The document discusses agile analytics and its benefits for businesses. Agile analytics involves rapidly testing hypotheses, analyzing results, and making improvements to gain a better understanding of customers, get results more quickly, and reduce risks. It recommends businesses focus on a single problem, develop small testable hypotheses, iterate testing every 2-4 weeks with specific changes, and use findings to direct the next round of improvements. Practicing agile analytics allows organizations to test, improve, fail, and succeed quickly.
7 Dimensions of Agile Analytics by Ken Collier Thoughtworks
We are in the midst of an exciting time. There is an explosion of very interesting data, and emergence of powerful new technologies for harnessing data, and devices that enable humans to receive tremendous benefits from it. What is required are innovative processes that enable the creation and delivery of value from all of that data. More often than not, it is the predictive (what will happen?) and prescriptive (how to make it happen!) analytics that produces this value, not the raw data itself. Agile software teams are continuously involved in projects that involve rich, complex, and messy data. Often this data represents innovative analytics opportunities. Being analytics-aware gives these teams the opportunity to collaborate with stakeholders to innovate by creating additional value from the data. This session is aimed at making Agile software teams more analytics-aware so that they will recognize these innovation opportunities. The trouble with conventional analytics (like conventional software development) is that it involves long, phased, sequential steps that take too long and fail to deliver actionable results. This deck will examine the convergence of the following elements of an exciting emerging field called Agile Analytics:
sophisticated analytics techniques, plus
lean learning principles, plus
agile delivery methods, plus
so-called "big data" technologies
Learn:
The analytical modeling process and techniques
How analytical models are deployed using modern technologies
The complexities of data discovery, harvesting, and preparation
How to apply agile techniques to shorten the analytics development cycle
How to apply lean learning principles to develop actionable and valuable analytics.
Big Data Agile Analytics by Ken Collier - Director Agile Analytics, Thoughtwo...Thoughtworks
We are in the midst of an exciting time. There is an explosion of very interesting data, and emergence of powerful new technologies for harnessing data, and devices that enable humans to receive tremendous benefits from it. What is required are innovative processes that enable the creation and delivery of value from all of that data. More often than not, it is the predictive (what will happen?) and prescriptive (how to make it happen!) analytics that produces this value, not the raw data itself.
Agile software teams are continuously involved in projects that involve rich, complex, and messy data. Often this data represents innovative analytics opportunities. Being analytics-aware gives these teams the opportunity to collaborate with stakeholders to innovate by creating additional value from the data. This session is aimed at making Agile software teams more analytics-aware so that they will recognize these innovation opportunities.
The trouble with conventional analytics (like conventional software development) is that it involves long, phased, sequential steps that take too long and fail to deliver actionable results. This talk will examine the convergence of the following elements of an exciting emerging field called Agile Analytics:
•sophisticated analytics techniques, plus
•lean learning principles, plus
•agile delivery methods, plus
•so-called "big data" technologies
Learn:
•The analytical modeling process and techniques
•How analytical models are deployed using modern technologies
•The complexities of data discovery, harvesting, and preparation
•How to apply agile techniques to shorten the analytics development cycle
•How to apply lean learning principles to develop actionable and valuable analytics
•How to apply continuous delivery techniques to operationalize analytical models
7 Dimensions of Agile Analytics by Ken Collier Thoughtworks
We are in the midst of an exciting time. There is an explosion of very interesting data, and emergence of powerful new technologies for harnessing data, and devices that enable humans to receive tremendous benefits from it. What is required are innovative processes that enable the creation and delivery of value from all of that data. More often than not, it is the predictive (what will happen?) and prescriptive (how to make it happen!) analytics that produces this value, not the raw data itself. Agile software teams are continuously involved in projects that involve rich, complex, and messy data. Often this data represents innovative analytics opportunities. Being analytics-aware gives these teams the opportunity to collaborate with stakeholders to innovate by creating additional value from the data. This session is aimed at making Agile software teams more analytics-aware so that they will recognize these innovation opportunities. The trouble with conventional analytics (like conventional software development) is that it involves long, phased, sequential steps that take too long and fail to deliver actionable results. This deck will examine the convergence of the following elements of an exciting emerging field called Agile Analytics:
sophisticated analytics techniques, plus
lean learning principles, plus
agile delivery methods, plus
so-called "big data" technologies
Learn:
The analytical modeling process and techniques
How analytical models are deployed using modern technologies
The complexities of data discovery, harvesting, and preparation
How to apply agile techniques to shorten the analytics development cycle
How to apply lean learning principles to develop actionable and valuable analytics.
