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Building Enterprise Analytics Structures, Processes and Organization for the Best in Deliverables As Enablers for Productivity, Market Growth, Revenue and Profitability

Designing and Building Strategic Analytics Organization As Part of Corporate Strategic Transformation:
To effectively, efficiently and proactively build Enterprise Analytics Organization within a company as an enabler for productivity, efficiency, customer acquisition, customer retention, market share increase, revenue and profit, there is the need for the Chief Analytics Officer/SVP of Analytics to understand the Strategic Direction of the company in terms of where the company is heading next.
The inital engagements of the Chief Analytics Officer/SVP of Analytics include working closely with the Executive and Leadership Teams and of course, with the Board, to understand the strategic plan and the strategic transformation of the entire company as the company moves into a new level of excellence in meeting the shareholders’ values

The Chief Analytics Officer(CAO)/SVP of Analytics has the responsibility to align the Analytics Strategic Roadmap with the strategic needs of the company. 

The detailed Data, Analytics, Modeling and Business Intelligence (BI) strategic roadmap or frameworks will have to follow a very good understanding of the strategic direction of the company.

Therefore, the CAO will need to have:

  • Clear understanding of what capabilities the company or the leadership are trying to develop as the company leadership tries to leverage Data, Analytics, Modeling and Business Intelligence (BI) structures, organization, systems and processes. Essentially the focus on Data, Analytics, Modeling and Business Intelligence (BI) structures, organization, systems and processes is not an end in itself.

    • The focus is to:

      • Use Data, Analytics, Modeling and Business Intelligence (BI) structures, organization, systems and processes to drive the company results to the next level of higher performance. 

      • Be able to sustain the new level of performance. So it very important that there is a very good understanding of what the Board and the Senior Leadership Team (SLT) really want after discussions and consensus with the Board, SLT and other relevant Stakeholders on how best to achieve the greatest benefits and impacts from Data, Analytics, Modeling and Business Intelligence (BI) programs.

  • Open and active communication with the Board, SLT and all stakeholders: For Data, Analytics, Modeling and Business Intelligence (BI) structures, organization, systems and processes to be designed, developed, built or/ and sustain, there is the need for active involvement of all the Senior Leadership Team members as partners and stakeholders.

In working together with the members of the Senior Leadership Team as partners and stakeholders, there are some questions below that the CAO will have to design to provide inputs into the Analytics Strategic Plan for actionable and measurable performance. The questions below are needed to elicit robust responses as inputs. The questions include but are not limited to:
1: Where is the Company Today? 

1a. Where is the company, among others, in terms of?:

  • Global Strategic Process and Execution in areas that include:

    • Strategic financial health including balance sheet health

    • Production or/and service capacities

    • Strategic positioning compared to the industry performance

    • Strategic capabilities within the context of competitive climate

    • Uniqueness and value added services delivered to strategic customers

    • Adaptiveness and responsiveness levels in deployment of strategic capabilities

    • Enabling technologies and processes that support the strategic capabilities

1b. Where is the company, among others, in terms of?:

  • Brand Strength and Service Improvement in areas that include:

    • Degree of customer sophistication

    • Customer loyalty

    • Knowledge of customer expectation

    • Size in segments served

    • Degree of technological sophistication

    • Capabilities of new products

    • Product positioning

1c. Where is the company, among others, in terms of?:

  • Marketing Prowess which includes but not limited to:

    • Market performance

    • Product/brand development and deletion decisions

    • Physical distribution and channel decisions

    • Sales level

    • Market penetration

    • Options for the companies regarding:

      • Product decisions

      • Core product concept and development evaluated in terms of:

        • Features/benefits (functional, aesthetic)

        • Engineering/design

        • Package

        • Brand name

        • Product lines (styles, features, price)

        • Legal compliance

    • Product-market strategy decisions

    • Pricing decisions

      • Overall price

      • Price structure

      • Price promotions

    • Promotion decisions

      • Promotion mix

    • Distribution decisions

      • Channels selection

      • Selectivity

      • Logistics

    • Communication decisions

    • Performance against forecast

    • Marketing cycles

1d. Where is the company, among others, in terms of?:

  • Competitive Environment

  • Growth Opportunities

    • Knowledge of consumer needs

    • Skills in distribution, promotion and advertising

    • Breadth of product line

    • Competency in marketing

    • Market share building

    • Market share holding

    • Market share harvesting

    • Capable sales and service force

  • Overriding Balance Sheet Power as reflected in the quality and composition of earnings which translates to:

    • Stability of income and trend of income

      • Most current balance sheet and income statement

      • Most recent budget, variance reports, and related financial items

      • Most recent quarterly and annual reports

    • Off-balance-sheet items

    • Changes in balance sheet composition

    • Earnings from high-risk lines of business

    • Analysis of earnings composition with focus on:

      • Core earnings – Where are the earnings coming from? Are they coming from where Analytics can help to sustain? Can Analytics be used to find more opportunistic routes?

