RiskView ® Architecture : Data Model

1. RiskView® Architecture: Data Model September 2012 Robert Cruickshank CEO & CTO, robert.cruickshank@rev2.com (703) 568-8379

2. RiskView Data Model Introduction RiskView provides a mechanism to: • Collect data from a variety of sources • Normalize and store them in a coherent fashion • Present the data in advanced visualization formats • Manipulate the visualization in order to model various scenarios • Conduct analytics on the data set The underlying data model that supports this is described in this presentation. It includes the following 6 steps: • Identifying the Data Sources • Correlating the Data Sources • Configuring the Data Model • Importing Data Using Adapters • Setting Up the Visualization • Analytics Confidential 2

3. RiskView Architecture RISKVIEW ADAPTERS COLLECTION & ABSTRACTION • ANALYTICS • INCIDENT PRIORITIZATION ACCORDING TO MATERIALITY • CHRONIC & INTERMITTENT DEVICE FAILURES • LOSS OF FACILITIES • CUSTOMER, COMPETITOR &MARKET RISK • OPERATIONS ISSUES • PROCESS GAPS & CHANGES • ETC. • DATA SOURCES • CONNECTIVITY CALLS • WORKASSURE® TRANSACTIONS • MAINTENANCE TRANSACTIONS • SERVASSURE® SUMMARIES Quantifiable business justification, demonstrable & immediate ROI Highly extensible platform for fact-based, scalable, repeatable risk management decisions. Confidential 3

4. Step 1: Identifying the Data Sources • A typical deployment involves congregating diverse data sets in order to glean insights that otherwise would not be apparent. • For example, in a typical Multiple System Operator (MSO) deployment, the data sets are sourced from: • Call Center ‘Connectivity Call’ records • Field Service Activity from WorkAssure® or other system including: • ‘Trouble Call’ Service Truck Rolls • Service Department Escalation to Maintenance Department • Voluntary Disconnects • Planned and Demand Maintenance • Summaries of Failed Telemetry data from ServAssure® or other NMS • Each Data source provides Issue, Resolution and often Cause Confidential 4

5. Step 2: Correlating the Data Sources • Once the data sources are identified, it becomes necessary to form a basis to build correlation across the data sets. • In the use case presented above, the following correlations readily come up: • By Hub/Node • By Street/Geography • This provides the ability to group by various criteria including: • DOCSIS Serving Groups • Geographic Management Areas Confidential 5

6. Step 3: Configuring the Data Model • Each of the data sources provide a variety of fields that need to be located in the RiskView database. • RiskView uses the concept of a vulnerability record to map these fields into a larger abstract that can then be used for analysis. • RiskView provides the following data field types: • Integer • Text • Date • Vectors • Percentage • Boolean • Date Range • Integer Range Confidential 6

7. Step 4: Importing Data Using Adapters • Setting up the data model in RiskView makes it possible to import data. • RiskView uses Adapters to accomplish this. • Adapters… • Are highly flexible • Perl-script based • Can run in real time or batch mode • Platform independent • Adapters also provide the ability to normalize data, if needed. Confidential 7

8. Step 5: Setting Up Visualization • RiskView provides an easy-to-read “Radar Chart” based set of views that directly present the most material aspects of the data. • The Radar Chart has the ability to drill into items of interest to look at the data detail driving a particular score. • In both levels of presentation, filters provide the ability to rapidly visualize specific areas of interest. • Additional analytical tools include Histogram analysis and the ability to export data via the build in web-service to feed into external mechanisms. Confidential 8

9. Step 5: Continued... The outliers representthe most material risk. Confidential 9

10. Step 6: Analytics • Filters also provide an excellent mechanism to conduct What-If analysis. This is valuable in “What to fix, in what order” scenarios. • RiskView provides the ability to manipulate data using formulas. • These formulas are used to calculate the scores that rank the data in the “Views” and in the “Detailed Table” View. • When a View is invoked, the data is ranked and presented in real time. • The process of conducting an analysis revolves around: • Identifying outliers of interest • Using filters to make incidents and issues easy to identify • Drilling into the specific areas of interest • Grouping and Sorting the data detail to formalize conclusions leading to next steps. Confidential 10

11. RiskView Data Model: Details Confidential 11