This week’s curator, Sam Thomas, looks at some of the DOs and DON’Ts of Big Data analytics, especially as it pertains to enterprise social networks; he also picks 5 links for further reading on the topic.
How do we traverse the minefield of Enterprise Social Network data?
Do you want meaningful insights from your enterprise social network data? Tread carefully!
Look at data too broadly – without initial direction, purpose or hypotheses – and you’ll rarely find insights that connect to your strategic objectives (and therefore add business value). Look too narrowly and you’ll only confirm your preconceived ideas. After all, who doesn’t love being right? If we see one instance of data that confirms our beliefs, then our brains are programmed to ignore any data that disagrees. And we rarely recognise spurious correlations unless they are truly absurd. (Total US crude oil imports correlate with the total per capita consumption of chicken. Who knew?)
Enterprise social network packages come with an abundance of accessible metrics. Many help us gauge levels of collaboration or strengths of networks within our organisation. However, unless you know how your associates really engage with the platform, this data will be riddled with red herrings and false conclusions.
You must build an understanding of the intent behind the data points. Then formulate a working hypothesis that you can prove – or disprove. Failing to do so means your analysis will contain unacceptable amounts of guesswork and assumption. This approach still leaves room for discovery: unexpected patterns can raise questions and spark new branches of investigation.
- The solution is not better technology. What’s required is a shift in responsibility for data quality away from IT folks and into the hands of managers, who are highly invested in getting the data right. Data’s Credibility Problem
- Even academic studies get distorted by researchers’ biases. This Is Why You Can’t Always Trust Data
- Big Data yields great insights on everything from consumer habits to operational processes. Knowing how to use the data is the real key to leveraging its power. Beware these 6 Big Data mistakes
- Analysing internal social networks provide business intelligence that was not formerly available. The Value Of Enterprise Social Networks and Big Data
- The rise and fall of Google Flu Trends demonstrates that Big Data is not immune to small data problems. A theory-free analysis of mere correlations is inevitably fragile. Big data: are we making a big mistake?
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