1e. Where is the company, among others, in terms of:

  • Diversity in Portfolios and Management with Operating Excellence

  • The Level and Consistency of Pofitability in Relation to Business Volumes

1f. Where is the company, among others, in terms of:

  • How much importance and emphasis do the Senior Leadership Team put among others on?:

    • Annual Strategic Performance Reviews

    • Maturity of the company for example:

      • Is the company focusing more or less in planning for growth?

    • How much efforts are focused on the business planning?

    • Willingness to assume risks?

    • Talent retention?

2: Where does the Company want to go? 

2a. Where Does the Company Want To Be, among others, in terms of?:

  • Global Scale and Presence

  • Sales Level

  • Market Penetration

  • Profitability

  • Corporate Stature

  • Degree of Customers' Sophistication

  • Customer Loyalty

  • Knowledge of Customers' Expectations

  • Size in Market Segments Being Served and Exapansion/Penetration of New Market Segments

  • Degree of Technological Sophistication

  • Capabilities of New Products and Services

  • Product Positioning

3: How Will the Company Get There? 

3a. How Will the Company Get There, among others, in terms of?:

What are the Options for the Companies Regarding:

  • Product Decisions

    • Core Product Concept

    • Features/Benefits (functional, aesthetic, etc.)

    • Engineering/Design

    • Package

    • Brand Name

    • Product Line(Styles, Features, Price, etc.)

    • Legal

  • Product-Market Strategy Decision

  • Pricing Decisions

    • Overall Price

    • Margins

    • Price Structure

    • Price Promotions

  • Promotion Decisions

    • Promotion Mix

  • Distribution Decisions

    • Channels Selection

    • Selectivity

    • Logistics

  • Communication Decisions

    • Message Goals

    • Budgeting

    • Scheduling (placement, timing, etc.)

    • Creativity

    • Media

    • Advertising

    • Sales Promotion

    • Effectiveness Measures

  • Customer service decisions

    • Pre-Sale Service

    • Post Sale Service

    • Relationship Building.

What Does the Company Needs to Know to Get There? – How Can Analytics Add Value?

1. Strategic Gap Analysis: Are there Strategic Gaps To Be Able to Leverage Data, Analytics, Modeling and Business Intelligence (BI) to Support the Strategic Direction of the Company?

Gap Analyses include:

  • Review of current and future state of Data, Analytics, Modeling and Business Intelligence (BI)  structures, operations and processes that best align with the strategic direction of the company in current and future states 

  • Review of the current and future internal Data, Analytics, Modeling and Business Intelligence (BI) capabilities to manage the analytics structures and processes to support the strategic needs of the company

    • Access the relevance of the:

      • Current Analytics Strategic Plan

      • Current Governance Model

      • Current Operating Model

      • Current Organization Models

      • Current Business Deliverables

      • Current Process Deliverables

      • Current Technical/Technology Deliverables

      • Competence and Talent Inventory in terms of Hiring Needs, Training, Development and Promotion

  • Performance Measurement/Assessment of Key Indicators in Data, Analytics, Modeling and Business Intelligence (BI) capabilities to also include the listed below among others:

    • Workforce Performance Management and Measurements

    • Process Work Flows especially with the identification of the basic or primary processes

    • Operational data, metrics, reports and decision support process capabilities

  • Inventory the usage of relevant technologies, software, tools and applications

2. Link the Strategic Direction of the Company with Abilities that Derive from Management of Analytics Capabilities

  • Link the strategic direction of the company with capabilities that derive from strategically designed integration of data management solutions, advanced analytics best practices, advanced modeling, applied optimization programing, business simulation, optional scenario generators, scenario testing plus scenario planning and business intelligence reporting processes and products that include dashboards, scorecards, operations command center and mobile channels.                    Use the capabilities to identify, assess, prioritize, measure, model, apply and provide robust information to support the business leadership in strategically managing effectively and profitably the followings:

  • How will the customers' behavior change?

