If we had to choose two of the biggest trends in recent years, our decision would be quite easy: artificial intelligence and data. And that’s because the impact these two technologies have had on the world are basically immeasurable and significantly impact the way in which we work and live. In addition to being powerful on their own, artificial intelligence and data science, when paired together, have the potential to be incredibly powerful and transformative.
In this article, we’ll dive into AI and data science, exploring how they’re both individually transformative and, of course, how they’re working together to shake up the status quo.
Ready? Let’s dive right in.
Artificial Intelligence & Data Science
Before we can dive right into the synergies of artificial intelligence and data science, it’s important to first understand how each works individually to best comprehend how they complement each other:
As you know, artificial intelligence technology seeks to teach computers how to mimic human intelligence and decision-making processes, eventually reaching a point where computers can substitute humans in certain areas.
Data science, on the other hand, is the practice of finding trends and insights in large amounts of data through complex machine learning algorithms or statistics.
Both are incredibly powerful and have tremendous potential when used together. Artificial intelligence tools are trained on data and learn from past experiences, meaning they need large amounts of data to be accurate with their outputs. And while data sets are valuable, they need to be understandable or automated to fully reach their power. This is why, when paired together, the two are unstoppable.
Wondering how exactly they work together? Let’s dive a bit deeper into the connection between the two in five key areas:
Focus: artificial intelligence wants to make sense of the data collected through data science techniques.
Strengths: with more available data, the better AI tools can mimic human intelligence. And with the power of AI, trends and patterns in data can be easily identified.
Needs: to better analyze and draw conclusions, both data science and AI tools need large amounts of varied, trustworthy data.
Techniques: both data science and AI use complex machine learning algorithms to reach their desired outcomes.
Uses: both data science and AI can help a wide range of industries deliver better results, such as finance, healthcare, and ecommerce.
Instead of thinking of how data science and AI work in a linear fashion, it’s best to understand their synergy as a circle that’s constantly cycling through different phases. Confused? Let’s break it down:
For AI tools to work, they need large amounts of data on which to be trained; data science models are some of the only ones that are capable of handling so much data.
When AI tools do have the right amount of data, they’re able to quickly and efficiently process this data, helping improve the algorithm for the next data collection cycle.
AI tools can then transfer this information to the data tools, helping its next data analysis go more smoothly and be more efficient.
This cycle continues over time, continuously improving the overall performance of both.
Why should companies bet on AI and data science?
We hope that it’s clear exactly how incredible the potential of these two are, but just in case you need a bit more convincing, let’s dive deep into some of the tangible and clear benefits of using AI and data science in your company.
Better decision making
Do you want everything you do to be backed by data? We hope so! And by betting on data science and AI, you can ensure that your business decisions are backed by firm data; with the help of easily dissected data sets, you can also make better predictions for the future to help you anticipate future needs or problems and get ahead of them.
Better efficiency
Want to free up your employees’ time and ensure they’re focusing on their more pressing and demanding tasks? With the help of AI, you can speed up your processes through automation–and, of course, use data analytics to figure out exactly what areas can be automated to help you reach peak performance.
Better user experiences
As companies gain more information about what users want, they’re better able to understand their users and provide personalized experiences to them. This not only creates a better user experience, but also encourages brand loyalty and a feeling of belonging that will cause the user to return to the company in the future. And where does this information come from? Data, of course.
Better market positioning
An increasingly high number of companies are offering new products and trying to stay on the front of the market, but without data to learn exactly what users want and what future trends are likely to be, it can be hard to prepare for the future. In order to stay competitive in today’s full market, it’s important to use data (and updated data!) to stay relevant.
Better risk management
In addition to making better business decisions for the future, data can help identify potential threats and problems before they happen to give companies a chance to fix or prevent them and avoid serious consequences and save time and resources.
Practical Questions of Using AI and Data
It all sounds really great, but you might be wondering if it’s all sunshine and butterflies. Well, it’s not and there are certainly some considerations to keep in mind. Let’s discuss:
‘Are AI outcomes infallible?’
The knowledge of AI machines is limited to the data on which it is trained. And in a practical sense, this means:
If AI systems are trained on biased data, outcomes will reflect this bias and this can have potentially catastrophic results.
To get an accurate picture of a business’ status, complete and thorough data is needed; if there are holes in the data or incorrect inputs, the AI tool’s analysis won’t be accurate.
‘Is it ethical and safe to use personal, consumer data to train AI systems?’
One of the most common concerns surrounding AI relates to the issue of ethics. Is it ethical to use personal data to train AI systems? And is the data used in AI training protected? Finding the balance of using data to provide more personalized and accurate experiences while protecting users is the main challenge facing companies today.
‘Will AI replace jobs?’
Another main concern facing the field of AI, especially when it comes to data, is the belief that AI is stealing jobs. And while this is a legitimate concern and something to keep in mind when discussing the ethics of AI, what we’ve learned from the tremendous effort and skill it takes to properly employ AI is that you’ll need skilled professionals to handle the challenge of properly using AI and data for your business.
As you can see, data science and artificial intelligence make quite the pair; together, they can completely transform your company’s potential for creating more dedicated and personalized experiences for users. But skilled AI and data science professionals are needed, and this is where you come in.
Ironhack’s new AI School aims to teach working professionals the AI skills they need to bring new technologies to their roles, speeding up their processes and making better business decisions. Sound like something you’d like to add to your toolbox? Check out our courses and make the best decision for your future–betting on artificial intelligence and data science..
