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How AI Can Improve Your Annotation Quality?

Smart Data Collective

Consistency and agreement Establish an agreement metric (e.g., Cohen’s Kappa) to measure inter-annotator agreement. Organize regular workshops to keep annotators updated with the latest guidelines and any changes in annotation strategies. This will reduce inconsistencies and errors in annotations.

Metrics 64
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What Is An Operational Report? A Complete Guide

FineReport

Identifying areas for enhancement : By utilizing operational reports to track key performance indicators (KPIs), businesses can pinpoint areas that are underperforming and take necessary measures to improve them. Operational reports incorporate tools such as dashboards, metrics, and Key Performance Indicators (KPIs).

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Utilize The Effectiveness Of Professional Executive Dashboards & Reports

datapine

By gaining the ability to gather, organize and analyze the metrics that are most important to your organization, you stand to make your business empire more intelligent than ever before – and executive reporting and business dashboards will help you do just that. We are indeed living in a time rich in invaluable digital data.

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Business process reengineering (BPR) examples

IBM Big Data Hub

A comprehensive supply chain overhaul might involve rethinking procurement strategies, implementing just-in-time inventory systems, optimizing production schedules or redesigning transportation and distribution networks. BPR initiatives generally boost key performance indicators (KPIs). Technology can also be used to automate processes.

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8 Ways Successful Online Business Leverage Big Data

Smart Data Collective

Use Data Analytics to Optimize Your Social Media Strategy. You want to use data analytics to monitor engagement on your social media profiles to make the most of your marketing strategy. Check Your Metrics. There is no point in creating a fantastic website if you fail to measure its success. Find A Mentor.

Big Data 104
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Why you should care about debugging machine learning models

O'Reilly on Data

Residuals are a numeric measurement of model errors, essentially the difference between the model’s prediction and the known true outcome. 8] , [12] Again, traditional model assessment measures don’t tell us much about whether a model is secure. That’s where remediation strategies come in. Residual analysis.

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Data Quality vs Data Condition: The Power of Context

Anmut

Both measure and compare the current state of your data against the desired state, but the approaches — and the results — are quite different. Data quality assesses your data against standard technical performance measures such as completeness, accuracy, timeliness, validity, and uniqueness. How data quality assessments work.