High Maturity in complex Information Management environment Standardization is the key

Transcription

1 High Maturity in complex Information Management environment Standardization is the key Chandu Mukkavalli Monica Kavatkar Neetu Kar Deloitte Consulting India Pvt Ltd. High Maturity Best Practices (HMBP) 20122

2 Agenda Introduction Challenges Approach and Solution Critical Success Factors 2

3 Introduction 3

4 Background Objective was to build an effort forecasting model for all technology framework projects in Information Management (IM) practice. The model would enable converging the difference between planned and actual effort across the following phases: Project Planning phase (Initial effort planned by projects) Execution phase (Additional effort due to planned / unplanned scope changes) Closure phase (Total actual effort expendedd in the project) This translated to the need of having a Process Performance Model for predicting the probability of meeting effort across lifecycle phase 4

5 Challenges in IM IM practice faced some of the below challenges in realizing the objective: Varied capabilities (BI/DW, EDM, ECM etc.) with multiple technologies, adding to the complexity Estimation model and Work Breakdown structure common across individual capabilities, but not across combination of capabilities BI/DW Business Intelligence/Data Warehousing EDM Enterprise Data Management ECM Enterprise Content Management 5

6 Challenges in IM - Project complexity IM has varied work streams with multiple technologies, which adds to the project complexity Business Intelligence Enterprise Data Warehouse/Data Marts Enterprise Information Management Analytics ETL for BI/DW Solutions Business Intelligence (BI/DW) Enterprise e Information Management Planning Management Technique(PMT) Planning, Budgeting, & Forecasting Corporate Scoreboards, Dashboards Management & Financial Reporting Financial Performance Mgmt Data Conversion, Retention & Archiving Metadata Management Master Data Management Data Quality Management Enterprise Data Management (EDM) Enterprise Content Management (ECM) Enterprise Collaboration Document Management Broad range of capabilities across BI/DW, PMT, EDM, ECM further integrated with various technologies like IBM, SAP and OPT 6

7 Challenges in IM Project Estimation and Data Collection Effort estimated at the planning stage changes through the remaining stagess Inconsistent set of tasks defined in projects due to different nature of projects Historical data was disparate and could not be utilized directly to build a standard PPM for IM practice Multiple estimation methods (capability dependent) In absence of a consistent historical data for each capability, it is difficult to envision or forecast if the project would meet the business objectives 7

8 Approach 8

9 Approach A phased approach was identified as the solution to overcome the existing challenges Maturity Institutionalization Stabilization Implement PPM across eligible projects Initiation Homogenize projects based on technology / frameworks Understand the current estimation mechanism Identify common tasks and base of estimation Build a PPM on top of the common estimation framework Implement PPM in projects Collect data from projects Re-calibrate PPM model using data received Pilot the model in additional projects Re-calibrate PPM model using data received Compare results of PPM implementation Standard Work Plan to be developed based on common tasks PPB to be revised based on the data collected as per common estimation framework PPM Model to be enhanced with additional control knobs (for what-if analysis) PPM to be enhanced to predict additional parameters (like defects for each phase) PPM to be enhanced to include additional capabilities Time 9

10 Initiation Creation of Baseline 1 of PPM 10

11 Initiation Homogenize projects based on technology / frameworks Understand the current estimation mechanism Identify common tasks and base of estimation Build a PPM Baseline 1 on top of the common estimation framework 11

12 Initiation Selection of common framework Most commonly executed component identified as BI/DW. Key tasks pertaining to BI/DW capability were taken up for creating the PPM: Plan Phase Define reporting and data conversion requirements Assess application landscape Design Phase Application Architecture & Infrastructure requirements Conceptual, Logical and Physical Data Modeling Report Functional / Technical Specifications Data Transformation/Conversion solution architecture Unit test plan and cases Build Phase Develop connections, mappings, workflows, semantic layer and reports Develop unit test scripts and conduct unit testing Deliver Phase Functional Integration Testing Workflows and Reports User Acceptance Testing Workflows and Reports Implement mappings, workflows, user security and reports Deployment and cutover activities 12

