How to use Microsoft Power BI semantic models to analyse data from multiple sources

Unlock deeper data insights with Microsoft Power BI semantic models

Microsoft Power BI is a game-changer when it comes to analysing departmental data. One of its great strengths is that it enables you to analyse data from more than one source. That means you can bring in data from say an Excel spreadsheet, an Access database and a line of business application, and analyse it as if it were just one data file. 

Working with more than one data source enables you to unlock more value from your data. It also enables you to use powerful AI visuals such as Power BI’s Key Influencers which needs a wide range of different attributes to give good results. 

But having several data sources can make the data more complicated to work with. Common problems include not being able to get Power BI to “see” parts of your data, or selection visuals not working as you expect. 

Power BI semantic models unlock more value from your data

A semantic model, or more simply a data model, puts data into a format that makes business sense. It is a business-friendly layer between the raw data and the report visuals. 

For example, if you are interested in sales trends, it is likely to want to know sales per day rather than deal with thousands of individual transactions. Creating a DAX measure called “DaySales” within the model makes the data easier to work with, and improves accuracy. You might also want to view “DaySales” by attributes from other files, so the semantic model needs to include those relationships.

Relationships may be one to many, or many to many, and the key to a good semantic model is how best to handle these relationships to enable meaningful analysis of the data. Data in a semantic model is often in a star schema format, with one or more fact tables, and several dimension tables that provide context to the data you want to analyse. Modelling data using a star schema provides a wide range of ways of analysing key metrics. 

So how do you go about creating a semantic model? 

Semantic models step-by-step

Creating a semantic model is step-by-step process, which needs to be done in the right order: 

1. Understand the business needs 

2. Clean and transform data

3. Create relationships, measures, calculated columns, and hierarchies 

4. Add security restrictions

5. Test and optimize.

The first, and arguably most important step, is to understand the business needs. Who will be working with the data, and why? What are they trying to achieve? Who will view the reports or use the analysis? What restrictions should be included, for example should some people see only part of the data set? Analysing the business needs provides a wish-list of requirements that can then be used to create the semantic model. You may be tempted to skip this step, feeling that you know your own business. Although that may be true, analysing your requirements for data analysis is always worth the effort.

Secondly, the data must be clean. This means that it must have the right data types, duplicates are removed, and decisions made about any data that is missing or obviously wrong. As clever as Power BI is, it is only as good as the data it is given.

Thirdly, the semantic model is created by joining data tables to create a star schema, and adding DAX measures, aggregates, and additional columns. Columns can be renamed or hidden to make the data easier to work with. Hierarchies can be created to make common tasks easier, such as managing dates. 

After the semantic model has been created, security such as row-level security can be added if needed. 

Finally, and crucially, the model needs to be tested to ensure it provides the expected results.

As with many things related to data, the order in which you do things is vital. Trying to work with raw data that hasn’t been cleaned will not produce good business results. Equally, providing high quality data that doesn’t make business sense will not produce good results either.

How to create the right semantic model for your needs

Power BI offers excellent visualization and analysis capabilities, making it the go-to tool for departments that want to make better use of their data. With the right semantic model, it’s easy to ask questions, dig deeper, and use AI to analyse your business data. Our step-by-step process allows you to figure out what you need from your data and assess what additional steps you need to optimize your reports. And if you want to understand more about why star schemas are so powerful when it comes to analysing data, have a look at Chris Adamson’s book “Star Schemas: The Complete Reference”. 

Here at Anatec AI we have many years of experience in modelling data, including business analysis and data preparation for both people and AI. So, if you think your data might not be in the right format to deliver your business goals, get in touch for a chat.

How to prepare data for the Key Influencers visual

AI visuals in Power BI are powerful tools for uncovering hidden patterns — but they depend on having the right data. Power BI’s Key Influencers visual uses machine learning to determine which attributes are most important for a selected outcome.

Unlike traditional visuals such as bar charts or line graphs, where the relationship between data and display is obvious, data for AI visuals require more care and attention. The machine learning model powering Key Influencers can only generate useful insights if your data is clean, well-organized, and rich enough to find patterns.

