Amarjeet Singh Khalsa, Solutions Architect, Gathr Data IncModern organizations are strategically building comprehensive “Data to Outcome” platforms that span the entire process, from data collection and processing to business insights, predictions, and actionable intelligence. Instead of managing standalone components of data management, analytics, and business intelligence (BI), we capture a continuous pipeline view of data and insights. This changed paradigm includes a holistic view of an integrated ecosystem that seamlessly integrates separate functions.
A key component powering these modern end-to-end ecosystems is Gen AI. We help organizations realize the full potential of their data and overcome traditional limitations with advanced analytics and processing capabilities.
Gen AI-powered interactions improve the user experience with intuitive interfaces to complex data, analytical tools, and BI tools, empowering a wider range of users to make data-driven decisions.
Enhanced pattern recognition
Gen AI excels at identifying complex patterns and correlations in data. This helps uncover hidden but extremely powerful business insights that cannot be scrutinized by humans or traditional analytical models.
Instant results with rapid engineering
Traditional analytics use cases such as entity extraction, classification, and sentiment analysis require machine learning (ML) skills and custom model training. Prompt Engineering and Gen AI make these accessible to users without ML expertise. This democratization of advanced analytics enables non-technical users to leverage complex insights and fosters a holistic and collaborative approach to decision-making.
Prediction and predictive analytics
Traditional ML models often lack consideration of comprehensive variables. Gen AI outperforms traditional prediction methods by considering a variety of factors to predict outcomes.
Additionally, Gen AI steadily improves prediction accuracy over time by dynamically adapting to evolving data patterns, taking into account both historical and emerging factors.
AI-powered personalized interactions
Gen AI takes traditional market segment analysis to the next level by understanding and predicting user behavior at a granular level. This enables improved and highly personalized customer experiences, interactions, products and services over traditional methods, leading to increased customer engagement, loyalty and satisfaction.
automated decision making
Gen AI extracts insights from diagnostic and predictive analytics and sets standards for prescriptive actions rather than letting users choose the best option. With appropriate control and access, this capability can also be used to autonomously make decisions and initiate actions.
Continuous learning and evolution
Gen AI models continuously learn and evolve using vast amounts of data, increasing efficiency and effectiveness over time. As the amount of data increases, this persistent learning allows it to handle tasks better. Therefore, it is emerging as a strategic asset for industries that require adaptive, data-driven solutions in the face of dynamic and unpredictable challenges.
For example, Gen AI-powered BI and analytics systems in the medical supply chain first predict demand patterns based on historical data. Over time, as systems encounter real-time data, models become increasingly capable of predicting demand spikes, optimizing inventory levels, and proactively addressing supply chain bottlenecks.
Challenges of building with Gen AI
Gen AI promises to build a smarter analytics data-to-results platform, but implementation also presents challenges, including:
Integration complexity: A sophisticated integration framework is required to integrate and process data from a variety of sources, including unstructured data such as social media, audio, and video.
Data accuracy and timeliness: The quality of insights you get from Gen AI is directly dependent on the cleanliness and currency of your data. Maintaining a consistently high quality and updated data repository is essential. This requires robust functionality to synchronize source changes and quickly update target systems.
Rapid advances in Gen AI: Gen AI's rapid advances pose ongoing challenges. To take advantage of evolving capabilities, analytics solutions must be agile and adaptable to incorporate new capabilities and techniques as they emerge.
Data privacy and security measures: Protecting data privacy and protecting intellectual property from potential infringements stand out as important challenges.
Ethical challenges: Gen AI-powered chatbots or agents must be designed to prevent abusive, racial, or unethical responses. We ensure that interactions remain neutral, respectful, and fair.
Tackling challenges
Comprehensive data engineering strategy
Dealing with diverse data formats requires a robust data engineering strategy. Natural language processing (NLP) must be used for audio and video text and speech recognition algorithms. Additionally, users should have the freedom to choose the best LLM based on their specific use case.
Continuous data quality assurance
It is important for data processing platforms to establish automated pipelines that synchronize data changes and apply data quality checks between source and target systems. This ensures timely updates and accuracy of insights. Employing LLM for data quality checking enhances the system's ability to identify and correct discrepancies, ensuring a consistent and reliable dataset for analysis.
Agile evolution of the Gen AI stack
Gen AI-powered data-to-outcome platforms must embody agility as a core tenet. To keep up with the rapid advances in Gen AI, we need to continually evolve while integrating new advances. You should use reusable AI solution blueprints with modular and scalable architectures.
Enhanced data privacy
Enterprises need to establish a clear governance structure for provisioning data for Gen AI use cases. Policies and guardrails must be strictly enforced for platforms that derive results from data. Use a centralized gateway to validate all AI-generated responses to prevent leakage of personally identifiable information (PII), identify and stop unintentional bias, and reduce the risk of generating inappropriate responses. can be reduced.
Gen AI propels the BI and data ecosystem into faster and smarter lanes. However, several challenges must be addressed for successful implementation, ranging from technology, design, and process to skill availability and the associated high costs. It's important to get buy-in from stakeholders across the organization.
It is important to carefully evaluate the potential impact and value of your Gen AI use case in terms of measurable ROI within a defined time frame. Selecting high-ROI cases to improve operational efficiency, optimize processes, and strengthen decision-making capabilities is critical to stakeholder alignment and successful implementation.
Choosing the right initial integration point is a practical and successful entry strategy.
The author is Amarjeet Singh Khalsa, Solutions Architect at Gathr Data Inc.
Disclaimer: The views expressed are solely those of the authors and ETCIO does not necessarily agree with them. ETCIO is not responsible for any damage caused directly or indirectly to any person/organization.
