As Knowledge Graphs / Network Analytics gain increasing traction among analysts looking for meaningful connections and lynchpin actors/nodes, a few key features about networks need to be appreciated a lot better to analyze them effectively.
Over 70% of M&As fail. With companies spending over 2 trillion dollars on M&A every year, this failure rate is a massive call-to-action. One of the key elements to making M&As work is to improve the effectiveness of the integration between the two workforces.
Organizational Network Analytics (ONA) enables leaders to create a Social Graph of the company and leverage that to identify who is “central” to the org and which are the informal communities driving the zeitgeist. This means ONA is ideally positioned to help leaders visualize and analyze the quality of collaboration throughout the integration journey.
Organizational Network Analysis (ONA) has become a key tool for leaders to visualize and analyze the Social Graph of a firm. Some of the specific advantages of doing ONA are >> 1) Understanding key communities in a firm 2) Understanding who are the “central” employees in a firm. 3) Identifying employees who are “isolated” and hence need to engage more effectively and many more. Here is a primer on ONA.
Advocacy of a firm has, for a long time, been associated with measuring how individual employees would respond to an NPS (Net Promoter Score) question like the one given below and then collating that data to drive individual-oriented actionables. Depending on the score provided — the difference between the % of Promoters (individuals who have given a high score) and % of Detractors (individuals who have given a low score) gives the overall NPS of the organization.
Measuring NPS has the advantage of simplicity — two-thirds of Fortune 1000 companies, considered this a benchmark for measuring customer / stakeholders advocacy…
Why do organizations / brands need an ‘Influencer Program’ (i.e. the ability to identify / incentivize / engage with Influencers to drive a specific message)?
If we analyze social media content — there are few observations that pop us almost every time. Given below is an analysis of 10,958 Twitter interactions around COVID. An “interaction” is defined as a Tweet, Retweet, MentionsInRetweet, Mentions and Replies.
While there are quite a few generic articles / blogs on how to evaluate Machine Learning Classifiers, there are hardly any that explain model evaluation in the context of a specific use case — for example Attrition Prediction. Would like to use this article to do exactly that. Having said so, the high level principles can be extended to any use-case.
Why is it important for everyone (even folks who are not Developers / Data Scientists) to know how to evaluate an ML model?
With the increasing adoption of predictive solutions — there is a pretty good chance that you…
While Organizational Network Analytics is becoming increasingly relevant for Community Managers to identify influencers and drive change — the allied field of Natural Language Processing is a major force-multiplier for ONA.
While ONA can help with identifying influencers, the next logical step is to work with them to drive change and manage perceptions. One way to do this is to analyze conversations with NLP and provide that feedback to influencers to close the feedback loop.
NLP primarily helps with sensing the mood (using Sentiment Analysis) and identifying what the community is talking about (using Topic Modelling and Word Clouds). There…
Given the recent discussions around Pegasus — its high time organizations re-evaluated how employees are informally using WhatsApp (and other informal Social tools) at work and its possible impact on privacy and data security.
Brasstacks first — what is Pegasus? This is an Israeli software that was used to snoop on 1400 devices across 20 countries (some belonging to prominent Indians) using a vulnerability in the WhatsApp voice call feature.
We are currently experiencing The Problem of Plenty when it comes to data, here are a few interesting stats:
We are at a time when employees are generating a lot of digital data (eg: Mails / Messages / Connections / Digital Media etc) that give a good insight into how they are engaging with their teams and the larger…
In a previous article (http://bit.ly/onamedium) I had highlighted the importance of Organizational Network Analytics (ONA). One area where ONA can make a big impact is identifying, leveraging & tracking the effectiveness of Influencers in an organization.
A few highly connected and visible associates can have a disproportionate say in shaping opinions on Social Platforms. While at times this may seem a little lopsided, this also presents an opportunity of spreading an organizational message without having to reach out to everyone.
So the million dollar question is how do we identify these “influencers” and how do we utilize them to predict…