The (near) future of AI in data analytics

Having disrupted countless industries, artificial intelligence (AI) is now transforming data and analytics, making it faster, more accessible and more accurate − which in turn is driving business strategy, innovation and growth.

As this technology continues to infiltrate almost every sector, and some trends come to the fore, it's important for data analysts and professionals to understand how it will impact their field moving forward.

There are many potential uses for AI in data analytics which can assist with small-scale projects, as well as large-scale enterprise initiatives. Recent advances in AI − such as Dall-E and GPT3 − have shown the power of AI is truly incredible, and that its growth and learning is exponential, making it difficult to truly predict the long-term future of AI in data analytics.

However, in the short- to medium-term, we are able to make some educated guesses. Here are some of the top predictions for the (near) future of AI in data analytics:

As more businesses look to scale implementation of AI across their entire organisations quickly, it will be increasingly important for them to consider how they can automate the implementation of their data analytics algorithms to improve efficiency.

While some aspects of data analytics can be automated, machine learning algorithms are currently notoriously difficult to automate given their dependence on carefully-crafted data sets that represent the broader population.

As a result, organisations looking to scale implementation of machine learning algorithms − at least in the medium-term − will require the expertise of data engineers to enhance high-quality data sets and algorithms manually.

The demand for data scientists will only increase as AI continues to be adopted at scale. Given the scarcity of experts in this field, organisations will likely need to increase wages and offer more competitive compensation packages to attract and retain data scientists.

While today’s data analytics algorithms have already been greatly impacted by AI, we will start to see AI-powered machine learning dominate the field. As more businesses adopt AI, they will look to scale existing algorithms across their organisations.

This will require them to find ways to simplify the process of collecting and analysing data. While the implementation of AI-powered algorithms will undoubtedly impact the day-to-day lives of data analysts and professionals, it will make their jobs easier as well.

As AI advances and is able to grasp a larger number of skills, it is likely to become an integral part of the data analytics process.

For example, they will be able to use AI-powered algorithms to automatically analyse a company’s data to find potential customers.

This also means data analysts will need to understand how to implement these algorithms across their organisations, and work closely with software engineers and other team members to ensure data is collected in a standardised manner.

While data analytics has traditionally been used to help organisations make better decisions, AI can use real-time data analytics to help companies make faster and better decisions.

For example, if an airline experiences a spike in demand for flights to a certain destination due to a special promotion, the company’s computer systems can use real-time data analytics to automatically send an alert to the relevant department and suggest rerouting some flights.

However, at present, real-time data analytics is usually possible only when an organisation has standardised its systems to a large extent. While this may not seem like a significant hurdle, it can actually be quite difficult for businesses, given the wide variety of computer systems and technologies they use.

Once an organisation has standardised its systems, it can use AI algorithms to analyse data in real-time, allowing for faster and more accurate decisions that can help them to stay competitive.

Organisational change and culture shift

While some companies are already working with AI, many of them are likely still in the early stages of implementation.

As they look to implement AI at scale, they will need to prepare for an organisational change and culture shift. This includes carefully considering how to adapt their organisational structure − as AI will likely completely transform how data analytics is done, having the right structure in place will be crucial to take advantage of these benefits.

One way to ensure a business is prepared for a culture shift is to carefully consider the types of AI algorithms it wants to implement, how these algorithms can be incorporated into the organisation, how it will impact existing jobs, and what future roles are required to fully capitalise on the implementation of AI algorithms.

As AI advances and is able to grasp a larger number of skills, it is likely to become an integral part of the data analytics process.

However, technology cannot do everything by itself, which is why humans will − at least for now − continue to be an essential part of the analytics process.

While AI can help with a variety of tasks, it cannot currently replace humans entirely. As such, there will be an even greater need for data analysts to make sure their algorithms are properly implemented and working correctly.

For example, data analysts will need to carefully consider how they implement real-time data analytics algorithms across their organisations.

With all of these changes in mind, data analytics will become faster, more accurate and even more accessible than it already is.