  • How will the costs change?

  • How will the segmentation be affected?

  • How will the competitors redefine their activities?

  • How will the competitors’ functional strategies change?

  • How will the competitors change their investment strategies?

  • How will the product technologies change?

  • How will the process technologies change?

  • How will the financial performance be affected?

  • How will the strategic direction impact::

    • Widespread/geographic spread

    • Rapidity of penetrating new markets

    • Increase in cash flow

    • Closeness to the customers

    • Long-term continuity with the customers

    • Broadest possible perspective of a targeted market.

  • Provide the thought leadership, with primary responsibility for developing and executing innovative, financially sound, executable, and data-driven corporate strategies to achieve business objectives, meet company goals, and improve shareholders’ values

  • Work with the SBU Leadership, other Operations and Support Leadership as business partners to utilize analytics to identify and implement operational processes and procedures designed to drive revenues and reduce costs.

  • Align analytical activities with corporate objectives and provide analyses that guide and provide alternative scenarios for strategic business decisions

  • Apply quantitative rigor, risk measures and financial due diligence on all proposed initiatives to ensure that decisions are made in the best interests of the company

  • Interface based on facts with external stakeholders and influencers as appropriate, including press, wall street analysts, customers, partners, and investors as needed

  • Help the organization achieve appropriate balance on short-term goal-setting and long-term strategic planning

3. Design Phase: Designing Blueprint for Data, Analytics, Modeling and Business Intelligence (BI) Capabilities

  • Map out the Current State through Transitional State to the Future Strategic Needs: Understanding the company’s strategic needs, alignment and prioritization for Data, Analytics, Modeling and Business Intelligence (BI) Capabilities deliverables

  • Identify and Design Conceptual Capabilities Models in terms of the Goals for the Future State:

    • Enterprise Data Infrastructure: This covers the sources of data, data integration that includes connectivity layers for integration platform, application adapters, database connectors; application integration layers that include data integrity, data standardization among others and data workflow capabilities models with input to data management platforms that involve:

      • Data Governance

      • Data Stream Capabilities

      • Master Data Management (MDM)

      • Enterprise Data Marshalling Domain

      • Enterprise Data Acquisition Domain

      • Enterprise Data Processing Domain

    • Process Workflow Capabilities Models which include:

      • Business Process Management

      • Quality Management

      • Requirements Management

      • Document Management

    • Workforce and Performance Management Capabilities Models

    • Modeling and Decision Support Capabilities which entail:

      • Descriptive Modeling

      • Operations Research and Decision Support Modeling

        • Management Science Modeling

        • Predictive Modeling

          • Predictive Process Modelers

          • Predictive Process Stimulators

          • Predictive Model Markup Language (PMML)

      • Prescriptive analytics

        • Business Rules Engines Capabilities

  • Identify Performance Metrics Catalog Capabilities including Key Performance Indicators (KPI)

  • Design Reporting Dashboards for Operations, Workflow, Management and Executive Dashboards

  • Identify and Design Real-time Monitoring Reporting for Operations, Operations Command Center, Command Center War Rooms, Executives and other Decision Makers

4.Establish Stage for Tools, Software and Technologies including Platforms to Support the Above Capabilities

Identification, Testing, and Acceptance of Relevant Tools, Software and Technologies including Platforms to Support the Above Capabilities. These include:

  • Software and Technologies to Enhance Enterprise Data Infrastructure: Sources of Data including Data Migration from the old legacy systems and  Enterprise Data Management Platform, or EDMP

    • Data Stream Capabilities

    • Data Governance

    • Master Data Management (MDM)

