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ONA ( organizational network analysis ) to enable individuals to impact their organization - part 2

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ONA - organizational network analysis - is becoming an important topic for HR-technology. Simply put, ONA provides insight into how organizations really function.

Embedding ONA capability has the potential to enable employers and employees to organize themselves more effectively, communicate more impactfully, and to lead their companies forward.

Published in: Data & Analytics
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ONA ( organizational network analysis ) to enable individuals to impact their organization - part 2

  1. 1. Facebook social widget For content discovery Form of social pressure (2 friends like this) Two of your close friends like this - You are more likely to discover the content - You are more inclided to believe/like the content
  2. 2. What if we can do the same?
  3. 3. Create a blueprint for Future of Work in Factories. Test the prototype in the customer production Factory, and obtain first feedback of the prototype from end-users. Similar Contributions: Future of Onboarding Learning on Shopfloor Alicia Young Bob Stone Joe McNamara Yves Delacroix Learning: Learn to make software Prototypes Prototyping Future of Work Factories of the Future Experimental Design Text is analyzed in real time - Extract Topic Meaning - Keyword Extraction Keywords are displayed Key Influencers are found. You can: - See their contribution - Contact them directly Related Marketplace Projects Relevant Learning is found. Add to MyLearning Create a «Future of Work in Factories» Concept
  4. 4. Similar Contributions: Future of Onboarding Learning on Shopfloor Alicia Young Bob Stone Joe McNamara Yves Delacroix Future of Work Factories of the Future Contact Alicia directly through the systemCheck Alicia´s contributions - See if you shoud align Align & Connect Align with people working on the similar things Find people from my network (that are in my communication neighborhood) Find key influencers company-wide Circle Indicates people from my immediate network
  5. 5. New Marketplace Project Title Description Working on similar topics: Bob Stone Joe McNamara Self-driving buses The future of public transportation is in self-driving buses, that are driving through metropolitan and rural areas. The goal of this project is to create software for self-driving buses, which would be the backbone of the public transportation in metropolitan areas. Self-driving Public Transport Buses Interested in Contributing: Yves Delacroix Text is analyzed in real time - Extract Meaning from text - Keyword Extraction - Keyword translation to users language ONA network finds key people to contact - Based on keywords - Based on Impact/Influence Yves is interested in the topic - Shown as suggested for the project Invite Yves to contribute
  6. 6. Our Approach: Privacy and GDPR Easier than feared! Privacy-by-design (GDPR): a) Article 4(5) defines pseudonymization as “the processing of personal data in such a way that the data can no longer be attributed to a specific data subject without the use of additional information.” b) For summary statistics or groups (non-PII) analytics: Recital 26 defines anonymized data as “data rendered anonymous in such a way that the data subject is not or no longer identifiable.” Cleanup PII Utilize PII re-bind ONA/NLP/ML Pseudonymize Dataset User Consent (PII Separation) NOTE: PII - Personally identifiable information 7
  7. 7. What next? 8 1. Roll-out a GDPR-aware MVP 2. Learn from it and grow Data Product Managers 3. Apply ONA metrics & ML to pioneer new products
  8. 8. NLP and Keyword extraction Input: Training email dataset • Sanitization, header removal • Filter outbound data Processing: • Simple NL preprocessing (lowercase, remove special characters, tokenize) • Named entity extraction and removal • Stopwords removal
  9. 9. . ... continued Processing: • Lemmatization and part of speech filtering • Identify bigrams and trigams Now the data should be clean enough to do actual NLP on it.
  10. 10. . LDA and RAKE LDA – Latent Dirichlet Allocation: • Topic modeling algorithm • Extracts relevant topics from a corpus • Unsupervised learning • Needs number of topics beforehand RAKE – Rapid Automatic Keyword Extraction: • Statistical modeling of text • Creates rank of keywords • Easy to run, not actually ML
  11. 11. . Word embeddings and TF/IDF Word embeddings: • Used fasttext embeddings – multilanguage • Expand found keywords by similarity vectors • Threshold value for similarity TF/IDF: • Ranking and relevance for documents • Using any Lucene based solution • Get free n-grams and typos
  12. 12. More Info? Visit our Article Series: bit.ly/2Rwhg2R 13 Vladimir Mijatovic | Alexandru Filip | Agron Fazliu February, 2019 Freiburg

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