Data mining in BABOK v3 (technique 10.14) serves three purposes: descriptive, diagnostic, predictive. Descriptive mining, such as clustering, shows patterns in data. Diagnostic mining, such as decision trees, shows why a pattern exists. Predictive mining, such as regression or neural networks, estimates how likely something is in the future [1, 3, 4]. On the 120-question CBAP exam, the scenario's question tells you which purpose fits [2].
Last verified: October 2026 (technique list on iiba.org [1], exam facts on the IIBA CBAP page [2]).
This is part 2 of a short series. For the five elements of the technique, its strengths and its limits, start with the overview of the Data Mining technique. This article goes deeper into the three purposes only.
Why the three purposes matter on the CBAP exam
The three purposes matter because CBAP questions describe a business problem, not an algorithm, and you have to pick the analysis that answers it. The Certified Business Analysis Professional (CBAP) exam has 120 case-study and scenario-based questions in 210 minutes [2]. Data Mining is one of 50 techniques in BABOK chapter 10 [1], so you will not see many questions on it. When it does appear, the question is usually "what kind of result does the analyst need?"
| CBAP fact | Detail |
|---|---|
| Questions | 120 scenario-based multiple choice [2] |
| Time | 210 minutes [2] |
| Largest knowledge area | Requirements Analysis and Design Definition, 30% [2] |
| Strategy Analysis / Solution Evaluation | 15% / 14% [2] |
| Technique number | 10.14 Data Mining [1] |
A data mining scenario could sit, for example, in a task such as Analyze Current State (6.1) or Analyze Performance Measures (8.2) [1]. To see where Data Mining sits next to the other 49 techniques, use the map of all BABOK techniques by knowledge area.
Descriptive, diagnostic, predictive: what BABOK 10.14 says
BABOK 10.14 says data mining can be used for descriptive, diagnostic or predictive purposes, and gives one technique family as the example for each. The BABOK text is for IIBA members, so the table below follows two independent study notes from IIBA training providers and a flashcard set, which agree with each other [3, 4, 5]. It is a paraphrase, not a quote.
| Purpose | Question it answers | Example technique in 10.14 notes | Example in 10.14 notes |
|---|---|---|---|
| Descriptive | What patterns are in the data? | Clustering | Similarities between customers [3, 4] |
| Diagnostic | Why does this pattern exist? | Decision trees | Traits of the most profitable customers [3, 4] |
| Predictive | How likely is this to be true in the future? | Regression, neural networks | Probability that a claim is fraudulent [3, 4] |
Two more distinctions in the same section help with exam reading. First, an investigation can be supervised, where the user poses a question, or unsupervised, where patterns are allowed to emerge [3, 4]. Second, the notes list typical modelling techniques: classification and regression trees (CART), C5 and other decision tree analysis, linear and logistic regression, neural networks, support vector machines and predictive scorecards [4]. They also say analysts often try several techniques to see which works best [3].
What BABOK 10.14 does not list is a fourth, prescriptive purpose. None of the 10.14 notes we checked mention it [3, 4, 5]. Prescriptive analysis appears in general analytics models, not in this technique.
A worked example: one dataset, three kinds of analysis
The clearest way to separate the three purposes is to run all three on the same data. The example below is invented for illustration; the numbers are not from a real company.
A meal-kit subscription company has 40,000 active customers. Cancellations rose over two quarters, and the head of retention asks the business analyst for help. The analyst runs requirements elicitation first: what decision will the results support? The answer is "who should get a retention offer, and what should we fix?"
Step 1. Descriptive: clustering the customer base. The analyst and a data colleague build an analytical dataset (orders, plan type, delivery history, support tickets) and run clustering. Four groups appear, for example "weekly family plans", "occasional singles", "discount hunters" and "lapsed gifts". The output tells the team what the customer base looks like. It does not say why anyone cancels.
Step 2. Diagnostic: a decision tree on cancellations. Next the analyst asks why the "weekly family plans" group cancels more. A decision tree trained on past cancellations splits the data into readable rules, such as "two or more late deliveries in 60 days" and "plan price increased this quarter". Decision trees suit this step because a tree can be read as plain rules, while a neural network is harder to explain [8]. The result points to delivery reliability as a likely driver. It is still a pattern, not proof of cause, so the analyst confirms it with logistics staff, in the same way as root cause analysis and the 5 Whys.
Step 3. Predictive: a cancellation score. Finally the team fits a logistic regression that gives each active customer a probability of cancelling in the next 30 days. This answers the forward-looking part of the request: who is at risk now.
