Most teams get the idea to automate part of the process, but then they sort of… stop. They schedule a report to refresh, but still transform data manually, or they build great dashboards, but still manually email people when a number moves. Analytics Process Automation (APA) looks at the whole chain of transforming data to insights as one process – no more, no less.
At Vidi Corp our digital analytics consultants helped over 1000 businesses automate their analytics – and I’m going to share the lowdown with you. This article will cover what APA is, just how to automate each bit, and where AI really starts to come in handy once you’ve got the rest sorted.
Analytics Process Automation is all about systems running the analytics process from start to finish with minimal or no human interference at all. It covers collecting data, cleaning it, analyzing it, refreshing reports, and setting off the action that follows.
As you’d expect, the data analytics implementation process itself remains the same – you still collect data, clean it, calculate KPIs, visualize the results and take action on what you’ve learned. APA is all about stripping out the manual effort from each of those stages, rather than just automating the reporting bit.
For example, instead of logging into five different platforms to download CSV files, a system can just pull the data on a schedule and clean & combine it in the background, refresh your dashboards, and give the right person a shout when a key metric suddenly takes a nosedive. That’s APA in action.
These terms get used a lot together, which can cause some confusion when teams start scoping out a project.
Robotic Process Automation (RPA) is about automating the manual tasks a person would usually do (for example copying data from one system and pasting it to another) using software. It’s all about the task, not about getting any insight.
BI automation usually means automating report refreshes and dashboards. It automates the reporting bit, but leaves the data prep and follow-up bits to be done by hand.
Analytics Process Automation covers all that – automating the whole analytic work process, plus the process that runs around it, so data prep, analysis, reporting and the action that follows are all just one smooth workflow. In practice, setting up APA means combining BI tools with automation tools rather than having to choose between them.
Every APA setup we build is made up of the same basic things.
Data Prep Automation – getting data out of source systems and cleaning it up without any manual effort – this is where most of the time gets saved.
Analytics Automation – doing calculations, creating KPIs and dashboards that update themselves instead of having to rebuild them each time.
Action Automation – the bit most teams seem to miss: automating what happens after you’ve got your insight – whether that’s sending out alerts, getting approvals or writing updates back into another system.
Maintenance and Governance – so that the workflow keeps running smoothly and if something goes wrong you can figure out quickly
Automated analytics starts with working out which tasks take up the most time, then tackling them in a structured way. Five areas tend to be the most time-consuming:
In our experience most manual work goes on in data extraction and transformation – and that matches a survey by CrowdFlower which found data scientists spend about 80% of their time collecting and cleaning data – automating those two bits and the rest of the process pretty much looks after itself. We’ve taken clients from 14 hours of manual data prep a week down to zero, and set reports to refresh at a rate of every working hour.
We take you through each of these 5 steps in detail, with the tools and setup for each, in our guide to automated analytics.
This is the bit that separates analytics process automation from ordinary reporting. The insight isn’t the end of the process – someone still has to do something with it.
Once a metric crosses a threshold, an automated workflow can take action on it – whether that’s sending a reminder, creating a task, sending an approval request or writing an update back into the source system. We build these with Power Automate flows and when a system has no API with RPA.
In our experience this is where the biggest time savings come in after the reporting is already automated – a report that tells five people to do something is still five bits of manual work – and automating the handoff removes the delay between seeing a number and responding to it.
We can rely on AI to automate a bit more of the analysis process: answering the types of questions that people would otherwise have to ask an analyst. We’ve tested not one but two approaches, & both really work much better when we’re sitting on top of a solid reporting foundation.
Build your dashboard first – then you can let AI do its job
We reckon the first thing you should do is get your KPI dashboards up & running. A dashboard is where a human data analyst has already coded the key definitions – like, what counts as an active customer or which revenue figure is the one that matters. That verified layer is what makes AI answers trustworthy. Without it, you’re essentially letting the model pick & choose its own columns and just hoping it picked the same ones your finance team would have.
