Data Analysis & ML — Consultant Field Kit
Understand ML at the right altitude, recognize the charts, pick the tool, and run a value‑oriented workshop.
1. What is ML (Consultant's Definition)
Machine Learning (ML) = algorithms that learn patterns from data and make predictions or decisions without being explicitly programmed — think advanced automation with feedback.
- Rules‑based logic (classic configs) = fixed.
- ML = adapts from historical data.
- Role for IT/Business Consultant: you don't build models, but you know where they fit into processes.
2. Typical Business Cases
| Case | Type | Tool / Library | Output |
|---|---|---|---|
| Sales Forecasting | Predictive (Supervised) | Python (pandas, scikit‑learn, Prophet) / SAP AI Core | Line chart: predicted vs. actual sales |
| Customer Segmentation | Unsupervised | scikit‑learn (KMeans), SAP Datasphere | Cluster chart / scatter plot |
| Invoice Classification | Supervised | SAP DOX / scikit‑learn | Auto‑tagged invoice categories |
| Anomaly Detection in Deliveries | Unsupervised | IsolationForest / SAP Data Intelligence | Alerts for outliers in lead times |
| Churn Prediction | Supervised | scikit‑learn (LogReg, XGBoost) | Probability score per customer |
| Text Analysis (Tickets) | NLP | Python (NLTK, spaCy) / Service Ticket Intelligence | Word cloud / auto‑assigned category |
3. Core Python Libraries (for Consultants)
- pandas → data manipulation (
df.groupby('region').sum()). - matplotlib / seaborn → quick charts (bar, line, scatter).
- scikit-learn → basic ML (classification, regression, clustering).
- prophet (by Meta) → time-series forecasting (business-friendly).
- NLTK / spaCy → text analysis (sentiment, keyword extraction).
- Even if you don’t code, knowing these helps you guide data teams.
4. Charts & Visuals You Should Recognize
- Line Chart → trends, forecasting.
- Bar Chart → comparison across categories.
- Scatter Plot → correlation / clusters.
- Histogram → distribution of values.
- Boxplot → detect outliers.
- Word Cloud → text data overview.
Tools: Excel, Power BI, SAP Analytics Cloud, or Python (matplotlib/seaborn).
5. Consultant's Techniques & Templates
Problem Framing
Business Question → Data Needed → Method → Action
Example: Why are deliveries late? → Historical delivery times, routes → Anomaly detection → Alert in SD/Logistics
Data Validation Checklist
- Is master data clean (BP, Material, Vendor)?
- Are time periods aligned (fiscal vs. calendar)?
- Are outliers real events or errors?
ML Use Case Fit Test
- Do we have enough historical data (> 500–1,000 records)?
- Is there a clear target variable (e.g., delivery on time: Yes/No)?
- Will prediction change business action?
6. Tools Outside of Python (Low‑Code / No‑Code)
- Power BI / SAC → dashboards & quick forecasting.
- Analytics Cloud Predictive → built‑in ML for analysts.
- Google AutoML / Azure ML Studio → drag‑and‑drop ML.
- Jupyter Notebooks → industry standard for exploration.
7. Quick Consultant Action Plan
- Learn to read charts (line, scatter, histogram).
- Play with one dataset (sales CSV in Excel/Power BI).
- Try a Jupyter Notebook with pandas + matplotlib.
- Run a ready ML model (forecasting with Prophet).
- Map ML back to process (forecast → procurement → supply chain).
You're not becoming a data scientist; you're becoming dangerous enough to lead value‑oriented conversations.
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Dzmitryi Kharlanau
Lead SAP transformations and AMS modernization across S/4HANA logistics (SD/O2C), MDG/MDM, integrations, and operations. Focus: outcomes over ticket volume, reliable releases, and AI/agentic workflows for triage, knowledge retrieval, and safe automation with approvals and audit.