Big Data Agile Analytics by Ken Collier - Director Agile Analytics, Thoughtwo...Thoughtworks
We are in the midst of an exciting time. There is an explosion of very interesting data, and emergence of powerful new technologies for harnessing data, and devices that enable humans to receive tremendous benefits from it. What is required are innovative processes that enable the creation and delivery of value from all of that data. More often than not, it is the predictive (what will happen?) and prescriptive (how to make it happen!) analytics that produces this value, not the raw data itself.
Agile software teams are continuously involved in projects that involve rich, complex, and messy data. Often this data represents innovative analytics opportunities. Being analytics-aware gives these teams the opportunity to collaborate with stakeholders to innovate by creating additional value from the data. This session is aimed at making Agile software teams more analytics-aware so that they will recognize these innovation opportunities.
The trouble with conventional analytics (like conventional software development) is that it involves long, phased, sequential steps that take too long and fail to deliver actionable results. This talk will examine the convergence of the following elements of an exciting emerging field called Agile Analytics:
•sophisticated analytics techniques, plus
•lean learning principles, plus
•agile delivery methods, plus
•so-called "big data" technologies
Learn:
•The analytical modeling process and techniques
•How analytical models are deployed using modern technologies
•The complexities of data discovery, harvesting, and preparation
•How to apply agile techniques to shorten the analytics development cycle
•How to apply lean learning principles to develop actionable and valuable analytics
•How to apply continuous delivery techniques to operationalize analytical models
"Making Data Actionable" by Budiman Rusly (KMK Online)Tech in Asia ID
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
According to recent research report by Wall Street Journal, AI project failure rates near 50%, more than 53% terminates at proof of concept level and does not make it to production. Gartner report says that nearly 80% of the analytics projects are not delivering any business value. That means for every 10 projects, only 2 projects are useful to the organization. Let us pause here a moment, rather than looking at what makes AI projects to fail, let’s look at the challenges involved in AI projects and find a solution to overcome these challenges.
AI projects are different from traditional software projects. Typical software projects, as shown in Figure 1, consist of well-defined software requirements, high level design, coding, unit testing, system testing, and deployment along with beta testing or field testing. Now, organizations are adopting Agile process instead of traditional V or waterfall model, but still steps mentioned are valid.
However, AI and Machine Learning projects’ methodology is different from the above. Our experience working on many AI/ML projects has given us insights on some of the challenges of executing AI projects. Also, we are in regular touch with senior executives and thought leaders from different industries who understand the success formula. The following discussion is based on our practical experience and knowledge gained in the field.
Successful execution of AI projects depends on the following factors:
1. Clearly aligned Business Expectations
2. Clarity on Terminologies
3. Meeting Data Requirements
4. Tools and Technology
5. Right Resources
6. Understanding Output Results
7. Project Planning and the Process
Data Quality Analytics: Understanding what is in your data, before using itDomino Data Lab
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
Slides for the presentation given at the Data Science Scotland Meetup (https://www.meetup.com/Scotland-Data-Science-Technology-Meetup/events/256269263/).
This talk aimed to give some general advice, tips, and tricks about how to run a successful data science project.
Hosted by:
Incremental Group - https://www.linkedin.com/company/incremental-group/
MBN Solutions - https://www.linkedin.com/company/mbn-recruitment-solutions/
The Datalab - https://www.linkedin.com/company/the-data-lab-innovation-centre/
Data-Driven Requirements for User-Stories on JustAnswerVlad Mysla
Process of switching to Data-Driven Requirements for User-Story creation. It has information about internal JA tools, which isn't useful for anyone outside the company.
Supporting innovation in insurance with randomized experimentationDomino Data Lab
Recent technological advances, a dynamic competitive landscape, and an evolving regulatory environment have led to a period of rapid innovation for many insurance providers. Here, we’ll explore how data scientists may use randomized experiments to rigorously assess the causal impact of innovations on business outcomes. Particular emphasis will be placed on experimentation in “offline” channels, with some of the challenges and mitigation strategies highlighted.
How to Use AI in Product by Intel Product ManagerProduct School
This presentation covers what it's like to use AI in Product for a company and the different ways they can be implemented within an organization and we'll also touch on some of the misconceptions that come with using AI in Product.
