Can you trust your data?

If you’re relying on data to make decisions, here’s a question for you:

Can you actually trust your data?

Bad data leads to bad decisions. And if you’ve ever tried to build a Power BI dashboard using unprepared data… you know what I'm talking about. That’s because good analytics starts before the dashboard—with trustworthy data.

So, let’s talk about what makes data trustworthy—and how to get there.

The 3 C’s of trustworthy data

To be useful, your data needs to be:

• Correct

• Current

• Constant

These three qualities are the backbone of any solid reporting system. Miss one, and your insights could be misleading at best—or flat-out wrong.

1. Correct data: accuracy isn’t optional

Data errors creep in more easily than you think. A common name mix-up might credit someone with attending a course they never showed up for. Or a stock item might never be recorded because “we were going to use it straight away.”

Sound familiar?

Then you’ve got things like:

• Null values

• Duplicates

• Outliers

• Inconsistent fields between systems

All of these distort the truth your analytics are supposed to reveal. Cleaning and validating your data isn't optional—it's foundational.

Question for you:

What’s the most unexpected data error you've ever uncovered?

2. Current data: how fresh is “fresh enough”?

Everyone has a different definition of “up-to-date.”

• For a factory floor, real-time data might be essential.

• For HR reports, yesterday’s numbers might do just fine.

But what matters most is transparency—do you know how current your data is, and can you trust that timestamp?

3. Constant data: reliable, available, and secure

Once your data is cleaned and verified, you need to keep it:

• Securely stored

• Regularly backed up

• Available wherever it’s needed

You don’t want your cleaned dataset disappearing on a lost laptop or overwritten by mistake. Constancy means your data is dependable and accessible, day in and day out.

Choosing the right data platform: why Azure?

The Azure data platform gives you flexible, scalable ways to store your analytics-ready data, depending on your requirements:

Azure SQL Database

• Great for datasets up to a few terabytes (TB)

• Geo-redundant and cost-effective

• Easily scalable up or down

• Supports Medallion architecture using schemas or databases

(If you’re curious about that approach, I wrote more about it here.)

Microsoft Fabric

• Ideal for high-performance analytics at scale

• Better suited for large volumes of data

• Higher cost, but better performance

• Also supports the Medallion architecture.

Once your data is in the cloud, everything gets easier—from sharing semantic models to boosting Power BI performance.

Don’t skip the foundations

Data visualisation tools like Power BI are only as strong as the data underneath them. Trustworthy data isn't just clean—it's correct, current, and constant.

So, here's a challenge:

What’s your biggest headache when it comes to data quality or reporting?

Drop it in the comments—I’d love to hear what you're wrestling with (and maybe swap ideas on how to fix it). And as always, if you want to talk about data quality, you can get in touch here.

Anatec AI has worked with data quality issues for many years. We focus on helping companies make better use of their data to improve their performance and resilience.

Key words: reporting, analytics, data quality, Power BI, Microsoft Fabric, dashboard design

Learning data is valuable data

Learning data may be the most underused strategic asset in your company. The world’s most powerful companies are increasingly evidence-led, yet learning and development (L&D) is often under-represented when it comes to data analytics. Right now, it has never been more important to nurture and retain good people. AI tools such as ChatGPT can assist skilled and knowledgeable people; but they don’t replace them. 

Blended learning - messy data

The move from classroom training to on-line or blended learning has reduced costs and facilitates less time away from work. But it has made learning a lot harder to track. Micro-learning results can be difficult to evaluate when you want a strategic view of your learning.  E-learning, YouTube, and MOOC courses are all more complex than pure classroom training when it comes to managing data. 

The rise of the learning management system

Learning management systems (LMS) make it possible to centralise learning data. An LMS not only enables you to distribute your own learning content but also track other learning in different formats. The only challenge is to turn this valuable asset into actionable insights. 

LMS data and Power BI 

Whilst every LMS has a variety of reports, there’s no replacement for the ability to interrogate data in analytics tools such as Microsoft Power BI. The ability to visualise the data in different ways, add multiple “slicers”, and use AI to find patterns and trends is tremendously powerful. Plus, you can match your learning data with data from other sources such as performance data, HR retention data etc. This flexibility can unlock real insight into what your learning programmes are achieving. Power BI can help you to make sense of a more complicated world of learning, massively improving your chances of finding patterns amongst the noise. 

How Anatec AI can help

If you want to do more with your LMS data and are stuck somewhere along the way – we can help. There are multiple ways of getting data from your LMS and into Power BI, depending on your needs. We can design data interfaces and help you choose the right way to store reporting data. We build semantic models that focus on your business needs and can help you make better use of Power BI AI capabilities.

Whether you’ve got a simple question, or an analytics project you need help with, get in touch to see if we can help. We’d love to hear from you. 

Anatec AI has worked in the learning and development space for many years. We designed and built two custom learning systems before LMS’s were even a thing. Now we focus on helping companies make better use of their data to improve their performance and resilience.

Key words: learning analytics, LMS data, Power BI for L&D, evaluating learning impact

Real-time intelligence: why speed matters more than ever

How real-time analytics is reshaping decision-making, competitiveness, and customer experience

Modern tools like Microsoft Power BI have revolutionised the way we analyse and visualise data. So much so that we’ve almost forgotten that we are looking at historical data that can be days, weeks, or even months old. After years of having no alternative, we’ve become desensitised to the delay between data collection and making sense of what it means.

But in today’s fast-paced and data-driven world, decisions are only as good as the data they are based on. That means that reducing data latency is more important than ever. When it comes to data, speed really does matter.

Having real-time or near real-time data has become a source of competitive advantage, a way of improving products and services, and a way of staying relevant. Data latency is no longer just an IT issue, it is now firmly on the boardroom agenda.

One company that has transformed its product offering with real-time analytics is The New York Times. By migrating to the cloud, and getting real-time data about how their readers behave, they have improved their content and tailored it to user preferences. The result has been greater reader loyalty and greater relevance in an industry that has had to make huge adjustments from print to digital. Whilst the New York Times is a multi-billion dollar business, the principles they are using are universal.

In the past there were good reasons for analysing data in batches: processing streaming data was complex and expensive, in memory storage didn’t exist, and cloud computing was less mature. But these technical barriers have now gone. And new technologies like Microsoft Fabric make it much easier for businesses to benefit from real-time data analytics. Microsoft Azure enables businesses to migrate to the cloud incrementally, delivering business value at every step.

If you’re exploring how to reduce data latency in your business — or how to build real-time dashboards from streaming data — we’d love to help. We are experienced developers for the Azure data platform and can turn ideas into robust data solutions for a range of needs. Why not get in touch for a no-obligation exploratory chat? It can only speed things up ....

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!