RiskView ® Architecture: Data Model September 2012 Robert Cruickshank CEO & CTO, (703)

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: 1.Identifying the Data Sources 2.Correlating the Data Sources 3.Configuring the Data Model 4.Importing Data Using Adapters 5.Setting Up the Visualization 6.Analytics Confidential 2

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

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: 1.Call Center ‘Connectivity Call’ records 2.Field Service Activity from WorkAssure 2.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 3.Summaries of Failed Telemetry data from ServAssure® or other NMS Each Data source provides Issue, Resolution and often Cause Confidential 4

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 Find & Fix More Issues, Reduce Calls, TCs, Disconnects Field Activity Failed Telemetry Connectivity Calls Step 2: Correlating the Data Sources Confidential 5

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

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

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

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

Step 6: Analytics 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. Filters also provide an excellent mechanism to conduct What-If analysis. This is valuable in “What to fix, in what order” scenarios. Confidential 10

RiskView Data Model: Details Confidential 11