Let’s look at how to prepare your data — and what to do if the “No influencers found” message appears.

What is the Analyze field in Power BI key influencers?

The “Analyze” field (also known as the Metric) is the target outcome you're investigating. This could be:

• A categorical field (e.g., Green, Amber, Red)
• A binary outcome (True/False)
• A numeric field (e.g., satisfaction scores)

Best practice: Use a field with low cardinality, that is few unique values. This helps the model detect meaningful patterns more reliably.

If your field is continuous like revenue or age, create binned categories. For example, instead of raw scores, use “High”, “Medium”, and “Low” categories.

Also, ensure each row in your dataset represents a unique observation — no duplicates for the same individual, customer, or case.

Choosing Key Influencers “Explain By” fields

The “Explain by” section contains the fields that will be evaluated to understand what might be influencing the thing you want to analyse.

Tips for choosing “explain by” fields:

• Include as many attributes as you have relevant (and clean) data for
• Avoid columns with many nulls or blanks
• Use correct data types (text, numeric, categorical)
• Look for attributes that have sufficient data. More rows give better performance with AI visuals. Aim for at least 100 observations per category — this gives the AI model enough depth to detect reliable patterns.

What to do when Power BI says “No Influencers Found”

Seeing the “No influencers found” error usually means the model couldn't find any statistically significant relationships between your Analyze field and the explain-by variables.

Here’s how to troubleshoot it:

1. Is your data distribution too even?

If your analyze field is too evenly distributed (e.g., 50% Yes / 50% No), there may not be enough variation for the model to detect influence.

2. Clean your data

Missing values reduce the model's ability to draw accurate conclusions. Clean up your data by intelligently filling in blanks or remove incomplete rows.

3. High cardinality in the metric column

If the Analyze field has too many unique values (e.g., raw revenue numbers), you could get the "No Influencers Found" error. Bin or group your metric into logical categories (like “Above Average” / “Below Average”).

Improve AI accuracy with data breadth and depth

When working with AI visuals like Key Influencers, think about both:

• Breadth: the number of attributes (columns) you provide
• Depth: the number of observations (rows)

A wide, clean, and deep dataset gives the machine learning model room to detect complex patterns and relationships — leading to more meaningful and actionable insights.

And if you would like to talk to us about getting your data AI-ready, then get in touch. We love a good data problem! 

How does Key Influencers work?

Microsoft Power BI includes the Key Influencers visualization, which uses machine learning to create insights from your data. The training of the data is done within Power BI, making it quicker and easier to harness the power of AI. There’s no programming or selecting machine learning models – it’s all done for you. That means you have more time thinking about the business opportunity (or problem) and less time worrying about getting it to work. All of which means that Key Influencers is worth another look. 

What problem does Key Influencers solve?

Before we start doing something more efficiently, let’s think why we would use Key Influencers at all. The visualization is designed for businesses who want to track important metrics. There are different names for these metrics, including:

• Key performance indicators

• Performance measures

• Objectives and Key Results (OKR’s)

• Balanced Scorecard measures including financial, customer, internal, and learning and growth

• Scorecards

• Lead and lag indicators

I’m sure there’s more – if you track metrics under a different name, put it in the comments and I’ll add it!

But whatever you call the metric; these are all ways of tracking things or processes that are important to your business. But tracking a metric is only part of the problem. The other half is knowing what to do to improve it, and that can be difficult. Everyone has a view, but it’s expensive to try all possible combinations.

A data-driven approach is more effective. By collecting data on what’s actually happened, you can cut through the decision-making process and get results faster. And that’s what the Key Influencers visual does. You provide it with data that includes actual results for the metric, plus lots of factors that may, or may not, contribute to improving it. Key Influencers uses machine learning to tell you which factors are important.

It doesn’t matter whether you want to influence the metric to be high (like sales) or low (like errors), the Key Influencers visual works in the same way. For example, if your metric is absenteeism, you might have data that includes work patterns, commute distance, job profile, etc. Some of these factors that may be contributing to the problem and Key influencers helps you understand which are important. 

