Self-Service Business Intelligence Program Overview. Director, Enterprise BI Cummins, Inc May 2015

Transcription

1 Self-Service Business Intelligence Program Overview Director, Enterprise BI Cummins, Inc May 2015

2 Revenue ($ Billion) Cummins - Profitable Growth Build a sustainable future for all stakeholders Revenue and EBIT $20 B Sales in 2014 $20 $15 $10 $5 $0 Revenue EBIT % % 20.0% 15.0% 10.0% 5.0% 0.0% Record Level Revenues Profits Grow Faster Than Revenues Strong Shareholder Return 1 EBIT excludes restructuring charges in 2009 and 2014 (in Power Generation), and the gains from the divestiture of two businesses and flood insurance recovery are excluded from Also, Q2 12 EBIT excludes $6 million pre-tax additional gain from the divestiture of two businesses in 2011, and Q4 12 EBIT excludes $52 million in restructuring charges. 2

3 Cummins Complementary Businesses Global Power Leader Engine Power Generation Components Distribution 3

4 Cummins Market Applications Global Power Leader Heavy-Duty Truck Medium-Duty Truck & Bus Light-Duty Automotive & RV Rail Mining, Marine, Oil & Gas, Government Construction & Agriculture Stationary Power 4

5 Why Self-Service BI? The Most Effective Way to Deploy Analytics to a Global Organization countries and territories employees worldwide Develop, design and manufacture products on continents Business Units Regional Organizations Corporate Functions Engine Applications Market Segments

6 Self Service BI at Cummins

7 Why Self-Service Business Intelligence? Our current business intelligence and analytical platforms are not delivering on our business needs Current State Business requests for new data analysis require analyst engagement and IT development to source and deliver data Sourcing of new data sources into the data warehouse requires modeling, coding, and testing Multiple end user tools for analytics requires substantial licensing, maintenance, and training Limited capacity for analyzing of ad hoc and what if scenarios for exploring business data Microsoft Self-Service BI Platform Existing data sources are directly accessible by business users; minimal to no analyst and IT work needed New data sources can be rapidly sourced into ad hoc data area for quick access; formal modeling into warehouse if needed Common set of analytics tools focuses investments in training and increases reusability across Cummins Full featured suite of data analysis tools that build on and around a widely-understood Excel platform

8 Self-Service BI: Guiding Principles Focus on the end users, think like an analyst Build and foster an analytical organization Reduce IT complexity Business owns the data Work together to govern the data and process Drive BI technology innovation

9 Self-Service BI Distinctions Introduces a new analytical paradigm Rapid prototyping, agile deployment Connect to any data source, connect any data source Spreadsheets with unlimited columns, unlimited rows Eliminates costly ETLs, replaced by Extract, Load, then Transform Faster development, easier to reconcile data, easier to adapt to changes Leverage analysts and business community Reduce dependency on 3 rd party developers Grass-Roots deployment, word of mouth communication

10 What is Self-Service BI? Cummins uses the Microsoft Business Intelligence built in Azure to provide: Business friendly reporting, analytics, and modeling tools Web front end for scheduling data refreshes and migrating analytical models Flexible and scalable Analytical platform Ability to connect to any data set (Source Systems, Data Marts, Data Warehouse, Teradata, Hadoop, etc.) Source Transform Model Store Consume Self-Service Analytics Data Data Data Data Minimal Transformation Engine Test Bed & Performance Manufacturing Control Systems Operational Reporting on MES Self-Service Reporting Tools Excel Reporting & Analysis PowerViews & PowerPivots Visualization & Analysis Tools Reporting & Analysis Storage Enterprise Data Warehouse Etc. Customer Data Transformation and Conformation to Enterprise Model Data Quality Assurance and Monitoring Sales Order Management Processing Operational Reporting on Sales Orders EDW Product Sales Etc. Source Systems and Processes across CMI

11 Microsoft Self-Service BI Stack

12 Why Self-Service BI Benefits the Business Business Effective data driven decisions Faster time-tomarket Quality data on which to make decisions Business accountability and responsibility of the data they already own SSBI improves time to market (introducing agile processes) New engagements: empirically gives the business their data up to 27 weeks faster Change requests: empirically realized by the business up to 34 weeks faster SSBI allows data cleansing without impacting source data Time to Data Analysis Decision Time to Data Analysis Decision

