Agile data science
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The document discusses the principles of Agile Data Science, emphasizing the importance of collaboration, flexibility, and continuous improvement in data analytics. It outlines the role of business champions, integrated environments, and analytics experts in creating successful data products while highlighting the significance of efficient data handling and interpretation. Through a case study approach, it illustrates the process from data acquisition to actionable insights, promoting a culture of data democracy and shared learning.
Agile data science
- 1. Agile Data Science INFORMS BIG DATA CONFERENCE 6/23/2013 ! Presented by Joel S Horwitz Follow me @JSHorwitz Alpine Data Labs
- 2. Agile Manifesto We are uncovering better ways of developing software by doing it and helping others do it. Through this work we have come to value: ! Individuals and interactions over processes and tools Working software over comprehensive documentation Customer collaboration over contract negotiation Responding to change over following a plan ! That is, while there is value in the items on the right, we value the items on the left more.
- 3. We are uncovering better ways of developing software models by doing it and helping others do it. Through this work we have come to value: ! Individuals and interactions over processes and tools Working software models over comprehensive documentation Customer collaboration over contract negotiation Responding to change over following a plan ! That is, while there is value in the items on the right, we value the items on the left more. Agile Manifesto
- 4. Business Analytics Technologist Linear workflows in non-agile culture ???? Business Owner Business Analyst Data Owner Data Scientist Business Stakeholders < / > 001011 = 5 = 10
- 5. Agile is about continuous interactions Analytics • Feature Creation • Model / Scoring • Evaluation Technologist • Wrangling • Interpretation • Pipelines Business • Presentation • Deployment • Productize
- 6. Minimally Viable Data Products (MVDP) crossfilter.js or D3.JS Tableau Dashboards Product Recommendations Also, Product Recommendations People Recommendations One model, many use cases.
- 7. Agile Data Science Feedback Loop Instrumentation / Data Collection Analysis & Design Results & Interpretation Cultivating Data Intuition
- 8. What do you need? 1. Business Champion 2.Integrated Environment 3. Analytics Ninjas
- 9. 1. Business Champion(s): Defining the Problem Executive Sponsor(s) who have a vested interest… ready to take action from results, has an impact to their business goals. Chief Technology Officer SVP of Product Chief Marketing Officer VP of Sales Problem statement… Monthly active user count growth was stagnating. Evidence of where to look… acquisition funnel, user engagement, and customer loyalty. Question to answer… How do I connect each part of the acquisition funnel? We started with the acquisition funnel… web visits, downloads, installs, and activations.
- 10. 2. Environment: Before • Many data silos and technology limitations • Data definitions not well defined. • Multiple data formats. • No place to build MVDPs at scale.
- 11. 2. Environment: After Open Source Technologies Flume, Sqoop, Oozie, and others Analytics Sandbox Relational Database Minimally Viable Data Products Selectively include data that is Relevant
- 12. How was our analytics platform deployed and connected to the network of systems. First there was FTP… dump raw log files from web analytics, content delivery network, and application log files. ! Second there was MS SQL… Most of the data was in Microsoft SQL databases. ! Third there was Hadoop (with Hive)… Engineering and Development backup to Hadoop. ! Now there is web based analytics…
- 13. What is Hadoop? • Overview: Apache Hadoop is a framework for running applications on large cluster built of commodity hardware. • Storage: Hadoop Distributed File System (HDFS™) is the primary storage system used by Hadoop applications. HDFS creates multiple replicas of data blocks and distributes them on compute nodes throughout a cluster to enable reliable, extremely rapid computations. • Applications: Apache Hive is a large scale Data Warehouse system and Apache Mahout is a machine learning system.
- 14. 3. Analytics Ninjas Build a TIGER Team… subject matter expert, modeler, technologist, and storyteller Curiosity Willingness to learn Resourceful Risk Takers ! Schedule Standups… daily is best depending on the project it can vary Take notes! Open dialogue (peer review) Share knowledge ! Centralize and Version Control Your work… files, code, knowledge, and data lineage are key to success Create a Wiki Work backwards from the end goal to the analysis to the data Share, share, and share! Stand on the shoulder of Giants! Why start new, there is plenty of boilerplate to go around.
- 15. What is Analytics? Analytics is the application of computer technology, operational research, and statistics to solve problems in business and industry. ! Historically, Analytics was heavily used in banking for portfolio assessment using social status, geographical location, net value, and many other factors. ! Today, Analytics is applied to a vast number of industries and is re-emerging due to the phenomenal explosion of data from our connected world. ! Big Data consists of data sets that grow so large and complex that they become awkward to work with using on-hand database management tools ! McKinsey Global Institute estimates that big data analysis could save the American health care system $300 billion per year and the European public sector €250 billion.
- 16. Analytics Example: Platform Monetization Search Lifetime Value • 3rd Party Distribution • SEM • Organic Traffic Quality Analysis • PPC • PPD • PPI • PPA 1. Business Champion • Sales & Product 2.Integrated Environment • Web analytics / app data and SQL Database. 3. Analytics Ninjas • Search engine marketers, business analysts, and statisticians.
- 17. Examples of Analytics: Campaign Management 1. Business Champion • Sales, Product, Marketing, and Project Managers. 2.Integrated Environment • Data Warehouse, SQL DB, Hadoop, and Tableau 3. Analytics Ninjas • Web analytics, product managers, business analysts, and business intelligence.
- 18. Examples of Analytics: Customer Sentiment Keywords by Ratings 1. Business Champion • Product and Marketing 2.Integrated Environment • Mobile analytics, app logfiles, and Hadoop. 3. Analytics Ninjas • Web analytics, product managers, business analysts, mobile developers.
- 19. What to avoid Its all about plugins for web analytics… Google Analytics, App Stores (iOS, Android, others), Social (Twitter, Facebook FQL, others?). Analysis lifecycle… 1. Give me data… import data and ETL it into submission. 2. What does this data mean… Crunch, Blend, Join, Pivot, Predict, Count, Map, or whatever 3. Show and Tell… Static (powerpoint, excel, etc.) or dynamic (trends, filtering, drilldown). (No more dashboards)... known knowns = data puking (dashboards) Interestingness rocks! Tell me where to look (I’m feeling lucky…) get people to "make love to data" to make actual good use of it Viva la revolucion! Data democracy!
- 20. Thank you! Any Questions? Want to jump start your Agile Data Science project? Head over to http://start.alpinenow.com Follow me on @JSHorwitz