AI’s Impact on the Evolution of Data & Analytics

It is no secret that AI empowers organizations with faster, deeper insights and democratizes access to data for a wider range of user. But the high cost of developing and deploying advanced AI systems and the required infrastructure is a significant barrier for many organizations.

This, combined with the knowledge that AI models are only as good as the data they are trained on and bias in training data can lead to skewed or unfair outcomes, requiring strict governance and monitoring creates a significant challenge for data & analytics teams.

AI’s Role in Data & Analytics Lifecycle

To be effective, Generative AI relies on complex models, making it difficult to fully understand how certain outputs or recommendations were generated, necessitating human oversight to validate AI-generated insights. New challenges also arise related to data privacy, intellectual property, and compliance given the huge amount of data required for AI to be effective so architectures must have robust security measures and governance frameworks built in.

Unfortunately, most data is stored in silos that don’t communicate with one another. These data silos prevent businesses from getting a full picture of how their business is performing, which means they're making decisions based on inaccurate insights. Plus, when data is stuck in silos, it can be hard to access and use efficiently, which wastes time and resources. Siloed data can also cause businesses to miss out on opportunities for growth and optimization. They could be overlooking potential areas for improvement or new markets to tap into, all because their data is locked up.

These data silos also cause businesses to make errors in decision-making, which can be costly in the long run. The more time a team spends hunting down data, the less time they have to work on important projects. And if they're working with incomplete or incorrect data, they end up wasting even more time on projects that won't deliver real value.

To become truly data-driven, organizations need to provide decision-makers with a 360-degree view of data that’s relevant. By breaking down data silos, businesses can gain a comprehensive view of their operations, allowing them to identify trends, patterns, and anomalies in data. Businesses can then uncover more opportunities for growth and optimization. Real-time access to accurate data enables faster data-driven decision-making, leading to improved efficiency, increased productivity, and a reduced risk of errors across the board.

Data Architectures Will Adapt or Fail

AI is not only the consumer of data but is also embedded throughout the entire data analytics workflow, automating and enhancing processes at every stage, so it is changing the landscape of what constitutes and effective data architecture.

Industry analyst Gartner Group reported in a webinar “By 2027, organizations that emphasize AI literacy for executives will achieve 20% higher financial performance compared with those that do not.” And also, that “By 2030, AI agents will replace 30% of SaaS application UIs relegating the SaaS application to a semantically enriched domain data source.”

To further emphasize this point, a recent analyst report by Cloudera titled “The Evolution of AI: The State of Enterprise AI and Data Architecture” further supported the predictions above with the following data reality survey results:

• Culture: 86% say they are at least moderately data-driven; “extremely” rises to 24% from 17%. Trust in data is also up, with 24% “much more” and 41% “somewhat more” than last year.
• Where data lives: private cloud 63%, public cloud 52%, data warehouse 42%, on-prem mainframe 38%, on-prem distributed 32%, data lake 25%, lakehouse 24%. Hybrid is the norm.
• What leaders want from architecture: integrated MLOps 52%, automated pipeline orchestration 51%, granular governance 44%, unified access 41%.

The report then goes on to highlight the following roadblocks:

• Biggest technical limits: data integration 37%, storage and compute 17% each, lack of automation 17%, latency 12%.
• Only 9% have 100% of data available to AI: 38% say “most” is available.
• Cost to access compute for model training: spikes from 8% to 42% year over year.
• Security worries are steady: data leakage during training 50%, unauthorized access 48%, insecure third-party tools 43%. Confidence is improving, and the share calling security the “biggest challenge” drops from 66% to 54%

Bottomline: AI is fundamentally transforming data and analytics architectures by moving them from static, manual systems to dynamic, self-optimizing ecosystems. This shift empowers organizations with faster, deeper insights and democratizes access to data for a wider range of users. To accomplish this, new advances in no-code solutions for a data intelligence cloud for AI are being deployed.

Time to Value Is Paramount

A data intelligence cloud is a cloud computing platform that leverages to power of GenAI, Reinforcement Learning and Agentic AI to enhance its capabilities and transform data into usable information accessible by the average person starts with ensuring you have the right data to the right person at the right time in the right format. More importantly, it can deliver measurable value in days, if not hours, instead of months to years.

Starting with UBIX DataSpace to ensure all data sources are engaged and contextualized in real-time, and then leveraging the power of ChatUBIX makes that a reality. ChatUBIX transforms data & analytics access with the ability for an average business executive to “converse” with a machine using natural language (NLP) to get the analytics they need to gain actionable insights and make better decisions by inspecting hundreds of variables instantaneously from across all systems within the enterprise and combine that with data that is publicly available.

Learning how GenAI and emerging advancements like Reinforcement Learning and Agentic AI can deliver on the promise of a data intelligence cloud for increasing levels of autonomy and enterprise impact, especially to your data & analytics architecture. Download our free eBook titled “Solving the Problem of Data and Decision Making” to help better understand the nuances of emerging AI and Agentic AI concepts/technologies and offer a set of best practices for consideration to ensure data & analytics transformation with business-led AI success. Or if you can spare 22 minutes for a mini–Agentic AI Readiness Workshop, you can contact one of our AI experts today.