Deployment. BABOK notes say that many mining results become business rules deployed through a business rules management system [3]. Here the rule might be: when the cancellation score is above 0.6 and the customer had a late delivery, offer a free box. Writing that rule down well is a job for decision modeling and decision analysis, not for the mining model.
| Step | Purpose | Technique | Output for the business |
|---|---|---|---|
| 1 | Descriptive | Clustering | Four customer groups |
| 2 | Diagnostic | Decision tree | Rules that explain higher cancellations |
| 3 | Predictive | Logistic regression | A 30-day cancellation probability per customer |
| 4 | Deployment | Business rule | Who gets a retention offer |
Which mining technique fits which purpose
The purpose decides the analysis, and the algorithm only follows. The same algorithm can serve two purposes, which is where many candidates slip.
| Technique | Typical purpose in 10.14 notes | Can also serve | Watch out |
|---|---|---|---|
| Clustering | Descriptive [3, 4] | Input to diagnostic work | Groups are not explanations |
| Decision tree (CART, C5) | Diagnostic [3, 5] | Predictive, as a classification tree [4] | Trees can overfit and need pruning [8] |
| Linear or logistic regression | Predictive [3, 4] | Diagnostic, by reading coefficients | Correlation, not cause |
| Neural network | Predictive [3, 4] | Rarely diagnostic | Harder to interpret [8] |
| Predictive scorecard | Predictive [4] | - | Needs a defined outcome to score |
On the exam, ignore the algorithm name in an answer option until you know what the stakeholder wants. If the stem says "understand why", diagnostic is right even when the answer mentions a tree. If it says "estimate which customers will", it is predictive even when the option mentions a tree.
Experts disagree on how far a BA should go
Practitioners agree that BAs should frame the question and use the results, but they disagree on who should build the model. Practitioners in a business intelligence community say models get built mostly for targeted uses such as marketing response, credit risk, pricing and logistics [12].
1. BAs can and should do predictive work. A supply chain analytics lead argues in a 2026 IIBA member article that predictive analytics is a natural extension of business analysis, and that BAs can contribute with spreadsheets and BI tools without becoming data scientists [9].
2. BAs can build models, but only with guard rails. An industry research analyst argues that analysts need training, review before production and limits by risk, with high-stakes models left to statisticians [11]. This answers position 1 directly: capability alone is not enough. A customer experience manager on the IIBA blog adds data quality, governance, bias and keeping humans in automated decisions to the list [10].
3. The BA's value is framing and adoption, not modelling. One BI practitioner's team built a late-delivery prediction model with an outside firm, and business users barely used it [12]. A model that nobody acts on has no value, so elicitation and deployment matter more than the algorithm. BABOK's own elements, which start with requirements elicitation and end with deployment, fit this view [3, 5].
4. Explainability beats accuracy for most business decisions. Several practitioners prefer simple models such as logistic regression that the business can read [12], and technical documentation agrees that trees are easier to explain than neural networks [8].
Common confusions about the three purposes
Most wrong answers come from mixing up the question with the tool.
- "Segmentation is always descriptive." Grouping customers is descriptive. Explaining what makes a group profitable is diagnostic [3, 4]. Read what the stakeholder wants to learn.
- "A decision tree is always diagnostic." It is the 10.14 example for diagnostic use [3, 5], but a classification tree can also predict [4]. The purpose decides.
- "Diagnostic mining proves the cause." It shows patterns linked to an outcome. Confirm cause with domain experts or root cause analysis (10.40) [1].
- "Prescriptive is the fourth BABOK type." The general analytics ladder has four steps: what happened, why it happened, what will happen and how to make it happen [6]. Prescriptive analytics advises what to do [7]. The 10.14 notes list only three purposes [3, 4, 5]. When a CBAP scenario asks which option to choose, look at other techniques such as Decision Analysis (10.16) or Business Rules Analysis (10.9) [1].
- "This is the same as the CBDA analytics types." The IIBA business data analytics exam frames analytics types around the research question. For a broader view of that exam, see the CBDA exam guide. For CBAP, stay with the three 10.14 purposes and the mining techniques.
Practice Questions
These are original questions written for this article.
Question 1. A bank's product owner asks the business analyst to find natural groups among small-business customers before the team designs new account packages. No outcome is defined yet. Which use of data mining fits best?
