Ihs Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at IHS? The IHS Business Intelligence interview process typically spans 5–7 question topics and evaluates skills in areas like data warehousing, dashboard and report design, stakeholder communication, and translating complex analytics into actionable business insights. Interview prep is especially important for this role at IHS, as candidates are expected to demonstrate expertise in designing scalable data pipelines, synthesizing information from multiple sources, and presenting findings that drive strategic decision-making within diverse business environments.

In preparing for the interview, you should:

• Understand the core skills necessary for Business Intelligence positions at IHS.
• Gain insights into IHS’s Business Intelligence interview structure and process.
• Practice real IHS Business Intelligence interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the IHS Business Intelligence interview process, along with sample questions and preparation tips tailored to help you succeed.

IHS Markit is a global leader in information, analytics, and solutions serving the major industries and markets that drive economies worldwide. The company partners with clients in business, finance, and government to deliver comprehensive insights that support informed and confident decision-making. Serving over 50,000 key customers in more than 140 countries—including 85% of the Fortune Global 500—IHS Markit is headquartered in London and is dedicated to sustainable, profitable growth. As a Business Intelligence professional, you will contribute to transforming data into actionable insights that help clients navigate complex markets and make strategic decisions.

As a Business Intelligence professional at IHS, you will be responsible for gathering, analyzing, and interpreting complex data to support strategic business decisions. You will work closely with various teams to develop analytical reports, dashboards, and visualizations that provide actionable insights into market trends, operational performance, and customer behaviors. Your role involves transforming raw data into meaningful information, ensuring data quality, and supporting leadership with data-driven recommendations. By leveraging your analytical skills and industry knowledge, you help IHS optimize business processes, identify new opportunities, and maintain its competitive edge in the market intelligence sector.

2.1 Stage 1: Application & Resume Review

Your application will be evaluated for relevant experience in business intelligence, data analytics, and technical skills such as SQL, data visualization, and data pipeline development. The review process emphasizes experience in designing data warehouses, building dashboards, and communicating insights to both technical and non-technical stakeholders. Demonstrating a track record of handling diverse datasets, collaborating with business teams, and solving real-world business problems will help your resume stand out.

2.2 Stage 2: Recruiter Screen

A recruiter will contact you for a 20-30 minute phone conversation to discuss your background, motivation for applying to IHS, and alignment with the company’s values. Expect to summarize your experience with business intelligence tools, data modeling, and cross-functional communication. The recruiter will also assess your interest in the specific business domains IHS serves and clarify your salary expectations and availability. Prepare by clearly articulating your career progression, key BI projects, and reasons for wanting to join IHS.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one or two interviews with business intelligence professionals or data team leads. You may be asked to solve SQL problems, design data warehouses for hypothetical business scenarios, or demonstrate your ability to translate business questions into analytics solutions. Case studies often focus on retail analytics, operational dashboards, pipeline design, and data quality issues. You might be asked to walk through designing a reporting pipeline, optimizing OLAP aggregations, or analyzing multiple data sources. Preparation should include practicing whiteboarding system designs, writing efficient SQL queries, and structuring your approach to solving open-ended analytics problems.

2.4 Stage 4: Behavioral Interview

You will meet with a hiring manager or cross-functional leader to assess your interpersonal skills, leadership potential, and ability to communicate technical insights to non-technical audiences. Expect to discuss experiences collaborating with stakeholders, overcoming data project hurdles, and making data accessible through visualization and storytelling. Be ready to share examples of resolving misaligned expectations, adapting presentations for diverse audiences, and driving actionable business outcomes from analytics work.

2.5 Stage 5: Final/Onsite Round

The final round often includes a panel interview or a series of back-to-back sessions with key team members, such as senior analysts, business partners, and technical managers. This stage may involve a practical exercise—such as presenting insights from a case study, critiquing a dashboard, or designing a BI solution in real time. You’ll be evaluated on your technical depth, business acumen, and ability to synthesize complex data into clear recommendations. Preparation should focus on refining your communication skills, brushing up on advanced analytics concepts, and being ready to respond to follow-up questions on your technical and business logic.

2.6 Stage 6: Offer & Negotiation

If successful, the recruiter will reach out to discuss the offer package, including compensation, benefits, and start date. You may have the opportunity to negotiate terms and clarify any remaining questions about the role or team structure before accepting.

2.7 Average Timeline

The typical IHS Business Intelligence interview process spans 3-5 weeks from initial application to offer, with each stage generally taking about a week to schedule and complete. Fast-track candidates with highly relevant experience or internal referrals may move through in as little as 2-3 weeks, while standard timelines can extend if there are multiple panel interviews or a take-home case component. Flexibility in scheduling and prompt responses can help expedite the process.

