What is AI in data analysis?

Traditional data analytics has long been a fundamental business practice, tracing its roots back to a pre-technology era of paper records and filing cabinets. The way we collect and store data has evolved over the years, but the guiding principles have remained the same: using past data to predict future outcomes.

So, too, the limitations of data analytics have remained much the same, namely time-consuming manual processes, high knowledge entry barriers, and conclusions vulnerable to human error.

AI-powered analytics has the potential to eliminate all of these long-standing roadblocks. Machine learning can identify patterns and connections in a fraction of the time. Natural language processing allows users to query complex datasets in plain language whereas predictive analytics make the future clearer than it has ever been.

How is AI used for data analysis?

AI data analytics is transforming how we approach data, from collecting and sorting to what we can interpret and how we use that information to shape future outcomes.

Data collection

Business Intelligence (BI) tools have historically been limited to using highly structured data. AI analytics eliminates this constraint by introducing unstructured and semi-structured data into the equation. Additional formats that AI can analyze include documents, meeting notes, video, and audio formats.

Data cleaning

By utilizing machine learning, AI-powered analytics can find patterns and connections between widely disparate sets of structured and unstructured data in much the same way the human brain can, only in a fraction of the time. Less resources need to be spent on manually cleaning and preparing datasets as AI is far less dependent on highly structured data.

Data analysis

AI for data analysis will drive a new age of advanced predictive analytics in much the same way that the enterprise data warehouse revolutionized business intelligence. It will shape the future with near real-time data analysis that is not only faster, smarter, and more adaptive, but can also recommend actionable insights.

Data interpreting

For a long time, technological complexity has acted as a bottleneck to broader data analytics. AI is changing that by fully democratizing data access. Advanced self-service analytics solutions like Veezoo allow users to query complex datasets in plain language and receive valuable insights with minimal technical ability.

Common AI technologies and tools used in data analytics

Under the umbrella term ‘Artificial Intelligence,’ there are numerous subfields that go into making AI-powered analytics successful. These are some of the most important concepts and the technologies that enable them:

Machine Learning: Transforming data insights

Machine learning is a branch of AI focused on pattern identification and facilitated by access to substantial amounts of data. Integrating machine learning is often one of the first steps toward greater automation.

Deep Learning: Uncovering complex patterns

While machine learning makes use of just one or two computational layers, deep learning models utilize hundreds or even thousands of layers to simulate the complex decision-making of the human brain. Deep learning models can use unsupervised learning, allowing them to grow in complexity without human intervention.

Natural Language Processing: Making sense of unstructured data

Natural Language Processing (NLP) is a field of AI that enables computers and users to communicate in everyday language. This allows for more natural two-sided conversations, much like talking with a friend. NLP is a revolutionary concept that democratizes access to data by removing technological barriers to database querying.

Predictive Analytics: Forecasting future trends

Predictive analytics is one of the most promising uses of AI-powered analytics. In short, this process can predict events by analyzing existing data and statistics and creating models that suggest the most likely future outcomes. This can include predicting customer behavior, shifting trends, and even global events.

Popular AI tools for data analysis: An overview

AI analytics is only as valuable as the data that underpins it. Ensuring that data is well integrated within a fast and accessible data warehouse architecture is essential. That data can then be used alongside a suite of AI tools to train ML models to identify the trends most relevant to a business. Finally, BI visualization tools or NLP interfaces allow those trends to be queried and put into terms that can be communicated across departments and disciplines.

Real-world applications of AI analytics

The impact of AI can be felt across almost every industry. The following are just some examples of where AI data analysis is reshaping how we do business:

Manufacturing and supply chain

AI analytics are powering real-time insights within manufacturing and supply chains capable of drastically improving inventory management, route planning, and global collaboration.

Not only does introducing AI into the supply chain improve operational efficiency, but it is also helping businesses meet their environmental aims by optimizing trade routes and product distribution.

E-Commerce and retail

AI-powered predictive analytics are transforming operations in the e-commerce and retail space. By using historical trends to predict future markets and seasonal fluctuations, retailers can ensure they always have the right stock to meet demand.

What is more, advancements in personalized pricing and recommendations, combined with behavioral insights, are maximizing profit margins, customer conversions, and overall satisfaction rates.

Financial services and banking

Risk management has long been a time-consuming task with human intervention or slow machine algorithms often required to detect potential fraud. While necessary, this often leads to a reduction in customer satisfaction and even a potential loss in business.

AI-powered algorithms, when partnered with a high-performance database, are now able to detect fraud in <50ms, protecting customer conversion rates and improving effective risk management.

Healthcare and medical research

The patient experience is dependent on rapid access to quality data. Exasol is transforming how data is used in the healthcare industry by enabling faster query performance, improved data accessibility, and self-service analytics.

More healthcare professionals can now use AI analytics to query complex scenarios such as patient characteristics, existing treatments, and drug interactions in seconds, freeing up time to focus on patient care.

Challenges and considerations

As with any new technology, it is important that the benefits do not overshadow the potential risks. AI can vastly improve both processes and outcomes, provided the following challenges are addressed:

Data quality and integrity issues

AI for data analytics, just like traditional data analysis, is only as valuable as the underlying data. If the database is flawed or outdated, those shortcomings will reduce the ability to extract accurate and actionable insights. To get the most out of AI, businesses should also favor a data approach that allows for real-time data analytics.

Ethical considerations and bias in AI

With the widespread adoption of AI-related technology, the concept of AI bias has entered the public consciousness in a big way. Eliminating bias is dependent on the data used to train AI. If that data stems from historical prejudices, the AI model will inevitably reproduce those shortcomings. Ethical considerations must be taken to ensure that existing biases are not proliferated, and that AI is trained to the highest data quality standards.

Data privacy and security concerns

As artificial intelligence unlocks seemingly endless data utilization opportunities, it also raises important questions about data privacy and security. Effectively using AI for data analytics is dependent on access to vast amounts of data. However, some data is more sensitive than others and access restrictions should be maintained. Navigating between effective AI training and upholding data privacy and security protocols is a complex issue each business must carefully consider.

Overcoming technical and integration challenges

Investing in the future of AI data analytics can feel like a monumental task. While the potential benefits are widely understood, integrating AI into existing technical infrastructure and processes can be a complex undertaking. Exasol has consistently been ranked as a leading data management and analysis tool provider by the BARC annual survey, thanks in no small part to our comprehensive deployment and operations support. What is more, with a broad range of deployment options, we offer solutions to overcome any technical challenges.

The transformative impact of AI on the future of data analytics

AI is already transforming the way we approach data analytics. In our latest study, in collaboration with Vanson Bourne, we surveyed 800 business and tech professionals on the impact AI is having on their business. A staggering 91% of respondents now view AI as critical for their organization over the next two years.

Gen AI & Analytics

Generative AI offers many compelling improvements to analytics. Gen AI can be used to power natural language queries for conversational analytics. This lowers the barrier for entry for analytics and helps democratize data and insights.

Gen AI can also be used for data transformation and mining insights from large amounts of data. Gen AI and LLMs can be trained to extract critical pieces of information from unstructured data to transform it into analytics-ready data. This saves considerable time and resources – by augmenting or replacing expensive ETL processes.

The main takeaway? Investing in AI for analytics is no longer optional – it is essential.