How does Key Influencers work?

The key influencers visual is actually two visuals in one: 

1. Key influencers

2. Top segments 

There is a tab at the top of the visual that allows you to toggle between the two. They both use the same set of data, however, and they both work by using supervised machine learning. This means that the model is trained using data where the outcomes, in this case absenteeism rates, are known. The model then learns the patterns and correlations between the outcome and influencing factors.

• The key influencers visual uses linear regression to show which attributes in your data correlate most strongly with your metric.
• The top segments analysis uses decision trees to segment the data into groups based on the attributes in your data. 

In both cases a sample of the data is used to “train” the machine learning model, before processing all the data and generating the results. All of this goes on behind the scenes, within the Power BI engine. And it does it all impressively quickly. There’s a huge amount of power within this single visual!

What data works best with Key Influencers?

Supervised machine learning works by crunching as much data as you can find. However, it must include actual results relating to whatever you are analysing. For example, if you are analysing absenteeism, you need data for your chosen metric, say number of days absent in a given period (such as month or year). And related to each case of absenteeism, you also need the factors that might affect the metric, such as demographics, job profile, etc.

When data includes known results, it’s called supervised learning. The machine learning algorithms take these past observations and figure out why they happened.

Let’s talk!

Are you using the Key Influencers visual? What do you think? Leave a comment about your experiences – good or not so good! And if you interested in finding out more, let’s talk. 

Unlock the power of AI with Key Influencers

AI is nothing short of revolutionary when it comes to solving tricky problems. Anyone who has been using generative AI such as ChatGPT knows that it’s an order of magnitude more powerful than what went before. But as with any ground-breaking technology, AI takes some getting used to. 

Traditionally, we develop software to carry out tasks based on our own assumptions and knowledge. Now, AI is using data to provide better answers to our problems. It’s still our data, but the power of AI is such that it can make more sense of it than a human can. AI's ability to provide more accurate, data-driven solutions to our problems and opportunities is incredibly powerful.

AI: A new approach 

Let’s say your goal is to increase sales. Traditional marketing would tell you to define your target market using market research and then craft a message tailored to that audience. While this approach works, it’s not always straightforward and by necessity has to include some assumptions. 

AI, on the other hand, uses a different approach: it sifts through large datasets, identifying the segments that are most likely to respond to your message based on past behaviour. And that’s the difference: AI doesn’t rely on what people say they will do; it looks at what they’ve actually done.

It’s not so different to what a market research expert once told me long after he had retired: "Look at what people do, not what they say." That’s precisely what AI does—it analyses historical data to provide insights that are grounded in reality, not assumptions.

Get started with AI with Microsoft Power BI

We tend to think of AI as being expensive and requiring teams of data scientists. In fact, Microsoft Power BI is a great way of getting started. It includes several powerful AI visuals: Key influencers, decomposition tree, Q&A, and Smart Narrative.

The Key Influencers visual uses AI to pinpoint the factors that influence the metric you're tracking. Whether it’s sales volume, net profit, or error rates, the Key Influencers visualization can help you identify what drives your key business outcomes.

What Problems Can Key Influencers Solve?

The Power BI Key Influencers visual works by analysing data to uncover the factors that either increase or decrease your chosen metric. Here's how it works in a nutshell:

1. Identify your key metric – eg sales volume, customer satisfaction, or product defects.
2. Gather relevant data – collect information on various factors that could impact your metric. This could include customer demographics, process efficiency, or external conditions.

Data can come from existing process or created for the project, such as:

• To increase sales – which customer demographics can you identify?
• To reduce errors – which data are you already monitoring? What could usefully be added?
• To increase productivity – which factors might impact employee performance that are already known?

Get your data in shape

For AI to work its magic, your dataset needs to meet certain criteria. Fairly obviously, there has to be some possible cause and effect between your data. There's no value in adding observations that do not influence your metric. In addition:

• Data may be continuous (numeric) or categorical but each row must represent a unique case.
• Data must be clean with no errors, duplicates or outliers that could skew the results.
• Get as much data as you can – AI thrives on large datasets. At an absolute minimum, Microsoft recommends having at least 100 observations for any one factor and at least 10 observations for comparison purposes. But large datasets give better results.