      • Metadata

      • Data quality

      • Data Architecture

      • Transactional Data Architecture

      • BI Data Architecture

      • Metadata Server

    • Enterprise Data Marshalling Domain

      • Enterprise Data Storage

      • Enterprise Data Warehouse

        • Cloud-Based Enterprise Data Warehouse

        • Data Store and Data Mart Consolidation

    • Data Content Management

  • Enterprise Data Acquisition Domain

    • Data Stream

    • Data Mirror

  • Enterprise Data Processing Domain

  • Software and Technologies to Enhance Process Workflow Capabilities Models

    • Business Process Management

      • Configurable Workflow Dashboard

      • Resource Management

      • Process Activity Monitoring

      • Approval Process Control

      • Event-Based Notifications

      • Asset Management

    • Quality Management

      • Corrective Actions based on new design, redesign or improvements

      • Defect Tracking

      • Customer Complaint Tracking

      • Action Item Tracking

      • Compliance Management

      • Calibration Management

      • Incident Management

      • ISO 9001 Management

      • Internal Audit Management

      • Supplier Quality Control

    • Requirements Management

      • User Defined Attributes

      • Multiple Projects

      • Traceability

      • History Tracking

      • Status Reporting

    • Document Management

      • Compliance Management

      • Access Controls

      • Archiving & Retention

      • Document Assembly and Conversion

      • Document Delivery

      • Document Indexing

      • Security & Encryption

      • Version Control

  • Software and Technologies to Enhance Workforce Force Capabilities Models

    • Associate/Agent Management System

      • Human Resource Integration

      • Scheduling

      • Labor Projection

      • Skills Tracking

      • Productivity Reporting

      • Employee Location Tracking

      • Forecasting

      • Budgeting

  • Software and Technologies to Enhance Modeling and Decision Support Capabilities

    • Descriptive Modeling

    • Operations Research and Decision Support Modeling

      • Management Science Modeling

        • Mathematical Optimization

        • Simulation

        • Queueing Theory

        • Stochastic Process Models

        • Markov Decision Processes

        • Econometric Methods

        • Data Envelopment Analysis

        • Neural Networks

        • Expert Systems

        • Decision Analysis

        • Social Network

        • Analytic Hierarchy Process

    • Predictive Modeling

      • Predictive Process Modelers

      • Predictive Process Stimulators

      • Predictive Model Markup Language (PMML)

  • Prescriptive analytics

    • Business Rules Engines Capabilities

  • Technologies Designed to Support Performance Metrics Catalog Capabilities including Key Performance Indicators (KPI)

  • Business Intelligence (BI) Technologies to Provide Reporting Dashboards for Operations, Workflow, Management and Executive Dashboards

  • Technologies to Support Real-time Monitoring Reporting for Operations, Operations Command Center, Command Center War Rooms, Executives and other Decision Makers

  • Technologies Designed to Support On Demand Capabilities for Analytics in terms of Performance Metrics, Predictive and Prescriptive Scenarios

5. Phased Implementation Plan (Tasks, Timeline, Dependencies, etc. aligned to the company’s project prioritization/approval format)

Development of an Implementation plan that best integrate the company's culture with the company's Data, Analytics, Modeling and Business Intelligence (BI) requirements, processes and objectives

  • Roadmap

    • Assess and finalize the company's Data, Analytics, Modeling and Business Intelligence (BI)  readiness capabilities at the Corporate and Business Unit Levels through gap assessment to determine the strength level of the current internal capabilities to meet high level requirements defined in implementation plan with criteria that include preliminary sequencing of business processes:

      • Value to Business

        • Deployment of high priority capabilities

        • Maximum impact from synergistic deployment

      • Type of Technology Needed for Implementation

        • The necessary tools, software and environment needed  for optimum deliverables before, during and after implementation

​Implementation or/and Project Readiness Before the Building Out of the New Capabilities:

I. Business Readiness:

  • Identify key business uncertainties surrounding strategic Analytics implementation

  • Assess and prioritize stakeholders' expectations based on preparedness and capabilities assessment

  • Assessment of the current environment for changes

  • Assessment of the costs and benefits of implementation

  • Validation plan for actual funding commitments

  • Assess timeline of important external and internal dependencies and the consequences on major execution of major projects

  • Coordinate the project schedules, obtaining concurrence and commitments from all parties involved

  • Develop core Analytics Team preparedness and capabilities. Deploy the capabilities to implement the project plans

  • Assess the needs for training (for the Analytics team and project team) if needed

 

II. Operational Readiness:

  • Determine the key stakeholders' involvement. Assess where the stakeholders are in terms of level of commitments. Get a sign-off by all or most members of the SLT on the master project plan, business units' and functional areas project charters

  • Focus on project management activities to ensure that project objectives are met within the parameters of approved scope, budget, quality and timeline

  • Assess economic, technical and company's feasibilities that are needed for projects initiations

  • Identify key process uncertainties

  • Identify and evaluate Work Breakdown Structure (WBS) elements using the project risk areas to determine risk events with assignment of probability/likelihood and consequence/impact of each risk event to establish a risk rating

  • Map out projects to be executed on several key levels (Corporate, business units and functional levels) within the master project plan with a consistent focus on cost, schedule, quality, risk and resources

