Differences between AI, Data Analytics, Data Science & Data Visualisation for Marketing
Understand the differences between key data-driven marketing phrases to improve your application of each discipline.
Phrases like artificial intelligence, data analytics, data science and data visualisation are buzzwords that blend into a soup of similarity, describing technical details that are best put into an industry context to truly understand.
The jargon of data-centric nature of marketing can sometimes be enough to set your teeth on edge and activate eyerolls, but defining data science versus data visualisation and the differences between business analytics and statistics is worth taking the time to do.
(Ahem, especially before we embark on the complexity that sits beneath the umbrella of artificial intelligence and the amazing tools it can unleash for business and marketing.)
Data science relates to research, intelligence, modelling and analytics but it is worth understanding why it is different to the other terms - and not just because data scientists get paid more than business analysts or researchers.
Data science lecturer at University of Technology Alex Scriven says analytics and data science overlap in terms of exploratory tools, techniques and data visualisation - and he has just written a book called From Business Intelligence to Data Science (Manning Publications) which outlines this in detail.
The simple way to sum up data science versus data analytics is that analytics looks backwards while data science tends to look forwards.
“Analytics is still answering questions of your data, such as 'what kind of products do consumers buy?' whereas data science may ask a question like 'can you predict what this customer will buy?',” Scriven says. “Even something like correlation matrices and some hypothesis testing could be considered analytics, which also has a place in data science.” So it does cross over and tends to get confusing.
NSW Government Chief Data Scientist Ian Oppermann says the opportunity for data science exists when you have an impossible question. The crunching of large datasets to build models or predict outcomes using data science is powerful.
“Data science is good at building predictive models and looking at root cause and ‘what if’ scenarios. Statistics and analytics looks at things differently,” he says, using the example of a model he built to prevent fire and rescue teams wasting time attending false fire alarms.
Statistics and analytics showed that 97% of fire alarm callouts were people burning toast rather than a genuine fire. Yet data science enabled a team to build a predictor model to try to determine when each alarm would be false or genuine.
The data scientists aggregated social media data, weather data, lunar cycles, pollen counts and fire alarm panel data to predict when the fire alarm callouts would be genuine.
“The model predicted with 77% accuracy, with the lunar cycles accounting for 2.5% of that accuracy,” Oppermann says. “It made a sceptical fire commissioner learn to love what we could do with data science.” Analytics, by comparison, typically only allows you to look at the data and draw out metrics that exist in it.
Data means nothing if you can’t see the meaning
Technical tools like Tableau and Power BI are powering business enterprise visualisations that go beyond the average Excel chart, making business enterprise reporting way more interesting than it used to be.
Savvy business leaders can move beyond a stagnant monthly sales report graph to see their own business dashboards updated in real time - you can even find beautiful examples on Pinterest.
The rise of data visualisation goes beyond business reporting. Take a look at David McCandless’s Information Is Beautiful, Flourish Studio or The Pudding to see how big data is changing media and storytelling.
Data visualisers like Priya Ramakrishnan - who worked in Nasdaq’s innovation lab - says the real skill of data visualisation comes from being able to clean and structure the data properly before analysing it to find the story hiding in all the numbers.
So what about Artificial Intelligence & Machine Learning
If there is one little term - AI - hiding big opportunities, it is artificial intelligence, which is allowing a multitude of business processes to be automated in new and efficient ways.
Machine learning (also called ML) is a branch of artificial intelligence, but typically the terms all get used to refer to the same thing.
“Recommendation engines, customer churn models, sales forecasting, machinery maintenance are all areas that classic ML can be used (across business),” says Scriven.
CSIRO Data 61 Senior Principal Researcher Dr Surya Nepal believes AI is where we will see many Australian businesses advance quickly to automate and solve bigger business problems.
AI also refers to deep learning, a powerful form of machine learning based on mathematical operators that simulate the structure of the human brain and can make amazing predictions when you have large quantities of (reasonably) structured data.
Sales forecasting, attribution modeling and lead scoring can make more powerful predictions with deep learning than analytics or statistics alone. There is a catch, though as many neural nets don’t actually let you know the precise factor or cause – it often just “is” or “isn’t”.
The Centre for the Future founder Richard Hames says the 2020s will be a perfect storm of disruption with the rate of apps, tech platforms and knowledge channels continuing to accelerate.
“What marketers need to do is stretch their imaginative capabilities and embrace what’s possible,” he says.
Look to upskill in data driven marketing and analytics? Here are our recommended courses from ADMA IQ: