Artificial Intelligence is the simulation of human intelligence by a machine or computer.
It is synonymous with Machine Learning and Data Science, however AI is the current buzz word of choice.
These articles portray robots doing human tasks, when actually, they are algorithms crunching mass amounts of data to make automated decisions.
Google Search Trend: Artificial Intelligence News ArticlesEveryone is talking about how AI can revolutionise your company and claiming AI is the silver bullet.
CEOs are demanding tech teams put in place AI to increase sales.
CFOs want them to automate manual tasks.
And CTOs want to be using the latest in neural nets or deep learning.
So can’t we go ahead and hire a data scientist?Missing foundationsPhoto by Francesco Ungaro from PexelsMost companies might think they are ready for AI, but their data will tell a different story.
Machine Learning requires a good amount of clean, historical data.
Before you even hire a data scientist it’ll make sense to have a data warehouse or data lake in place.
That means they aren’t hunting around for data.
It also makes sense to have a set of KPIs — otherwise, it will be difficult to measure the impact of your AI Project.
The data scientist is the missing pieceIf your data is in a good place you’ll be forgiven for thinking the data scientist is the missing piece in your puzzle.
Whist the scientist is a key player on the journey to artificial intelligence, his or her talents will be wasted without the support from stakeholders.
The most successful AI projects are born from a business or customer problem.
The role of the data scientist is to ease these problems and solve them with data science and machine learning.
Yet, it is often the case that data scientists can do more than solve the problems you know about — they can identify solutions to problems you’re not even aware of.
Create a backlogPhoto by Startup Stock Photos from PexelsJob role aside, there is nothing more frustrating for a data scientist than boredom at work.
A data science backlog is important to ensure that your scientist has enough work to do.
It allows you to review and prioritise what is important.
This will identify what will drive the biggest return on investment.
Feature engineeringPhoto by rawpixel.
com from PexelsFeature engineering can be one of the more time-consuming parts of data science.
Don’t fall into the trap of thinking that a data scientist will start building algorithms instantly.
They will need to spend time interrogating and exploring the data to understand how it can be fed into the right algorithms.
This will help them find the right features to train the model — they may even build new features into the dataset.
RetrainingPhoto by Lukas from PexelsThe project does not end once the model has been built, tested and released to production.
The model needs constant care and attention.
It will need to be retrained, tweaked and sometimes rebuilt as more data is produced and the processes change.
Data scientists are in for the long game.
Companies of all sizes are starting to either hire data scientists or engage with agencies to build their first models.
Before rushing in,Take a look at your dataGet your company involvedCreate a backlogDon’t forget to factor in engineering timeAnd finally — once you hire one — they are there for good.
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