www.AdvizorSolutions.com Presents a webinar on Visual Analysis and The Rise of Data Discovery
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www.AdvizorSolutions.com Presents a webinar on Visual Analysis and The Rise of Data Discovery

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www.AdvizorSolutions.com provides visual analysis and data visualization software. Advizor products combine data visualization software with in-memory data-management and predictive analytics to ...

www.AdvizorSolutions.com provides visual analysis and data visualization software. Advizor products combine data visualization software with in-memory data-management and predictive analytics to provide problem solving capabilities. Advizor products offer large-scale enterprise solutions as well as benefits to individuals and small businesses.

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  • Business Intelligence : http://en.wikipedia.org/wiki/Business_Intelligence The term business intelligence (BI) dates to 1958.[1] It refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information and also sometimes to the information itself. The purpose of business intelligence is to support better business decision making. D. J. Power explains in "A Brief History of Decision Support Systems,"[2] BI describes a set of concepts and methods to improve business decision making by using fact-based support systems. BI is sometimes used interchangeably with briefing books , report and query tools and executive information systems . Business Intelligence systems are data-driven DSS . Decision support systems are a class of computer-based information systems including knowledge based systems that support decision making activities. BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data. Software elements support reporting, interactive "slice-and-dice" pivot-table analyses , visualization , and statistical data mining . Applications tackle sales, production, financial, and many other sources of business data for purposes that include, notably, business performance management . [1] http://www.research.ibm.com/journal/rd/024/ibmrd0204H.pdf [2] http://dssresources.com/history/dsshistoryv28.html For a BI technology system to work effectively, a company should have a secure computer system which can specify different levels of user access to the data warehouse , depending on whether the user is a junior staffer, a manager, or an executive. Also, a BI system should have sufficient data capacity and a plan for how long data will be stored (data retention). Analysts should set benchmark and performance targets for the system. Business intelligence analysts have developed software tools to gather and analyze large quantities of unstructured data, such as production metrics, sales statistics, attendance reports, and customer attrition figures. Each BI vendor typically develops Business intelligence systems differently, to suit the demands of different sectors (e.g., retail companies, financial services companies, etc.). Business intelligence software and applications include a range of tools. Some BI applications are used to analyze performance, projects, or internal operations, such as AQL - Associative Query Logic, Scorecarding, Business activity monitoring, Business Performance Management and Performance Measurement, Business Planning, Business Process Re-engineering, Competitive Analysis, User/End-user Query and Reporting, Enterprise Management systems, Executive Information Systems (EIS), Supply Chain Management/Demand Chain Management, and Finance and Budgeting tools. Other BI technologies are used to store and analyze data, such as Data mining (DM), Data Farming, and Data warehouses; Decision Support Systems (DSS) and Forecasting; Document warehouses and Document Management; Knowledge Management; Mapping, Information visualization, and Dashboarding; Management Information Systems (MIS); Geographic Information Systems (GIS); Trend Analysis; Software as a service (SaaS) Business Intelligence offerings (On Demand) — which is similar to traditional BI solutions, but software is hosted for customers by a provider[3]; Online analytical processing (OLAP) and multidimensional analysis, sometimes called "Analytics" (based on the "hypercube" or "cube"); Real time business intelligence; Statistics and Technical Data Analysis; Web Mining; Text mining; and Systems intelligence. Other BI applications are used to analyze or manage the "human" side of businesses, such as Customer Relationship Management (CRM) and Marketing tools and Human Resources applications.
  • National Visualization and Analytics Center (NVAC™), http://nvac.pnl.gov/about.stm
  • So how can you use these visual components? With simple point-and-click effort…

www.AdvizorSolutions.com Presents a webinar on Visual Analysis and The Rise of Data Discovery www.AdvizorSolutions.com Presents a webinar on Visual Analysis and The Rise of Data Discovery Presentation Transcript

