Ever felt like you’re drowning in data but starving for insights? You’re not alone. Many business functions have more data than they know what to do with. And that means they’ve been missing out on some big opportunities.

But the game has changed, and it’s changed fast. If you’re still crunching numbers the old-fashioned way, you’re missing out on a revolutionary new way to work.

The arrival of AI into cloud data analytics has transformed the way organisations look at themselves and the world. From natural language interaction to real-time monitoring, the opportunities are endless.

Below, we’ll look at how new AI innovations are opening up massive new potential for understanding your data.

Crunching data in the old days

Things have changed a lot in the last few years.

Let’s go back five years, to the vintage era of pre-AI data analytics. Even though it was powered by the cloud, it wasn’t always a walk in the park.

Analysts would have to set up queries, create data models, and run statistical analyses, often writing custom scripts for specific tasks. While these methods were effective to a certain extent, they had several limitations:

• Scalability issues: As data volumes grew exponentially, traditional methods struggled to keep up. The computational power required for these tasks often exceeded the capabilities of standard servers, leading to performance bottlenecks.
• Lack of real-time analysis: Traditional systems weren’t really designed for real-time data processing. This lag in analysis could result in missed opportunities or delayed responses to market changes.
• Complexity and skill barrier: The complexity of the job would often require highly skilled data scientists. This not only increased operational costs but also created a barrier for smaller organisations that couldn’t afford top-tier expertise.
• Inability to handle unstructured data: Traditional data analysis tools were adept at handling structured data like spreadsheets and relational databases. But they weren’t so great at processing unstructured data like text, images, or video, which make up a significant portion of today’s data.
• Limited predictive capabilities: While statistical methods could offer insights based on past data, they weren’t very effective at predicting future trends or identifying complex patterns within the data.

AI techniques like machine learning only really became mainstream during the cloud boom of 2020. Since then, we’ve seen a vast and astonishing growth in analytics capabilities.

The cloud will always play a massive role as the foundation of data analysis. The arrival of artificial intelligence and all its wonders is a smart layer on top of that foundation, and brings a whole new world of productivity and insight.

What is AI, and what can it do for data analysis?

When we’re talking about data analysis, ‘AI’ generally refers to the use of algorithms and computational models to automatically identify patterns, make decisions, and generate insights from data.

The cool thing here is that AI can learn from the data it processes, improving its accuracy and effectiveness the more you use it. There are several techniques within AI that are particularly relevant to us here:

• Machine learning: This is the most commonly used AI technique in data analytics. Machine Learning algorithms can be trained to recognise patterns and make predictions or decisions based on new data.
• Natural Language Processing (NLP): This technique is used to analyse and understand human language, making it invaluable for processing unstructured data, like raw text from customer reviews or social media comments.
• Deep learning: A subset of machine learning, deep learning uses neural networks with multiple layers to analyse various forms of data, including images and videos.
• Data mining: Though not exclusively an AI technique, data mining is enhanced by AI to sift through large datasets and identify correlations or patterns that might not be immediately obvious.

These AI techniques have made a massive difference in data analysis, bringing capabilities far beyond those of traditional methods.

Let’s take a closer look at the real use cases, from the different types of analysis techniques to the industries it’ll have the biggest impact on.

What can AI do for data analysis?

Here are three data processing skills that AI is brilliant at, for organisations of any scale.

Automated data structuring

This is an area of AI with a big ‘wow’ factor for anyone seeing it for the first time. LLMs (Large Language Models) can hoover up vast reams of text-based data and make sense of them in seconds.

Consumer AI tools like Audiopen can show you the magic of this in real-time; just babble into your phone’s microphone for a few minutes, and the AI processing will annotate what you’ve said. But it doesn’t just give you a transcript – it fully summarises and organises your messy thoughts into structured, readable prose.

Now imagine a process like that applied to large datasets. The implications are staggering.

Automated data structuring algorithms can sift through terabytes of raw, unstructured data—be it text, numbers, images, or sensor readings—and categorise it into meaningful segments. It’s more than just sorting or filtering; it’s about understanding the context and relationships within the data.

Supercharged metric monitoring

For businesses, AI systems means that vast amounts of customer feedback, market trends, and operational data can all be analysed and summarised in seconds. The AI can identify patterns, anomalies, and even predict future trends, turning what was once a laborious manual process into an automated, efficient operation.

No matter what type of business you’re in, this level of automation opens up new avenues for your data-driven decision-making. You’ll be able to track your KPIs, see real-time customer sentiment analysis, and automate report generation. In a way, it democratises data analytics, making it accessible to individuals without a technical background. They can now make informed decisions based on complex data analysis, which was previously the domain of data scientists.

Automated data structuring is like having a super-powered analyst that never gets tired or bored.

Working better with spreadsheets

Spreadsheets are enjoying some time in the spotlight.

Web-based databases like Airtable can be linked via API or connectors like Zapier to AI services like OpenAI. This means any data stored can be sent out, processed, and returned in seconds.

Google Sheets is starting to gain more integration with Bard’s conversational AI, but it still has a way to go before it gets genuinely transformed.

We’re most excited by the latest AI features powered by Azure, though, which bring exciting changes to Microsoft’s productivity software line-up.

Anyone who’s spent an afternoon trying to figure out pivot tables or make a formula work in Excel will love its new AI features. Microsoft Copilot for Office 365 is set to transform the way users interact with spreadsheets.

