Information Management Building a Successful Foundation

1. Information ManagementBuilding a Successful Foundation Presentation by Gordon Brown 13th May 2004

2. Agenda • Increased Demand + Increased Possibilities • Information Management • Aspects of Information Management • Challenges + Discussion

3. Business Drivers – Increased Demand • Asset Risk Management • Not just operational – probability and consequences of failure • Customer • Level of Service • Communication and Information • Quality of Service • Cost and Price • Avoiding “Unfortunate Incidents” • Increased collaboration and role separation

4. Once appointed by new Supplier Send Meter Technical Details & Mapping to new NHHDC If invalid reading obtained NHHDC Produce and send Invalid data report to PDSO, Supplier, Old NHHDC (as applicable) Notification of Change to other parties If valid meter reading obtained If applicable If applicable prior to SSD + 8 Where actual meter reading required NHHDC Produce and send Valid data report to PDSO, Supplier, Old NHHDC (as applicable) Either send instruction to obtain C.o.S Reading Or Supplier Customer own reading Request for metering system related details Send meter readings and EAC/AA historical data+ Historical data requirements Notify old agents of deappointment By SSD + 8 New NHHDC selects the C.o.S. Reading Process & validate reading using historic data and Meter Technical Details D0151 D0148 D0205 D0058 D0055 D0057 D0217 D0260 D0072/ D0071 D0153 D0010 D0209 D0209 D0011 D0010/D0086 Meter Technical Details D0155 D0011 D0149 D0010 D0150 D0170 D0170 D0152 D0155 D0010 D0010 D0010/D0086 Diagram courtesy of Elexon e.g. Complexity ! Change of Supplier - Electricity New Supplier Existing Supplier PDSO SMRA Supplier Supplier NHHDA NHHDA MOA MOA NHHDC NHHDC Send confirmation of appointment NHHDC Request Metering System Details By Settlement Start Date By Settlement Start Date If Validation unsuccessful Between Settlement Start Date (SSD) minus 28 Calendar Days (CD) and SSD - 1 WD On successful validation and within 1WD of Registration Notification Data Aggregator Appointment Notify each agent of their appointments Update registration Details Validate Registration Flow Notify originator of rejection using D0057 Inform: PDSO = New Supplier, MPAN & SSD Confirm Registration & Notify old registration details Confirm deappointment of old NHHDA Notification of Termination of Supply Registration D0055 Registration Flow sent from New Supplier to SMRA On receipt of D0055

5. Technical Advances – Increased possibilities

6. Data – the under-represented viewpoint Increasing Business Value

7. Data Quality Tools Enterprise Application Integration Activity Monitoring Business Intelligence Tools Reporting Reference Model Business Processes Data Architecture Model Policies Technical Blueprint Data Management Monitoring & Analysis Quality & Integrity Integration Security Knowledge / Intelligence Bases Operational Data Data Warehouse

8. Occam’s Razor • One should not increase, beyond what is necessary, the number of entities required to explain anything William of Occam – 14th Century Philosopher Or, to be crude: • Keep it Simple, Stupid

9. Guiding Principles • Define what data the business needs to deliver on its outputs, not what it thinks it needs based on historic judgement • Data governance controls are essential given the operational impact of poor data • Data should be treated as a critical business asset, subjected to the same financial governance as all other investment decisions • Legacy data is not the starting point for determining data needs • Costs of collecting data should be calculated and measured against the benefits

10. Recommendations • Leadership • Prioritisation • Value Chain • Performance Management • Motivation • Approach

11. Data Quality • Not a Binary Attribute • Data should be evaluated in context of its use • Prevents effective Asset Management and prevents strong customer relationships • Needs to be a focus as part of “steady state”

12. Analysis & Enhancement Analysis Outputs Quality Analysis Quality Reports Data Profiling Assess data completeness, conformity, consistency d Input Data Files Data Matching Assess data duplication, Integrity, accuracy Data Extracts Low Quality Data Quality Enhancement d Output Data File Improve data completeness, conformity, consistency Data Standardisation High Quality Data Remove duplicates Improve integrity Replace inaccurate data Data Consolidation

13. DQ Scorecard & KPI’s Business Unit1 DQ Score Datasource1: Completeness Asset_ID Asset_Location Conformity Datasource2: Date_Last_Mnt Consistency Overall DQ Score: Business Unit 1 Attribute Completeness Chart Datasource1 : Asset Location Accuracy 90% 92% 90% 85% 75% 75% 75% 70% 60% 65% Duplication 65% 55% Integrity Wk1 Wk2 Wk3 Wk4 Wk5 Target Wk1 Wk2 Wk3 Wk4 Wk5 Target

14. The Data Quality Challenge • Data Management strategy across the organisation • Agreed DQ standards • Multiple coordinated DQ initiatives Increasing Business Value Managed • Data issues felt accutely by some groups • Prioritise DQ in certain projects • No coordination of DQ efforts Reactive • Aware Data issues causing some pain • No understanding of business impact • No formal DQ initiatives Aware

15. Challenges + Discussion How do you score ? • Understanding of Data • Ability to Trust your Data • Appropriate Data • Ability to Execute Change