The Data Odyssey Developing a Culture of Data Analytics

Transcription

1 The Data Odyssey Developing a Culture of Data Analytics Greg Flay, Chief Technology Officer Austin Energy September Austin Energy

2 Austin Energy at a Glance Customer Driven and Community Focused Serving Austin since 1895 Rates set by City Council Revenues contribute to the General Fund Leader in Renewable Energy Led by City Council mandate Today 40% renewable portfolio Targeting 51% by 2020 Targeting 65% by 2027 Leader in System Reliability SAIFI 0.68 interruptions/year SAIDI 58 minutes/year SATLPI 3.0 faults/100 miles 2

3 Strategic Context Financial Health Customer Collaboration Grid Modernization Business Excellence AA S&P Bond Rating JD Power 2 nd Quartile SAIDI 58.0 SAIFI rd Quartile or Lower on Price Environment Employee Engagement 65% renewable by 2027 DSM of 800MW by 2020 Gallup Engagement > 4.2 Gallup Participation > 80% 3

4 Data Analytics Use Cases Financial Health Customer Collaboration Grid Modernization Business Excellence Environment Employee Engagement What are the key drivers behind our debt service coverage ratio? Where are our biggest gaps in customer satisfaction? Where are our most troublesome feeders? What is driving change in O&M costs? Which DSM programs have been the best performing? Are there attributes in common among the highest scoring business units? With our current capital spend plan, where is out debt-tocapital ratio going over the next 5 years? What effect did our recent two-way outage notification system have on customer satisfaction? Which feeders are most susceptible to vegetation issues? What is a 5% increase in renewable portfolio likely to do to price? Which customer segments have the greatest propensity to engage in DSM programs? Which type of communication seem to be most effective in encouraging participation? 4

5 Data Related Challenges What Data Do We Have? How Can I Get Access to Data? What Tools Do We Have for Data Analysis? Who Else is Doing Analytics? What Have They Done? 5

6 What Data Do We Have? Problems Dispersed data sources Previous analyses are inaccessible, creating an environment that encourages duplicate work efforts Solution Implement common BI tool Assign appropriate data sensitivity classification Enable browsing of analytics and data sources Result Implementing Tableau Server in single site configuration to enable staff to securely browse across business units for previously created data sources and analyses Remaining Challenges Completion of Tableau Server implementation Publication of a comprehensive, global data dictionary 6

7 How Can I Get Access to Data? Problems Undefined, inconsistent, and frustrating data access processes across the enterprise Solution Create an enterprise-wide Data Governance Council Leverage the DGC to develop data access policy and process Result Staff have a known, reproducible path to data access For AE Internal data sources, no data access request needed Remaining Challenges Implementation of the standard process across the organization 7

8 What Tools Do We Have for Data Analysis? Problems 45+ tools for data analytics deployed in the environment Uneven tool support by internal staff Solution Define organization requirements for data analytics tool Leverage EA-based selection process to identify BI platform Implement BI support staffing Result Rapid adoption of a common, supported tool across the enterprise Improved support for common BI tool Remaining Challenges Rationalization of BI tool set 8

9 Who Else is Doing Analytics? What Have They Done? Problems Siloed analytics methods Siloed analyses Solution Develop a Visual Analytics Team from across AE as well as other city departments Develop a collaboration platform for analysts across the enterprise Result Developing culture of analytics Did I use data? Improved sharing of BI tool knowledge across the organization Remaining Challenges Migration from Tableau Desktop to Tableau Server Widespread adoption of Did I use data? thinking 9

10 Questions? 10