  • advizorsolutions.com The Rise of Data Discovery and Analysis Tools -- Enabling Better and Faster Decisions Presenter: Doug Cogswell, President & CEO, ADVIZOR Solutions, Inc. Email Questions To: [email_address]
  • Agenda
    • The Landscape:
      • Data tells “stories”
      • End-users often stuck
      • “ Cycle of Pain”
    • New Technologies:
      • In-memory-data-management
      • Data Visualization
      • Predictive Analytics
    • Examples and Use Cases
    • Q & A
    Email Questions To: [email_address]
  • Agenda
    • The Landscape:
      • Data tells “stories”
      • End-users often stuck
      • “ Cycle of Pain”
    • New Technologies:
      • In-memory-data-management
      • Data Visualization
      • Predictive Analytics
    • Examples and Use Cases
    • Q & A
    Email Questions To: [email_address] Higher Ed Financial Services Transportation Healthcare
  • Business Intelligence
    • “ The term business intelligence (BI) dates to 1958. [1] It refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information and also sometimes to the information itself.
    • The purpose of business intelligence is to support better business decision making .” *
    [1] http://www.research.ibm.com/journal/rd/024/ibmrd0204H.pdf * http://en.wikipedia.org/wiki/Business_Intelligence
  • Information Needs Vary Management Business Analysts Frontline Staff Strategic Decisions Tactical Decisions
    • Focus on Management : (1) operational reports, and (2) scorecards / dashboards.
    • However, staff need to slice and dice data, look at trends, etc. to make decisions
    • Reports, Scorecards and Dashboards fall short:
      • Summary, not Detail,
      • Time consuming and difficult to implement
      • Consume a lot of core IT resource
    • Key staff largely unsupported
    • And, when they then go to IT or central reporting for custom reports they often get:
      • The wrong data late, or at best . . .
      • . . . the right data through a time consuming iterative process
    Today . . .
  • There are 4 key aspects to Business Intelligence Get Data Store Data Understand Information Make Decisions Most effort has been here Most value still to come!!
    • Many organizations have two layers of this “stack”, and use them to attempt to fill the other two.
    Understanding Information . . . SAS, SPSS, Filemaker, etc. Cognos, Hyperion, Business Objects, etc . ? Operational Reporting Performance Reporting Discovery & Analysis Advanced Analytics
  • Problem: the “Cycle of Pain”
    • Custom Report Requests:
      • Custom query submitted to IT
      • Backlog – 5 days to get answer
      • Begs another question, back through the cycle
      • Frustration on both sides
    • Excel:
      • Download “extracts”
      • Slice and dice in Excel
      • Time consuming and challenging
      • Don’t have all the data
      • Hard to show to management
      • “ Shadow data systems”
  • New technologies to the rescue!! SAS, SPSS, Filemaker, etc. Cognos, Hyperion, Business Objects, etc .
    • In-Memory-Data
    • Data Visualization
    • Predictive Analytics
    Operational Reporting Performance Reporting Discovery & Analysis Advanced Analytics
  • Not “One Fits All” Management Business Analysts Frontline Staff Strategic Decisions Tactical Decisions Operational Reporting Performance Scorecards/ Dashboards Data Discovery & Analysis Advanced Analytics
    • More people
    • More decisions
    • More often
    • Strategy is implemented through tactics
    • Underfunded: Why??
    Staff Tactical Decisions:  Critically Important
    • Management Strategy gets focus
      • “ Clean Summary”
      • NOT
      • “ Tactical Detail”
    • Leverage Legacy Systems
      • “ The Reporting System will be ready in six months”
    • Fear of “information democracy”
      • Aka “The Unknown”
    Why Underfunded?
  • From Gartner BI Summit:
    • #1 IT priority (for 4 th year)
    • Penetration of end-users will increase 2.5x (by 2012)
    • 4 Key driving technologies:
      • (1) in-memory-data-management
      • (2) data visualization
      • (3) social software
      • (4) search
    • New models are emerging (the “Information Buffet”)
      • Structured Decision Making  Autonomous Decision Making
      • Controlled / Qualified Access  Open / Unqualified Access
    • Gartner BI Summit keynote address, Kurt Schlegel and Bill Hostmann
    • In-Memory-Data-Management
  • In-Memory-Data-Management
    • “ Supports the concept of end user analysis and visual discovery™ by enabling very fast interaction , slicing and dicing, and calculation.
    • No predetermined structure is required, so the analysis can be completely ad hoc against any combination of any elements in any table in the memory pool.
    • Since the detail is all in memory, as the end user slices and dices the data the detail list is constantly changing . When done, the end-user can easily export his / her list (of customers, products, underperforming employees, etc.) to another system for action.”