13 Why Self-Service BI Benefits IT IT Security Governance Planning for the future Security and auditing is streamlined The infrastructure and environment isolation in place The business owns the responsibility for the user-level security and auditing of their data and access rules IT has governance visibility into the environment Complete records of who is using the system, what and when they have accessed, etc. Complete visibility on the creation of data models and all connections to source systems Gold standard Data Warehouse can be developed over time based on actual usage IT can focus on the infrastructure and providing a service to the business

14 Highlighted Success Stories

15 Parts Pricing and Analytics Category: Large entity, high visibility What: Expensive, inflexible 3 rd party system delivered no result How: Designed a Self-Service solution that the Parts business teams (Pricing, Product Management, and Analytics) now support Value: Enabled the Parts business to analytically price 80,000 parts and begin building the history to achieve optimal pricing User Experience Third-Party Proprietary Self-Service Analytics Parts Pricing Parts BI $6.5M $1.4M Parts Consulting Fees $2M $300,000 Implementation Time 3.5 Years 12 Weeks $0* Parts Priced 0 80,000 Year 1 Revenue $0 $33M

16 Corporate HR Analytics Category: Large scope What: Manual process with continual churn and no analytical capabilities How: Designed a Self-Service solution that allowed for automated global business efficiencies Value: In my 4 years at Cummins, this is the first time that we have successfully moved forward from a system perspective on analytics. This is very exciting, the possibilities are huge! - Brian Hamilton, Director - HR Reporting & Analysis User Experience Past Experience Self-Service Analytics Efficiencies Gained Manual Reporting Workforce Analytics BI Engagement No Capability/Support Capable in 4 Weeks AOP Process Manual Automated Decision Cycle Time No Ad-Hoc Responses 5 Minutes Data Quality/Governance 1 Day 5 Minutes

17 Distribution BU Global Inventory Category: Do it yourself, never had BI AOP funding What: Manual inventory and cleansing collection process against disparate systems. Error prone, time consuming, and demanded user compliance. How: Designed a Self-Service solution that pulls and aggregates the data automatically from multiple ERP systems Value: Automatic data aggregation allowed for the resolution of many customer down incidents. Additionally, it provides for a secure data environment that reduces user error and enhances work satisfaction (Cummins employees can now work strategically to benefit the business rather than spend their time with data entry). User Experience Past Experience Self-Service Analytics Efficiencies Gained 46 Support Personnel 1 Person Part-Time Process Compliance Chronic Weakness Automated Data Quality Manual Entry - Worse than Source Same as Source

18 Enterprise Remedy Analytics Category: Global IT Production Support group supporting multiple BI reporting applications, reporting requests, and data warehousing services What: Analytics capability on large volume of support requests (1k/mo) How: Pull daily tickets direct from source into the MSBI environment for visibility to SLA performance and all other KPIs Value: Significantly improved ability to focus improvement work, reduce support costs, improve customer satisfaction User Experience Past Experience Self-Service Analytics Availability of data Limited by vendor Full access to all relevant data Reporting interval Monthly per vendor Daily refresh in MSBI Ability to drill down None Full Visualizations on data Minimal Unlimited

19 Engine Warranty Analysis Category: New capability, cross-bu/cross-functional unsatisfied need What: Existing Big Data solution was limited and not delivering end-user data or linking with other CMI data sets How: Built a small Hadoop solution in Microsoft Azure within 3 weeks, including reprocessing all engine INSITE logs for 3 years Value: Extracted and delivered all engine information to Engineering, Reliability, and Six Sigma teams to use in their investigations. Data used in one Six Sigma project with projected $5.6M in savings. User Experience Past Experience Self-Service Analytics Linkage to CMI Data Not Designed Fully Capable Effort Realization Continual Churn 3 Weeks New Capability Cost Estimate >$270K $87K Savings * >$5.6M

20 Engine Warranty Analysis (con t) Phase I work completed in 3 weeks for under $60K Over 2600 parameters per engine combined into a single analytical model to enable correlation of engine faults and failure codes to specific components Engine service logs pulled from EDW 93M and from CloudOne Customer information loaded from multiple sources (ERP s, EDW, Customer Masters, and National Accounts) POLK / VIN data Reliability Warranty events Expert Diagnostic data Work order details/headers Warranty campaigns Vehicle registrations/oem info Plasma/Genealogy Part sales/product Coverage; some portions of the BOM/SBOM

