Today businesses around Australia depend on the likes of Salesforce, Oracle, Microsoft enterprise systems to manage their operations, sales, marketing and more.
However, almost every business I have encountered throughout my time in technology has struggled with one thing. Getting shit into the system.
Middle Management is constantly toiled with this task with constant shouting on the floor “guys please update your CRM”, “team, why isn’t this in the system”, “our reports are all out of whack”, “our data needs so much cleaning its so messy”.
Where I believe Gen AI really serves a strong purpose is about transforming our everyday lives into data.
Just think about it for a second. Everything is data.
Talk to someone on the phone? data.
Chat to someone in a room? data.
Type an e-mail out to a beloved customer? data.
So what’s really the pattern here? These are all types of qualitative data.
Qualitative Data Analysis
Qualitative data is really a mystique known to us. Conversations with several close data scientist friends of mine have alluded that the best ways to assess and visualise qualitative data are:
- Word Clouds
- Word Frequency Count
- Sentiment Analysis
- Tree Diagrams
But if the core of data is about taking action, I’m not sure as a business leader how I would even take action around this sort of data.
I mean think about it for a second. Sentiment analysis says that “hey your Google reviews seem positive”. But what can you take from that? What can you action from that?
Thematic Data Analysis
Thematic Data Analysis has been around for some time, but has become more applicable in our modern day world.
Historically, Thematic Data Analysis was done painstakingly manually. The core concept behind thematic data analysis is really around understanding themes in certain blocks of qualitative data.
A thematic analysis of this blog post itself may arise themes of: “Artificial Intelligence, Gen AI, Qualitative Data Analysis.”
But imagine doing this for large sets of data! Imagine hundreds, thousands, hundreds of thousands of rows of data in a database. I would need a team of 1000 people to simultaneously process that data before the data becomes irrelevant to my business.
Well guess what? Gen AI can do that for you.
Running a database through Gen AI, suddenly helps to appear themes of data that are apparent off your dataset. If you work at McDonalds you may not be aware that customers actually dislike the curve in the road of the drive through based at every McDonalds, which gives them the anxiety to even drive-in in the first place!
Now with thematic analysis, I can actually get some actionable insights/data that are useful to me!
And not only that, I can collect and format properly our everyday world data, like conversations, e-mails, chatbot data, and harness them into a format that ensures that our key systems, CRMs, ERPs, HCMs etc, are all data rich. So I can finally take accurate actions towards my customers or clients that they are yearning for.
What an amazing thought!