In addition to reading this article about becoming a speech analytics analyst, you should also check out the video series Speech Analytics Best Practices. Jim Rembach, discusses the importance of identifying the key personnel required for a successful Business Intelligence implementation and the traits or attributes of these personnel:
1. Analytic / math skills,
2. Project management skills,
3. Persuasive story-telling ability (art), and
4. Business acumen.
Analytic skills are integral to any business intelligence implementation not only because those skills guide decisions on how to interpret results and what to do with data, but more importantly, what not to do with data.
As Jim Davies, research director at Gartner says about the speech analytics analyst:
“The organisation has to have a degree of expertise in-house – there has to be the analytics guru that is using the system as their day job. And if the company hasn’t got that time and expertise internally then maybe having a managed service is the way to go, rather than an on-site or Cloud-based deployment.”
Mistakes with data typically result from either ignorance or intent. We’ll start with the easy part – intent.
Data Mistakes – Intent
During my first undergraduate statistics course, the book How to Lie with Statistics was required reading and I’ve kept it on my bookshelf ever since, requiring any analyst on my team to read it as well. The book is a quick read with 144 pages of nostalgically-dated examples of blatant data bias and abuse, the perfect way to introduce a discussion on the ethical handling of data. From using loose arguments to justify exclusion of outliers, to ignoring severe violations of the assumption of data normality, mishandling measurement errors, to “toying” with the statistical power of a test to either hide or uncover differences, there are many ways to avoid letting data get in the way of a good story.
Data Mistakes – Ignorance
That leaves us with (data) mistakes due to ignorance. There are plenty of books and online courses promising to teach you everything you need to know about statistics in 30 days or less / in just 12 hours of online class time / etc. (As of today, there are not any on being a speech analysts analyst). In fact, they teach you just enough to be dangerous. Knowing which statistical methodologies to apply to the data set and business problem in question is not just about following a flow-chart, although there are plenty of flow-charts out there for that distinct purpose. Statistical analysis requires understanding of data, the assumptions underlying statistical methodologies and interpretation of statistical outputs, yes, but more importantly, it requires making decisions and judgments about data and analytic methodologies that only solid education and experience with “real-life” data sets can provide.
Speech Analytics for Dummies
In many ways the wide-spread availability of point-and-click statistical software (while Analysts shout for making it more actionable) has contributed to the popular opinion that anyone who can use a mouse can do statistical analysis, but the hard part, the part that no software package can do for you (will it ever), the part that requires a “marriage” of both art and science is in the interpretation of the statistical outputs. Remember…people make it actionable, not the software.
Your Business Risk
So is it possible to become a Speech Analytics Analyst in 30-days or less? Well, I have never seen it happen. However, I have seen some pretty certificates that say it has. That’s the risk your business has to be willing to accept.
You can obtain more insights on Speech Analytics Software right now by clicking here.
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