Data Intelligence Platform

In today’s rapidly evolving economic landscape, large enterprises must transition from a backward-looking data warehouse model to a more encompassing approach that supports forward and backward chaining techniques. This shift is essential for forming predictive views that can be operationalized for a variety of uses.

In response to this, some organizations are already adopting AI-driven analytics to enhance their existing data intelligence capabilities, such as machine learning, natural language processing (NLP), and predictive analytics and integrating these with traditional tools, to provide the kind of deeper insights and more accurate predictions necessary in today’s increasingly complex society.

This is helped by the emergence of new hybrid Data Intelligence Platforms that are designed to accelerate this transition, moving from traditional, retrospective data analysis to a more dynamic, real-time approach, by integrating advanced AI, blockchain, and data services.

The Need for Transition

Traditional data warehouse models focus primarily on historical data, limiting the ability to make proactive decisions. While advanced data analytics can provide some insights, they often fall short in delivering real-time, actionable intelligence. The need for a more dynamic approach is driven by several factors:

• Digital Society Volatility: The pace of changes to the way we live, earn and consume is accelerating, requiring public and private sector organisations to continuously adapt.
• Customer Expectations: Modern customers demand personalised and timely interactions.
• Operational Efficiency: Real-time data is essential to enhance operational processes and decision-making in more informed and valuable ways.

The case for a new hybrid approach encompasses several needs: -

• Real-Time Insights; Instead of merely analysing historical data, we should combine historical and current data to predict trends and enable immediate, informed decisions. For example, a retail company can use real-time insights to adjust inventory levels based on current sales trends and predicted future demand.
• Operationalising Data; Turning insights into actionable strategies is crucial for enhancing operational efficiency. This allows organisations to implement data driven strategies in real-time. For instance, a logistics company can optimise delivery routes dynamically based on real-time traffic data and historical delivery performance.
• Predict and Prevent; Predictive analytics enable organisations to foresee potential issues and opportunities, allowing for proactive measures rather than reactive ones. For example, a manufacturing company can predict equipment failures before they occur, reducing downtime and maintenance costs.

All three of these examples have analogy in the public sector where they can manifest as Customer, Process and Risk Management use cases

Realising the Vision

However, it’s crucial to recognise that big organisations have spent significant time and money on Data Intelligence landscapes, so the trend toward adopting a hybrid approach, utilising AI tools, should be additive not disruptive.

New AI technologies should be integrated with existing systems rather than completely replacing them. This approach leverages strengths in established systems and new AI tools. The integration of AI should enhance and complement existing systems, adding value without causing major disruption. This means improving efficiency, accuracy, and capabilities without necessitating a complete overhaul of the current infrastructure.

For a truly successful integration, it’s crucial to implement AI tools in a way that aligns with the organization’s goals and existing workflows. This ensures a smooth transition and maximizes the benefits of both the old and new systems.

Getting this fusion right, ensures that data is then transformed into a dynamic resource for continuous improvement across a range of operational functions. But it is important to enrich these functions through a governable Data Insight Framework that enables that insight to be surfaced in a consistent way across disparate disciplines such as: -

• Customer Management: Enhancing customer interactions through personalised insights.
• Case Management; Surfacing relevant insight cues in case processes to support decision points.
• Process Management; Driven dynamically or conditionally by insight cues surfaced from relevant data sets.
• Knowledge Management; Leveraging data to improve organisational knowledge and decision-making.
• Risk Management; Identifying and mitigating risks proactively.
• Quality Management; Ensuring product and service quality through continuous monitoring and improvement.
• Supply Chain Management; Optimising supply chain operations with real-time data.
• Change Management; Facilitating smoother transitions during organisational changes.
• Project Management; Improving project outcomes through data-driven insights.

Conclusion

In conclusion, a Data Intelligence Platform that embraces a fusion of traditional Data Analytic and AI Tools, represents a significant advancement in how large enterprises can industrially distil insight from large datasets. By transitioning from a backward-looking model to a dynamic, real-time approach, organisations can gain this in real-time, in effect operationalising data, and predicting and preventing issues.

This transformation requires a comprehensive, governable framework that integrates advanced AI, blockchain, and data services. As new products continue to develop and expand, use cases will clarify, helping organisations to achieve continuous improvement and innovation.