    • In-Memory Advantages:
    • Ad Hoc
    • Fast
    • Flexible
    • Cross Table
    • Simple Query
    • Summary AND Detail
  • Lots of Information, but Fragmented and Hard To Access . . . Call Records Alumni Data Email Appeals Student Caller Data
  • First Step: Knit the Information Together . . . Affinity Capacity Activities Demographic Call Records Alumni Data Email Appeals Student Caller Data
  • . . . Enabling Easy Answers to Key Questions. Q: Who is not doing well with my top donors? Call Records Alumni Email Student Caller Data
    • Example #1: pre-built call center “project”
      • Team of users
      • “ Pool” of ~70 tables from 3 systems:
        • Call record details
        • Alumni Data
        • Student Data
      • Loaded and refreshed each night
      • Access by either:
        • Client application (used in the demo)
        • Web portal
      • No pre-set hypothesis. Will have to:
        • “ Fish”
        • Let the data tell its story
        • Collaborate
    Let’s Take a Look . . .
    • Demo . . .
  • Key Benefit: Collaboration
    • Data Visualization
    • “ Recognizing that humans have a keen ability to process visual information , data visualization tools have been developed that allow people to interpret and analyze vast amounts of data. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. People use visual analytics tools and techniques to:
      • Synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data.
      • Detect the expected and discover the unexpected .
      • Provide timely, defensible, and understandable assessments.
      • Communicate assessment effectively for action.” *
    Interactive Data Visualization * National Visualization and Analytics Center (NVAC™), http://nvac.pnl.gov/about.stm
  • Let’s take a look . . .
    • Example #2: ad hoc analysis
      • Desktop analyst
      • Portfolio of mutual funds . . .
      • . . . and find and categorize the “dogs”
      • 10 minutes
      • No pre-set hypothesis. Will have to:
        • “Fish”
        • Let the data tell its story
        • Share findings
    • Demo . . .
    • Data Visualization
    • Drill Down . . .
  • Charts > Pages > Projects ADVIZOR Charts 15 interactive Charts solve any data display need ADVIZOR Pages Combinations of Charts that address specific business issues ADVIZOR Projects Combinations of Pages that address the performance needs of a business unit
  • 15 Visualization Charts Bar Chart:
  • Pie Chart: Line Chart:
  • Map:
  • Heat Map:
  • TextFilter, DataSheet, and Counts:
  • ScatterPlot: TimeTable:
  • Parabox: Multiscape: Histogram:
  • DataConstellation:
  • SummarySheets:
  • Visualization: Finding Patterns in Data
    • Example #3: Airline Network
      • Overbooked Flights
      • Changing data
      • Single flights don’t matter . . .
      • . . . groups of them do.
  • Oversold Flights . . .
  • Oversold Flights . . . Grouping Matters
  • Boston . . .
  • Flight Details . . . Take Action
    • Predictive Analytics
  • Predictive Analytics
    • “Uses mathematical tools and statistical algorithms to examine and determine patterns in one set of data . . .
    • . . . in order to predict behavior in another set of data
    • Integrates well with in-memory-data and data visualization”
  • Predictive Analytics
    • Concepts :
    • Target
    • Base Population (“excluded” data not used)
    • Explanatory Fields
      • Core Table
      • Calculated and joined from other tables*
    • Regression Based Model
    • Examine results against original data*
    • Predict*
    * Key advantage of an in-memory application since it has all of the underlying data
    • Example #4: Medical Claims Data
      • Who are our high cost members?
      • What makes them unique?
      • Who else fits the same profile?
    Let’s take a look . . .
  • 1.8mm Claims; 21k Members
  • Wide Range of Behaviors
  • 95 Members  Lots of Claims, and High $$
  • Build Model
  • Predict Others
  • List of 220 Other Members with similar profile
  • Predictive Analytics
    • Fast Answers
    • Complements Visual Discovery™
    • Don’t need to know statistics
    • Integrates well with In-Memory
    • Highly collaborative
    • Business Staff Can do This!!!
  • What We Covered:
    • The Landscape:
      • Data tells “stories”
      • End-users often stuck
      • “ Cycle of Pain”
    • New Technologies  BREAK THE CYCLE OF PAIN!!!
      • In-memory-data-management
      • Data Visualization
      • Predictive Analytics
    • Examples and Use Cases
    Email Questions To: [email_address]
  • What We Covered:
    • The Landscape:
      • Data tells “stories”
      • End-users often stuck
      • “ Cycle of Pain”
    • New Technologies  BREAK THE CYCLE OF PAIN!!!
      • In-memory-data-management
      • Data Visualization
      • Predictive Analytics
    • Examples and Use Cases
    Email Questions To: [email_address] Higher Ed Financial Services Transportation Healthcare
    • Questions and Answers
    Email Questions To: [email_address]