"We're Data-Rich but just drowning in reports and tables of data, without any insights."
"We do it this way because that's the way we've always done it."
"We're looking to use AI, but aren't sure where it makes sense in our business."
We build a data-model off your system exports (csv/Excel) and any manually-tracked spreadsheets, delivering significant improvements to data-visibility across the business.
For whichever datasets are deemed important, we build a data-model of the business.
This could mean:
We tag & enrich your data in simple, practical ways for the end-users of any report or dashboard.
This could mean:
There's often a data-integrity/cleanliness aspect to begin with, that most businesses aren't expecting, but are often very grateful for.
Analytics is tightly woven with the Automation side of our business, given that most of the time we save clients, is from manual data-processing in Excel.
This is the core of our business; we do it very well.
Taking system exports from Accounting/ERP Software to deliver superior Financial Reporting & Time-Series Analytics.
Say you have a WIP Export on open jobs. It comes out as a poorly formatted CSV, no data-visibility until someone spends 20-mins manually formatting it & calculating figures, then sending it out. Sometimes it's done quickly, sometimes it's not done at all.. We'd build you a WIP Snapshot, not only so that it's processed instantly, but likely with vast improvements to your previous view.
Or maybe you want to track debtors, not just with a point-in-time snapshot, but you'd like to see the trends over time, slicing by different vectors of the business. If what you're looking for isn't part of your software package (common), we solve that by exporting the raw data to csv/excel and building a debtors dashboard tailored to your needs.
For example, Slice on Customer-type vectors like;
Very dependant on the business/industry. Non-Financial Analytics is a broad topic.
Revolves around order/case/job/project/deal/etc... tracking.
However your business operates, you'll likely be operating off a distinct operational ID of some kind, (job_number, case_number, order_ID etc).
We build that into the data-model and do two things:
Operational analytics feeds into broader process automation
CRM/ERP Exports, with Historical Data Mining and Database Enrichment.
Things we work on:
One data model, multiple lenses. Board sees trends and risk. Managers see KPIs and comparisons. Teams see today’s priorities. Definitions are fixed, so when the numbers change between meetings, it's because the numbers changed. Drill down from totals to the line item. Export the packs you need without rebuilding them each week.
Without a doubt, the most time we save for clients is by automating previously manual data-processing inside of Excel.
We broadly classify anything that starts & ends in Excel as "Reporting Automation".
Everything else, we'll group as:
But all automation, we like to think of as a data-pipeline, with three parts:
This is our bread & butter for saving you time.
The approach is one of PowerQuery for automated data-processing (And excels data-model for analytics). We do very little in what most people think of when they hear the word "Excel". Excel is just the best end-user interface for business data-workflows, it's simple, ubiquitous & dynamic.
For perspective, we work with a few accounting firms, none of them, nor most other businesses we work with, understood what can be done to improve spreadsheet-related workflows until we started working together.
Point being, if accounting firms are having us help with their financial reporting & excel-workflows, we're doing something right.
Ties in with Operational analytics.
Outside of analytics & data-process automation with the above approach, automation is done with either custom python scripts or your standard suite of workflow automation tools (Zapier, Make, Power-Automate etc).
For the standard workflow automation tools, we generally suggest that clients have some staff members learn the basics. It's a waste of our time and your money to be building drag & drop automations. From experience it's usually a younger, tech-savvy team member who's keen on filling that role, often they've already built a few automations for themselves or the business. We can help them here.
Pipelines can be simply transferring data from point A to point B.
Ie, an event is triggered by the arrival of an email, a field (or fields) need to be extracted from that email, then updated to either an Excel spreadsheet or into your CRM/ERP. Sometimes requiring an internal notification that said fields have been extracted & updated so that the next step in the process can commence.
Any time your business receives a system-generated email, requiring someone to monitor, search the inbox, manually extract information from the email (or attachment), maybe save down said attachment and then action the data that was contained within...
Then that's an opportunity to not only free up time, but significantly improve the timeliness & consistency of the whole process.
Most commonly, emails from suppliers. System generated or otherwise.
On the flip side, it's less common, but if your business is manually generating emails from templates, with only the fields of data changing from client-to-client or case-to-case.
Then the population, sending & monitoring for responses in a more structured way is something we can help with.
PDF parsing can sometimes require AI, it depends on the variability of the input docs.
Ie, A highly structured govt/compliance document, same headers/formatting each time, no AI Necessary. But for a Semi-structured purchase order pdf, AI might be the best option.
The most common type of docs we've done these projects for are docs required in Onboarding procedures.
On the web-scraping side of things, it's often for scraping prospect lists, but it depends on your business. There's been some really creative ways people have gotten value from our web-scraping projects.
Start Small, Keep it Simple, Practical & Low-Risk
Our approach for everything is to keep it simple, same goes for AI.
For context, whenever somebody says AI nowadays, they almost certainly mean a Large-Language-Model (LLM).
The concept of a Large-Language-Model (like ChatGPT) only entered existence in 2017 with the release of the research paper, "Attention is all you need".
And most businesses really only started hearing about or using AI from 2022/'23 onwards.
It's a very common occurrence when a business asks us, "Can this be automated with AI?"
9 times out of 10, our answer is "No, but it can be automated without AI."
Many businesses still lack a unified understanding of:
Tom's conducted over 100 AI Basics training sessions to businesses ranging from small one-man-band realestate agencies in Perth, to $1b plus ASX listed entities.
The combined feedback from those sessions has been very helpful, not to mention that most recipients find the training itself extremely valuable.
Tabular data (database/csv/excel) & highly-structured text data are not things we need AI for.
It's the semi-structured & unstructured end of the data-spectrum where AI dominates.
For automation via python scripts, AI's not always required, and we'll tell clients if that's the case. Suggesting instead, a much more reliable, deterministic pure-python script (No AI) when that's the best option.
But sometimes those python scripts will require an AI step or two. Simply put, any semi-structured or un-structured text data:
... these are the types of things we consider using AI for.
Often the most involved part of those pipelines is in building the pre & post-processing steps.
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