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Data Automation


Why 2022 is the year we spend less time crunching numbers, and more time telling stories.

Data automation defined

Traditional data processing and analysis can be broadly broken into 3 tasks:


  1. Extraction: pulling data from a source

  2. Transformation: converting raw data into a format that you can mine for business intelligence

  3. Loading: The data has to live somewhere - data loading gets information into your system of choice

What data automation does is simple: it performs data extraction, transformation and loading without any need for manual support or supervision.  Understanding what data automation is capable of is a little more complicated (but a lot more exciting, too).

Benefits of data automation

The biggest benefit of data automation is the same as any automation, winning back time for the parts of work you actually enjoy.

Manual Data Extraction, Transformation and Loading (ETL) can force you to spend more time crunching data than analysing it.  And the more systems you have to extract data from, the longer the process. Generally, customers with legacy systems tend to struggle with this the most.

Data Automation cuts that time down substantially

That isn't just valuable because it saves time and money (although it does both).  Data automation is valuable because it frees up more time for analysts to do what they love - uncovering brilliant insights in data.  They get to spend more time being motivated by the most creative and rewarding aspects of their job, and less time just moving data around. 

How to get the most value from data automation

Spend less time crunching numbers, and more time telling stories.

We're going to try and live up to the point we're making.  Throughout this article, we'll spend less time talking about the process of data automation and more time telling stories about how organisations are using it to get ahead. First up, Amazon.  Here’s how 98.2% of all website interactions go.

Someone will enter a website, click 6 times, look at 5 pages and then leave.  That's it. It’s not long either - in fact most of the time, that interaction will only last 4.41 minutes before it’s overThe interaction is only different 1.8% of the time - when a user will make a purchase or contact the brand.  Now ask yourself - what could you learn about a customer if you knew exactly what pages they looked at during that session, or how far they scrolled, and where they lingered?  Probably a lot.

On it’s own, that’s not so useful - we’re a world of individuals and who’s to say if the next visitor wants something completely different.  Fortunately, the average business website gets 694 visits per month.  That’s 600+ hours of non-stop website interaction per year. What could you learn about your customers in 12 months if you looked at that information closely? What could you do with that knowledge? The short answer is lots.  Amazon asked themselves this same question 20 years ago, and it helped them become the 4th largest company in the world.


Depending on your personality type, Andreas' weekly meetings either sound like an interesting night or a really dry way to spend your time. But there’s no doubt that it’s valuable.

Amazon’s focus on data gave rise to:

  1. Personalisation
  2. Targeted product recommendations

  3. Customised front pages for every customer

Any Amazon customer knows about those front-end uses of data automation, and I think we're all guilty of clicking 'buy now' on one of those 'suggested' products that pops up on our homepage so we all know how effective that personalisation is at converting.  What might surprise you is what data automation Amazon performs on the back-end of its website.

As soon as you start browsing a new type of product, Amazon starts automatically forecasting the inventory they’ll need in 3-6 months.  The reason?  Because they’ve looked at millions of transactions and worked out exactly how long you’re likely to wait until you ‘unexpectedly’ decide to buy that product.  Data automation at it's finest.  And here's some good news, this type of data automation was cutting edge when Amazon first got started, but every business can access the tools needed to do the same thing today.

Our favourite data automation tool

Microsoft Power Automate

Winning back time for customer care

Case study:  Thirteen Group


What if the data you need to analyse isn't digital at all? Suppose your organisation still collects data with pen and paper forms. How could you possibly use data automation?  Three words - Optical. Character. Recognition (OCR). One of the main reasons we love Microsoft Power Automate is how easily it lets you detect and extract data from scanned documents using OCR. 

Here's a quick overview of how OCR-based data extraction can work as part of an automated process:

Microsoft's Power Automate logo

Automate anywhere.


Power Automate in practice

In 2020, we helped Thirteen Group start their digital transformation with these two technologies . They manage 34,000+ properties as a landlord and housing developer - each one with dozens of associated documents. 

Thirteen's agents need fast access to those documents to deliver outstanding customer services, so efficient storage and document management is essential.  Thirteen's property management process was mostly analogue, relying on agents processing paper documents and manually importing each into an SQL database.  Slow and boring, staff often lost concentration during the process and made mistakes.  These mistakes made it difficult for other agents to find data quickly when they needed it to help customers.

As a Gold Microsoft Partner, we knew that Microsoft SharePoint and Power Automate could help Thirteen transform this process.  Combining the two with OCR, we built Thirteen a system that automatically:

  1. Extracts written or printed text from a scanned document

  2. Transforms the data into a machine-readable format, and

  3. Loads that data into a customer folder on SharePoint automatically

With it, Thirteen's agents won back roughly 250 days of working time a year from manual data management.  Less time hunting data, more time helping customers.


"Working with boxxe has been a fantastic experience, they saw my vision and brought it to life, adding their own innovative and forward-thinking ideas.  It’s paved the way to Thirteen Group being Microsoft-first in our future developments."

Jayne Allport , Head of Service, Systems & Application Improvement, Thirteen Group

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