Nexlytix | AI Readiness Calculator | AI Maturity Calculator

How would you rate the accuracy and consistency of your data across your retail systems?

Data is consistently accurate and reliable across all systems.

Data is generally accurate but may require validation in some cases.

Data accuracy is frequently questioned, leading to confidence issues.

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How frequently do you perform data validation and cleansing to ensure data integrity?

Regularly, through automated processes and periodic audits.

Occasionally, with some manual efforts involved.

Rarely, only when issues are noticed.

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To what extent do data silos exist across departments or functions (e.g., marketing, sales, supply chain)?

Regularly, through automated processes and periodic audits.

Occasionally, with some manual efforts involved.

Rarely, only when issues are noticed.

Do Not Know.

Is there a centralized source of truth that all departments refer to for data-driven decision-making? Is that source of truth cataloged for Retail?

Yes, a centralized system is used consistently across all departments.

Partially; some departments use a central source, others rely on their own data.

No, each department operates with its own data sources.

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How well are your retail KPIs (e.g., sales, inventory turnover, customer satisfaction) defined and aligned with your business objectives?

Well-defined and aligned; KPIs are standardized and regularly reviewed.

Moderately defined and aligned; some KPIs lack clear business relevance.

Poorly defined or inconsistent across the organization.

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How frequently are your KPIs monitored, analyzed, and adjusted to reflect changes in business goals or market conditions?

Frequently, through automated dashboards and monthly/quarterly reviews.

Occasionally, with manual processes and ad hoc reviews.

Rarely, KPIs are only reviewed annually or less often.

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How would you describe your current ability to derive actionable insights from your data?

High: Insights are frequently derived, actionable, and impact decisions.

Moderate: Some insights are actionable, but analysis is limited.

Low: Insight generation is infrequent or rarely impacts decisions.

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To what extent does your retail organization leverage advanced analytics (e.g., predictive modeling, machine learning) to forecast trends or customer behaviors?

Extensively: Advanced analytics are actively used in forecasting.

Somewhat: Basic analytics are used, but advanced techniques are limited.

Not at all: Predictive analytics are rarely or never used.

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How closely does your organization monitor data-related expenditures to prevent over-spending in areas like software licensing, infrastructure, and data management?

Closely monitored; we have defined budgets and track spending regularly.

Partially monitored; some areas are tracked, but there are gaps.

Not closely monitored; spending often exceeds the planned budget.

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How effective is your organization at identifying areas of over-spending based on data analytics (e.g., cost of marketing campaigns, operational inefficiencies)?

Highly effective: Regularly identify and adjust for over-spending areas.

Moderately effective: Sometimes identify areas but adjustments are ad hoc.

Rarely effective: Over-spending is only noticed post facto.

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How well-defined are your organization's access controls for managing AI models access to your data?

Extensively: Fully Established.

Moderately: Partially in place.

Minimally: Limited or none.

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What is your organization’s current capability in training, deploying, and maintaining AI models for various use cases?

High: AI models are regularly trained, deployed, and maintained across use cases.

Moderate: Some AI models are maintained, but capabilities are limited to certain areas.

Low: Minimal AI capabilities; models are rarely maintained or updated.

Do Not Know.

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