Project planning and scoping are crucial steps in applying analytics to business scenarios. They involve defining the problem, setting objectives, and identifying stakeholders. These steps ensure the project addresses real business needs and has clear goals.
Effective planning also includes creating a detailed project plan, defining milestones, and assessing data quality. This groundwork sets the stage for successful analysis, ensuring the right data is available and the project stays on track to deliver valuable insights.
Defining the Business Problem
Understanding the Problem
- A business problem is a challenge or issue that a company faces which can be addressed through a project or initiative
- Defining the problem involves understanding root causes (process inefficiencies, market changes), current impacts (lost revenue, customer churn), and desired outcomes (increased market share, cost savings)
- Techniques for problem definition include root cause analysis (5 Whys, Ishikawa diagrams), impact analysis (financial modeling, customer surveys), and goal setting (SMART objectives)
- Example: A retail company is losing market share to online competitors, resulting in declining revenue and store closures. The desired outcome is to regain market share through an e-commerce initiative.
Establishing Project Objectives and Scope
- Project objectives are specific, measurable goals that describe what should be achieved for a project to be considered successful
- Objectives should be directly tied to solving the defined business problem and aligned with organizational strategy (increase online sales by 20%, launch e-commerce platform within 6 months)
- Defining project scope involves specifying which goals and deliverables are part of the project (website development, product catalog) and which are out of scope (supply chain optimization, brick-and-mortar store updates)
- Scope helps keep the project focused and manageable by setting clear boundaries and avoiding scope creep
- A problem statement is a clear, concise description of the business problem, its impacts, and the desired outcome that will signify the problem has been addressed (Online competition has reduced in-store sales by 15% over the past year. Launching an e-commerce channel within 6 months is expected to recover market share and increase overall revenue by 10%.)
Identifying Key Stakeholders
Stakeholder Analysis
- Stakeholders are individuals or groups who can influence the project or who are impacted by the outcomes of the project
- Identifying all relevant stakeholders is critical to project success (executive sponsors, project team, end users, external vendors)
- Stakeholder analysis involves understanding each stakeholder's role, their level of influence on project decisions (high, medium, low), their interest in the project outcomes (advocate, neutral, resistant), and their communication needs and preferences (weekly status reports, monthly steering committee presentations)
- Techniques for stakeholder analysis include power/interest grids, stakeholder interviews, and persona development
- Example: For the e-commerce project, key stakeholders include the CEO (high power, high interest), Marketing (high power, medium interest), IT (medium power, high interest), and customers (low power, high interest)
Defining Stakeholder Requirements
- Stakeholder requirements are the specific needs and expectations that each stakeholder has for the project deliverables and outcomes
- These requirements help define project success criteria and inform key design and functionality decisions (website must be mobile-responsive, order fulfillment should be automated)
- Techniques for gathering requirements include interviews, focus groups, surveys, and workshops
- Requirements should be documented in a clear, concise format (user stories, use cases) and prioritized based on importance and feasibility (MoSCoW method: Must Have, Should Have, Could Have, Won't Have)
- A RACI matrix is a tool used to define and document project roles and responsibilities, specifying who is Responsible, Accountable, Consulted, and Informed for each project task and deliverable
Project Planning and Deliverables
Developing a Project Plan
- A project plan is a formal document that details all project goals, milestones, deliverables, resources, timeline, budget, assumptions, constraints, and dependencies
- It serves as the single source of truth for managing the project and helps ensure that all stakeholders are aligned on project scope, approach, and expectations
- Key components of a project plan include a project charter (high-level overview), work breakdown structure (hierarchical decomposition of deliverables), project schedule (timeline with milestones and dependencies), resource plan (roles and responsibilities), and communication plan (information sharing and reporting)
- The project plan should be reviewed and approved by key stakeholders before project execution begins and updated regularly throughout the project lifecycle
- Example: The e-commerce project plan includes milestones for website design approval, product catalog completion, platform testing, and launch. It also details the budget for software development, content creation, and digital marketing.
Defining Milestones and Deliverables
- A project milestone is a significant point in the project which marks the completion of a major deliverable or phase (website design complete, beta testing finished)
- Milestones are used to track and communicate overall project progress and serve as key decision points for go/no-go determinations or change requests
- Project deliverables are the specific outputs, products, or artifacts that must be completed to achieve the project objectives (functional website, product descriptions, user guides)
- Each deliverable should have clear acceptance criteria that define the requirements and quality standards it must meet to be considered complete (zero high-severity defects, 90% automated test coverage)
- A Gantt chart is a type of bar chart that displays the project schedule, showing the start and end dates of each task as well as any dependencies between tasks (content creation must be completed before website testing can begin)
- A project budget outlines the total financial resources allocated to the project as well as how those resources will be used across different cost categories (personnel, software, hardware, outsourcing)
Data Availability and Quality
Identifying Data Sources and Systems
- Identifying available data sources and systems is a key step in planning a data project, as the available data will determine what types of analysis and insights are possible
- Data sources may include internal systems (CRM, ERP), external providers (market research firms, government agencies), or public datasets (census data, weather data)
- For each data source, it's important to understand the data format (structured, unstructured), storage location (on-premises, cloud), access method (API, batch export), and update frequency (real-time, daily, monthly)
- Example: For the e-commerce project, relevant data sources may include website clickstream data, customer transaction history, product catalog data, and competitor pricing data
Assessing Data Quality and Preparing for Analysis
- Data profiling is the process of reviewing a dataset to understand its structure, content, relationships and quality
- It provides a clear picture of data characteristics (data types, value distributions) and any quality issues that need to be addressed (missing values, inconsistent formatting)
- Data quality dimensions are the different aspects of data that can be measured and managed, such as accuracy (data reflects reality), completeness (all required data is present), consistency (data is the same across systems), timeliness (data is up-to-date), and validity (data conforms to business rules)
- Each dimension may require different quality criteria and thresholds (95% accuracy, 99% completeness)
- A data quality assessment is a formal process of measuring a dataset against predefined quality criteria and scoring the results
- The assessment helps identify and prioritize any data issues that need to be resolved before analysis (standardize address formats, impute missing values)
- Data preparation is the process of cleaning, transforming, and normalizing raw data into a format suitable for analysis
- It may involve handling missing values (deletion, imputation), standardizing formats (date/time, units of measure), combining datasets (joins, unions), or creating calculated fields (total spend, customer lifetime value)