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Predictive Analytics

About this topic

Predictive analytics refers to advanced techniques that examine data to answer the question, "What is likely to happen?" It includes methods such as regression analysis, multivariate statistics, pattern matching, predictive modeling, and forecasting.

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Sept 2024

How are U.S. CISOs Addressing Liability Risk?

New regulations taking effect in the U.S. mean that cybersecurity leaders could face legal liability in the event of an incident. What strategies are they using to protect themselves?
How are U.S. CISOs Addressing Liability Risk?

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Has anyone implemented a Computer Vision solution that will find particulates in a clear liquid? Would like to hear pluses / minuses with the implementation.

How do I transition from batch to real-time analytics processing?

I've built this framework that my firm leverages for AI and Predictive Analytics Initiatives as a structure for AI adoption/governance

1. Looking for feedback on is anything missing, is it useful.
2. Sharing with the group to leverage for their own needs.

1. AI Strategy
Set Business Goals 
Do Opportunity Assessment
Develop Strategy 
Develop Roadmap

2. AI Readiness
Audit Data
Evaluate Infrastructure
Assess Organization readiness
Select Use Cases

3. Solution Design
Design Solution Architecture
Select Models
Build Models
Integration Strategy

4. Adoption and Change
Transform processes
Train users
Build Change Strategy
Develop CD/CI

5. Governance
Ethics and Compliance Policy
Model Monitoring and Tuning Framework
Ensure scalability
Design Ongoing support

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Does supply chain security require AI, blockchain or process analytics?

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It's time for 2024 predictions: Note that these are just ideas or off-the-wall predictions. However, choose the one you think is most likely to come about December 29th, 2024.

The First Human (Universal Translator) will be created using AI10%

Generative AI, will be added to children's Toys and be the hottest toy in 202435%

AGI (Artificial General Intelligence) will be created (Denial, Scared, and Hope) may come out of this. 16%

AI and GenAI will be able to communicate with (an animal species) in 2024. -There is work going on now to do this. 12%

Lawmakers will force AI Platforms to provide extensive lists of what information is in use, or being used within any AI model. If the company or entity cannot provide such information there will be fines. (This will force AI Models to be built on known data or purchased data)16%

Employment concerns spike in 2024. Concerns with companies asking for 10+ years, no job training, people let go and AI hired on, Schooling not keeping up with the innovation of this new technological innovation. 11%

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With the current hype in AI technologies, how is your organization tracking demand for internal AI solutions?

Excel33%

Word44%

Sharepoint48%

Internal intake process (home grown system)38%

Third Party Vendor8%

Other5%

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What is the one indispensable action you would emphasize for a successful Data Strategy, which would have a lasting impact on your organization's long term competitiveness?

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Does the term "novel attack" resonate with security teams? or does "unknown attack" resonate better? What would you consider novel/unknown attacks? Does your current threat detection solution detect novel/unknown attacks?

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Anomaly Detection / Mindbridge: We’re exploring tools that support continuous monitoring of financial transactions (e.g., inventory adjustments, journal entries, credit memos, disbursements) to help us identify anomalies earlier than our traditional periodic audits allow. We are a $20B company with 50+ different ERP systems globally (some data is mastered). Our goal is to enhance fraud detection and internal controls using modern, AI/ML analytics tools. We've started a vendor assessment process and MindBridge seems to be the only vendor that fairly fits the bill. I'd appreciate if someone can share any feedback/experience on the solution/vendor.

For data lakehouse platform utilization, our use cases vary between 1. respond to smart applications' data enquiries and 2. AI/ML data exploration. The first use case type mandates low latency responses, while the second consumes computational resources for long periods.  Should we create two different lakehouse platforms to serve both use case types?

As a general rule, do you think the impact of regression modeling on decision-making should be evaluated more heavily on quantitative or qualitative factors?

More quantitative (ROI, significance of coefficients, etc.)20%

They should be balanced70%

More qualitative (domain expertise, stakeholder feedback, etc.)7%

It should vary based on context and goals3%

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