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Turning information chaos into reliable data: Tools and techniques to interpret, organize, and increase reliable business results

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With evolving technology, many people are overloaded and overwhelmed with information and data. Businesses now have access to large amounts of feedback from internal and external sources. How do we make sense of the all of the information? Is the data reliable? How can we manage and utilize the data in order to impact business goals, visions, and mission? This seminar with help you turn your information overload into powerful and reliable data that you can use to meet organizational goals.

Learning Outcomes: Increase professional effectiveness, data management, and analytical skills

At the end of this seminar, participants will be able to:

a) Assess and categorize data and information
b) Identify tools and techniques to organize and interpret data
c) Explore productivity tools and techniques
d) Examine common data management challenges and solutions

Published in: Business
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Turning information chaos into reliable data: Tools and techniques to interpret, organize, and increase reliable business results

  1. 1. TURNING INFORMATION CHAOS INTO RELIABLE DATA Nannette Kelly - Northrop Grumman Roderick McLean - Lockheed Martin William Patrick, Sr. – Northrop Grumman Brian Keller – Booz Allen Hamilton February 27, 2014
  2. 2. Information Overload • Data creation/delivery exceeding standard management tools • Volume, variety, velocity, and variability • Interesting facts: – Every 6 hours, the NSA gathers as much data as is stored in the entire Library of Congress – Facebook’s photo collection has over 140 billion photos – In 2012, every day 2.5 quintillion bytes of data created, with 90% of the world’s data created in the last two years alone – Twitter averages 500 million tweets per day
  3. 3. Analysis Evolution • Derived from stable, fixed sources From Business Intelligence Actionable Information • Fixed types Big Data Data To Data Analytics Collection • Varied types • Serial Processing • Iterative Analysis • Pattern Analysis Reporting • Derived from diverse, dynamic sources Actionable Information
  4. 4. Business Relevance • • • • • • • Provides customer/environmental insights Establishes a competitive advantage Shapes marketing strategies Reduces uncertainty Enables optimization Improves decision making Increases productivity Ref. Turn Information into a Strategic Asset - SAP Provides Critical Information to Drive Positive Business Outcomes
  5. 5. Data Management Framework 1 • 2 Organization 3 Process/ Methods System 5 3 • Define data usage in analysis, process control, and business management. Establish processes to monitor and ensure data quality. Develop data structures to address company-wide requirements • 5 Select, design, and implement software applications to accomplish strategic objectives Strategy Controls 4 Define process/data owners, roles, and responsibilities 4 • 1 Define objectives; Confirm data strategy alignment to business strategy 2 • Objectives Data Architecture Applications
  6. 6. Strategy • Define key business objectives or problems to solve • Clarify data required for strategic choices • Identify what’s required to establish a competitive advantage Acquire, Grow, Retain Customers Create New Business Models Improve IT Economics Manage Risks Optimize Operations and Reduce Fraud Transform Financial Processes Ref. IBM Use Cases (IBMbigdatahub.com)
  7. 7. Controls • Proactively secure data and comply with privacy regulations • Understand retention requirements • Incorporate Data Quality Management and define quality metrics • Document organization roles/responsibilities • Define data reporting, access and latency requirements • Establish analytics driven business processes • Fight bureaucracy and organizational silos
  8. 8. Data Architecture • Categorize data and usage – Content format: structured, semi-structured, or unstructured – Type: transactional, meta data, – Analysis: real-time or batch – Processing methodology: predictive analysis, analytical, query/reporting – Data source: web, machine generated, data entry, etc. • Define data structures to support cross-business needs • Document data definitions
  9. 9. Applications • • • • • Web Crawlers Social Media Network Logs Sensor Networks SAP Acquisition • • Data Management • • • • R Python SQL MapReduce/Hive/ Pig Analytics Visualization • • • • • Flat Files Relational Databases Hadoop/NoSQL MongoDB Jpg/png BI (Spoyfire, Jaspersoft) Web Apps (ext-js, d3.js) Various Toolsets are Available to Fulfill Data Intelligence Needs
  10. 10. http://datacommunitydc.org/blog/2013/05/stepping-up-to-big-data-with-r-and-python/
  11. 11. http://datacommunitydc.org/blog/2013/05/stepping-up-to-big-data-with-r-and-python/
  12. 12. Various Choices Available to Implement Analytics… Approach Ease of Learning Availability on Systems Java Hive Pig Commercial Tools Streaming frameworks Streaming Also works outside of Hadoop with no code changes! Analysis Flexibility
  13. 13. Implementation Methodology Motivation/ Constraints Business Discovery Data Discovery Design Build New/Changing Operational Reqmts What does Customer seek to accomplish? What data is available to work with? Data Architecture Architect Data Exploration Problem Statement Where is data located? Infrastructure Architecture Infrastructure Data Analytics Ingest Data Process Existing Tools, Custom Code Data Mining, Scientist Techniques Analytics, Visualization Presentation Visualization Tools/Product Selection Deliver, Train Result Evaluation Legal & Compliance Regulations Pain Points Security Concerns Organization’s Culture Market Pressures & Mission Expansion Data Ecosystem Budget, Resource Reductions Existing Data Architecture Limitations What architecture to support data? What additional data is required? What type of analytics used, needed? Decision Support Predictive Modeling Action Planning Continuous Improvement
  14. 14. Summary • Begin with the end in mind • Incorporate controls to drive data quality • Protect the data

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