Who is to blame?

When someone makes an expensive mistake, it’s easy to react. 

“Who is to blame?” 
“Who do they report to?” 
“How did you let this happen?"

Blaming someone isn’t difficult. Sometimes the person themselves thinks it’s their fault. 

But a more useful question might be:

“Which system allowed this to happen?”

Quite often we already know there are flaws in the system. Or there’s no system where there should be one. Yes, there are always exceptions, but dig deep enough and often there’s a sub-optimal system somewhere.

What is a system?

Systems are everywhere. Some we recognize as systems, such as the accounts payable system, or the payroll system. They have names and often have software to make them work. 

Other systems are less obvious, or even invisible.

Think about how you start your day. Is that a system? 

It doesn’t have a name and may or may not involve software. You might start by switching on your computer, scanning your email for important messages, figure out what’s urgent or outstanding, then decide what you want to achieve that day. 

That’s a system, just not an obvious one. 

Systems thinker Daniel Kim defines a system as:

“Any group of interacting, interrelated, or interdependent parts 

that form a complex and unified whole that has a specific purpose.”

That definition gives us four things:

• Parts – different elements within the system, including people.
• Relationships – how the parts interact and depend on one another. 
• A unified whole – a recognizable system with boundaries and outcomes. 
• Purpose – its reason for existing.

It’s a decent starting point when figuring out whether something is a system or not. Not whether it’s a good system, just whether it’s a system.

Identify the system that’s to blame

Once you can identify and recognize a system, you can start to improve it. You can optimize it, fix it, or create a system. 

Preventing the same mistake happening again may be more important than playing the blame game. Well-designed systems help people do a good job, not just once but every time. Which is worth a lot.

Poorly designed systems make mistakes possible or inevitable.

Start with objectives

A system that delivers the intended business benefits starts by understanding the system you're trying to improve. Only then do you decide whether the answer is new software, better data, improved reporting, applying AI, or a better way of working.

That's how we've always approached projects, and it's why we start every discussion with the same question:

“What are you trying to achieve?”

We are software engineers who specialize in implementing Microsoft technologies. Only by understanding what you are trying to achieve can we design a system that is right for your objectives and your organisation. For us this is fundamental, and in the long run will be a major factor in the success of the system. 

If you are concerned that some aspect of your systems is letting you down, get in touch. It costs nothing to find out if our skills could help.

Small bets, big wins

The most successful systems we’ve developed began with a Visionary.

Not always someone with a grand title, just someone doing their job who saw a better way. Not just better for themselves, but for their team, and the organisation.

We call them Visionaries because plenty of people notice problems, but very few actually do something.  But then a lot of ideas aren’t worth the effort. The hard part is knowing when one is.

From idea to system: the process

When a Visionary comes to us with an idea, we follow a simple discipline:

1. We understand what they want to achieve — and whether we’re the right people to help. Not every problem needs a system, and not every system needs us.
2. We design a high-level architecture. A good system with a poor architecture will almost always fail.
3. Then we build a prototype — a deliberately small part of the system. This is often to test out a high-risk area, but it could also be part of the UI so people can see how it would work. But it’s always limited, and always designed to move the thinking forward.

The power of the prototype

Today this is often called a proof of concept. Sometimes it’s just “let’s see if this works”.

Names don’t matter. What matters is that the prototype tests whether something actually works — technically, operationally, or commercially. (In theory there’s no difference between theory and practice. In practice, there is.)

Because it’s a small bet, it can be thrown away. We often throw several prototypes away, because each one is small. But each time, we’ve learned something and the next step is clearer. We might refine a prototype or build something else. Each is a small bet, designed to reduce risk and build confidence.

Eventually, the prototype stops being a prototype and becomes a system. The Visionary can get budget to develop it further because everyone can see how it will work. 

Do you have a visionary idea?

Many of the systems we’ve built have gone on to have long, productive lives. Mostly well after the original Visionary has moved on. They didn’t know at the start whether their idea was large or small; they only found out by making small bets.

If you have an idea that might be a better way, and want to explore it further, let’s have a conversation.

A light-weight way to solve heavy data problems

Most people want more from their data, but knowing how best to use it can be difficult. There’s always a question mark over whether the development effort will have a big enough pay-back. Smart leaders know that everything has a cost – in management attention and budget.

But doing nothing is not the answer. Data-driven organisations are out-performing those less skilled in data analysis. So, what’s the answer? 

At Anatec AI we are big fans of the proof of concept or prototyping. The idea is to do a relatively small amount of work that does a lot of heavy lifting. It might focus on analysing a limited amount of data to assess the data quality. Or it might be to test out a theory on how two data sources could identify patterns. Or it might be a difficult interface that absolutely has to work for the solution to be a success. Each project will be different, because each problem is different. The approach, however, is consistent: do a small amount of high-risk work that tells you whether or not you are on track. 

The cloud provides a great way of doing light-weight small projects – you don’t need to buy servers or install operating systems. You don’t need to commit up-front. You can just use the cloud’s in-built flexibility to do just as much as you need to do, then decide what to do next. This is a lot safer, and easier to justify than ambitious projects that can end in mediocre results.

Azure containers are a great fit for that way of working. They are reliable, whether running locally or in the cloud. And they can securely access data that’s held in databases, cloud storage, queues or APIs. Of course, containers are just one example of a light-weight approach to our small-project ethos, but they are a good example of how smart Azure technology can produce big results at a small cost. 

The Anatec AI design approach underpins everything we do when architecting modern cloud systems. Our solutions need to be reliable, portable, and consistent across environments - and developed one bit-sized step at a time. Because our clients need to be in charge of the direction of the project and the budget, not the technology.