Our first approach is to use Microsoft Fabric data agents to build a chatbot that people can ask questions in plain English. The agent then goes off and finds the relevant tables and calculations in the data model – and answers accordingly.
What makes this tick is the instructions you pass to the agent. Our Microsoft Fabric consultants usually give it rules on how to format answers, business jargon like ‘sales’ and ‘revenue’ so it knows they’re the same thing, and guidance on which tables and columns to use for each type of question.
And it turns out that how the data model is built is pretty important too – the model should be as simple and unambiguous as possible, which in practice means things like keeping one single calendar table rather than having loads, so the agent never has to try & guess which one to use.
The second approach is to give AI your existing Power BI reports and let it see the measures, formulas and columns your team has already agreed on. This means the AI can reuse all that logic rather than writing its own version of it, and it can also pick up the default filters that need applying – which is a common source of wrong answers when a model works directly against raw tables.
We reckon AI should be the layer on top of the work that’s already been verified, not a replacement for it. Get your dashboards up & running, confirm the definitions, then connect a chatbot so the AI references that work and performs some straightforward calculations on top.
For companies that already have their reporting in place, this is a pretty sensible next step. It gives people quick answers to routine questions without an analyst in the loop, while keeping the numbers anchored to definitions that a human has signed off on.
Automation of the analytics process really frees up a lot of time. The collecting, cleaning and refreshing that consumes most analyst time stops being a manual job.
Consistent results – processes run the same way every time, so numbers don’t change depending on who prepared them.
No single person dependency – reporting keeps going even if someone is on holiday or leaves.
Shorter gap between data & decision – with the follow-up actions automated too, insight turns into action without having to wait on a person to notice.
Automated marketing analytics
A marketing team was spending hours a week on cleaning and combining data from different sources. Our Power BI consultants built a Power BI report that automated the whole chain from start to finish.
They saved over 30 hours a month and could spend that time analysing the data rather than preparing it. The automation also removed recurring errors and made the reporting easier to act on. Check out the Vidi Corp Clutch review.
Automated financial analytics
We automated financial reporting for another client, who then identified 50k EUR worth of cost savings and 20k EUR of new MRR opportunities from it. We worked closely with their team to confirm the reports were accurate and genuinely useful before automating them. See the Vidi Corp Clutch review.
We assess your data sources – we take a look at where your data comes from and what it would take to reach it. We have a library of pre-built connectors and build custom ones where a source isn’t covered.
We automate data preparation – we map the manual steps your team currently performs to clean and organise data, then rebuild them in tools like Power Query and SQL so they run on their own.
We build reporting that refreshes itself – we develop dashboards in Power BI or Tableau, set the refresh schedule to match how your business runs, and confirm the definitions behind every metric with your team.
We automate the follow-up actions – we add the alerts, approvals and workflows that turn a number into a next step, using Power Automate or RPA depending on the systems involved.
We add an AI layer where it makes sense – once the reporting is verified, we can connect a chatbot so people get answers to routine questions directly.
It’s shocking to say this, but most analytics teams are still spending far too much time on manual tasks – mainly in the data preparation phase and in the aftermath of report reading.
If you want to see which bits of your analytic process could be taken off your hands and automated, give our team a shout.
It’s the automation of the entire analytic process: collecting data, preparing it, analysing it, reporting on it and acting on the result. We often abbreviate it to APA.
RPA essentially just automates routine software jobs. APA on the other hand, automates the whole analytic process – and guess what? all of a sudden it’s probably going to include RPA as one of the tools it uses to perform the actual task.
Not at all and no. the term APA is often associated with certain providers but the reality of the matter is the approach itself is perfectly agnostic to any particular platform. We actually deploy it using our preferred set up with the Microsoft universe which includes Power BI, fabric, Power Automate and RPA, and also with Tableau – but only were clients are already established with it.
To be honest we recommend kicking off with data extraction and transformation. This is usually where the bulk of the manual effort sits, and once you get this bit sorted everything else falls into place a lot easier.
