
• Netwerk: TM_BICC met als key BICC1
• Twitter hastag: #BICongres15
• Twitter user: @BICC_ThomasMore
21. What is OSS?
• “Open-source software (OSS) is computer
software with its source code made available
with a license in which the copyright holder
provides the rights to study, change and
distribute the software to anyone and for
any purpose”
• Free “as in speech” rather than “as in beer”
24. OSS Licensing
• Moving from copyleft (GPL family) to permissive (e.g. Apache
v2)
• 2015: Apache v2 (do wtf you want)
• Professional/Commercial OSS:
• Dual licensing/Open Core:
• Free Community Edition:
• go your own way
• Only free (as in beer) to download, use has a cost
• Paid Enterprise Edition:
• Pro approach (consultancy, support, training…)
• Enhanced functionality
• Beekeeper model
25. Evolution of OSS
• Infrastructure: Linux, OS on low level
hardware
• Middleware:
• databases (PostgreSQL, MariaDB)
• application servers (JBoss, Tomcat)
• Applications: Firefox, LibreOffice, GIMP
• OSS is ubiquitous: increased need for
standardization forces towards OSS
26. OSS Market Share
Open Source dominates in
• Supercomputing (485 of top 500
run Linux)
• Cloud computing (75% Linux)
• Web servers (65% Apache)
• Mobile
• Embedded
• IoT
• …
30. • Pentaho components:
• Data Integration (Kettle)
• Reporting OLAP (Mondrian)
• Data Mining (Weka)
• Dashboarding (CTools)
• BA server (security, scheduling)
• Community contributions (marketplace)
31. • Only complete OSBI platform in the market
• Founded in 2004
• (to be) acquired by HDS in 2015
• Open core:
• CE: OSS engines
• EE: CE + support, enhanced functionality
• Strong community in (ao) EU
42. Sessie 2
Sales & Marketing Analytics
Join the Big Data revolution!
Carl Sablon, Senior Consultant, Keyrus
Peter Poppe, Principal Consultant, Keyrus
43. AGILITY I COLLABORATIVE INTELLIGENCE I INNOVATION I PERFORMANCE
CONSULTING I TECHNOLOGY
SALES AND MARKETING ANALYTICS
/ JOIN THE BIG DATA GENERATION!
PIETER VANDAMME
MARCH 2015
73. Sessie 2
Sales & Marketing Analytics
Join the Big Data revolution!
Carl Sablon, Senior Consultant, Keyrus
Peter Poppe, Principal Consultant, Keyrus
76. “Education is not the piling on of learning, information, data,
facts, skills or abilities – that’s training or instruction – but is
rather a making visible what is hidden as a seed...”
- Thomas More
81. Agenda
●Introduction
●Value for the Customer
●Data Mining vs Predictive Analytics
●Learn from Experience
●Use Cases by Function
●Use Cases by Market
●Return on Investment
Predictive Analytics
82. Fit IT at a glance
Predictive Analytics
Strategic PartnersFacts & Figures
● 3 Activities
Business Analytics
Systems Engineering
Business Applications
● > 14 Mio Revenue
● 90 FTE’s
Locations
Ghent
Brussels
Antwerp
83. Predictive Analytics
Value for the Customer
Reporting
Query, Search,
Reporting
COMPLEXITY
BUSINESS VALUE
What
happened
Why did it
happen
What’s
happening
now
What might
happen
Analysis
OLAP
Visualisation
Monitoring
Dashboards,
scorecards
Prediction
Predictive
Analytics
84. Predictive Analytics
Data Mining versus Predictive Analytics
“Which products are
bought together”
=> CORRELATIONS
“Who buys a certain
product and why”
=> INFLUENCE
Predictive
Analytics
Data
Mining
86. Predictive Analytics
Use Cases by Function
Product Mix
Marketing
Predicting Life Time Value
Up Selling
Channel Optimization
Reactivation Likelyhood
Customer Churn
Risk
Credit Risk
Accounts Payable Recovery
Fraud Detection
Anti-Money Laundering
Treasury or Currency Risk
Churn
HR
Resume Screening
Training Recommendation
Talent Management
Employee Churn
87. Predictive Analytics
Use Cases by Market
Product Mix
Life Science
Drug/chemical Discovery &
Analysis
Diagnostic
Targeting
(CRM)
Predicting prescription adherence
with different approaches to
reminding patients
Predicting drug demand in
different geographies for
different products
Churn
Retail
Merchandising
Shrinkage Analytics
Location of New Stores
Pricing
Market Basket
Analysis
Next Best Offer Analysis
Warranty Analytics
Insurance
Claims
Prediction
Investments
Product Mix
Agent and Brand Performance
Price Sensitivity
95. 97
Data and Analytics Evolution
Application
Centric
Integration
Centralized
Decentralized
Capability
Rigid Agile
Data and
Analytic
Centric
96. 98
Organization has a full fledged analytic architecture that is enterprise wide, fully automated,
integrated into processes, and sophisticated
Organization has high quality data. An enterprise wide analytics plan, governance principles, and
some automated analytics
Proliferation of BI tools and data marts but most data remains unintegrated. Non standardized, and
inaccessible
Organization collects transaction data efficiently but often lacks the right data for better decision
making
Organization is plagued by missing or poor quality data, multiple definitions of its data, and poorly
integrated systems
Stages of Analytic Maturity
Source: Davenport Harris, Competing on Analytics, Harvard Business School Press, 2007, pp156
97. 99
FINANCE
Revenue
Expenses
Customers
CUSTOMER CARE
Customer
Products
Orders
Case History
SALES
Orders
Customers
Products
MARKETING
Customers
Orders
Campaign History
OPERATIONS
Inventory
Returns
Manufacturing
Supply Chain
Which plants are
using which suppliers
for EV batteries?