  • Determine the phases of Analytics implementation - Divide the Analytics implementation projects into several major phases to be done serially and within each phase, set to accomplish a variety of individual tasks simultaneously. Estimate duration of the each phase of the projects

  • Identify the key principles and dependencies that can influence the implementation roadmap

  • Develop and/or adopt operation support processes

  • Integrate Metrics and Analytics requirements with operational support structures and processes

  • Identify all resources that will be required during each phase of the implementation and provide an estimate of efforts for these resources

  • Identify the pre- and post- implementation training that may be recommended for Analytics solutions. Describe the purpose and audience for each training requirement

  • Describe the preferred approach to plan, manage, deliver and/or coordinate training required

  • Determine which Analytics structures and processes will require to be changed directly in order to achieve the Analytics implementation objectives or to support the required changes in other processes that lie within the Master Project Plan

  • Determine the nature of changes that will have to be undertaken, when, how, by who, in what order and with what possible constraints

  • Finalize requirements and scope of implementation

  • Develop and execute communication plan. Schedule update with all stakeholders

  • Adopt target measurement and regular reporting schedules

 

III. Technical Readiness:

  • Assess and determine the Analytics Technical Requirements. Share the Technical Requirements with appropriate teams or/and individual and have a consensus on a high level overview of the technical design requirements

  • Identify key technical uncertainties

  • Describe the infrastructural architecture of the proposed solutions. Include infrastructure architecture diagrams, assumptions for the infrastructure architecture, preferred major application components/modules and model deployment

  • Develop a detailed Architecture Design for the integration of Analytics Technical Requirements with the technological capabilities of the company. Assess the need for upgrading of appropriate risk facilities

  • Work with the IT/Business Leadership Team to define and establish required environments for implementation areas that will require development, test, production and training among others

  • Put together an Enterprise Architecture strategies and tradeoffs for implementing robust, secure, high-performance, and high-availability solutions that are relevant to the successful implementation of Analytics objectives

  • Work with the IT Leadership Team to test and validate the technical environment

  • Assess and make recommendations that will be needed for the development of the metrics and reporting dashboards

  • Assess the sources and stability of data quality that will be needed for the Analytics processes that include identification, measurement, control and reporting

 

IV. Change Management Readiness:

  • Work with different corporate and business unit leaders at various levels on the analytics processes as they relate to the consequences in how the deliverables of the stakeholders will change.

  • Collaborate with the necessary stakeholders in putting together Enterprise Architecture strategies and tradeoffs for implementing robust, secure, high-performance, and high-availability solutions that are relevant to the successful implementation of Analytics solutions. These covered the data, metrics and reporting requirements

  • Treat operating and culture change as proactive processes and structured approaches to address the people (leadership vision, employees’ engagement and stakeholders’ concerns) and organizational risks inherent in any change effort, to optimize business benefits realization/ROI, and to sustain long-term performance within a constantly changing environment

  • When necessary, influence without authority to successfully secure the sponsorship and support of various business and technical partners to achieve project goals and objectives

  • Demonstrate flexible approaches and resilience to setbacks and ability to drive operating and culture change

 

V. Internal Enterprise Project Management Office (EPMO) – Analytics Project Portfolio Management Readiness:

  • Focus on analytics project management activities to ensure that the objectives are met within the parameters of approved scope, budget, quality and timeline

  • Map out analytics projects to be executed on several key levels (Corporate, business units and functional levels) within the master project plan with a consistent focus on cost, schedule, quality, risk and resources

  • Determine the phases of analytics implementation - Divide the analytics implementation projects into several major phases to be done serially and within each phase, set to accomplish a variety of individual tasks simultaneously. Estimate duration of the each phases of the projects

  • Identify the key principles and dependencies that can influence the implementation roadmap

  • Develop and/or adopt operation support processes for the effective implementation of analytics solutions across the company

Plan Execution 

  • Execution Plan recommended should combine speed and sustainability

    • Look to minimize resource conflicts early in the projects

    • Look to minimize staff involvement except where absolutely necessary given other strategic initiatives

    • Identify other company's in-flight projects with potential overlap

    • Institute process transformation with the objective of achieving “World Class” for key priority areas

  • Deploy Master Project Plan and project management skills and tools to:

    • Identify and select high profile Analytics, Modeling and Business Intelligence (BI) projects

    • Prioritize and schedule major Analytics, Modeling and Business Intelligence (BI) projects

 