- A. Predictive, using a neural network
- B. Descriptive, using clustering
- C. Diagnostic, using a decision tree
- D. Prescriptive, using optimization
Answer: B. The stakeholder wants to see patterns, with no outcome to explain or predict. That is descriptive use, and clustering is the 10.14 example [3, 4]. D is not a 10.14 purpose.
Question 2. After a pricing change, an insurer sees more policy cancellations in one region. The sponsor asks the analyst which customer traits are linked to these cancellations. What should the analyst propose?
- A. A diagnostic analysis, such as a decision tree on past cancellations
- B. A descriptive dashboard of cancellations by month
- C. A predictive score for next month's cancellations
- D. A survey of all customers before any analysis
Answer: A. The question is "why is this pattern happening", which is diagnostic [3, 4]. B only describes what happened. C looks forward and does not explain the cause.
Question 3. A claims team wants each new claim flagged with the probability that it is fraudulent, so investigators can prioritize. Which statement is correct?
- A. This is diagnostic because it uses past fraud cases
- B. This is descriptive because claims are grouped into high and low risk
- C. This is predictive, and regression or neural networks are typical techniques
- D. Data mining does not apply because fraud is a business rule
Answer: C. Estimating how likely something is to be true in the future is the predictive purpose, and the fraud example appears in 10.14 notes [3, 4]. Using past data does not make it diagnostic. A fraud rule can come out of deployment, which is why D is wrong.
Conclusion: the gap between BABOK 10.14 and real data mining work
BABOK gives a clean three-way split, while real teams mix the purposes in one project and argue about who builds the model.
| Area | Standard | Reality | Delta |
|---|---|---|---|
| Purposes | Descriptive, diagnostic, predictive [3, 4] | Teams use all three in one project; forecasting and targeting dominate [12] | No gap |
| Who builds models | Not assigned to a role in notes [3, 4] | Often specialists or outside firms; BAs build with guard rails [11, 12] | Reduced to framing and review |
| Elicitation and deployment | First and last elements [3, 5] | Unused models are a common complaint [12] | Under-used |
| Explainability | Listed as a limitation [4] | Practitioners prefer readable models [12] | No gap |
| Data governance | Privacy listed as a limitation [4] | Stressed by BA practitioners [10] | Added by employers |
For the exam, learn the three purposes and their example techniques, and read every scenario for the question the stakeholder asks. At work, the BA's part is to define that question, prepare the data with others, and make sure the result becomes a rule or decision someone uses.
Related topics
FAQ
What are the three types of data mining in BABOK?
BABOK v3 technique 10.14 describes descriptive, diagnostic and predictive data mining. Descriptive shows patterns, for example with clustering. Diagnostic shows why a pattern exists, for example with decision trees. Predictive estimates how likely something is in the future, for example with regression or neural networks.
Is prescriptive analytics part of BABOK data mining?
Study notes of BABOK 10.14 list only three purposes, and prescriptive is not one of them. Prescriptive analytics comes from general analytics models, where it answers "what should we do". In a CBAP scenario, choosing between options points to other techniques, such as Decision Analysis.
Is a decision tree diagnostic or predictive?
It can be either. BABOK notes use decision trees as the example of diagnostic mining, because a tree shows readable rules behind a pattern. A classification tree can also predict outcomes for new cases, so on the exam the stakeholder's question decides the answer.
Does a business analyst need to build data mining models for CBAP?
No. The CBAP exam tests when and why to use the technique, not how to code a model. In practice, BAs usually frame the question, help prepare the data and turn results into rules, while data specialists build complex models.
How is this different from the CBDA analytics types?
The CBDA exam focuses on analytics types chosen from the research question and uses a four-step view that includes prescriptive. CBAP and BABOK 10.14 use three data mining purposes tied to example techniques. The ideas overlap, but the exams frame them differently.
Sources
- BABOK Guide v3 techniques list (IIBA KnowledgeHub)
- Certified Business Analysis Professional (CBAP) (IIBA)
- BA Techniques: Data Mining (training provider notes, 2020)
- Data Mining (training provider notes, 2023)
- Techniques: Data Mining flashcards
- Gartner analytics maturity model factsheet
- Descriptive vs prescriptive vs predictive analytics explained (2021)
- Decision Trees, scikit-learn documentation
- Predictive Analytics for Business Analysts (IIBA member article, 2026)
- The Rise of Machine Learning: A Game-Changer for Business Analysis (IIBA blog, 2024)
- Who Should be Building Predictive Models? (2014)
- Does anyone actually build/use predictive ML models? (r/BusinessIntelligence)