Generative AI for Data Analytics: Let’s Gauge the Impact

AI runs the gamut of practicality: utterly useless in some areas, bordering on magic in others. As a prominent software company, it’s our job to distinguish between the two. We also like to inform our audience – clients, prospects, readers, and those who’ve stumbled here looking for actual symphony concerts – about the latest, genuinely promising AI trends. One of these is using generative AI for data analytics (DA). These pre-trained models can finally make DA accessible to regular people, something organizations have grappled with for years. 

Before we explain how GenAI can give you better data insights, let’s rehash some general definitions. 

What Even Is Data Analytics and BI? 

In short, both terms refer to the processes companies use to collect, prepare, analyze, and transform data to enable information-driven decision-making. BI focuses on studying what is going on now, while DA explores why certain things are happening and uses stats from the past to predict what can happen next. 

There are multiple personas involved in these proceses, but for simplicity’s sake, we’ll narrow them down to three here. 

Data stewards and data engineers. These professionals take the first lap in this relay race. They collect, clean, transform, structure, and augment datasets, preparing them for analysis down the line. 

Analysts. Next, the baton is handed to the analytics experts. On the DA side, their role mainly revolves around querying and interpreting datasets. Contextualizing the data at hand. In BI, the focus is on turning data into graphs and dashboards and answering specific ad hoc questions from line-of-business (LOB) users. Good DA and BI experts deeply understand the needs of the LOB users they work with and give them highly detailed yet comprehensible reports. 

LOB users. Finally, we come to the line-of-business users, the consumers. They’re the ones for whom all the data work was done. They review reports and dashboards, figuring out how to apply new insights to improve operations. Sometimes, they may tinker with the data, adjust visualizations, tweak dashboards, add filters, slice and dice it, and so on. But mostly, they just interpret the insights that came pre-chewed for them. 

Here’s the key point: despite years of vendors offering no-code, self-serve analytics tools, only 25–30% of business users actually use data analytics to improve decision-making, according to different estimations. This is true across industries. And this figure hasn’t changed in 7 years. 

Why Has the Adoption of Data Analytics Tools Been So Slow? 

One big barrier has always been the complexity of data prep. It’s tedious, manual, and demands skills many companies lack – and often don’t care enough to acquire. 

Another challenge lies in the tools themselves, their unintuitiveness. While there are typically some solid features offered to simplify data analysis, users can’t really leverage them fully unless they have an intricate understanding of business logic, metric and KPI definitions, and other technical details of the task. Which kind of defeats the purpose. 

Most users aren’t particularly interested in these things. What they want are clear recommendations or actionable insights – without needing to tweak dashboards or reshape data to draw conclusions. 

Finally, there’s the gap. The space between raw data and real insights is just too wide. By the time data makes it to LOB users, its relevance is often gone. 

The good news is that AI and GenAI have the potential to simplify and optimize analytics experiences for each of those roles. And that can happen fast. 

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Generative AI for Data Analytics 

Let’s first cover how generative models specifically can streamline what is being done in each phase of the analytics process, role by role. 

Engineering Phase 

GenAI can massively simplify data preparation. It generates code – snippets or full scripts – for routine tasks like cleaning, transforming, and loading data. It optimizes pipelines by spotting bottlenecks and recommending fixes – advising where applying parallel processing, caching, or partitioning would make the most sense. 

It also enables automated data profiling. Models can analyze datasets at lightning speed, and they can identify key data object characteristics like no human could.  As far as dataset augmentation goes, GenAI can pick up patterns in your data and generate as many realistic, tailored data variants as needed. 

Analysis Phase 

At the analysis stage, GenAI enhances report authoring. It creates SQL queries, dashboards, visualizations, and, again, code. Data analysts can, therefore, use it to automate hypothesis generation, for feature engineering, and even to get suggestions on the right statistical or ML models for the task. 

BI engineers and those involved in descriptive and diagnostic analytics can gain from GenAI’s ability to automate report customization. Models can suggest narratives to improve data storytelling and uncover non-obvious, predictive insights that might otherwise go unnoticed. 

Consumption Phase 

End-users – the consumers of analysis – are arguably the biggest winners here. GenAI can empower them, finally, to interact with data in plain language. 

Here’s how: 

• With a simple prompt, GenAI can determine which data sources should be visited, which queries to perform, and even what type of statistical analysis fits the specific problem. It can do all that and then return a plain-language answer. 

• If the output isn’t perfect on the first try, the user can refine it on the spot. And they can keep changing the answer till it’s completely tailored to their particular requirements and business role.  
   

So, while there is going to be an impact on each part of the process, GenAI will likely change the game the most for LOB users. It is democratizing data analytics. 

Types of AI Analytics 

Let’s zoom out from GenAI and talk about what AI does for different types of analytics in general. 