13 Initiation Phase Activities Identify a common base of estimation / data collection Understand the standard estimation framework (PEPS) in IM method Understand the project s estimation mechanism BI/DW Component Shortlist common tasks for data collection across phases Select past projects on BI/DW components Build the PPM on top of PEPS framework Baseline 1 of PPM created 13

14 Initiation Phase Outcome - PPM Baseline 1 Baseline 1 of PPM was based on the standardd Project Estimator application in IM, used for scoping, estimating, and planning projects. Standard estimator tool had complete list of applicable tasks for BI/DW components Each of the task had a basic estimated hours defined as per complexity level Sizing (IM points) based on the simplest task in DW components was introduced, with help of SME s Input parameters identified (based on number of items and complexity) Based on the actual effort provided by the modified. past projects, the estimation tool was Guidance for using the estimation tool was defined and shared with selected projects 14

15 Stabilization Creation of Baseline e 2 and Baseline 3 of PPM 15

16 Stabilization Baseline 1 PPM Baseline 1 shared with past projects Baseline 2 PPM Baseline 2 created by from Baseline 1 recalibrating data received Baselinee 3 PPM Baseline 3 created by recalibrating data received from Baseline 2 16

17 Stabilization Phase Activities PPM Baseline 2 Data collected from Past Projects based on PPM Baseline 1 Distribution updated for tasks which had effort from at least 5 past projects Remaining tasks were calibrated with triangular distribution Baseline 2 of PPM was created 17

18 Stabilization Phase Activities PPM Baseline 3 Baseline 2 of PPM was used for forecasting across new projects The output of the model was to predict effort with certainty percentage across build and design phase Metric data collected (based on tasks in PPM) from all projects Distribution updated for tasks which had effort provided Baseline 3 of PPM was created 18

19 Baseline Results Comparison 19

20 PPM Results Comparison A BI/DW project was identified to run through all baseline versions of the PPM Objective was to compare the effort variance between the forecasted effort and the project actual effort at phase level (mainly Design and Build) Inputs of Project X was run across the threee different baselines to forecast effort Actual effort was collected and compared against forecasted effort of Project X, across all baselines 20

21 Results Validation for Project X Overall Effort Variance Project X Overall Effort Variance - Inferences There is a consistent decrease in effort variance across baselines for the identified projects This indicates that the forecast (planned) value is closer to the project actuals through baselines Baseline 1 Baseline2 Baseline Phase-wise Effort Variance Design Build Phase wise Effort Variance - Inferences Effort variance range has reduced across baselines for build phase Effort variance has reduced with Baseline 3 for design phase, Baseline 2 had some additional design tasks, due to which higher effort variance was seen Baseline 1 Baseline2 Baseline3 21

22 Institutionalization Road Ahead 22

23 Institutionalization Activities Implementation Plan Roll out Baseline 3 of PPM across new projects Modify metrics report at organization level, in line with PPM tasks / phases Revise Process performance baseline (PPB) for BI/DW tasks in IM Practice Model Enhancement Plan Recalibrate the model, as appropriate Introduce control parameters in the PPM (for what-if analysis) Include additional parameters for prediction as applicable, like defects per phase Pilot PPM for additional IM capabilities like PMT, EDM etc.. 23

24 Critical Succes ss Factors 24

25 Critical Success Factors High Maturity is NOT just about numbers and statistical tools Strong Leadership commitment and stakeholders support Inculcating a culture change for using standard framework and tools Collecting and reporting accurate data as per suggested framework Diligent support by process consultants in enabling the projects to use the PPM 25

26 Any questions? s?

27 Contacts Chandu Mukkavalli Director Monica Kavatkar Manager Neetu Kar Manager Deloitte Consulting India Pvt. Ltd. U.S. India Quality Fairmont Level 2, Hiranandani Business Park, Powai, Mumbai India Tel (India): Mobile: cmukkavalli@deloitte.com Deloitte Consulting India Pvt. Ltd. U.S. India Quality Fairmont Level 2, Hiranandani Business Park, Powai, Mumbai India Tel (India): Mobile: mkavatkar@deloitte.com Deloitte Consulting India Pvt. Ltd. U.S. India Quality Fairmont Level 2, Hiranandani Business Park, Powai, Mumbai India Tel (India): Mobile: nkar@deloitte.com 27

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