  • Develop core Data, Analytics, Modeling and Business Intelligence teams preparedness and capabilities based on the final "strategically designed packages"

  • Manage and deliver analytics projects through the entire life-cycle to include scoping, structuring, budgeting, project team assignments and analytical resolutions (e.g. constructing predictive models, new visualization techniques and applying external research to business issues)

Governance Models:

Develop and implement Governance Model that can best integrate Data, Analytics, Modeling and Business Intelligence (BI) Capabilities in:

  • Data Governance Model

  • Analytics Governance Model

  • Modeler Governance Model

  • Business Intelligence (BI) Governance Model

These governance models by default include:

  • Governance framework

    • Structure 

    • Principles 

    • Architecture 

    • Processes and procedures

    • Quality tracking

  • Deployment and Adoption of the Governance Models

Enterprise Analytics Organization:

  • Establish short and long-term strategy, vision, and goals for entire Analytics Team which ensures that the organization is effectively utilizing latest research techniques, analytics and technology to achieve corporate strategic goals and objectives.

  • Leverage new research techniques and analytical methodologies to drive strategic vision and marshal change

Operating Models:

Develop and deploy Operating Model which maps out the detailed process levels, procedures and activities that enable the Enterprise Analytics Organization to direct and drive the transformation of the Team's deliverables in terms of relationship to Data, Analytics, Modeling and Business Intelligence (BI) Capabilities. The Operating Model can be sub-leveled to include:

  • Data Management Team Operating Model

  • Analytics Team Operating Model

  • Modeler Team Operating Model

  • Business Intelligence (BI) Team Operating Model

These operating models by default include:

  • Core services deliverables - current, transitional and future state

  • Interaction with relevant and identifiable stakeholders

  • Core processes to manage stakeholders' facing operations and practices

  • Service measures and performance management

  • Deployment of Operating model

Organization Models:

Develop and deploy Organization Model which maps out the mission, vision, objectives, structure, roles, responsibilities and skills of the key strategic players that enable the Analytics Team to direct and drive the transformation of Data, Analytics, Modeling and Business Intelligence (BI) Capabilities. The Organization Model can be sub-leveled to include:

  • Data Management Team Organization Model

  • Analytics Team Organization Model

  • Modeler Team Organization Model

  • Business Intelligence (BI) Team Organization Model

These organization models by default include:

  • Mission, vision and objectives that are linked to the company's strategic goals, priorities, fulfillments and culture

  • Roles, responsibilities and skills

  • Training requirements

  • Organization model deployment

Examples of Team Structure in Post Implementation – Managing for Success
Strategic Data Management Team
Corporate Analytics (Or Business Analytics)  Team
Advanced Analytics (Or Decision Sciences  Team)
Business Intelligence  Team
6. Some Suggestions on Building Analytics Teams that Leverage Data and Analytics Tools, Methodologies and Processes To Deliver Superior Services to the Customers, Drive Efficiency, Support New Revenue Opportunities and Sustain Profitability Margins

  • Define staffing objectives by determining the mix and level of talent required to support current and future business objectives

  • Continually assess the key strengths and gaps of team members to ensure a long term succession and internal talent pipeline

  • Leverage Data, Analytics, Modeling and Business Intelligence (BI) resources efficiently and effectively to address customers’ drivers and initiatives

  • Collaborate with direct reports and other appropriate stakeholders in defining annual strategic and tactical deliverables, learning objectives and developmental goals

  • Consult regularly with direct reports and other appropriate stakeholders on operations to provide clarity in direction and address developmental needs at the right time

  • Perform coaching and mentoring activities to direct reports from time to time or when deemed necessary

  • Facilitate performance feedback to direct reports and other appropriate stakeholders (as needed) based on needs or based on defined organizational schedule for annual individual performance review

  • Manage to the strength of the individual Analyst to be able to find her/his strength as part of the Team

  • Provide opportunities for them to be on top of the world which they actually deserve!

  • Implement the governance and operating models for the Analytics organization as approved by the senior leadership to provide for robust operational and control systems

  • Facilitate series of presentations and meetings with direct reports and the employees on the strategic directions of the Analytics Organization and the company, the alignment between the Analytics Organization and the company and how the Analytics Organization can provide benefits to the company to be able to achieve and sustain its strategic objectives. During these presentations and meetings, the operational and analytical deliverables should be discussed, analyzed, with risks assessed and deliverables mapped into projects with timeline and resources

  • Share the entire presentations with the staff during the annual Strategic meeting

  • Develop and implement effective communication mechanism within the Analytics organization to ensure plans and feedbacks are relayed to the proper person or stakeholder at the right time

  • Provide teaming approach to critical review and analysis, win or lose with the regional, national or international teams and shares best practices within the team irrespective of their locations.

