1. Implementing Business Intelligence and Data Management at SSC May 28 th, 2015

2. Agenda Background Business Model and Vision Data Management Tools available Analytics Maturity curve Organization of data POC Dashboard Next steps 2

3. Background Mandate is to implement the Enterprise BI platform and tools to support and enable analytics needs at SSC. To achieve this mandate we began to: ♦ Coordinate the collection of data; ♦ Collaborate with interdepartmental stakeholders to improve the data quality; and ♦ Implement the technology as a standard BI platform. 3

4. Business Model and Vision 4

5. Data Management Data Management:  Data Quality Assessment; ♦ Our methodology consist of evaluating the following components of data; – Data Reliability ( Ensuring that the process is automated and repeatable) – Data Accuracy ( Does the data captured make sense) – Data Consistency ( Ensuring the data structure is consistent) – Data Uniqueness (Ensure there are no duplication or double counting) – Timeliness (Data is up to date) – Completeness (Are we getting the data we need)  Data Governance; ♦ Data governance Working Group  Data Warehousing and Business Intelligence Management ♦ Data dictionary ♦ Data manipulation (ETL) 5

6. Tools Available Traditional SAS tools SAS Stat, Graph, ETS, OR SAS Enterprise Miner, Forecast Server SAS Cost and Profitability, Strategy Management Data Management SAS Data Integration Studio + Dataflux Self-Serve Visualizations SAS Visual Analytics SAS Visual Statistics 6

7. Maturity Curve Proof of concept – 3 case studies and growing Build DW for self-serve reporting - Dashboards and Scorecards Provide Self-serve Analytics – Forecast, Correlation, Decision trees 7 We are here

8. Organization of Data for Self-Serve 8 Enterprise data is accessible to anyone in the organization. Demands data governance and privacy impact assessments Team data is accessible to anyone in the group. Loosely based on organization and secret clearance

9. Next Steps Continue training, change management Continue to grow in an organic fashion (needs dictate approach, not trying to boil the data ocean) Models and Data  Activity-based costing model  Dashboards and Advanced Analytics on operations/transformation programs  Refine our service delivery model 9