Main takeaways:
- Multidisciplinary Product Manager
- Managing a product with invisible software, vague requirements in AI/IoT,
- Customer vs Industry
- Difference between technology and product; When to productize?
- AI as a feature vs AI as a product
- Product Management for the Internet of Things
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
Is Agile Data Science just two buzzwords put together? I argue that agile is a very practical and applicable methodology, that does work well in the real world for all sorts of Analytics and Data Science workflows.
http://theinnovationenterprise.com/summits/digital-web-analytics-summit-london-2015/schedule
"Making Data Actionable" by Budiman Rusly (KMK Online)Tech in Asia ID
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
According to recent research report by Wall Street Journal, AI project failure rates near 50%, more than 53% terminates at proof of concept level and does not make it to production. Gartner report says that nearly 80% of the analytics projects are not delivering any business value. That means for every 10 projects, only 2 projects are useful to the organization. Let us pause here a moment, rather than looking at what makes AI projects to fail, let’s look at the challenges involved in AI projects and find a solution to overcome these challenges.
AI projects are different from traditional software projects. Typical software projects, as shown in Figure 1, consist of well-defined software requirements, high level design, coding, unit testing, system testing, and deployment along with beta testing or field testing. Now, organizations are adopting Agile process instead of traditional V or waterfall model, but still steps mentioned are valid.
However, AI and Machine Learning projects’ methodology is different from the above. Our experience working on many AI/ML projects has given us insights on some of the challenges of executing AI projects. Also, we are in regular touch with senior executives and thought leaders from different industries who understand the success formula. The following discussion is based on our practical experience and knowledge gained in the field.
Successful execution of AI projects depends on the following factors:
1. Clearly aligned Business Expectations
2. Clarity on Terminologies
3. Meeting Data Requirements
4. Tools and Technology
5. Right Resources
6. Understanding Output Results
7. Project Planning and the Process
Data Quality Analytics: Understanding what is in your data, before using itDomino Data Lab
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
Slides for the presentation given at the Data Science Scotland Meetup (https://www.meetup.com/Scotland-Data-Science-Technology-Meetup/events/256269263/).
This talk aimed to give some general advice, tips, and tricks about how to run a successful data science project.
Hosted by:
Incremental Group - https://www.linkedin.com/company/incremental-group/
MBN Solutions - https://www.linkedin.com/company/mbn-recruitment-solutions/
The Datalab - https://www.linkedin.com/company/the-data-lab-innovation-centre/
Data-Driven Requirements for User-Stories on JustAnswerVlad Mysla
Process of switching to Data-Driven Requirements for User-Story creation. It has information about internal JA tools, which isn't useful for anyone outside the company.
Supporting innovation in insurance with randomized experimentationDomino Data Lab
Recent technological advances, a dynamic competitive landscape, and an evolving regulatory environment have led to a period of rapid innovation for many insurance providers. Here, we’ll explore how data scientists may use randomized experiments to rigorously assess the causal impact of innovations on business outcomes. Particular emphasis will be placed on experimentation in “offline” channels, with some of the challenges and mitigation strategies highlighted.
How to Use AI in Product by Intel Product ManagerProduct School
This presentation covers what it's like to use AI in Product for a company and the different ways they can be implemented within an organization and we'll also touch on some of the misconceptions that come with using AI in Product.
Main takeaways:
- Multidisciplinary Product Manager
- Managing a product with invisible software, vague requirements in AI/IoT,
- Customer vs Industry
- Difference between technology and product; When to productize?
- AI as a feature vs AI as a product
- Product Management for the Internet of Things
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
Is Agile Data Science just two buzzwords put together? I argue that agile is a very practical and applicable methodology, that does work well in the real world for all sorts of Analytics and Data Science workflows.
http://theinnovationenterprise.com/summits/digital-web-analytics-summit-london-2015/schedule
Agile Big Data Analytics Development: An Architecture-Centric ApproachSoftServe
Presented at The Hawaii International Conference on System Sciences by Hong-Mei Chen and Rick Kazman (University of Hawaii), Serge Haziyev (SoftServe).
The Briefing Room with Colin White and Composite Software
Live Webcast Feb. 26, 2013
The modern business analyst needs data from all over the place: yes, the data warehouse, but also the Web, big data, production systems, as well as via partners and vendors. In fact, the typical analyst spends more than 50% of the time chasing data, which slows delivery of analytic insights and limits the time available for thorough analysis. Some practitioners refer to this conundrum as "the data problem."
