In the last two years alone, the world has generated more than 90% of the data it holds today. The jargons that describe data may be cliched — “Data is the new oil”, “Data is not the new oil, it’s the new soil” — but one cannot deny that real value lies in the meaningful insights that can be extracted from massive volumes of structured and unstructured datasets.
The question is, how can organizations unlock the hidden value of the humungous volumes of data that deluge them? How can they efficiently process unstructured data to reveal valuable enterprise data intelligence that often remains hidden and unutilized?
AI for data analysis makes this happen.
How AI-powered data analytics can unlock data’s hidden value
The dynamic world of unstructured data is severely handicapped by a singular lack of context and insights. It is estimated that a good 80 percent of enterprise data is unstructured, making it extremely challenging to analyze and to uncover value.
Only close to 1/3 of collected data is really utilized, while the rest goes unleveraged. Data silos of unstructured data results in poor data utilization, slow and ineffective decision-making, and loss of competitive edge.
AI makes the winning difference for unstructured data analysis. By embedding AI and machine learning for data analysis across every phase (requirement analysis, design, development, testing and deployment), enterprises can rapidly implement scalable solutions, and unlock significant productivity gains at every stage of the SDLC cycle. This is why enterprises are embracing next-generation AI platforms for data analysis to accelerate software development.
For example, AI and Gen AI can:
- Expedite the understanding of business logic, and decode and simplify its complexity
- Streamline data model design
- Automate code conversion and development to boost productivity
- Generate realistic and synthetic test data with zero delays and risk of data privacy
- Seamlessly integrate with CI/CD tools to ensure faster, safer, and smarter delivery
Our experience shows that these technologies can deliver up to 40% higher productivity in discovery, development, and testing, along with a 30% faster build time for data models.
Additionally, generative AI for enterprise data analysis and reporting can swiftly and accurately cull out patterns, predict incisive trends, and automate smart decision-making with real-time insights generation to unleash powerful competitive edge for organizations across industries. For instance, retailers can optimize supply chains, personalize customer experiences, and improve demand forecasting, while banks and financial institutions can detect anomalies, prevent fraud, manage risk, and automate compliance.
When combined with AI for data analysis and visualization, organizations gain vivid, automated data storytelling that generates dynamic visual insights of patterns, trends, interpretation, and communication.
In short, AI-driven business decisions can revolutionize an organization’s entire strategy and operations in a big way. For example, Netflix is reported to make USD 1billion annually from its AI-driven and automated personalized recommendations.
With great advantages come great responsibility
As advantageous AI-driven business decisions can be, they are also fraught with challenges. Wisdom lies in being aware of the possible pitfalls and minimizing their impact.
AI technologies depend heavily on a lot of sensitive and personal data. Data privacy through robust measures, is thus vital, especially in today’s digital era. Responsible, transparent and ethical practices must be in place to gather, use, store and safeguard data. Algorithmic bias must be avoided. Accountability must be embedded through meticulous data classification, access controls, and compliance to industry standards and regulatory mandates.
Adopting privacy-by-design principles in AI applications - where privacy concerns and measures are firmly embedded in development lifecycle is a good proactive approach to adopt.
Most importantly, AI technologies must not just work for business but equally for societal well-being.
Present and future success in an increasingly disruptive and competitive landscape belong to organizations that strategically invest in AI. Look at it as a core driver of business innovation and intelligent decision-making — and not just as a game-changing tool. Smartly blend AI's might with human ingenuity and learn continuously from such collaboration. That is how we can create new paradigms of the extraordinary with immense trust and confidence.
For enterprises exploring how to leverage Gen AI for data analysis, the opportunity lies in responsibly combining advanced AI with strong governance — turning hidden data into sustained business advantage.
