How Can I Make My Organization's Data AI-Ready?
What Are Some Boxes To Check As You Make Your AI Journey?
Over the years and many conversations, one I’ve learned about AI is that most organizations – even larger ones – that are embarking on the AI Journey are not ready from a data perspective.
This topic is a common one when the subject of AI and machine learning pops up.
“Our data is in different departments.”
“It’s siloed.”
“It resides in different platforms and repositories.”
“It’s all in Excel, etc.”
So, that’s the next topic for this piece - how can we make our data AI-Ready?
It Starts with DATA
Companies want to jump right into the “Fourth Industrial Revolution” to prove that AI can deliver ROI in their businesses.
But, AI commercially, is still in its early stages and many companies are just now becoming AI-ready.
For many of them, it’s internal company data holding them back.
Many leaders know they have to “prepare their data” in order to train a model. But how to go about it?
This is where the difficult part starts.
Creating a unified data warehouse – a central area in your operation where all company data flows in and out and is stored – is the goal here.
Within this data center, your database is cleansed, updated, consolidated, and labeled in a uniform way across every department. This provides a great baseline for all training data going forward that will be used for any machine learning model you test and deploy.
This makes your company data accessible to all and begins to turn it into a tool working for your business.
INTERNAL POC
Once the data is aligned, test the strength (efficacy) of your models with an internal Proof of Concept (POC).
The point of a POC is to just prove that it truly is possible to find business efficiencies, save money, or potentially improve a customer experience using AI.
This is not an attempt to get the model to the level of accuracy needed to deploy it -- just to show the project can work for the business.
This is all about testing. Seeing what works and what doesn’t. You can use off-the-shelf algorithms, find open source training data or purchase a sample dataset, and/or create your own algorithm with internal staff if you have it.
Find what works for you to prove that your project will achieve the overall goal.
A successful POC is the baseline to get the rest of the project launched.
WATCH OUT FOR BOTTLENECKS
Training data can be a pain.
It could take tens of thousands of records to train your model, depending on what project you initially choose.
Just be aware of this going in – data is paramount to project success.
It is understandable that data science teams often underestimate the quantity and quality of training data they will need.
As a leader, it’s important to choose initial AI projects where you can pull sufficient amounts of data to train your models.
While not enough training data is one common roadblock, there are others.
It is essential that you are watching for and mitigating any bias in your data as you go along.
Your team will want to implement process practices to make adjustments on the fly.
CONCLUSION
That should get you started on thinking about your organizational data and how you’ll want to format it before beginning a project.
As with anything, there will be time and financial investment here.
But, with persistence and patience, running a few successful AI tests will help out the business and your team exponentially.
Get ready to get as “AI-ready” as possible. Stay open-minded. Encourage internal learning and up-skilling. Stay up-to-date on the constantly changing AI environment.
Lastly — Stay patient and enjoy the AI Journey..