Check out the slides from this episode of The Briefing Room to hear veteran Analyst Colin White of BI Research as he explains why analytical sandboxes and data hubs can be an analyst's best friend. He'll be briefed by Bob Eve of Composite Software who will discuss his company's mature data virtualization platform, which includes a number of capabilities that help organizations leverage agile analytics. He will discuss why time-to-insight is fast becoming the battle cry of analysis-driven organizations.
Visit: http://www.insideanalysis.com
Agile Analytics: Delivering on Promises by Atif Abdul RahmanAgile ME
Big Data is all the hype in town yet the real value still remain with delivering analytics that create business impact. Agile Analytics sets out to unleash the true promise usually lost in lengthy, elephantine projects and years of data management purists' pursuits of perfection. That is exactly what separates these big data technologies: They promise greater agility. But is a supportive technology enough or even mandatory to become more agile? We will go through the value chain of delivering high impact analytics using agile practices and devise a jumpstarter kit for you to adopt and adapt.
Estamos presenciando inovações tecnológicas que possibilitam utilizar ciência dos dados sem a necessidade de antecipar grandes investimentos. Este contexto facilita a adoção de práticas e valores ágeis que encorajam a antecipação de insights e aprendizado contínuo. Nesta palestra, iremos abordar temas como times multi-funcionais, práticas ágeis de engenharia de software e desenvolvimento iterativo, incremental e colaborativo no contexto de produtos e soluções de ciência dos dados.
Agile data science: Distributed, Interactive, Integrated, Semantic, Micro Ser...Andy Petrella
Distributed Data Science…
* A genomics use case
* Spark Notebook
* Interactive Distributed Data Science
Distributed Data Science… Pipeline
* Pipeline: productizing Data Science
* Demo of Distributed Pipeline (ADAM, Akka, Cassandra, Parquet, Spark)
* Why Micro Services?
* Painful points:
* Data science is Discontiguous
* Context Lost in Translation
* Solution: Data Fellas’ Agile Data Science Toolkit
This is the presentation I shared at the SAP Influencer Summit. The presentation discusses how we are seeing companies in APJ utilize our BI/Analytics solutions.
Agile analytics : An exploratory study of technical complexity managementAgnirudra Sikdar
The thesis involved the reviewing of various case studies to determine the types of modelling, choice of algorithm, types of analytical approaches and trying to determine the various complexities arising from these cases. From these reviews, procedures have been proposed to improve the efficiency and manage the various types of complexities from using agile methodological perspective. Focus was mostly done on Customer Segmentation and Clustering , with the sole purpose to bridge Big Data and Business Intelligence together using Analytic.
Lean Product Management for Enterprises: The Art of Known Unknowns Thoughtworks
Natalie Hollier presentation was given at the Lean Strategy + Design Salon meetup in New York: http://www.meetup.com/LeanStrategyPlusDesign/events/200913392/
Check out Natalie's website: http://www.nataliehollier.com/
You Can't be Agile When you are Knee Deep in Mud Thoughtworks
To be effective with agile software development, you need to have solid technical practices. But many organisations are still only implementing process changes to their software delivery cycles. Rachel Laycock will explain why you need technical practices like testing, refactoring, continuous delivery and evolutionary architecture. She will cover a brief history of these practices and explain how without them you will end up with "ball of mud architectures" that slow you down no matter what process changes you make.
- Planned and prepared an email marketing campaign which included creating email marketing content, creating a campaign calendar, drafting an email, using MailChimp to create the campaign, A/B testing, and conducting an evaluation of the email campaign results.
During the SaaS.City Customer Success bootcamp on Monday the 18th of September 2017, attendees at SaaStock 2017 found out how to manage customer risk, map customer health scores, justify the expense of Customer Success, and so much more.
The Customer Success Bootcamp mentors include Dan Steinman, GM of Gainsight EMEA; David Apple, VP of Customer Success at Typeform; and Cristina Georgoulaki, Head of Customer Success at Typeform. This event was exclusively for SaaStock conference ticket holders.
