This guide was created by May Jean Gimoto, Bachelor of Library and Information Science for the subject Information Literacy.
This guide has been checked and evaluated by the Instructor and Librarian in-charge before inclusion.
1. Data teams that are more agile and adaptable will be better equipped to stay competitive and realize the benefits of AI across the enterprise.
The ever-changing nature of business over the past year convinced enterprises across industries of the necessity of data and AI in decision making. However, not everyone has mastered how to use these tools effectively yet.
2. You need the data to be readily available and high-quality
An obvious first step to working data, and AI algorithms trained on that data, is to have data to use. It’s rare to find an enterprise today not actively gathering data on most aspects of their business, so this shouldn’t be a major concern anymore.
3. You need the expertise and technical skills
Even with the right data assets in place, you’ll struggle to get any value out of them without the right talent. The speed with which AI is deployed across industries heavily depends on the expertise and technical resources to deploy ML.
We are in the age of Data. There is no doubt about it. We are collecting more data at greater rates each year. You need to be ready for this but being ready has many aspects.
When you look at collecting data, start by looking at your business. You have been doing what you are doing for years most likely. That means you have your first dataset at your very fingertip – your own knowledge and a team with experience.
If you start with what you know you can begin to understand what you WANT to know. From here, you can begin the process of planning to collect useful data that can be interpreted for meaningful insights that create actionable outcomes.
Ensure that you are up-to-date with the latest changes in regulation and compliance as you begin to grow your data sets.
Keep in mind you can’t know everything and technology changes rapidly which affects the way and types of data collected. Do yourself a favour and find a trusted partner to help you walk the road of robust and effective data collection.
