Automated analytics revolutionises data processing by leveraging advanced tools and technology to swiftly and accurately interpret large datasets with minimal human intervention.
Unlike manual analysis, which is time-consuming, repetitive, and prone to errors, automated systems transform raw, disorganised data into actionable insights faster and more reliably.
In this article, you’ll learn what automated analytics is and how to make it work for your projects or business.
Automated analytics is when systems and software do the entire data analysis process with little to no human intervention. That includes collecting data, cleaning it, updating reports, interpreting trends, and sharing results.
For example, instead of logging into 5 different platforms to download CSV files of data for analysis, a tool can pull all the data for you on a schedule. It can clean and combine the data in the background, refresh your dashboards automatically, and send you alerts when there’s an unusual change in key metrics.
All that is automated analytics in action.
Automating data analytics starts with identifying the tasks that take up the most time and effort and then using tools to do them in a structured way.
At Vidi Corp, we’ve helped over 1,000 businesses automate their analytics, and we did so by focusing on five areas that are usually the most repetitive or time-consuming:
In our experience, most of the manual work happens during data extraction and transformation. This matches the CrowdFlower survey, which showed that data scientists spend about 80% of their time just collecting and cleaning data. Once these two steps are automated, your analytics process is mostly automated, reports update themselves, and you can focus on insights and action.
Now, let’s break down how to automate each of these five areas.
One of the biggest time drains in analytics is manually pulling data from multiple platforms: logging into accounts, downloading spreadsheets, and copying and pasting data into Excel. You don’t have to keep doing that.
You can set up systems that automatically retrieve data from various sources, such as websites, databases, or cloud applications. This can be done through:
We explained this in more detail in another article about automating data extraction, where we showed how to extract data using Power Query, custom connectors, and APIs.
After extraction, the next big chunk of time is usually spent cleaning and formatting data. This involves things like:
Doing this manually every week is not only repetitive and time-consuming, it also increases the chances of errors.
You can automate it by setting up rules once and letting the system apply them every time new data comes in. In Power BI, Power Query can be used to define all your transformation steps, including filtering, splitting, merging, and calculations. Once set, these steps will run automatically with each data refresh.
We had a client who used to spend 14 hours every week doing this manually. After we set up Power Query to clean and transform their data, that workload dropped to zero.
We covered this with examples in our article on automating Power BI reports. The same logic applies in Excel Power Query, Python scripts, or any BI tool that supports transformation logic.
Once your data is ready, the next step is to make sense of it. That’s where analysis comes in. This involves writing formulas, calculating KPIS, and creating charts from scratch. But you don’t have to do it all manually.
You can speed up the process by loading your data into pre-built templates. These templates have common metrics and visuals already set up, so your data just flows in. You can also customise these templates to show deeper insights and what matters to your business.
Tools like Power BI allow you to set up alerts, which means you’ll receive an email or notification when something important changes in your data i.e. when expenses spike suddenly. So you don’t have to check dashboards all the time.
Some businesses take it a step further and utilise AI to identify patterns or suggest insights. We’ll get to that later, but for now, know that even without AI, a lot of the manual analysis can already be automated.
Once your data is cleaned and your dashboards are built, you’ll want them to stay up to date without having to click “refresh” every time. That’s where automating the report refresh comes in.
Most analytics tools let you schedule data refreshes to run automatically. For example, with Power BI, you can set your report to refresh as often as eight times a day if you’re using the Pro version. That means your dashboards will always show the latest data, whether it’s first thing in the morning or just before your weekly team meeting.
If your data is stored on your computer or in a local system, some tools (like Power BI) may require a data gateway—a small bridge that connects your local files to the cloud. It sounds technical, but it’s straightforward to set up. We explained the full process in this article: how to automate Power BI refresh step-by-step.
At Vidi Corp, we’ve seen real impact from this. We set the Power BI reports of our client to refresh every working hour. This used to be a manual task that took hours each week. Now, it runs quietly in the background.
Automating report refresh helps you keep your dashboards up to date, reduces the risk of working with stale data, and saves your team time.
When your analytics workflow is up and running, it’s essential to keep it that way with minimal manual effort. Automated maintenance can do just that.
You can set up rules to handle errors automatically. If a data extraction fails because an API has reached its limit, for instance, you can have the system retry the process after 30 minutes. This type of retry logic is particularly useful for data sources with strict request limits.
You should also set up alerts, which will notify you immediately if something goes wrong. Tools like Power BI can send you an email if a dataset fails to refresh. You can then fix the issue before it affects your reports.
We break down these maintenance steps in more detail in our article on how to automate report maintenance, including how to avoid common issues and keep things running smoothly.
Your analytics system should be able to monitor itself, recover from common problems, and alert you only when something needs your attention.
Many data analytics tools like Power BI now have AI features to automate parts of the analysis. For example, you can click an “auto-create” report button, and Power BI will create a basic version of your report with common insights as shown in the video below:
This saves time by giving you a starting point, though you’ll usually want to customise it further for deeper and more tailored insights.
AI can also explain changes in your key metrics over time. Say you see a spike in sales on your chart. By clicking the “analyse” button, Power BI uses AI to dig into the data and find out what’s causing that increase. A practical use is shown in the image below. This saves you time trying to figure it out yourself.
Another helpful feature is the Power BI Key Influencers visual. It shows you which factors affect your main metric. Take the dashboard below as a case in point, where we wanted to see what influences our customer satisfaction score the most. The Key Influencers visual identified and showed that faster delivery times lead to higher satisfaction scores.
These AI-driven insights help you understand your data without needing to write complex formulas or run manual analyses.
As seen throughout the article, automated analytics brings clear advantages that help your data work harder and smarter for you:
Some real-world examples from our work at Vidi Corp show how automated analytics can save time and improve business insights.
One marketing team used to spend hours a week cleaning and combining data from different sources. We built a Power BI report for them that automated everything – from pulling in the data to keeping the report up to date. They saved over 30 hours a month and could focus on analysing the data, not just preparing it. The report also fixed common errors and made the data more understandable and actionable. See Vidi Corp Clutch Review.
We automated financial reports for another company. They saw cost-saving opportunities and new ways to grow their business. We worked closely with them to make sure the reports were accurate, useful and up to date. See Vidi Corp Clutch Review.
We make automating data analytics easy and simple for our clients by following this process:
With years of experience helping businesses automate their data analytics smoothly and reliably, Vidi Corp knows exactly how to get your data working for you.
Ready to save time and make smarter decisions? Reach out to us today and let’s get your automation journey started!
