Your Data Strategy Fix
In today’s Tech Pulse, gain insight into how:
Each of these articles is penned by members of Forbes Technology Council, key luminaries shaping the future of technology leadership.
Grab your coffee, and let's dive in!
Unlocking Business Insight: Leveraging Knowns & Unknowns in Decision Making
Navigating the complex business data landscape entails understanding a matrix of knowns and unknowns, essential for strategic decision-making. Stemming from ancient wisdom to modern business practices, classifying and interpreting these knowns and unknowns helps drive smarter, faster business decisions.
Key aspects of knowns and unknowns:
📘 Known-Knowns: These are clear, validated facts helping organizations make confidence-backed decisions. Businesses confirm operational safety by utilizing data like customer identities and historical behavior patterns.
🕵️ Known-Unknowns: Recognized but not fully understood, these elements often reveal threats. AI and machine learning are pivotal in transforming these into actionable insights, enhancing customer retention and optimizing selling strategies.
🔍 Unknown-Knowns: Often overlooked, these data points, when linked, unveil opportunities for upselling and customer engagement. Advanced data analytics can reveal hidden patterns, proving crucial for competitive advantage.
🌌 Unknown-Unknowns: Representing deep uncertainties and potential crises (e.g., COVID-19 impacts) require robust crisis management and predictive strategies to mitigate unforeseen disruptions.
🛠️ Handling Unknowns: Integrating and analyzing data enterprise-wide, utilizing AI, and empowering business users are key strategies for addressing unknowns effectively, maximizing decision-making, and enhancing ROI.
Building a Resilient Supply Chain with Real-Time Data & Hybrid Tech Strategies
Tim Bridges - Global Sector Lead, Consumer Products, Retail and Distribution Capgemini
Supply chain resilience has become a pivotal focus for executives, especially given recent disruptions and the fast-changing market dynamics. Modern supply chains need agility and efficiency, powered by real-time data and innovative technology, to remain competitive and reliable.
Exploring the integration of modern tech in supply chains:
🌐 Real-Time Data for Enhanced Visibility: Emphasizing the shift from internal data reliance to incorporating third-party data like retailer POS systems, enhancing accuracy and responsiveness to market changes.
🛠️ Small Tech as a Strategic Solution: Implementing small tech solutions like custom dashboards provides agility, complements large-scale systems, and facilitates rapid integration of new data sources for quicker adaptability to market shifts.
🤖 Leveraging AI & GenAI: These technologies are crucial in processing and analyzing data in real time, enabling dynamic decision-making and operational adjustments that align with current market demands.
⚙️ Unified Big Tech + Small Tech Strategy: Combining robust enterprise platforms with agile small tech tools helps automate and enhance data processes, driving significant productivity and operational efficiency improvements.
📊 Operational & Strategic Benefits: Improved demand forecasting and inventory management lead to reduced operational costs, optimized workforce efficiency, and enhanced sustainability through precise alignment of production with demand.
The MVP of AI Implementation: A Robust Data Strategy
Dan Higgins - Chief Product Officer Quantexa
As industries lean more heavily on data-driven decisions, a robust data strategy becomes crucial, particularly in the realm of AI projects, where challenges like siloed and poor-quality data can cripple implementation.
Ensuring data is collected and effectively integrated and utilized is paramount for enterprises aiming to leverage AI successfully.
Explore strategic elements of effective data management:
🌐 Connecting Data: Overcoming data silos through Entity Resolution (ER), which creates a 'golden record' or a unified source of truth by de-duplicating and resolving discrepancies across data entries.
🔍 Analyzing Data: Utilizing knowledge graphs to map relationships and contexts between data points, helping organizations understand complex data connections and boost contextual awareness.
🚀 Operationalizing Data: Transforming connected and analyzed data into actionable insights that can be operationalized across an organization, enhancing decision-making processes and workflow efficiency.
🌍 Democratizing Data: Employing GenAI tools to make data accessible to an organization's broader range of users, facilitating inclusive and informed decision-making across various departments.
Wrapping Up
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