Generative AI is the latest technology to disrupt data analytics, a field with a long history of combining technological advances with new ways of doing business. Data analysts must adhere to best practices for using generative AI in analytics operations if they want to reap the benefits.
Data analysis comes from decision support. It has expanded into data warehousing, BI, visualization and predictive analytics. At every stage, decision-makers demanded better information, so IT learned how to deploy and manage complex technologies. As a result, companies have discovered new efficiencies or entirely new business models.
Generative AI is the next evolution of data analytics – and data operations as a whole – but the effects it will have are unclear. Analysts must adapt to an ever-changing field of opportunities to learn how to take advantage of the new capabilities offered by AI.
The key to the power of generative AI is in its name. Powered by machine learning models, generative AI can create text, images, code, new data and much more. Many people are familiar with images and text created by AI, but generative AI is also proficient in analyzing information, an ability stemming from its training methods.
During training, generative AI learns to identify patterns, predict outcomes, and extract key features from large data sets. The same techniques applied analytically allow AI to effectively interpret and analyze new data. The dual capacity of generation and analysis makes generative AI a particularly versatile technology.
Uses of Generative AI for Data Analysis
Organizations can demonstrate the usefulness and effectiveness of generative AI in data analysis in several ways.
Generate synthetic data for analysis
A common barrier to creating and testing new analytics tools and effective machine learning models is the lack of good quality, freely available data. Available data is often limited in scope and does not reflect the complexity of real-world scenarios.
For machine learning, it is difficult to find data that contains rich and realistic patterns. Even if one data set meets the desired needs, it can be difficult to find more to test and validate. In any case, the use of real data poses ethical challenges related to the confidentiality and security of confidential data.
As a result, the same data sets are used repeatedly, such as software companies’ numerous demos all replicating the same analyzes on Olympic Games data, New York taxi data, or movie rentals.
Today, generative models can create large, synthetic, yet realistic data sets to power analytics and modeling initiatives. Synthetic data fulfills two crucial roles. First, it addresses privacy issues in data analysis, particularly in sensitive sectors such as health, by creating realistic but not real data, thus protecting the privacy of individuals. Second, it fills gaps in scenarios where real data is scarce or non-existent, such as unique market trends or emergency situations. Simulating rare scenarios enables more comprehensive modeling and analysis, significantly improving the usefulness and relevance of data-driven insights. The result is more interesting and meaningful analysis for data analysts.
Uses in enterprise BI
By generating charts, summaries, and dashboards, generative AI has the potential to automate routine BI reporting. The same technology can also identify patterns that escape human analysts or business users and explain the information in natural language. Automation allows data analysts to focus less on repetitive tasks and more on higher-value analysis.
However, the capabilities of generative AI go beyond reporting. Traditional BI focuses on descriptive analysis, summarizing and interpreting trends in historical data, providing insight into what happened. During the last years, predictive analytics has become mainstreamusing statistical algorithms and machine learning to suggest future trends or what might happen.
Generative AI does prescriptive analysis possible and practical. Prescriptive analytics provides guidance on predicted outcomes, recommending actions, tactics and strategies based on the predictions. Human analysts and strategists, working with prescriptions, can be more insightful, more confident and more innovative.
Generative AI’s authoring capabilities enhance the utility of data analysis.
Benefits of Using Generative AI in Data Analysis
It seems likely that generative AI will ultimately redefine the data analysis landscape. However, just as data warehouses remain fundamental to enterprise architecture more than 30 years after their initial development, we can expect current methods of analysis and reporting to be used in the years to come . Generative AI has potential benefitsnot only as a technology, but also as an enhancement to existing analysis techniques and tools.
Increased automation
The ability of generative AI to find patterns and trends, even in complex and messy data, reduces the need for manual data processing, leading to savings in time and labor. Instead of working on labeling, cleaning, and normalizing data, human experts can focus on high-value, strategic work. Automating mundane and repetitive tasks also ensures consistency; manual cataloging is fallible. Automated reporting and analytics enable organizations to make faster decisions based on more up-to-date data, driving greater agility across the enterprise.
Identify patterns, correlations or relationships
Generative AI excels at identifying complex patterns, correlations, and relationships in data that human analysts might not see. Generative AI can simulate different scenarios to identify risks before they occur, allowing businesses to proactively develop mitigation strategies. It can also identify growth prospects, such as new markets, products or services.
Human analysts and strategists, working with prescriptions (supported by generative AI), can be more insightful, more confident and more innovative.
For example, a financial institution could use generative AI to replicate patterns of real financial transactions as well as new similar patterns to train fraud detection models. Generative AI capabilities improve the organization’s ability to discern fraudulent tendencies and enable new financial products that are safer and better adapted to the realistic needs and behaviors of consumers.
Effective data catalogs
A data catalog is an organized inventory of data assets, which can discover and deliver relevant data to users with the appropriate permissions. A good catalog provides quick, self-service access to the right data with meaningful context. Generative AI can automate the cataloging process and intelligently categorize and label datasets, making the catalog more usable. Automation also ensures data quality and consistency, which is crucial for better data governance and management.
Best practices for generative AI and data analytics
As with any new technology, best practices in generative AI are developing as quickly as the technology itself. However, some basic guidelines should be helpful in any implementation to maximize benefits.
Use high-quality data
Generative AI excels at identifying patterns in complex data and can generate new data sets, but its effectiveness in prediction, pattern detection, and automated decision-making relies on the quality of the data entrance. High-quality business data enables generative AI to produce reliable and accurate results. Data cleansing, quality control, and data governance are essential investments for any organization using generative AI.
Integrate tools with generative AI
BI tools are catching up with generative AI. Tools that integrate generative AI with existing data infrastructure simplify adoption and streamline workflows. Organizations can choose between data analytics platforms with built-in generative AI capabilities or tools that integrate generative AI to enhance their existing data analytics operations.
Determine KPIs, objectives and use cases
Setting clear goals in the form of KPIs or objectives and key results before starting with generative AI is a useful step in managing the technology effectively. Consider who might use the tool, industry requirements, cross-departmental uses, presentation formats, speed or pace of the business, required accuracy, and training needs of human users.
Adapted to specific objectives and needs
Designing generative AI implementations and integrations for specific scenarios ensures the most effective use. Whether it’s enterprise BI, marketing, sales, customer experience analytics, or geospatial analytics, customizing generative AI assets maximizes their potential, rather than relying on generic models that may have limited understanding of the unique contexts and nuances of different Industries.
Generative AI has already significantly transformed data analysis, presentation and operations. As technology evolves, it is expected to continue to fundamentally change how businesses create value from their data assets.
Start experimenting with integrative applications of generative models, especially in some of the use cases described. The potential for improved decision-making through automation, deeper insights, and increased efficiency is truly exciting. Analytics teams ready to take on the challenge have the opportunity to radically change their own role and even the fundamentals of the business. It’s a unique and inspiring perspective.
Donald Farmer is the principal of TreeHive Strategy, which advises software companies, businesses and investors on data and advanced analytics strategy. He has worked on some of the market’s leading data technologies and at award-winning startups. He previously led design and innovation teams at Microsoft and Qlik.
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