Introduction to Business Analytics

Explore how companies set short-term and long-term expectations and monitor performance. Learn how stakeholders’ expectations translate into the long range plan and the annual business plan.

Identify key stakeholder groups—employees, suppliers, customers, investors, communities, and regulators—and map their expectations through targeted questions and interviews to build the firm’s business intelligence and guide strategy.

Explore how business intelligence turns data into valuable insights, guiding qualitative and quantitative analyses and the predictive analytical tool to assess risks and opportunities and shape enterprise performance management.

Identify stakeholders and their expectations, ask relevant questions to translate business intelligence into end-to-end processes with metrics and benchmarks, linking long-range and annual plans to performance.

Map end-to-end processes across departments to reveal dependencies and conflicts, and preemptively resolve issues before they derail operations. See how working capital targets shape payment terms between finance and sales.

Explore hire-to-retire (h2r) processes from hiring through retirement, covering onboarding, payroll, performance management, training, promotions, and exit procedures across hr, payroll, and departments.

Explore the source-to-pay process (S2P), from vendor sourcing and procurement to orders, invoices, and payment, and learn how to track supplier performance and optimize costs.

Explore record-to-report (R2R) by collecting and transforming data in an ERP to generate key financial indicators, such as profit and loss and balance sheets, for stakeholders.

Explore the order-to-cash process, from capturing customer orders and pricing with promotions to invoicing, delivery, and receiving payment, across sales, marketing, manufacturing, logistics, finance, and treasury.

Understand how end to end processes split into sub processes with metrics per sub process, and how global process owners align department targets to prevent conflicts and support performance measurement.

Identify value drivers that impact performance, link them to metrics, and use a value driver tree to assess predictability, materiality, and risk, including oil price.

Distinguish metrics from KPIs, noting KPIs are primarily non-financial measures of quality, efficiency, and cycle time, with examples like invoices without errors and journals per FTE.

Define metrics with quality over quantity, focusing on key performance indicators, key business indicators, and key risk indicators to deliver real-time visibility, transparency, accountability, and actionable intelligence.

Set performance targets by benchmarking against world-class peers to gauge efficiency and effectiveness. Compare internal and external benchmarks to identify gaps and adopt best practices for improvement.

Data drives value as firms leverage its potential; learn how companies acquire data and the key considerations to ensure data quality for analytics.

Define and enforce the master data governance function to deliver a single source of truth with standardized, high-quality data for analytics.

Explore descriptive, predictive, and prescriptive analytics to understand how businesses apply analytics across industries and create value, guided by a holistic framework.

Describe descriptive analytics as the first analytics type that explains what happened and uses historical data and variance analysis to reveal trends for management decisions.

Learn diagnostic analytics and how a SWOT analysis answers why it happened by evaluating a company's strengths, weaknesses, opportunities, and threats, using Starbucks as an example.

Predictive analytics uses current and historical data to forecast future events and model scenarios of volume and profit to guide product promotions.

Apply prescriptive analytics to identify the best actions among options, using machine learning, AI, and NLP to influence marketing and retail decisions.

Explore trend analysis with practical examples, then cover comparative and value based analysis, discuss time series, correlation, and regression, and introduce business decision trees, machine learning, and natural language processing.

Trend analysis uncovers patterns in historical data to monitor metrics and forecast future performance. It spans performance management, project management, and trading, reflecting internal and external factors that shape decisions.

Explore trend analysis in Excel by comparing revenue and COGS over three years, using date and EOMONTH, SUMIF, and a two-variable line chart to uncover non-proportional growth.

Use comparative analysis to compare two or more variables, with internal or external benchmarks, and identify value drivers like fuel costs and manpower to guide decisions.

Perform a comparative analysis in Excel to benchmark profitability across country units for 2020, using revenues, cogs, and gross profit margin to identify top performers and outliers like Brazil.

Learn value based analysis to identify value drivers with ROIC and working capital, map drivers with driver trees, and use correlation, leading and lagging metrics, and what-if analysis.

Explore value-based analysis in Excel through a practical, no-calculation example that links strategic goals to revenue, costs, sensitivity analysis, leading and lagging metrics, and what-if scenario planning.

Learn correlation analysis as a low-cost tool to map relationships between variables and outcomes using correlation coefficients. Recognize that correlation does not prove causation and requires predefined variables and data.

Apply correlation analysis in Excel to quantify relationships and interpret values between -1 and 1. Compare revenue with advertising spend and website traffic, noting causation and potential regression for prediction.

Explore time series analysis for forecasting future sales using naive, probabilistic Monte Carlo, deterministic, and hybrid models to quantify the impact of weather and marketing on revenue.

Explore time series analysis in Excel by building autoregressive models with one-period lag and twelve-period lag to predict monthly revenue using regression, and assess fit with r-squared and p-values.

Explore regression analysis in the predictive phase, using linear and logistic models to quantify how dependent variables are explained by independent variables in business data.

Learn how to perform regression analysis in Excel, from simple to multiple regression, and interpret intercepts, slope coefficients, r-squared, and p-values to gauge relationships.

Explore when to use machine learning over traditional statistical methods and learn the four ingredients: data, model, objective function, and optimization algorithm, through a practical archery analogy.

Explore the three major types of machine learning—supervised, unsupervised, and reinforcement—through labeled and unlabeled data, targets, and reward-based training for business analytics.

Learn how descriptive analytics uses simple techniques with Excel and BI tools, then explore diagnostic, predictive, and prescriptive analytics that automate and optimize decisions, with the analytics life cycle next.

Explore the six phases of the analytics project lifecycle: hypothesis development, situation analysis, current state analysis, blueprint and design, build and test, deploy and operationalize.

Develop a testable hypothesis as the first analytics step by formulating an if this, then that statement, tested against time, budget, and constraints to explain performance and guide action.

Phase two situation analysis identifies organizational needs and factors shaping performance in the German market, then builds a stakeholder list and a Raci matrix for roles and communications.

Learn to build a raci matrix by listing activities and stakeholders, assign r, a, c, i roles, and apply to a plant parking lot project.

Successful projects finish on time and on budget with all features and functions specified, driven by executive sponsorship, clear top-down communication, stakeholder engagement, strong planning, and realistic expectations.

Perform current state analysis to benchmark performance against peers, identify gaps, and plan future improvements. Assess capabilities, set targets, and socialize the benefits to drive world-class practices.

Explore blueprint and design to chart a future state, detailing how to meet requirements with governance, data integrity, and an enterprise information model while selecting appropriate analytics tools.

Build and test the solution through a readiness assessment, verify ERP and R-2r tool integration, and validate month-end closing improvements with stakeholder sign-off.

Explore the build and test phase step by step, outlining readiness checkpoints, data validation, data model loading, application logic, testing, reporting, quality assurance, and user acceptance with ERP reconciliations.

Plan, train, and communicate to ensure a successful deploy and go live by conducting readiness checks, testing, and post-deployment training for all stakeholders within change management.

Explore how data visualization uses interactive BI dashboards from Tableau and Power BI to deliver self-service insights with real-time data across the organization.

Interview the supply chain team to identify key drivers and compare performance to peers using budget, variance, trend, and descriptive analytics.

Enhance supply chain planning by linking sales forecast accuracy to optimal inventory and on-time case fill. Compare performance with peers using predictive models and benchmark studies, via ERP dashboards.