What do the results reveal?

The Key Influencers visual ranks the factors that influence your metric based on their influence, helping you quickly identify the most important drivers. But here’s where it gets really interesting: the AI doesn’t just stop at general insights—it can also segment the data to reveal deeper patterns.

For instance, it might show that "large companies" drive higher sales, which you might already know. But Key Influencers goes further by identifying more specific segments, such as "large companies in France’s textile industry" having a particularly strong impact on sales. These insights are tough to uncover manually, especially when dealing with large datasets.

Next steps: put AI to work

The Key Influencers visualization is just one of many ways that Microsoft Power BI can integrate AI into your business analysis. However, getting your data ready for analysis is crucial for success in any machine learning project.

At Anatec AI, we’ve got a ton of experience in data wrangling and data engineering, ensuring that your data is clean, structured, and ready for powerful AI insights. We also create semantic models and measures, so your data is in the right format for visuals such as Key Influencers. If you want to learn more about how we can help you unlock the full potential of Power BI’s AI features, don’t hesitate to get in touch for a chat.

Data is the new oil

Almost 20 years ago Chris Humby coined the phrase “data is the new oil” and it has been repeated like a mantra ever since. “Data is the new oil” has come to define a generation of business thinking. Data has changed the way we think, and the way we pay for things. 

Chris Humby was a data scientist before we knew that we needed data scientists. He worked with Tesco and found value in a place where no one else had looked: the weekly shop. Back then, each unremarkable purchase of toothpaste or bread was just that - unremarkable. Today we know differently. We now recognise the value in data that shows, for example, the purchase of nappies. We know that it indicates a high probability of household with a baby. Which in turn means a high probability of tired parents likely to redeem coupons against toys or premium brand nappies. And conversely, there’s no point offering money off toys or nappies to households without young children. 

The result was the Tesco Clubcard which became a win-win for both consumers and Tesco. And, of course, Chris Humby didn’t do too badly out of it either. Together, they had struck oil. 

Data needs to be refined

As with many catchy sayings, there’s more than a grain of truth in what he said. Like oil, valuable data is often hidden and needs research to be identified as such. And like crude oil, data also needs to be refined to be made useful. There was investment and experimentation in the Clubcard project before it became the success we know today.

Where is your data hidden?

A lot has happened since then, including of course AI. Whilst AI was around 20 years ago, it was not in the accessible format it is today. Azure AI Services make AI models available on a pay as you go basis, requiring an understanding of software engineering rather than machine learning to implement them.

But a lot of valuable data is still hidden. Some companies have made big investments in data analysis,  but many have not. And almost all businesses have vast amounts of data in databases, spreadsheets, legacy systems, and many other places. This is data that no one has yet identified as valuable.

Competitive advantage in an AI world

In a world where AI makes general knowledge easier to access, competitive advantage will come from business-specific knowledge. That is, the knowledge that individual businesses or organisations have built up over time and is unique to their organisation and industry. This is company-confidential data that has value to decision-makers - provided it can be easily accessed when it’s needed. 

Consider the needs of a medium sized business or department that wants to make in-house data easily available to their people. In the past, this might have been done using a combination of induction training, on-going training courses, operating manuals, and data shared on intranets.

ChatGPT has shown us a friendlier way to access data. AI now makes it much easier to access company data in the same way. Azure AI Search provides functionality to augment large language models like ChatGPT with domain specific data, such as company knowledge bases or information held in a database. 

Azure AI Search

Azure AI Search enables advanced querying of different types of data. It has clever ways of indexing data, which enables better performance when querying the data. And it integrates with other Azure services to provide natural language processing. 

It is no longer news that data has value. Now the question is how business should use their corporate data to best advantage. Azure AI Services, which includes Azure AI Search, puts better data within the reach of more businesses.

If you want to explore what Azure AI services could do you for you, then get in touch. We are Azure software developers with solid experience of software engineering and data manipulation. And we’d love to help you find your oil.