Many people think analytics is about gathering data using software tools and creating dashboards and reports. However, analytics is much more. Analytics goes beyond data; its primary goal is to enable business decisions based on that data. This involves working with stakeholders to understand the gaps in the business and using this knowledge as a guide to manipulate data, derive useful insights, and make recommendations – all key actions to increase revenue and lower costs.
Wherever you sit in your organization, what’s most important is the bottom line. And so whether you lead business or IT units or are in the trenches, the analytics profession has likely crossed your mind. What does it entail? Who are true analysts? How does one become an analyst?
Those of you specifically in a data management, data warehousing or business intelligence role may wonder how to further develop your analytics career. On the surface, an “analytics career” can be quite broadly defined, and the transition to it can seem very confusing. However, the structured approach we describe in this article will make it easy to choose your path – and give managers and leaders an appreciation for the developmental steps to success.
My first question to someone looking at an analytics career is usually, “Why do you want to change careers?” Once you understand your motivation to change and how well this career will fit your personality, you can consider your next moves.
Step 1: Align Your Disposition to Your Career
What are the telltale signs of a good analyst? Ask yourself these questions to see if you have what it takes:
- Are you a problem solver?
- Do you like puzzles and other games involving logical thinking?
- Are you generally curious?
- Do you like working with people and helping them solve their problems?
- Are you driven toward making an impact through your work?
If you answered “yes” to most of these questions, you will likely enjoy being an analyst.
Step 2: Get Trained
Once you know you will enjoy this career, it is time to get trained. To do that, you’ll need to decide how you want to use analytics. Do you want to play in business analytics, driving decisions in the business world, or would you prefer the technical challenges of a data scientist doing advanced analytics?
To give you some perspective, McKinsey Global Institute’s report on big data predicts that by 2018, there will be a shortage of 1.5 million analysts/managers who can make data-driven decisions versus 140,000-190,000 positions open for data scientists.
There are several key differences between the two tracks.
Data scientists need advanced analytics skills and thus they need formal education in statistics, computational mathematics or predictive analytics. Data scientists spend more time on computer algorithms than they do working with people. If you love working on data, software and systems, this is a good fit. Your education options depend on your situation:
- For full-time or part-time courses in analytics with core topics in statistics, algorithms, quantitative methods, data mining and predictive analytics – along with tools training in SAS®, R, etc. – consider North Carolina State University and Northwestern University, two well-known schools that offer master’s degrees in analytics. Stanford and many other universities offer professional development courses through their statistics, data mining or other departments.
- Training on software tools is widely available through vendors such as SAS, SPSS and Angoss.
- Short, hands-on courses in advanced topics such as logistic regression, decision trees and data mining are offered by analytics consulting companies, including Aryng, Prediction Impact and Abbott Analytics.
A business analytics professional/manager will need a basic understanding of analytical techniques that most data professionals can learn quickly. Analysts/managers spend more time interfacing with people than computers and are often working with broader business questions that can be solved using simpler analytics techniques.
This track requires less time for transitioning, especially if you already have experience working with data. Data professionals already equipped with SQL skills to manipulate data will need training in data analysis and people skills to start tackling business analytics challenges. Although 80 percent of business problems can be solved via business analytics techniques and don’t require advanced data analysis, historically there has not been formal business analytics training offered, and most people have learned it on the job. Even today there isn’t much in the marketplace.
You may find books and occasionally courses at conferences. At Aryng, we recognized this gap and created business analytics training classes. Our five-step analytics framework course marries years of practical business operations experience with technical data analysis techniques to quickly enable business and data professionals in data-driven, decision-making processes.
Step 3: Find a Job
Analytics is a hot field with many jobs available. To land a great analytics job, consider networking via LinkedIn. Use LinkedIn Jobs as well as LinkedIn analytics groups and highlight your analytics skills using tags. Also consider key job portals, such as Craigslist, icrunchdata, Indeed, Dice and Monster.
Things to Avoid
Here are my recommendations for things to avoid.
- Don’t expect to learn analytics from blogs and social chatter. There is a lot of information published online. Do your own due diligence.
- Don’t view conferences as a solution for training. Be choosy; attend only the best vendor-neutral conferences to get the real scoop on analytics. I recommend Predictive Analytics World, Strata and TDWI. If you are using a specific tool, such as SAS or R, attending those annual conferences may be a good idea as well.
In summary, know your strengths, get the right training, go get the job – and don’t forget to have tons of fun!