Next, let’s dive into the types of interview questions you can expect throughout the IHS Business Intelligence interview process.

3.1. Data Modeling & Warehousing

Expect questions that assess your ability to design, optimize, and manage data infrastructure for scalable analytics. Focus on structuring data warehouses, integrating diverse datasets, and ensuring performance for business intelligence use cases.

3.1.1 Design a data warehouse for a new online retailer
Begin by identifying core business entities (orders, products, customers), then normalize and star-schema your tables for analytical queries. Discuss ETL processes, scalability, and how you’d enable reporting for various business functions.

3.1.2 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Address localization, currency conversion, and compliance challenges. Outline how you’d handle multi-region data sources and reporting requirements while maintaining high data quality.

3.1.3 Design a dashboard that provides personalized insights, sales forecasts, and inventory recommendations for shop owners based on their transaction history, seasonal trends, and customer behavior.
Describe how you’d source and aggregate relevant data, build predictive models, and present actionable insights. Mention usability and customization for different merchant profiles.

3.1.4 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
Discuss tool selection (e.g., Airflow, dbt, Metabase), data flow architecture, and approaches to ensure reliability and scalability. Highlight how you’d monitor, maintain, and optimize the pipeline.

3.2. Data Analytics & Experimentation

These questions evaluate your ability to conduct rigorous analyses, design experiments, and translate findings into business strategy. Emphasize your approach to metrics, statistical testing, and actionable recommendations.

3.2.1 The role of A/B testing in measuring the success rate of an analytics experiment
Explain experiment design, control/treatment assignment, and how you’d interpret results to determine business impact. Discuss pitfalls like sample bias and statistical power.

3.2.2 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Describe your approach to experimental design, key metrics (retention, conversion, profit), and how you’d isolate promotion effects. Highlight how you’d communicate results to stakeholders.

3.2.3 How would you determine customer service quality through a chat box?
Identify relevant quantitative and qualitative metrics (response time, sentiment, resolution rate), and propose analysis techniques. Discuss how you’d present findings to improve service.

3.2.4 Assess and create an aggregation strategy for slow OLAP aggregations.
Outline your strategy for optimizing queries, indexing, and pre-aggregation. Mention trade-offs between speed and granularity, and how you’d monitor performance over time.

3.3. Data Engineering & Pipelines

These questions focus on your ability to design, implement, and maintain robust data pipelines for business intelligence. Expect to discuss ETL, data integration, and scalable infrastructure.

3.3.1 Let's say that you're in charge of getting payment data into your internal data warehouse.
Describe your approach to ingestion, transformation, error handling, and monitoring. Mention how you’d ensure data integrity and timely availability for downstream analytics.

3.3.2 Design a data pipeline for hourly user analytics.
Explain your architecture for capturing, aggregating, and serving user activity data. Discuss scalability, latency, and how you’d enable real-time reporting.

3.3.3 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Detail your process for data profiling, cleaning, schema matching, and integration. Emphasize how you’d validate data quality and synthesize insights across sources.

3.3.4 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Outline ETL steps, feature engineering, and model deployment. Discuss how you’d monitor pipeline health and ensure predictions are actionable for business users.

3.4. SQL & Reporting

Expect practical questions on querying, aggregating, and presenting data for business intelligence. Highlight your efficiency with SQL and ability to deliver clear, actionable reports.

3.4.1 Write a SQL query to count transactions filtered by several criterias.
Show your method for filtering, joining, and aggregating data. Clarify edge cases and ensure your query is optimized for large datasets.

3.4.2 Calculate total and average expenses for each department.
Demonstrate grouping, aggregation, and handling of missing or anomalous data. Mention how you’d present results to inform budget decisions.

3.4.3 Create a report displaying which shipments were delivered to customers during their membership period.
Explain your logic for joining membership and shipment tables, filtering by delivery dates, and summarizing results for business review.

3.4.4 Write a query to create a pivot table that shows total sales for each branch by year
Describe your use of aggregation and pivot techniques. Highlight how you’d ensure the report is flexible for different time periods and branches.

3.4.5 Write a SQL query to find the average number of right swipes for different ranking algorithms.
Explain your approach to grouping by algorithm, calculating averages, and handling missing or outlier values.

3.5. Data Visualization & Communication

These questions test your ability to translate complex analysis into actionable insights for non-technical stakeholders. Focus on clarity, adaptability, and storytelling.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss your approach to audience analysis, visual selection, and simplifying technical details. Emphasize tailoring your message for impact.

3.5.2 Making data-driven insights actionable for those without technical expertise
Describe strategies for demystifying analytics, such as analogies, visual aids, and focusing on business outcomes.

3.5.3 Demystifying data for non-technical users through visualization and clear communication
Highlight your use of dashboards, charts, and storytelling to drive understanding and adoption among business users.

3.5.4 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Explain your choice of visualization techniques (word clouds, frequency histograms) and how you’d surface key patterns for decision makers.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision. How did your analysis impact business outcomes?
Focus on a specific project where your insights led to a measurable change, such as process improvements or new initiatives.

3.6.2 Describe a challenging data project and how you handled it.
Highlight the obstacles, your approach to problem-solving, and the results you achieved.

3.6.3 How do you handle unclear requirements or ambiguity in analytics requests?
Share your process for clarifying objectives, iterating with stakeholders, and delivering value despite uncertainty.

3.6.4 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Discuss strategies for bridging technical and business perspectives, such as tailored visualizations or regular check-ins.

3.6.5 Describe a time you had to negotiate scope creep when multiple teams kept adding requests. How did you keep the project on track?
Explain your prioritization framework and communication methods for managing expectations and maintaining data quality.

3.6.6 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Share how you balanced transparency, interim deliverables, and resource management.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe how you built credibility, used evidence, and navigated organizational dynamics.

3.6.8 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Highlight your approach to trade-offs, documenting limitations, and planning for future improvements.

3.6.9 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
Show your accountability, corrective actions, and how you communicated the fix to stakeholders.

3.6.10 Describe how you prioritized backlog items when multiple executives marked their requests as “high priority.”
Discuss your criteria for ranking tasks, stakeholder alignment, and communication strategies.

4.1 Company-specific tips:

Familiarize yourself with IHS Markit’s core business domains, including market intelligence, analytics, and global data solutions. Understand how IHS partners with major industries and government clients to deliver actionable insights that drive strategic decision-making. Review recent reports, press releases, and industry trends relevant to IHS’s client base, such as energy, finance, and supply chain. Be prepared to discuss how business intelligence contributes to optimizing processes and identifying growth opportunities in these sectors.

Research IHS’s approach to data quality, compliance, and international reporting. Demonstrate awareness of challenges in managing multi-region data, localization, and regulatory requirements. Highlight your experience in supporting global operations or working with diverse datasets.

Learn about IHS’s emphasis on transforming complex analytics into clear, actionable recommendations for non-technical stakeholders. Prepare examples of how you’ve made data accessible and impactful for business leaders, emphasizing storytelling and visualization.

4.2 Role-specific tips:

4.2.1 Master data warehousing concepts and be ready to design scalable solutions for diverse business scenarios.
Practice structuring data warehouses and modeling data for analytical queries using star and snowflake schemas. Be ready to discuss ETL processes, normalization, and strategies for integrating multiple data sources. Show how you would enable reporting and dashboarding for various business functions, with a focus on scalability and data quality.

4.2.2 Demonstrate proficiency in dashboard and report design tailored to stakeholder needs.
Prepare to walk through the process of building dashboards that deliver personalized insights, forecasts, and recommendations. Emphasize usability, customization, and how you translate raw data into actionable business intelligence. Share examples of dashboards you’ve designed for different user profiles, such as executives, shop owners, or operational teams.

4.2.3 Show advanced SQL skills through efficient querying and aggregation for large datasets.
Expect practical SQL questions that require joining, filtering, and aggregating data across multiple tables. Practice writing queries to count transactions, calculate departmental expenses, and create pivot tables for sales data. Explain your approach to optimizing queries for performance and handling data anomalies or missing values.

4.2.4 Prepare to discuss your experience with data pipeline design and open-source tool selection.
Be ready to outline how you would build reporting pipelines under budget constraints using open-source tools. Talk through your decisions regarding tool selection, data flow architecture, and monitoring strategies. Highlight your ability to ensure reliability, scalability, and timely data delivery for analytics.

4.2.5 Exhibit strong analytical thinking in experiment design and translating findings into business strategy.
Review key concepts in A/B testing, statistical analysis, and metrics selection. Practice explaining how you design experiments to measure business impact, interpret results, and communicate actionable recommendations. Be prepared to discuss trade-offs, such as sample bias and statistical power, and how you isolate effects in complex business scenarios.