Do you want to get more from your data without breaking the bank? As qualified Microsoft developers, we’ve got a whole toolbox full of Azure tools to engineer data-driven solutions. If you’ve got an idea for your data that might be a game-changer, get in touch to find out more about how we work.

Keywords: Azure, containers, proof-of-concept, cloud native, data analytics, Azure AI

Three steps to optimising learning at work

Whilst everyone agrees that learning at work matters, not all organisations are good at knowing whether their learning programs actually work. That’s because optimising learning is messy. It needs data from multiple systems, departments, and stakeholders.

Historically, L&D hasn’t rushed toward that complexity. But that’s changing.

Three things have shifted the landscape:

1. Learning management systems (LMS) are now widespread, and they hold large amounts of learning data.
2. Cloud platforms enable data to be combined and analysed without the need for huge data warehouses. That makes learning analytics projects much more cost effective.
3. AI tools are available off the shelf and viable for small, focused experiments.

The result is that learning analytics is no longer theoretical or reserved for large enterprises. It’s affordable, practical, and increasingly hard to ignore.

So where do you start?

What is learning analytics?

It’s always useful to start with a definition, and learning analytics is pretty straight forward:

Learning analytics improves learning outcomes by analysing data.

It may not be complicated, but it’s still useful. And it also gives you the correct order to do things - an order many organisations still get wrong.

Step 1: Define what “better” looks like

You don’t start with data. You start with outcomes. What are you actually trying to improve?

• Fewer errors?
• Higher sales?
• Faster onboarding?
• New skills that are being used in day-to-day work?

There is no universal answer. It will depend on the business context and the problems you’re trying to solve. But until you can clearly describe what success looks like, analytics will only produce dashboards, not insight.

Step 2: Decide how you’ll measure improvement

If you can’t agree on a measure, you can’t tell whether learning made any difference.

Choose metrics that reflect the outcome you care about, and that you can actually measure. That might include:

• Knowledge gained
• Reduced error rate
• Skills demonstrated
• Business performance indicators

Your metrics don’t need to be perfect they just need to be useful. The goal is better decision-making, based on data.

Step 3: Identify the data that supports those metrics

Most organisations already collect far more data than they use: LMS activity, course attendance, assessment results, feedback surveys, HR data, performance metrics. The problem isn’t that the data doesn’t exist, it’s that it’s siloed. 

When data from multiple systems is brought together, patterns start to emerge. Training volume alongside retention. Learning pathways alongside performance. Timing of learning alongside error rates.

Putting it into practice

This approach works best when treated as iterative. Small proof-of-concept projects using modern business intelligence tools and AI can deliver fast feedback on existing learning initiatives.

You don’t need a multi-year transformation. You need a focused question, a clear metric, and enough data to test whether your learning is actually helping.

If you are looking at getting more from your learning data, and improving learning outcomes, get in touch for a no-obligation chat. 

Keywords: learning analytics, data analytics, learning management system, LMS, Power BI, L&D dashboard, Azure AI

Unlocking the ROI of Learning

Or how to free your data from the LMS

Relevant to: HR, L&D, Operations, Finance

You’re under pressure to show the impact of learning. You need to prove that training links to performance, that learning programmes improve retention, and that investment in learning is moving the profitability needle.

But when you go looking for answers, all roads lead to your learning management system (LMS) — and stop there.

You know the data is in there somewhere. Completion rates, assessment scores, time spent learning, department-level engagement — all the raw material for powerful insights. But what you get instead are clunky exports, static reports, and dashboards that don’t speak the language of the business.

If this sounds familiar, you're not alone.

You’ve got the data!

You know you’ve got the data, that’s why you have an LMS. The bad news is that it’s not always easy to get at – particularly if your LMS is software as a service (SaaS).

If you have found your valuable learning data locked in your LMS, I feel your pain. This wasn’t what you were expecting.

Using your data

There are different ways to use data stored in your LMS. For many, the standard reports are more than enough. But for those that want to analyse learning data together with other data, or do more in-depth analysis, you have to get data out of your LMS and into a reporting data store. Here’s how that might work:

1. API extraction – most modern LMS platforms provide RESTful APIs. With proper authentication (usually via OAuth 2.0 or an API key) you can programmatically extract learning records.
2. Data pipeline & transformation – Data is ingested into a reporting database such as Azure SQL Database, where it's cleaned, normalized, and enriched with metadata.
3. Semantic modelling – Using tools like Power BI, you can then build a semantic layer that defines business terms — e.g., "active learner", "average time to completion", "learning impact score".
4. Dynamic dashboards – These models power interactive visuals, filterable by time, team, location, or training programme — and update in real time or on a schedule.

If you are wondering why you need to store, check and clean data in yet another data store, I talk about that here: Can you trust your data? The bottom line is that your data needs to be clean, up to date, and readily available for analytics purposes. 

Power BI is L&D’s new best friend

With this approach, you’re no longer limited by your LMS’s front end. You get full control of your learning data — and the power to connect it to performance or finance data for deeper insights.

So, although you may sometimes feel that your data is locked in your LMS, there are ways to get at it and analyse it in friendly tools like Power BI.

Here at Anatec AI we’ve been working with data, interfaces, APIs, and learning systems for many years. So, we are well placed to help if you need it. And we can help with Power BI dashboards, scorecards, DAX queries and semantic models. There’s nothing we’d like more. 

If you have a question or want to chat about any challenges you’re facing, get in touch.

Keywords: learning analytics, data analytics, learning management system, LMS, Power BI, L&D dashboard, L&D scorecard