21 Self-Service BI: Success Stories

22 Self Service BI Deployment Status

23 Self-Service BI: Program Status

24 Self-Service BI: Program Status

25 Self-Service BI: Training Status

26 2015 Self-Service Engagements Engagement Time to Delivery (Weeks) I I Low Complexity Medium Complexity High Complexity Faster time to deliver Quicker to business value Higher satisfaction What could 6 months buy the business? ADSC SSBI $180,000 $160,000 $140,000 $120,000 $100,000 $80,000 $60,000 $40,000 $20,000 $0 Engagement Cost $168,300 $119,808 $88,128 $84,150 $48,960 $13,200 Low Complexity Medium Complexity High Complexity Initial cost will be higher only for complex projects 100 s of small projects vs. 15 high complexity projects ADSC SSBI

27 2015 Self-Service Cost Avoidance

28 Self Service BI - Next Steps Expand Global Training Program Formal Communication? Expand Azure Europe, Singapore, others Integration and alignment with traditional BI program Expand advanced analytics, Machine Learning, IoT Tools and processes

29 Self Service BI In Summary Increased Revenue Decreased IT Costs Millions in IT Cost Avoidance Increased User Satisfaction Global Deployment Business & IT Teams working together

30 Appendix

31 Microsoft Self-Service BI

32 Microsoft Self-Service BI Architecture Azure-West (Production) SharePoint 2013 Web Active GTM SharePoint 2013 Web Active Azure Azure-East (DR) SharePoint 2013 Web DR SharePoint 2013 SharePoint 2013 App App SQL Always On Availability Group SharePoint SharePoint SQL 2012 SQL 2012 Primary Secondary (Synchronous Commit) SharePoint 2013 App SharePoint SQL 2012 DR SP WFE/APP Active 8vCPU x 32GB C:-80GB, D:100GB SP WFE/APP Active 8 vcpu x 32GB C:-80GB, D:100GB SP WFE/APP Passive 8vCPU x 32GB C:-80GB, D:100GB SQL Agent Sync SQL Always On Availability Group SQL Always On Availability Group SQL 2012 SSAS Tabular Primary SQL 2012 SSAS Tabular Secondary (Synchronous) SQL 2012 SQL 2012 RDBS, MDS, DQS RDBS, MDS, DQS Primary Secondary (Synchronous) SQL Always On Availability Group SQL 2012 RDBS, MDS, DQS DR SQL 2012 SSAS Tabular DR SQL for SP/MDS/SUPPORT Primary Replica 8vCPU x 32GB C:80GB, D:300GB, E:100GB F:100GB, G:300GB SQL for SP/MDS/SUPPORT Secondary Replica 8vCPU x 32GB C:80GB, D:300GB, E:100GB F:100GB, G:300GB SQL for SUPPORT - DEV Primary (no AG) 8vCPU x 32GB C:80GB, D:300GB, E:100GB F:100GB, G:300GB SQL for SP/MDS/SUPPORT Secondary Replica 8vCPU x 32GB C:80GB, D:300GB, E:100GB F:100GB, G:300GB TFS 2013 SQL 2012 Management Primary SQL 2012 Management Secondary (Synchronous) ACDC on-premise servers (see other illustration) AT&T NetBond MPLS ExpressRoute Cummins Network MPLS AT&T NetBond MPLS ExpressRoute SSAS Tabular Primary/Query Replica 8vCPU x 64GB C:80GB, D:100GB, E:50GB, F:200GB SSAS Tabular Secondary/Processing Replica 8vCPU x 64GB C:80GB, D:100GB, E:50GB, F:200GB SSAS Tabular - DEV Primary Replica 8vCPU x 64GB C:80GB, D:100GB, E:50GB, F:200GB SSAS Tabular Tertiary Replica 8vCPU x 64GB C:80GB, D:100GB, E:50GB, F:200GB Cummins CIDC Cummins FTDC Pivot Tables PowerMap PowerView PowerPivot Model Enterprise Sync to SSAS Tabular ACDC PROD Delivery CED FTDC PROD DR CED

33 Microsoft Self-Service BI Architecture