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Financial Management and Business Data Analytics · Introduction to Data Science for Business Decision-making

Data Science Life Cycle and Process Explained

Updated 10 October 2026 · Fact-checked

The data science life cycle is the ordered set of stages a data project follows: business understanding, data collection, data preparation, exploratory analysis, modelling, evaluation, deployment and monitoring. It is iterative, so you often go back a stage. In exams, name each stage, say what it does, and tie it to a business example.

Understand Data Science Life Cycle and Process

A data science project is not just 'build a model'. It is a chain of steps that starts with a business question and ends with a model that is used and checked in real life. The life cycle gives that chain a name and an order, so a team does not jump to modelling before the problem or the data is clear.

The common stages are: business understanding (what decision, what goal, what success looks like), data collection (find and gather data from internal systems such as ERP, sales and accounts records, and external sources), data preparation (clean, fix errors, handle missing values, combine and transform), exploratory data analysis (summaries and charts to see patterns and spot problems), modelling (choose and train a technique), evaluation (test whether the model is accurate and answers the business need), deployment (put it into use) and monitoring (track performance and update it).

CRISP-DM is a widely used framework for this. It has six phases: business understanding, data understanding, data preparation, modelling, evaluation and deployment. Monitoring and maintenance is usually treated as part of, or following, deployment. Different books group the stages differently, so learn the logic rather than one fixed count, and use the stage names given in your ICMAI study material.

The cycle is iterative, not a straight line. Poor evaluation results may send you back to data preparation or even to redefine the business problem. Data preparation usually takes the largest share of effort in practice.

Example: an Indian retail chain wants to reduce unsold stock. Business understanding fixes the goal (cut dead stock). Data collection pulls sales and inventory records. Preparation removes duplicates. A forecasting model is built, tested on past months, deployed into the ordering system, and monitored as demand patterns change.

Key rules to remember

Life cycle sequence
Business understanding → Data collection/understanding → Data preparation → Exploratory analysis → Modelling → Evaluation → Deployment → Monitoring
Iterative: evaluation or monitoring can send you back to earlier stages.
CRISP-DM phases
Business understanding → Data understanding → Data preparation → Modelling → Evaluation → Deployment
Six phases. Use the grouping your study material gives if it differs slightly.

How to solve Data Science Life Cycle and Process questions

Use this method for any question that asks you to explain, list or apply the life cycle.

  1. 1Read the question and note whether it wants a list, an explanation of each stage, or an application to a business case.
  2. 2Write the stages in order, using standard names. If CRISP-DM is mentioned, use its six phases.
  3. 3For each stage, give one line on what is done and one on why it matters.
  4. 4Add a short business example (sales forecasting, credit risk, customer churn) and link each stage to it.
  5. 5State that the process is iterative and name one point where you would loop back.
  6. 6Close with deployment and monitoring: model drift, new data and periodic retraining.

Quickest way: Stage-purpose-example line

When to use it: For MCQs and short 3 to 5 mark answers when time is tight.

  1. Recall the order with a cue: Business, Data, Prepare, Explore, Model, Evaluate, Deploy, Monitor.
  2. Match the activity in the question to a stage: goal setting is business understanding, cleaning is preparation, testing accuracy is evaluation, tracking performance is monitoring.
  3. In an MCQ, eliminate options that put modelling before data preparation or deployment before evaluation.
  4. In a written answer, use one line per stage in the form: stage, what happens, example.

Common mistakes in Data Science Life Cycle and Process

  • Starting the life cycle with modelling or data collection.

    Students think data science means algorithms.

    Fix: Always begin with business understanding: the problem and the decision to be supported.

  • Treating the cycle as a one-way straight line.

    Lists in books look sequential.

    Fix: State that it is iterative and give an example of looping back, such as poor evaluation leading to more data preparation.

  • Forgetting deployment and monitoring.

    Students stop once the model is built.

    Fix: Add that a model must be used in decisions and tracked for falling accuracy as data changes.

  • Confusing evaluation with exploratory analysis.

    Both involve checking results.

    Fix: Exploratory analysis studies the data before modelling; evaluation tests the model's performance against the business goal.

  • Listing stage names with no explanation or example.

    Students memorise labels only.

    Fix: Give one line of purpose and a business example for each stage so the answer earns step marks.

Worked examples

Example 1

A bank wants to predict which loan applicants are likely to default. Explain the stages of the data science life cycle for this project. (Model answer, 8 marks)

Show the solution
  1. Business understanding: the bank defines the goal as reducing loan defaults while not rejecting good customers, and sets a success measure.
  2. Data collection: gather past loan records, repayment history, income and credit bureau data.
  3. Data preparation: remove duplicates, treat missing income values, correct errors and convert fields into usable form.
  4. Exploratory analysis: use summaries and charts to see how default varies with income, age or loan size.
  5. Modelling: build a predictive model on past data to estimate default likelihood.
  6. Evaluation: test it on data not used for training and check that it meets the business goal.
  7. Deployment: embed it in the loan approval process.
  8. Monitoring: track accuracy over time and retrain as customer behaviour or the economy changes. The process is iterative, so weak results may send the team back to data preparation.

Answer: Business understanding, data collection, preparation, exploratory analysis, modelling, evaluation, deployment and monitoring, applied to loan default as shown above, with the cycle being iterative.

Example 2

Choose the correct statement about the CRISP-DM framework. (a) Modelling comes before data preparation (b) Evaluation comes before deployment (c) Deployment is the first phase (d) It has no business understanding phase

Show the solution
  1. CRISP-DM runs: business understanding, data understanding, data preparation, modelling, evaluation, deployment.
  2. Option (a) is wrong because data preparation precedes modelling.
  3. Option (c) is wrong because business understanding is the first phase.
  4. Option (d) is wrong because business understanding is included.
  5. Option (b) matches the order: evaluation precedes deployment.

Answer: (b) Evaluation comes before deployment

Exam tips

  • Learn the stages in order and be ready to write them as a numbered list with one line each.
  • If a case is given, use its business context in every stage instead of generic text.
  • Mention that the process is iterative and that models need monitoring; many answers miss this.
  • For MCQs, test the sequence: any option that puts modelling before preparation or deployment before evaluation is wrong.
  • If the question says CRISP-DM, give the six phases; otherwise use the broader eight-stage list.

Practice questions from Introduction to Data Science for Business Decision-making

Data Science Life Cycle and Process in other exams

The same ground in other exams, if you are preparing for more than one or want another angle on it.

Data Science Life Cycle and Process: frequently asked questions

What are the steps of the data science life cycle?

The usual steps are business understanding, data collection, data preparation, exploratory analysis, modelling, evaluation, deployment and monitoring. Some sources group or name them slightly differently. Follow the order and logic, and use your study material's names.

What are the CRISP-DM phases?

CRISP-DM has six phases: business understanding, data understanding, data preparation, modelling, evaluation and deployment. It is used as a general framework for analytics projects. The phases can be repeated as the project learns from results.

Why is the life cycle called iterative?

Because results at one stage can force you back to an earlier one. For example, a weak model may show that the data needs more cleaning or that the business question was framed poorly.

Which stage takes the most time?

In practice, data preparation usually takes the largest share of effort, since real data is messy. Do not quote a fixed percentage in the exam; just say it is typically the most time-consuming stage.