Big data + data science startup focus points

Big data + data science startup focus points

• 1.
  BIG DATA &DATA SCIENCE START-UP FOCUS POINTS + BUSINESS AND TECHNOLOGY REFERENCE ARCHITECTURE @TomZorde
• 2.
  I HAVE ANIDEA FOR A DATA SCIENCE START-UP • Use these slides to focus conversation • What stage are you at? • What is the problem you’re trying to solve? • What type of business model would work? • Tools? – A rapidly evolving space. • Reference Architecture helps identify what level of the stack we’re talking about.
• 3.
  AREAS OF EARLYFOCUS SEED STAGE - Research & Development 1. Research & Define Concept, business model, internal & sourced capabilities 2. Define customer value proposition and identify target market ANGEL – Business Planning & Product Development 1. Identify services and products required and evaluate gaps for go-to-market readiness 2. Source funding partner to build minimum viable product and get commitment for round 2 funding 3. Assemble team and build MVP prototype exceeding expectations ROUND 1/ SERIES A FUNDING – Commercially operational ROUND 2 / SERIES B FUNDING – Fully Operational ROUND 3 / SERIES C FUNDING – Expansion IPO/ ACQUISITION
• 4.
  BUSINESS PLANNING &DEVELOPMENT - LOGICAL STEPS 1. Full business needs and information requirements analysis. Business Drivers • Revenue generation? Cost reduction? Customer retention? Compliance? • Process Improvement? Fraud detection? Analytics? Dashboard? • Solving a tough problem? Retiring/replacing assets, technologies and systems? 2. Technology Evaluation and Selection • Define requirements and objective first • Evaluation a variety of technology stacks – develop a framework first 3. Board Support for Start-up Resources 4. Prototyping, Discovery, and Planning • Rent Infrastructure in Cloud – VMWare, AWS, MS Azure and others • Use Spare Hardware and Network Bandwidth • Assessment, Proposal. Project/Program Plan for next steps • Start small and keep delivering 5. Architecture Design, Estimation, Business Case 6. Obtain funding and executive sponsorships, owners, etc. 7. SDLC, don’t forget Hardware, Security, Testing, Data governance etc.
• 5.
  FORESEEABLE CHALLENGES Business urgency,time to market pressures • Big Data /Data Science start up needs careful planning • Big Data needs infrastructure, software stacks, people, start up plan Lack of Big Data Resources, Lack of Sponsorships (except in some companies) • Big Data is complex and multiple skill sets (mostly new to many companies) – Infrastructure, Administration, Security, Programming, Testing, etc. • Skepticism about Big Data Integration with Existing Technologies and Systems • Can not develop isolated big data solutions • Integration with existing systems will be a top challenge (requires both sides to do additional work) Open Sources: Stability, Maturity, and Security
• 6.
  INFORMATION AS APRODUCT/SERVICE TYPES OF RELEVANT BUSINESS MODELS Differentiation New Services Customers Experience Contextual Relevance Brokering Raw Data Benchmarking Analysis and Insight (Meta Data) Delivery Market Place Facilitator Advertising
• 7.
  REFERENCE ARCHITECTURE Decisions &Insight Analytics & Discovery Data Access and Distribution Data Collection& Organisation Infrastructure Platform Monitoring,Alerts,Tools, Security,Governance • The technology stack is rapidly evolving with all traditional as well as new vendors providing offerings • Open source tools remain at the foundation layers. • Different use cases will require different technology tools.
• 8.
  REFERENCE ARCHITECTURE Decisions &Insight • IBM Watson • Industry Specific Analytics & Discovery • SAP Business Objects • IBM Cognos • SAS Analytics • Dell Statistica • Oracle Hyperion • Microsoft BI • KNIME • Pentaho • Informatica
• 9.
  REFERENCE ARCHITECTURE Data Accessand Distribution • Document: MongoDB, CouchDB • Graph: Neo4j, Titan • Key Value Pair: Riak, Redis • Columnar: Cassandra, Hbase • Search: Lucene, Solr, ElasticSearch Monitoring, Alerts, Tools, Security, Governance: • Hadoop:Apache, CloudEra, Hortonworks, MapR, IBM • SQL Mapping: Hive • Big Data Transformation: Pig • Hadoop Load: Sqoop • Realtime-ETL: Storm • Cluster Computing: Apache Spark • Languages: Python, Java, R, Scala
• 10.
  REFERENCE ARCHITECTURE Data Collection&Organisation (Batch & Real-Time) • Hadoop • Hadoop Map Reduce • Mahout Infrastructure Platform • AWS • Azure • Mortar • Google BigQuery • Qubole • Dell • HP • IBM
• 11.
  BIG DATA &DATA SCIENCE START-UP FOCUS POINTS @TomZorde Thank you