Friday, 1 May 2015

5 Steps Transition Your Career To Analytics

If you devour all things analytics, even to the point of setting up Google alerts to help you begin or progress in your analytics career, then you’ll find this five-lesson blog series helpful.
These lessons are part of Aryng’s Analytics series for individuals looking to transition to a career in analytics or who are new to an analytics role. I hope to answer all the questions I have received from readers of my blog on “Three Steps to Identify the Analytics Training You Need”. Before we go further, understand your fit to an analytics role by assessing your own analytics aptitude. If you don’t have high analytics aptitude, you won’t have fun being an analyst.

Lesson 1 – Understand the analytics landscape and identify your ideal analytics job

So, what constitutes an analytics job? Is it the same as big data job?

The analytics landscape is fraught with over-hyped and over-used terms, so before we go further, let me briefly clarify some of the terminology. (This subject is discussed in-depth in my book, “Behind Every Good Decision”, so feel free to start there as well. You can also download Chapter 7 of the book FREE here, which discusses analytics talent requirements in detail as part of the leadership toolkit.)

Believe it or not, “analytics” is not synonymous with “Big Data” even though these days it is often mentioned in the same breath. Let’s discuss that in a moment.

First let’s define “analytics” vs. “business intelligence” (BI). Business intelligence and analytics are actually two distinct processes that involve different tools and serve different purposes.

When a user interacts with a system (such as when you checkout groceries from your local supermarket), data is produced, collected, cleaned and stored using data solutions including Teradata, Hadoop and Oracle. Data is then accessed via reports and, increasingly, via graphical dashboards. BI includes all components of the operation, from when data is collected to when it is accessed.

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