How many EV
batteries are in
inventory by
manufacturing plant?
What is the trend of
warranty costs?
What is the Year-Over-
Year growth in hybrid
sales?
How many people
reported an issue with
EV batteries last
month?
How many people
made a warranty claim
on Hybrid cars last
week?
How many sales of
hybrid cars have
been made quarter
to date?
What % of after
market accessories
are sold to hybrid
customers?
Which customers should
get upcoming email
communication on
hybrid car extended
warranties?
Which of our customers
are likely to buy a
hybrid car in the next 3
months?
54 32 29 49 66
99. 101
2855
Given the rise in warranty costs, isolate the problem to be a specific plant, then isolate to a specific battery lot.
Communicate with affected customers, who have not already made a warranty claim on batteries, through
Marketing and Customer Service channels to recall cars with batteries.
Inventory
Returns
Manufacturing
Supply Chain
Customer Service
Orders
Revenue
Expenses
Case History
Customers
Products
Pipeline
Customers
Campaign History
FINANCE
SALESMARKETING
OPERATIONS
CUSTOMER
EXPERIENCE
2855
101. 103
Is not about Volume, Velocity and Variety anymore….
It is about how you use the data and analytics
102. 104
BIG DATA
WEB
Petabytes
CRM
Terabytes
Gigabytes
ERP
Exabytes
INCREASING Data Variety and Complexity
User Generated
Content
Mobile Web
SMS/MMS
Sentiment
External
Demographics
HD Video
Speech to Text
Product/
Service Logs
Social Network
Business Data
Feeds
User Click Stream
Web Logs
Offer History A/B Testing
Dynamic Pricing
Affiliate Networks
Search Marketing
Behavioral
Targeting
Dynamic Funnels
Payment
Record Support Contacts
Customer Touches
Purchase
Detail
Purchase
Record
Offer Details
Segmentation
DECREASING Value Density in the Data
Big Data: From Transactions to Interactions
108. 110 110
2855
MANUFACTURING
CAMPAIGN HISTORY
COSTS
PRODUCTS
FINANCE
SALES
MARKETING
OPERATIONS
CUSTOMER
EXPERIENCE
Given the rise in warranty
costs, isolate the problem to
be a specific plant, then
isolate the specific lot.
Result is two-thirds of the bad
battery lot are fine, and
exclude them from the recall.
Communicate with affected
customers, who have not
already made a warranty
claim on batteries, through
Marketing and Customer
Service channels to recall
cars with batteries.
CUSTOMERS
CASE HISTORY
SENSOR
116. Strategie = BOTTOM UP
• De vraag die zich stelt
is echter of het
allemaal zo helder was
van bij het begin…
• Hebben succesvolle
bedrijven niet altijd een
goede strategie?
• Komt het niet
van onderen
naar boven?
118. Strategisch management
• Huwelijk tussen
gehoopte toekomst,
haalbare toekomst en
noodzakelijke toekomst
• Hoe kunnen we het
verschil maken en
blijven maken?
119. Strategisch management
• Een zinvol business model kan maar worden gerealiseerd via
een passend organisatie model
120. Performance Management en
Strategisch Management
• Kan Performance MANAGEMENT hier een bijdrage leveren?
• Kunnen we iets leren van Strategisch MANAGEMENT?
• Natuurlijk, wat had je gedacht?
121. Strategieformulering
• Waar zijn we nu goed in?
• Waar zijn onze concurrenten goed in?
• Waar liggen nog opportuniteiten en in welke mate?
Ansoff
Product-
Markt matrix
BCG product
portfolio
123. Strategie(bij)sturing
• Faciliteren van de kwantitatieve beleidsprocessen door PDCA-
cyclus heen
o Plan
Budgettering
Opvolging
o Do
Operationele ondersteuning
o Check
Actual versus budget
Balanced Scorecard
124. Strategie(bij)sturing
• (Re)Act
o Performance MANAGEMENT
heeft ervaring met het
formuleren en opvolgen van
strategie
o Continu bijsturen van de
strategie
o Terugkoppeling naar de
strategieformulering
o Target setting om strategische
doelen ook effectief op te
volgen en te realiseren
o Evaluatie van hoe goed we
wel bezig zijn
o Predictie van potentieel
o Forecasting van wat mogelijk
resultaat van onze strategie
zou kunnen zijn
125. Strategie = TOP-DOWN
Strategie = BOTTOM-UP
• Performance MANAGEMENT
heeft ervaring met het formuleren
en opvolgen van strategie
• Strategisch MANAGEMENT helpt
creatieve vragen te stellen van
wat mogelijk zou kunnen zijn
• Beide zijn met elkaar getrouwd,
en dat is maar goed ook…
126. Afsluitende sessie & wrap-up
Nood aan meer strategische management?
Dries Van Nieuwenhuyse, Onderzoeker, BICC Thomas More