Descriptive 

This is about understanding past performance. Let’s suppose we run a manufacturing plant. We can feed many types of data to the AI: production data, sensor readings, employee feedback, client purchase records, and market trends. The AI will then help us figure out which production lines are the most effective during specific seasons. Then, we’ll be able to use the insights to effectively adjust inventory management, optimize schedules, and more. 

Diagnostic 

This type of analysis digs into causes. Why did a specific event occur? In our example, AI models can analyze sensor data – temperature, vibrations, pressure readings, etc. – and help us pinpoint the causes of machine failure: wear and tear, misalignment, overheating, or other issues. It does this way faster than traditional analytics methods. 

Predictive 

AI excels at spotting patterns and anomalies, and when fed with the right data, it can give us accurate predictions. 
For instance, it can help us anticipate equipment failure before it happens by picking up subtle changes in vibration that typically precede a problem. That means downtime will be avoided, and repairs will happen proactively. AI also learns from historical trends. It might forecast long-term issues, like cyclical operational slowdowns, helping our plant prepare and adjust. 

Prescriptive 

This is where AI provides actionable advice. It can recommend the best course of action when there’s an increased likelihood of a machine performance issue. It might suggest when to schedule maintenance, which parts to order, and even which maintenance approach to take. The suggestions will be based on urgency, staff availability, and the impact on production schedules. 

What Platforms Do We Use for AI in Analytics? 

When it comes to platforms for AI analytics, organizations nowadays are spoiled for choice. Let’s take a quick look at the most popular options. 

Microsoft Azure 

Known for its strong hybrid cloud capabilities, Azure is versatile and connects seamlessly to your existing infrastructure. If your organization already relies on Microsoft services, this is a no-brainer. Notable tools include Azure Synapse Analytics for robust data warehousing and analytics capabilities and Azure OpenAI Service and Copilot in Power BI, which offer cutting-edge AI-powered data analysis and data visualization. 

Google Cloud Platform (GCP) 

Google excels in Big Data and machine learning. If your business already uses Google’s ecosystem, sticking with GCP for analytics and AI is probably the easiest choice. Standout tools include AutoML, which simplifies creating custom models, and BigQuery ML, which enables the creation of machine learning systems with basic SQL tools. There’s also the Generative AI App Builder, which makes chatbot and search application development straightforward.  

Amazon Web Services (AWS) 

AWS is the oldest and most established cloud provider and also holds the largest market share. In the area of advanced analytics, its notable tools include Amazon SageMaker and AWS Bedrock, which simplify building and deploying advanced generative AI models. Additionally, DA experts use Amazon Forecast, which uses machine learning and generative AI to enhance forecasting accuracy. 

IBM Cloud 

IBM is also a key player. IBM Watson Studio offers a collaborative environment for developing AI-driven applications. It combines rich data analysis, visualization, and machine learning capabilities. The properties of IBM analytics tools are particularly valuable in heavily regulated industries like healthcare.  

So, what platform to choose? 

While “it depends” might sound like a cop-out, that is the answer we’d give. And we’re really not trying to be fence-sitters here. It’s just that, when working with clients on AI and analytics projects, we’ve learned that we must always account for the company’s unique circumstances when determining the technology stack. You see, most cloud and AI analytics platform providers offer similar storage and processing capabilities. Yet the performance of their proprietary AI models can vary a lot depending on which type of data is used and the ecosystem they run in. And that’s what we have to evaluate before making a choice. 

What Skills Are Needed to Use AI and Genai for Analytics? 

On an organizational level, the main prerequisite for using AI in any capacity – and especially analytics – is data maturity. This means your data collection processes must be seamless. Data flow should be consistent, with reliable streams and efficient ETL. 

While the platforms we discussed above can help abstract away many of the difficulties associated with enabling AI analytics, they still require clean, well-structured data to produce valuable insights. 

On the level of individuals, being familiar with the basics of data science and having a general understanding of data visualization would, of course, be helpful. Therefore, even businesses that are currently using generative AI tools to make analytics more accessible should not neglect to upskill business users with training sessions, workshops, etc. This will help to truly shift the analytical power within organizations from DA experts to LOB users. 

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To Conclude 

Data analytics and BI processes may start looking very different in the coming years. Simpler, more effective, more nuanced, and finally accessible without specialized knowledge. For this to happen, though, companies must know how to integrate their legacy systems with advanced AI and GenAI platforms.  

At Symphony Solutions, we’ve worked extensively with organizations across various sectors, helping them incorporate AI and GenAI capabilities into complex systems and infrastructures – even when they still relied on outdated tech stacks and proprietary legacy tools that didn’t fit well with modern AI frameworks. 

Beyond that, implementing AI brings a set of unique risks, particularly around security and compliance. This is especially relevant for organizations in healthcare, finance, and other highly regulated industries where firms handle sensitive data. 

We help companies address all these challenges. We build custom platforms with sophisticated predictive capabilities and modernize existing ones. If you’d like to level up your analytics and BI with cutting-edge AI and GenAI capabilities, contact us today. Let’s turn your data into a key asset for business growth!