7. Final Frontier in Building the New Capabilities to Support the  Transformation of the Company to Deliver Superior Services to the Customers, Drive Efficiency, Support New Revenue Opportunities and Sustain Profitability Margins

Drive Predictive Analytics and Performance Metrics for Profitability: Use advanced statistical analysis and modelling including predictive statistical methods to identify the factors that can drive up business-line profitability

Establish analytics processes that can efficiently be leveraged to provide the needed input for different stakeholders whose responsibilities are different areas of the company that include:

  • Portfolio management

  • Pricing

  • Reserving

  • Sales and Marketing

  • Distribution channels or logistics

  • Customers and Client Management

  • Underwriting

  • Capacity Management

  • Volatility Management

  • Enterprise risk management

    • Strategic Risk

    • Operational Risk

    • Premium Risk

    • Underwriting Risks

    • Marketing Risks

    • Asset/Liability Matching Risks

    • Underwriting Risks (Pricing)

    • Reserving Risks

    • Credit Risks

    • Market Risks

    • Interest Rate Risks

    • Exchange Risks

    • Liquidity Risks

  • Capital Management - (Allocation, Economic, Regulatory and Cost of Capital)

 

Leverage Analytics Best Practices to Measure the Impact on Business Results:

Use different types of analytics best practices to align with business strategies and also use analytics to measure impact on business results in the areas below:

  • Monthly Reviews and Action Plan

  • Quarterly Reviews and Action Plan

  • Annual Strategic Planning  

  • Account Review (Transaction)

  • Growth Playbook

  • Annual Portfolio Review (Annual Portfolio Risk Reports)

  • Capital Management (Economic Capital, Capital Allocation and Capital Requirements)

  • Loss Severity Forecasting

  • Different types of benchmarking analytics that included:  

    • Comparative Benchmarking (Comparing the company for internal Improvement)

      • Strategic benchmarking, Process benchmarking, Operational benchmarking and Risk Management benchmarking

    • Competitive Benchmarking (Benchmarking to seek Competitive edge or advantages)

      • Risk to Strategic Plan benchmarking, Risk to Rating benchmarking, Product Performance  benchmarking and Market Performance  benchmarking

         

Leverage the enterprise data through the application of sophisticated analytics methods, tools and processes to provide insights into strategic planning, strategic implementation, monitoring and reporting:

  • Provide input into strategic planning, refining and enriching decision making processes.

  • Customer Buying Behavior:

    • Quantitative predictive analysis of the drivers, barriers, location and dynamics of customer buying behavior so as to generate predictive and prescriptive recommendations to improve marketing processes, products distinction, services uniqueness and marketing effectiveness

  • User Experience

    • Customer Analytics provides the clients with deeper insights into their existing or prospective buyers and how best to retain them

  • Segmentation

    • Quantitative analysis of customer location, buying power and behavior are leveraged to generate segmentation grid and allows for probability of movement from one grid to the other iand ncluding when best to position the company to benefit from the movement in the grid

  • Market Demand Analysis

    • Qualitative and quantitative analysis of what the consumer wants or need and how best to position the company to benefit from the changing demand

 

Ensure performance management, applifying metrics sensors and driving timely corrective actionable plan:

  • Performance Metrics (PMs)

    • Identify, refine and monitor core performance metrics or “measures that matter” in alignment with the progress towards the achievement of organizational goals.

    • Provides both short and long-term targets to ensure a focus on continuous and breakthrough improvement.

  • Key Performance Indicators (KPIs)

    • Provide metric system for estimating and evaluating the level of achievement of quality targets in order to drive actions and decisions.

    • Ensure that current and future-oriented measures align to key operational principles and goals.

    • Develop series of indicators which will be exact, precise, easy to interpret and sensitive to significant changes.

    • Create indicators that identify the gap between performance and expectations with in-built mechanisms to ensure intervention and improvement.

  • Sensors

    • Develop and maintain integrated and algorithm based combination of Performance Metrics that allow or automate interventions to better manage operational workflow.

