How to Launch Your Data Analytics Career – Part 1
A “how to” series by analytics leader Piyanka Jain, President and CEO of Aryng
You live and breathe data analytics. Call it a passion, an obsession, the career you were made for—giving it a name is easy. Giving it life is the challenge. We at Aryng know, because every day we help people just like you channel that passion for data science into a successful and fulfilling data analytics career.
In this blog series, we’ll walk you through the steps you can take to begin or progress your analytics career. All that dreaming about an analytics career can become reality. So, let’s get going.
Step 1 – Identify Your Ideal Data Analytics Career
Understand the terminology
“Analytics” has become a buzzword, misused and overused to the point that before we can go further, clarity is required. “Analytics” isn’t synonymous with “Big Data” or “Business Intelligence”. We’ll get to those in a minute. First, let’s define analytics.
Data is the new currency. More so, data science is the science of deriving insights from data. Decision science is the science of collaborative decision making. Analytics drives results by using insights from data science to fuel business decisions leveraging decision science.
Drilling down a bit more, “business intelligence (BI)” and “analytics” are two distinct processes that involve different tools and serve different purposes. When a user interacts with a system (such as the self-checkout stand at your local supermarket), data is produced, collected, cleaned and stored using data solutions. Data is then accessed via reports and graphical dashboards. BI includes all components of the operation, from when data is collected to when it is accessed.
Analytics, on the other hand, is the process performed on data that has been delivered by BI for the purpose of generating insights to drive decisions, actions and, eventually, revenue or other impact. Data is converted to insights using analytics tools.
Now let’s talk about Big Data. Big Data’s ever-increasing volume, variety, velocity, (known as the 3 V’s) create issues of storage and visualization that make traditional business intelligence systems unstable. Big Data is thus a business intelligence issue, not an analytics issue. For this reason, this blog must exclude Big Data.
For more information on terminology, check out Chapters 1 and 2 of my book, “Behind Every Good Decision”.
Now, let’s find data analytics jobs that are right for you
“Interest” in analytics doesn’t always equate with a good career fit. It’s your aptitude for analytics that can determine whether you will enjoy or disdain your new data analytics career.
We know people with a strong aptitude for analytics exhibit some common traits. Ask yourself:
- Do you love solving puzzles?
- Are you a math fanatic?
- Do you enjoy problem-solving?
If you answered “yes” to the questions above, the next step is to assess your analytics aptitude. A strong result on this quick quiz could mean it’s more likely you’ll enjoy an data analytics career.
With your data analytics aptitude confirmed, start browsing popular job forums and search terms such as “analytics” or “data scientist”. The chart below maps some of the job titles you’ll find to three major categories.
Let’s look at “Marketing Analyst”. Most jobs with that title fall in the Business Analytics Professional job category. But some of these positions need advanced analytics skills and thus fall under the Predictive Analytics Professional category.
Now, let’s talk about the job categories—Data Analyst, Business Analytics Professional, and Predictive Analytics Professional. Each position needs different analytics skillsets, per the table below.
A business analytics professional, for example, needs strong business analytics skills, the ability to access data through a GUI-based BI tool, and the knowledge to analyze it in a basic analytics tool that uses smart spreadsheets. In addition, an understanding of basic statistics and testing skills may be required. Note that, as with any job, these positions need additional skills specific to the industry served and job function.
How do you navigate this landscape to zero in on the job that’s right for you?
My years of experience helping people transition to becoming successful data scientists has shown:
- If your background is in BI/Data or Engineering—data structure, information management, data architecture, another engineering degree —a Data Analyst Professional position will likely be your easiest transition.
- Next, if you have a business background—Product Management, Project Management, an MBA—consider a Business Analytics Professional
- Finally, if your experience has focused on statistics, operations research, computer science, or algorithms, a Predictive Analytics Professional job may suit you best.
As you browse through available jobs, look through the requirements of the position. What skills and tools are listed (expert knowledge of SQL, ability to drive decisions based on analysis, etc.)? Use that information and the tables above to identify the appropriate job category.
Next, consider your past work experience. What skills and expertise do you already bring to the table? Leadership, business acumen, customer-facing roles—all of these and more bring great value to an analytics position and should be considered as you get closer to your target dream job.
Congratulations! You are now one step closer to finding and landing your data analytics job. In my next blog, I’ll help you identify your data analytics career skill gaps and map job requirements to your own background. Until then, check out how to get an ACAP Professional Certification in data science, which includes the analytics assessment, online self-paced, hands-on training in business analytics, predictive analytics and testing, experience with a real-time client project, mentoring sessions with analytics experts and career coaching to find and land your analytics dream job.
A highly regarded industry thought-leader in data analytics, Piyanka Jain is a frequent keynote speaker on using data-driven decision-making for competitive advantage at both corporate leadership summit as well as business conferences.
To learn more about Piyanka: