Hey recruiters! Here’s a bonus installment to our D365 series, diving into a hot topic: the need for BI (Business Intelligence) expertise vs. Power BI-specific skills in Dynamics end user companies. With many companies still in the midst of their digital transformation journey, understanding these roles and what to look for in candidates can be a game-changer for building effective analytics teams.
For reference, here's the inspo for today's blog, thanks, Bola!
Why BI Expertise is Different from Power BI Skills
Most large companies aren’t running on a single ERP; they’re operating across multiple systems, each handling different facets of the business. For example, Boeing has five ERPs – a setup I call a “distributed ERP solution.” With data flowing from various systems, companies need to make sure everything comes together in a unified view. This requires both broad BI expertise and platform-specific knowledge (like Power BI).
Here’s why these two roles matter:
Complex Environments Need More Than One Tool:
Many companies have multiple ERPs, each with different types of data. A general BI expert has experience integrating data from various sources, while a Power BI expert is often focused on creating dashboards and reports within that specific tool.
Companies in the middle of a digital transformation typically need a BI expert to strategize at the data architecture level and a Power BI expert to visualize that data for end users.
Long-Term Decisions in Large Companies:
In complex organizations, reporting needs may vary widely across business units, and Power BI may not always be the final solution. Instead, companies might implement a combination of tools or build a centralized data warehouse that can handle data from all sources.
BI experts are crucial here—they bring the knowledge to structure this “big picture” architecture. Meanwhile, Power BI experts can help deliver insights on the front end.
Permanent Data Segregation Needs:
Large companies often divide their data and reporting by department or business unit. A good BI expert knows how to set up reporting structures that meet these needs without overwhelming users or compromising data integrity.
Power BI experts then focus on building user-friendly dashboards that fit within these parameters, bringing the data architecture to life for each department.
What to Look For in a BI Expert
A true BI expert should have:
Experience with Data Architecture and Strategy: Look for candidates who can talk about designing data pipelines and ETL processes to bring together data from multiple sources.
Tool-Agnostic Skills: A good BI expert can work with multiple platforms (e.g., SQL Server, Azure Data Lake, or Snowflake), not just Power BI.
Experience in Data Governance: BI experts should understand how to manage data access, security, and governance across multiple systems, especially in companies with diverse data sources.
Questions to Ask:
“Can you walk me through a project where you integrated data from multiple ERPs or platforms?”
“What tools have you used for ETL, and how did you ensure data accuracy across different systems?”
Listen for: Expertise in data architecture, governance, and flexibility with tools beyond Power BI.
What to Look For in a Power BI Expert
A Power BI expert should bring:
Proficiency in Data Visualization: They should understand how to create intuitive, insightful dashboards that communicate complex data in simple terms.
Experience with DAX and Power Query: These are Power BI’s primary languages for data manipulation and transformation within the tool, essential for handling complex data scenarios.
Understanding of Power BI Service and Workspace Management: A Power BI expert should know how to manage reports, datasets, and user access within the Power BI Service, crucial for large-scale deployments.
Questions to Ask:
“Can you describe a complex dashboard you created and the impact it had on decision-making?”
“How have you handled data transformations within Power BI? Are you comfortable with DAX and Power Query?”
Listen for: Specifics about creating interactive visualizations, managing Power BI workspaces, and using DAX/Power Query.
Real-World Scenario: Distributed ERP in Action
Consider a large company with a distributed ERP model, where different business units use different ERP systems. A BI expert might start by setting up a data warehouse, creating a unified data source for all these systems. From there, they’d ensure the data flows properly into a central repository with ETL processes.
The Power BI expert then takes this unified data and builds dashboards for each business unit, customizing them to the needs of the sales team, finance, operations, and so on.
This combined approach ensures the company has both a strategic, big-picture data framework and a user-friendly, actionable view for day-to-day decisions.
Data Science, Modeling, and Engineering (A Crash Course for Recruiters)
Alright, recruiters, let’s add a dash of data science to this BI stew. In addition to the classic BI and Power BI roles, you’ll often hear about Data Scientists, Data Engineers, and Data Modelers. Each plays a unique role in making sense of data, so understanding their differences will help you connect the dots in a candidate’s resume (and sound like a data whiz yourself).
Data Scientists: The Fortune Tellers of Data
Think of Data Scientists as the predictive wizards. Their job is to look at past data and try to predict the future. They do this by building models that identify patterns, using techniques like machine learning (the fancy algorithms that learn from data). They’re the ones who say, “Based on our past sales data, here’s what we expect next quarter.”
Typical Tools: Python, R, machine learning libraries, and sometimes good ol' Excel.
What to Look For:
Experience with Predictive Modeling: If they talk about things like “predicting customer churn” or “sales forecasting,” you’re in Data Science territory.
Machine Learning and AI Skills: Look for experience with libraries like TensorFlow, scikit-learn, or PyTorch.
Questions to Ask:
“Can you tell me about a model you created that impacted business decisions?”
Listen for: Details on how they made predictions, improved accuracy over time, or influenced decisions with their models.
Data Engineers: The Builders of Data Pipelines
If Data Scientists are the visionaries, Data Engineers are the architects and builders. They create the data pipelines—the systems that move, clean, and store data from various sources (like ERPs, CRMs, and external databases) into a central location for analysis. Their job is to ensure data flows reliably, so it’s always ready for BI reports or Data Science models.
Typical Tools: SQL, Spark, Hadoop, and cloud platforms like AWS, Azure, and Google Cloud.
What to Look For:
ETL (Extract, Transform, Load) Skills: Data Engineers are the ETL champions, pulling data from various sources, cleaning it, and loading it into a data warehouse.
Experience with Big Data Tools: They might mention tools like Apache Spark, Azure Data Factory, or SQL-based data warehousing solutions.
Questions to Ask:
“Tell me about a data pipeline you built and the challenges you faced.”
Listen for: Stories about moving or cleaning large volumes of data, managing different sources, or optimizing storage. They should mention words like “pipeline,” “data flow,” and “ETL.”
Data Modelers: The Organizers of Data
Last but not least, we have Data Modelers—the ones who decide how data should be structured so it makes sense to everyone else. They design data models that make data easier to access and analyze. Picture them like a librarian who organizes books in a way that’s intuitive for anyone looking for information.
Typical Tools: ER/Studio, IBM InfoSphere, and sometimes SQL and Power BI.
What to Look For:
Experience with Data Modeling: They’ll have a knack for structuring data tables and relationships, like a puzzle master organizing pieces to reveal the big picture.
Familiarity with Relational Databases: Modelers understand how data relates across tables (e.g., “customer” relates to “orders” and “products”), making it easy for everyone else to pull the right data.
Questions to Ask:
“Can you walk me through a data model you designed and why you structured it that way?”
Listen for: References to organizing data, creating relationships, or making data easier to query. They’ll often talk about “entities” and “relationships.”
How They All Fit Together
Here’s an easy analogy to remember:
Data Engineer: Sets up the kitchen, makes sure ingredients are in place, and everything is labeled and ready to cook.
Data Modeler: Organizes the ingredients, deciding which shelves they go on for easy access.
Data Scientist: The chef, creating recipes (models) that predict if your soufflé (or sales) will rise.
And remember, Power BI Experts are the servers, presenting the final dish to the diners (business users) in a way that’s appetizing and easy to understand.
Dad Joke of the Day
Why do ducks have feathers? To cover their butt quacks.
Happy recruiting!
DynamicsDad
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