  • Timely Corrective Actionable Plan

    • Use different types of analytics best practices to update reports about practices, trends and changes in the company portfolios and communicated the information in business friendly ways to the corporate, business units and functional areas. Many of these reports can be used in different degree as input into decision-making processes that accelerated and motivated timely corrective action plan to meet the expectations of internal stakeholders that included the Board, the leadership and external stakeholders that included the Rating Agencies and the Regulators

Analyze the enterprise data to understand the patterns, trend and “what next” to:

Create environment that allows for objective identification and measurement of drivers that can be influenced if there is the need to change the current course of action(s) through Command Centers:

Command Center Deliverables

  • Revenue and Workflow

    • Dynamically focus on meeting revenue goals during the operating hours

    • Dynamically re-distribute work to available resources to meet revenue goals during the operating hours

    • Align employee skill-set with the need of the business horizontal

    • Leverage standardized business processes across the company for supporting revenue performance

  • Productivity, Workflow and Workforce Management

    • Dynamically re-distribute work to available resources

    • Drive process and operational performance monitoring

    • Cross-trained personnel – Skills catalog and defined roles to enhance gap in personnel involvement

    • Ensure continuous process improvement and optimization

    • Drive for standardized business processes across the Company

  • Predictive Analytics and Performance Metrics

    • Leverage predictive interventions real-time sensors, alerts and business intelligence to drive Command Center Rapid Response Activities

    • Leverage dynamic data quality management to design and manage measures that matter – Productivity, Conversion Rate, PTP, RPC  etc.

    • Manage hierarchy of metrics data from executive/strategic down to employees

    • Connect clients' priorities as Command Center priorities

    • Use advanced statistical analysis and modeling including predictive statistical methods to identify the factors to ensure business-line profitability

 

Create structures and processes that allow for objective identification and measurement of drivers that can be influenced if there is the need to change the current course of action(s) through Operational Reporting, Dashboards and Scorecards:

Operational Reporting, Dashboards and Scorecards on the performance of the organization

  • Provide the Executives with report cards and scorecards on the performance of the organization. Design, develop and increase the use of dashboard across company to:

    • Enhance tactical view for advanced analytics with drill-down capabilities.

    • Display operational data and metrics in near real time.

    • Enhance the company capabilities to monitor enterprise sensors with alert system to ensure quick turnaround on major incidents with issue resolution.

  • Monthly Operations Reports (MOR)

    • Provide Monthly review of operations performance

Drive Change Management with the Adoption Plan

  • Leverage the Adoption Plan as the midwife in progressing the evolution of the transformation. The plan focuses on the timeline, players and major activities that will facilitate the phased introduction through planned schedules and tasks associated with its deployment in:

    • Building team dynamics, accountability, enthusiasm and involvement for change while instilling conflict resolution techniques in the team if needed to ensure process credibility

  • Ensure the existence of consultative implementation schedule which balances commitment with time management without scaring the Senior Leadership Team (SLT) because they believe that they are too big projects that will take all their time and resources and the need for the SLT to accept that the implementation is part of how business will be done going forward

  • Leverage Communication Plan

    • Promotion of open and active communication within the implementation team and between the SLT and the implementation team with constant and scheduled update with all stakeholders

  • Drive Clarity in Technology Statement of Direction

  • Allow for Training and Development Plan

  • "Over-focus" on Employee Buy-In and Culture Change Management Plan

Ensure Financial Obligation and Value Contribution

  • Oversee budgeting, forecasting, billing and invoicing processes in Data, Analytics, Modeling and Business Intelligence (BI) implementation activities

  • Own the responsibility for measuring the value coming from increased revenue and decreased cost as reflected in the NPV, IRR and Payback summaries in supporting business justification for investment in Data, Analytics, Modeling and Business Intelligence (BI) space.

  • Control staffing, budgeting, planning, manage expense priorities and recommend/implement changes as needed

  • Operational Excellence

    • Ensure that the Data, Analytics, Modeling and Business Intelligence (BI) operations are established to scale and facilitate smooth interaction between dependent groups especially in the SBUs and within Business Units

    • Design and manage Data, Analytics, Modeling and Business Intelligence (BI) workflows and automation projects in conjunction with the Enterprise Project Management Office (EPMO) to continuously improve efficiency, effectiveness, and accuracy in execution of deliverable requirements

    • Continuously refine Data, Analytics, Modeling and Business Intelligence (BI) service offerings and service level agreements with SBU teams

    • Ensure operational excellence through continuous refinement of best practices and tactics

    • Develop key performance indicators in reporting metrics

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