The words “artificial intelligence” are on everyone’s lips at the moment, not least in the business world – and it’s easy to see why it’s the case. The arrival of technologies like ChatGPT have not only left people feeling awestruck about the potential these tools hold. People across many different sectors have also felt somewhat afraid, or even fearful, about the impact they could have on their businesses. And on a more human level, those who don’t work in the tech sector are perhaps asking themselves how AI tools are even possible.
For those concerned with these questions, it’s essential to look at the flip side. AI can also play a big role in transforming the data analysis set up across your business, whether in terms of how quickly you can get through such analysis – or, indeed, how useful the extracted insights are.
And it’s also important for those considering a career in AI development to ensure that they are up to speed with the latest on the AI technology front so that they can plan their training in this field appropriately. This article, then, will consider just how AI – and many of the other emerging technologies in the field right now – can help you get your business data analysis shipshape, or point you in the right direction if you’re considering a career in this sector.
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What is AI?
Before plunging into an explanation of how AI and the other technologies that have emerged in recent years can transform a company (especially when it comes to data analysis), it’s first important to ensure that you have an accurate working definition of AI in place. Many people may think they know what AI is but don’t actually have a firm understanding of the definition; others, meanwhile, may have heard about it in the news but don’t feel they could explain it in a pithy way if asked.
In short, AI refers to the way in which a computer can behave like a human brain. According to McKinsey, AI is the act of a machine taking on this human quality. It is, they say, “a machine’s ability to perform the cognitive functions we usually associate with human minds.” It’s perhaps best understood as an example of technological advances that hold the potential to transform the way people do business, meet each other – and live their daily lives. And make no mistake: this is a technological trend that is impacting almost every sector. According to CNN, for example, around three-tenths of radiologists are using artificial intelligence in their work – suggesting it’s becoming widespread in healthcare in particular. These patterns are replicated across many sectors, and so it’s vital to stay ahead of the curve no matter what field your business operates in.
How can it impact data analysis in business?
But the question that many people are asking is not exactly what AI is, but how it can affect their business – and their bottom line. One of the many ways in which it can do so is through data analysis. Gone, of course, are the days in which data analysis was done manually by a person analyzing realms of data by hand. The arrival of personal computers and desktop technology like spreadsheets and databases has meant that patterns and trends in data can be extracted with great ease, and it isn’t new for people to automate processes like graph production and more using these methods.
But AI has had a huge impact, not least in terms of speed. One of the many ways this can happen is through risk prediction. Any business faces risk: this could be financial in nature, especially if you’re concerned about the risk that income streams will dry up, or perhaps regulatory if you operate in a highly regulated sector or a sector in which regulation changes all the time or is complex to follow. But the way in which AI can help is huge. AI is proving itself to be better than the vast majority of other such tools when it comes to modeling risk and predicting how this might play out. The difference between AI and other such prediction tools is that AI appears to be able not only to manage the data and extract patterns, but also – crucially – to learn how to make recommendations in the context of risk.
In this sense, AI is becoming part of your risk management team in an unprecedented way and it’s likely that, in time, AI will be one of the staff!
An interesting question here, however, is likely to be whether the risk management advice that AI gives is advice that will be taken seriously. If AI gets to the point soon where it can predict cash flow worries faster than staff members can, it is entirely possible that senior executives might decide that they do not want to listen – perhaps because they haven’t clocked AI’s potentially transformative power. Either way, there is likely to be a role here for humans: AI data analysis experts will be able to present their AI models work, explain how it was done, and provide some legitimacy – or otherwise – for the findings.
What’s more, AI has also reduced the risks involved when it comes to the possibility of human error in your analytics systems. Say you have 10,000 lines of sales data information in a spreadsheet that you want to turn into a pie chart for ease of understanding in the management accounts. A person still needs to be able to decide to go for a pie chart or to check a piece of graph production code for accuracy. Where AI is different is that it is starting to not only be able to produce the graphs but is also able to know when it’s wise to produce a chart, what sort of chart to make, and more. So this goes further than software: AI is starting to be able to make decisions about what is useful and what strategic direction should be taken (decisions that, up to now humans have been likely to make), as well as just executing them. AI can, in some cases, even write code – which, from a business owner’s perspective, is an obvious benefit, as it means that at some point soon, one machine may be able to do the work of multiple humans.
That said, there is a simple volume efficiency at play here too. To continue with the comparison example of the spreadsheet, it’s clear that a spreadsheet holding 10,000 lines of data is still likely to be hard to handle and probably clunky to manage, even with a computer with lots of processing power. But AI is in another realm: it can process this level of data with great ease.
