Omri Kohl from Pyramid Analytics provides an explanation of how AI has fundamentally changed the business data analytics workflow. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversations with AI.
AI has been a huge success in data analytics, but there are risks that could lead businesses to the wrong path.
Tech readers want to take advantage of atmospheric coding using advanced AI engines with easier, more accessible, lower cost, custom agents. This allows you to connect your custom chatbot or AI agent to the database and leave AI to answer decision makers and stakeholders' questions. However, unless your AI engine is connected to a proven decision intelligence engine connected to a well-prepared data source, it may return unreliable and inaccurate answers.
No, AI isn't killing the business intelligence star. Companies that are most valuable from today's business data analytics workflows are leveraging the increased power of BI combined with AI. Integrating AI into BI improves BI capabilities throughout the analytic lifecycle, from data intake and preprocessing to conversational analysis, automated reporting and real-time insights.
Below we present the main ways that AI can (and should be used) improve all aspects of data analysis. Putting it all together, new photos of AI are emerging that are fundamentally changing the way businesses engage in business analytics.
AI extends data collection
As data analysis operations expand, they are hungry for more data. Data is growing mercilessly, but analysts can have a hard time finding the right and valuable data. AI-assisted intakes help teams increase the number of data sources they consider incorporating into their analysis.
AI can gather data that can be siloed in places that otherwise would not be accessible and inspected only on its own, thereby improving data integration. Add unstructured data such as videos, images, audio clips, and other available data sources to deal with real-time data, bringing you the latest information as soon as it appears.
With AI, you can extract values from huge datasets that are not used, as huge volumes obscure insights. Enhance data semantics by understanding the sentiment of social media posts, sales call transcripts, and support tickets, ensuring that all analytical models use the same metric layer to promote consistent data interpretation.
By classifying and cataloging data more effectively and efficiently, AI applications make it easier to find existing data. Furthermore, synthetic data generated by AI can fill in gaps where data is missing, or create new datasets for training purposes.
AI speeds up data preparation
Preparing data for analysis includes a long list of tasks, including organizing data, cataloging and classifying, removing duplicates, outlier spots, normalizing data, and correcting errors. These are all important steps in your data analysis workflow, but when handled manually, they can be boring, slow and error-prone.
AI speeds up the entire process by using NLP and pattern recognition to automate cleaning, merging, validation, and even repetitive tasks of data. It can automate schema matching and data adjustments, propose standardized formats, and input missing information signals. AI-powered tools can recognize data types, understand relationships between datasets, assign metadata, and group similar assets to improve data classification and search.
Using AI has improved data modeling. This has traditionally relied on manual functional engineering and statistical techniques. AI automatically recommends the best model for each scenario and generates an initial data model. This removes the bottlenecks in data preparation and lays the foundation for more accurate analysis.
Machine learning (ML) can uncover hidden patterns, process unstructured data, and continually adapt models as new information arrives. AI-driven data semantics adapt models to a variety of user needs, improve reuse, and accelerate deployment.
These use cases provide a significant time savings for analysts, along with improved accuracy and reduced human bias. Using AI in data preparation supports regulatory compliance, streamlines workflows, and accelerates decision-making. Additionally, these tasks cannot be managed manually and require data scientists to wait for requests to respond, lowering barriers to data analysis for business lines (LOB) users.
Finally, as AI models learn as they work, tools with AI evolve to ensure that data preparation is relevant, effective, consistent and reliable throughout the analysis lifecycle.
AI democratizes data analytics
The impact of AI on data analytics continues all the way to creating and investigating data-driven insights. Generated AI models support conversational data analysis, allowing users to naturally enter word queries in text or speech. This removes the need for coding expertise and advanced data literacy in data analysis, further strengthening the power of LOB “civic analysts.”
When trained to understand your data analysis needs, AI-powered BI tools can automatically select the appropriate analytical method for each situation, such as regression and classification. Next, choose a visualization that is easy to consume and help users find insights and draw conclusions from the data.
With AI, analytics platforms process huge data volumes and extract relevant datasets from data that cannot be analyzed. AI allows decision makers to quickly identify data sets that need to be investigated more closely. It also advises users on how to select the best data model and integrate new data sources, highlighting more relationships between datasets.
In this way, AI-powered analytics eliminate barriers to data exploration, speed up time to insights, and focus analysts on complex challenges. AI allows decision makers to focus on their decisions rather than wasting time on boring tasks or freezing them in the face of complex data.
AI and BI belong together
Instead of swapping the latter, bringing AI into BI leads to more accurate insights, faster time to insights, and reduced costs. All of these are high on every executive wish list. In short, AI is surprising for data analysis, but only if it is applied to analysis and does not become independent. Companies need to ensure that investments in both technologies are in line with each other.