4.2.6 Showcase your ability to synthesize insights from multiple data sources and ensure data integrity.
Detail your process for profiling, cleaning, and integrating diverse datasets, such as payment transactions, user behavior, and fraud detection logs. Explain how you validate data quality, resolve schema mismatches, and extract meaningful insights that drive system improvements.

4.2.7 Refine your communication and storytelling skills for presenting complex analytics to non-technical audiences.
Practice simplifying technical concepts and tailoring your message for different stakeholder groups. Use visual aids, analogies, and clear business outcomes to make data-driven insights accessible. Prepare examples of how you’ve adapted presentations for executives, business partners, or cross-functional teams.

4.2.8 Be ready with behavioral examples that demonstrate your leadership, adaptability, and stakeholder management.
Reflect on situations where you influenced stakeholders, negotiated scope, or balanced competing priorities. Prepare stories that highlight your approach to resolving ambiguity, managing expectations, and delivering value under pressure. Emphasize your accountability, transparency, and commitment to data integrity even when deadlines are tight.

4.2.9 Practice critiquing dashboards and presenting insights from case studies in real time.
Anticipate panel interviews or practical exercises where you’ll need to evaluate existing BI solutions or present findings on the spot. Focus on synthesizing complex data, identifying key trends, and making clear recommendations. Show your ability to respond thoughtfully to follow-up questions on both technical and business logic.

5.1 How hard is the IHS Business Intelligence interview?
The IHS Business Intelligence interview is considered moderately to highly challenging, particularly for candidates without prior experience in scalable data warehousing, dashboard design, and stakeholder communication. You’ll be tested on your ability to synthesize complex data from multiple sources and present actionable insights that drive strategic business decisions. Candidates who are comfortable designing data pipelines, optimizing SQL queries, and translating analytics into business recommendations will find themselves well-prepared.

5.2 How many interview rounds does IHS have for Business Intelligence?
IHS typically conducts 5-6 interview rounds for Business Intelligence roles. The process includes an initial recruiter screen, one or two technical/case interviews, a behavioral interview, a final onsite or panel round (sometimes involving practical exercises), and an offer/negotiation stage. Each round is designed to assess both technical depth and business acumen.

5.3 Does IHS ask for take-home assignments for Business Intelligence?
Take-home assignments are occasionally part of the IHS Business Intelligence interview process, particularly for roles that require advanced dashboard design or data analysis. Assignments may involve designing a reporting pipeline, critiquing a dashboard, or analyzing a case study with real or simulated business data. You’ll be evaluated on both your technical approach and your ability to communicate findings clearly.

5.4 What skills are required for the IHS Business Intelligence?
Core skills for IHS Business Intelligence include advanced SQL, data warehousing, ETL pipeline design, dashboard/report creation, and data visualization. Strong analytical thinking, experiment design (such as A/B testing), and the ability to communicate insights to both technical and non-technical stakeholders are essential. Experience with open-source BI tools and handling multi-region datasets is highly valued, as is the ability to manage data quality and compliance.

5.5 How long does the IHS Business Intelligence hiring process take?
The typical IHS Business Intelligence interview process takes 3-5 weeks from application to offer. Each stage generally requires about a week to schedule and complete, though timelines can be expedited for candidates with highly relevant experience or internal referrals. Flexibility and prompt communication can help move things along more quickly.

5.6 What types of questions are asked in the IHS Business Intelligence interview?
Expect a mix of technical, case-based, and behavioral questions. Technical questions focus on data modeling, SQL querying, dashboard/report design, and data pipeline architecture. Case studies often involve real-world business scenarios, such as designing scalable solutions for retail analytics or optimizing reporting pipelines. Behavioral questions assess stakeholder management, communication, and adaptability in ambiguous situations.

5.7 Does IHS give feedback after the Business Intelligence interview?
IHS typically provides high-level feedback through recruiters, especially if you reach the final stages of the process. While detailed technical feedback may be limited, you can expect to receive insights on your interview performance and areas for improvement. Candidates are encouraged to follow up for additional clarification if needed.

5.8 What is the acceptance rate for IHS Business Intelligence applicants?
The acceptance rate for IHS Business Intelligence roles is competitive, with an estimated 3-7% of applicants receiving offers. This reflects the high standards for technical expertise, business acumen, and communication skills required for the position.

5.9 Does IHS hire remote Business Intelligence positions?
Yes, IHS offers remote opportunities for Business Intelligence professionals. While some roles may require occasional onsite presence for team collaboration or client meetings, many positions support flexible or fully remote work arrangements, reflecting IHS’s commitment to attracting top talent globally.

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Ready to ace your Ihs Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like an Ihs Business Intelligence professional, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Ihs and similar companies.

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