You can literally just write what you want it to do in natural language, and Copilot will write a formula or perform an action for you instantly. The productivity-boosting potential of this tech is massive; thanks to its integration with Microsoft 365, you could  automatically draft a reply to an email, automatically pulling in summarised data from an Excel spreadsheet.

Microsoft Excel is really having a moment right now – with its functionality being supercharged by Copilot, along with the addition of Python integration, your analytics abilities are about to get a whole lot stronger.

What else?

These are just some of the ways AI can manipulate data intelligently and at scale. Let’s look at some of the more specific use cases for different types of organisations.

Examples of AI cloud analytics in action

Some of the awesome industry-specific abilities AI brings to data analysis are:

• Fraud detection in banking: AI algorithms can keep an eye on transaction data to identify unusual patterns or anomalies that point towards fraudulent activity. This real-time monitoring means financial institutions can take immediate action and minimise financial loss.
• Contract reviews in legal firms: AI-powered tools can scan through legal documents to ensure compliance with regulations and flag any problematic clauses, reducing the risk of legal disputes. AI summarising large documents can save hundreds of hours a month – it’s not perfectly reliable, but it’s a much smarter way of sifting through legal paperwork than a simple ‘CTRL+F’.
• Risk assessment for investments: AI models can monitor financial markets and ongoing news to assess the risk levels of company investment portfolios, so you can make smart adjustments to investment strategies.
• Regulatory compliance monitoring: In various different sectors, AI can be used to automatically monitor and ensure that the company’s operations are in compliance with ever-changing laws and regulations. It’s a great way to watch for data leaks, vulnerabilities, and so on, keeping your business and reputation safe.
• Personalised recommendations in retail: Online retailers can use AI algorithms to monitor customers’ browsing and purchasing history. These algorithms can then generate personalised product or service recommendations, making for a much more engaging shopping experience and higher sales. For instance, if a customer frequently buys organic skincare products, the AI system might recommend a newly launched organic moisturiser.
• Image and video analysis in security: Security companies can use AI-powered image and video analysis to monitor surveillance footage for suspicious behaviour. Clever algorithms can identify unusual movements or activities (like loitering near a restricted area) and alert security personnel in real-time. This not only enhances the effectiveness of security measures but also allows for quicker response times in critical situations.

The downsides of using AI in cloud data analytics

While there are many benefits of integrating AI into cloud data analytics, there are some challenges and potential downsides.

One important issue is data verification. AI algorithms are only as good as the data they are trained on. Poor quality or biassed data can lead to inaccurate or misleading results, which could have severe consequences for decision-making. This is especially pertinent in sectors like healthcare, finance, or public safety. This is an area where manual intervention can play a part.

A related concern is what’s often referred to as “hallucinations”. These are instances where the AI model, particularly in deep learning, generates data or patterns that don’t actually exist in the original dataset. Basically, it makes up things that sound correct, but aren’t. This can happen when the model is overtrained or not adequately validated, leading to false positives or conclusions that are flat-out wrong. These hallucinations can be especially problematic in fields that need high levels of accuracy and reliability, like medical diagnostics or fraud detection.

The complexity of AI models can also make it difficult to see how they arrive at their decisions, leading to a real lack of transparency. This “black box” issue can be a pretty big hurdle in regulated industries where explaining the decision-making process is mandatory.

Lastly, the integration of AI into cloud data analytics raises questions around data privacy and security. As the algorithms become more sophisticated in their data collection and analysis, there’s an increased risk of sensitive information being misused or falling into the wrong hands. This could lead to an ethical issue, or a legal disaster – use at your own risk.

AI is reshaping the cloud data landscape

Remember the days when data analysts were swamped with manual tasks? Those days are fading fast. AI algorithms are now doing the heavy lifting, churning through mountains of data at breakneck speeds.

And it’s not just about doing it quickly; it’s about doing it smarter. These algorithms can spot trends and anomalies that would make even the sharpest human minds do a double-take.

But let’s not forget the human element. With AI taking care of the grunt work, data analysts are getting a new lease on life. No longer bogged down by mundane tasks, they can dive into the strategic, the insightful, and the downright fascinating aspects of data. It’s like giving them a magic wand that turns data into actionable business insights.

And let’s talk about democratisation. AI is breaking down the ivory towers and making data analytics accessible to the average Joe and Jane. No longer the exclusive domain of data scientists, analytics is becoming a team sport, and that’s a win for everyone.

So, while there are challenges, the upsides are too compelling to ignore. AI in cloud data analytics is like a turbo-charged engine on a solid foundation. It’s not just the future; it’s the here and now.

Where do you begin with AI cloud data analysis?

Pretty much all forms of business analytics have a massive opportunity for positive change with AI. Choosing where to focus isn’t easy.

Pricing is one area that AI can have an immediate big impact on. According to one MIT survey, AI pricing initiatives were found to be twice as successful as other AI projects, saving some tech companies millions.

So which area of your business is ripe for supercharged data analysis? Your AI transformation might only be a single analyst automating some Excel jobs. Or it could be an enterprise-wide overhaul of your entire data processing workflows. The opportunities are endless.

If you’re not sure where to start, and you’d like advice on AI implementation from an elite squad of cloud experts, we’d love to help. Contact us today.