Why do data analysts spend so much time preparing data? (and what can we do about it?)

Arthur Geneau — Mon, 20 Apr 2026 18:24:12 +0000

Data analysts spend a majority of their time cleaning data, not generating insights. According to a Forbes study, 60% of their time is dedicated to cleaning data. And more recent testimonials suggest this number may have gone up since the time of publication.

But why is this so important? That’s because insights are only as accurate as their underlying data. Bad data quality can mean reducing accuracy with fewer data points, or skewing results with outliers.

This is why every year US organizations incur average costs of $12.9 million each on poor data quality, according to a Gartner study.

Why does bad data quality cost so much?

The costs of bad data can be broken down as such:

Storage costs: ideally, organizations should be paying to store data that they can use. Increasing a balance sheet to store unusable data amounts to a waste of resources.
Cleaning costs: this is the work that analysts spend reviewing a dataset, filtering the undesired data, adapting some data points, and producing a usable dataset.
Fixing costs: this includes work like documentation, back-and-forth between teams to increase the quality of the data in the future, and other processes to that effect.
Opportunity costs: data value decreases over time. Leaders and managers need to make timely decisions based on data. If too much time is lost cleaning up a bad dataset, they will have to make uninformed decisions or abandon a project entirely.

At this point, some may have realized that the costs of poor data quality happen after data has been received.

Thus, the solution seems rather obvious: ensuring the quality of data before it is received. However, upstream validation also comes at a cost.

The costs of upstream validation

Upstream validation is a great technique to ensure a higher quality of data without incurring the costs mentioned in the previous section.

As any software engineer knows, shift-left testing – moving testing earlier in the lifecycle – is a great way to reduce the cost of detecting defects.

Implementing upstream validation comes with 2 costs:

Infrastructure costs: this can mean putting servers online, scaling with traffic and other engineering solutions.
Resource diversion: mainly from dedicating a team of engineers to fix data quality over improving a product.

While not perfect, this is already a significant improvement over simply fixing bad data.

Building a new solution

Bad data quality is the gap between the intention behind a data model and its implementation.

When analysts design a data model, their intention is clear, but there is no guarantee the implementation will follow. This is how bad data emerges.

That’s why Filasys was built to be a self-serve data analytics platform that treats data models as enforceable contracts, not just documentation.

This fixes the gap between modeling and implementation by letting analysts specify validation when modeling the data and then enforcing the validation when receiving the data.

You can take a look at our tutorial to see how to create enforceable data models with Filasys.

How to start collecting web analytics using only HTML and Filasys.

Arthur Geneau — Wed, 08 Apr 2026 01:31:17 +0000

Web analytics is a critical component in building any successful website.

Whether for SEO purposes or improving user experience, websites need to collect data in order to see how it performs, what works well, and what can be improved.

In this tutorial, we will see how we can start collecting enterprise-level web analytics in a website using only one (and we mean 1) line of html in a website.

Before we begin

The website used in this tutorial is a mock online shop used solely for the purpose of this tutorial. Its source code code can be found here.

Filasys is a data-analytics platform build for creating custom analytics.

Cookieless tracking will be used for this example. This means that sessions will be tracked without the use of cookies. Filasys uses a special technique to generate anonymized session ids when doing cookieless tracking.

For this tutorial, we will need:

Git
A Filasys account to manage data collection.

Setting up the project

In order to save time, we have a pre-made project set up on github.

We start by cloning the project by running the following command in the command line:

git clone https://github.com/Filasys/web-sdk-starter.git

Alternatively, we can also visit the github page and download the project as a zip file:

The contents of the project should be as follow:

- index.css
- index.html
- index.js
- lemons.jpg
- logo-icon.png
- oranges.jpg
- readme.md
- strawberries.jpg

The index.html file contains the html code for the website, index.css the styling and index.js is here to make the website reactive (not necessary for collecting data).

The various images are here to make the website more interesting than just showing text.

We can ensure that the project works by opening index.html from a file explorer into a browser. The following page should be on display:

Collecting data

In order to collect the data, we first need to create a project on Filasys to store it.

On the filasys console, we go to the "Projects" tab, a create a new project. In order to match the Web SDK, we need to make sure to select the Web Analytics preset before the project creation.

Then, in the project overview, we select "Web SDK" under Integration, make sure that "Auto Start" in selected, and copy the line of html under it.

We can then paste the line of HTML into the head of our document in index.html.

And we are done! Now opening the page will start collection session and performance information about the page. (Make sure to reload the page for the changes to apply)

If we open the page, visit the "Events" tab of the project, select "Session Events" and go to "Logs", you will start seeing some events (It may take a minute or two for the events to appear):

Additionally, if we go to the "Dashboards" tab, and visit the "Performance Analytics" or "Session Analytics" dashboard, we can start observing some charts:

Going the extra mile

The Web SDK can also track interactions with the website, alongside some details for the interaction.

To that effect, we can add two different attributes to buttons in our website: data-filaction and data-filadetails.

data-filaction is used to describe the kind of interaction we are tracking. We will choose to track click interactions.
data-filadetails is optional and needs to be set together with data-filaction. The value for this attribute can be anything and will be recorded as the interaction details.

Going back to the HTML, we can modify the buttons as follows:

<button type="button" class="add-to-cart" id="add-strawberries" data-filaction="click" data-filadetails="add-strawberries">Add To Cart</button>
<!-- ... -->
<button type="button" class="add-to-cart" id="add-oranges" data-filaction="click" data-filadetails="add-oranges">Add To Cart</button>
<!-- ... -->
<button type="button" class="add-to-cart" id="add-lemons" data-filaction="click" data-filadetails="add-lemons">Add To Cart</button>
<!-- ... -->
<button type="button" class="checkout" data-filaction="click" data-filadetails="checkout">Checkout</button>

Now interacting with the buttons on the web page will send some interaction events matching the configuration of the buttons.

Conclusion

In this tutorial, we've shown how to set up web analytics using only HTML. Now performance events and session events are automatically collected, as well as interactions with the website.

If you want to learn more about the Web SDK and the events being collected, you can check out our documentation.

DEV Community: Arthur Geneau