SaaS.City Customer Success Bootcamp at SaaStock 2017SaaStock
Presentation by Dan Steinman, Head of EMEA at Gainsight, David Apple, VP of Customer Success at Typeform, and Cristina Georgoulaki, Head of Customer Success Management at Typeform
Web Analytics: Free Yourself from Analysis ParalysisThe Loud Few
Erin "Loudfinity" Steinbruegge presents at MarketSTL on Web Analytics and How to Break Free of Analysis Paralysis. Includes a link to a free website anlaytics dashboard.
eBay Partner Network & Optimizely: Optimization Best PracticesSejal Patel
eBay Partner Network partnered with Optimizely to share insights and best practices for optimization. What we covered:
-What a world-class testing program looks like and why it is so important for an increasingly digital age
-Best practices on how to implement a testing and optimization program
-Success stories from publishers that are leveraging testing to significantly increase conversions and revenue
Email marketing is one of the oldest digital marketing tactics, yet it continues to deliver a higher ROI than just about any other online strategy. But this is not your father's email marketing anymore. That's right: this strategy has matured, and if you haven't revisited how you create email content since the early 2000's, you're most likely missing out on the major ROI and benefits of email. This topic takes an in depth look into six key areas of email marketing and how each has evolved. It's time to update your old email marketing strategy and move it to 2016 and beyond.
SaaStrU 301: Unlocking Growth in the Internet Economy: a Perspective from Str...saastr
The internet economy is experiencing explosive growth, and more business models are now possible online than ever before. Join Suzanne Xie, Stripe’s Business lead for their Invoicing products and a former serial entrepreneur, as she shares her lessons for how internet businesses can use new tools to make more money for less effort.
Making data sexy: Data Visualization for Digital MarketingMashMetrics
Are your stakeholders falling asleep while you explain your monthly reports? Are you having a hard time separating metrics from insights? Do your Excel charts look like they are from the 80's? This deck will explain some of the key pitfalls to a boring data presentation. We walk through a real-life Digital Marketing example of Google Analytics data and other insights to help you along your data-driven journey.
Five Ways Analytics Empowers Marketing LinkedIn eMetrics Jimmy WongJimmy Wong
As presented at the eMetrics Summit in Chicago on June 10, 2015, in this fast-paced session, Jimmy Wong from LinkedIn shares five ways in which marketers at the world’s biggest professional social networking company use custom-built analytical tools and techniques to acquire, delight, and retain B2B customers. See how a combination of open-source and commercial technologies can be leveraged to hack together optimal solutions for marketing. Learn how these in-house techniques can be applied to your own organization.
Jimmy Wong heads up the business analytics team for LinkedIn’s B2B marketing. At the intersection of marketing and technology, Jimmy has extensive experience in building scalable analytical products and optimizations for B2B marketing and sales. He also runs the local Toastmasters club and mentors college students on starting their careers in marketing and analytics. Jimmy is passionate about empowering marketers to succeed through analytics.
Agenda Course 6: Marketing Matrix - Communication Matrix
Course Objectives:
Build a humanized brand connected with the Purpose
Build an Ideal Customer Persona (ICP)
Build a Lead Generation Cycle that engages, generates, and qualifies leads
Build a Lead Conversion Cycle that produces sales, repeated sales, and brand ambassadors.
Community Program: 15 Min.
Frame the community project with an accountable action plan.
Work group session: Validate the Solution, Business Model, and Sales Pitch. 45 min.
Marketing Matrix Introduction 5 Min.
Canvases: Solution - Business Model - Marketing Matrix
Graph Theory for Small Businesses
Brand Matrix Matrix 10 Min.
Graph Theory for Small Businesses
Brand Persona and Brand’s Assets
Marketing Matrix: 15 Min.
Lead Generation Cycle
Lead Conversion Cycle
Marketing KPIs
B2C Flow
Bright Business Model —Solution Matrix - You do not sell a service or a product. You sell an experience.
Chuck Sharp, Right Intel CEO, discusses digital marketing from the perspective of a CMO. By using a predetermined performance framework to dictate the KPIs of a campaign, marketing team can better ensure they achieve the goals intended.
Similar to Agile Analytics: The Secret to Test, Improve, Fail & Succeed Quickly. (20)
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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).
5. Because the purpose of business is to create a
customer, the business enterprise has two–
and only two–basic functions: marketing
and innovation. Marketing and innovation
produce results; all the rest are costs.
Marketing is the distinguishing, unique
function of the business.”
Peter Drucker
6. Because the purpose of business is to
create a customer, the business
enterprise has two–and only two–
basic functions: marketing and
innovation. Marketing and innovation
produce results; all the rest are costs.”
Peter Drucker
Father of Business Management