And what about other technologies?
The way that the technology debate is going is clear: AI is, by and large, considered a major part of the new technology framework. But what many people fail to notice is that AI is, in reality, just one of many new technologies that are having an impact on the way that businesses conduct data analysis techniques. It’s in fact the case that a lot of other tech types are playing a role here – and it’s important for you as a business owner to know. “Big data” has been part of the business technology framework for decades, – and it’s clear that there’s more than just AI to think about here.
It’s worth noting that big data tools can be run on the cloud, these days. In the past, businesses looking to crunch through large amounts of data to identify customer preference trends, for example, were often likely to find themselves worrying about whether or not they had the space on their servers to actually keep the amount of data they might have felt they needed. These days, it is possible for services like Hadoop – which is a well-known data analysis tool for business – to be run on the cloud, meaning that companies can just pay for the amount of storage space they feel they need and go from there (rather than purchasing or running entire servers for this purpose).
There is a wide variety of choices when it comes to scalable big data systems. As a business seeking to decide on this sort of thing, it can often feel like the choice on offer is too vast. What’s the difference, you might ask, between the likes of MongoDB and Stats IQ? There are two things to think about here.
The first one is to ensure that you have appropriate staff in place. The right sort of big data professional will be aware of how data analytics help business development and be able to advise you on how to go about choosing the right provider. It’s worth ensuring your analytics team is on point and advanced education programs can help individuals develop their skill set. St. Bonaventure University for example offers a Master of Science in Business Analytics that will equip professionals with in-depth knowledge of the latest powerful data analytic tools and softwares. This expert knowledge can be readily utilized to identify & leverage right datasets using the right software, ensuring that complex business challenges are addressed efficiently.
The second thing to consider is the makers of the big data software you’re thinking of going for. Choosing a reputable provider is of course vital. If you’re adding customer or other data to a system, you’ll want to be certain that it’s watertight and is not at risk of being lost. But there are other things to consider here too. Cost is one: many big data tools are pricey, and it could be the case that using a free option such as an open-source package like Cassandra from the Apache Software Foundation might be more cost effective for your organization. Again, it comes down in part to people. Sometimes it can be a little tricky to maintain open-source big data platforms, especially if they prove to be glitchy or similar. If you have the right data professionals on hand, it’s likely that you’ll be able to sort out any problems.
A career in this field?
Finally, if you’re an early-stage analytic professional – or someone hoping to break into the industry – then it’s also worth thinking about this question from the point of view of your own career. Picking up a career in this sector is likely to allow you to acquire lots of useful skills. You may be able to learn about data science architecture, for example, which helps explain the tools used to “hold together” different parts of the datasets you’re working with. It’s also entirely possible that you’ll be given a training course in how to use a certain coding language, known as Python, which is a skill both useful for the analysis of data but also useful in a transferable sense when it comes to other technical jobs.
A career in data analysis is, it’s important to stress, something that marries different skill sets and parts of work. It’s mathematical, in some senses, especially when it comes to coding and working out what code is trying to express. It’s also creative, though, as it’s about finding new ways to see patterns, and to show them at a large scale. And it’s yet also commercial: it is a way of finding out how to cut costs for a business and allow that business to retain more profit. In short, a career in AI-based data analytics is likely to be a varied and stimulating one that cuts across different parts of the working world and allows you to have a stimulating and enjoyable experience of working in the sector.
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There are multiple things to think about if you’re considering embarking on a career in this sector. But perhaps the most important thing to remember is that a career in AI is in large part dependent on the quality of the training you receive.
Ultimately, artificial intelligence is clearly starting to play a huge and unequivocally influential role in the functioning of firms large and small. From ChatGPT to risk modeling tools, AI tools are doing dramatic things across sectors and industries – and it’s not looking likely that that will change any time soon. On the data analysis front, meanwhile, AI is just one of many types of technology that are changing the face of the business world – with many other big data available to choose from if you’re a company. And when the broader range of tech solutions to data analysis questions are brought into the equation, the question gets even more interesting – as this article has shown.
So, whether you’re a data analyst looking to improve your workflow, a business owner seeking to increase profits or a student hoping to pivot your career towards AI, it’s worth being on top of these trends and ensuring you’re getting the most out of the resources available out there on this topic. The use of AI will be increasing as of late, and it’s a good thing to get ahead of the game.
