The triple threat: a phrase frequently associated with athletes and entertainers. Move over Justin Timberlake, today it’s the analyst’s turn to shine. This is the first in a three part series on being a triple threat in data analysis by:
- being an expert in your business
- beefing up on your stats knowledge
- developing key technical skills
Being an expert in your business
Lucky you: the first step in becoming a data-analyzing triple threat should be the easiest.
Any data mining project starts with gaining a firm understanding of the context in which it’s taking place. After all, it’s pretty difficult to identify trends or build models without knowing what you’re interested in learning and what data is available.
By becoming an expert in your business (i.e. your organization and the broader field) you’ll be in a good position to find opportunities for analytics to boost your work.
Unless you’re starting out in a new job, you are probably pretty familiar with your particular organization, so this is the easy part.
Evaluate your role:
What are your responsibilities? What are some of the challenges you’ve come up against? How could your work become more productive? Data analysts typically display great curiosity – so what in your own job are you curious about?
Listen to your colleagues:
Not by eavesdropping on their random personal calls, but in meetings and maybe even over lunch. What are they working on? What are their challenges and where do they see opportunities?
These conversations may spark ideas for you in terms of how data analysis could improve your organization, and help you identify future allies. When you get ready to build that first predictive model, what will it measure and who will be its champion?
Survey the data:
At this stage, you don’t need to know every item in your database, but it’s helpful to have an idea of what the organization is currently tracking and what seems to be missing. If you’re looking to get into data analysis, you’re probably already fairly tech savvy and familiar with the database, but if not have a chat with IT.
Understanding the strengths and weaknesses of your organization’s data may give you an idea of the sort of data analysis projects you could feasibly attempt.
A final suggestion is to keep an eye on your organization’s strategic goals. These indicate what the current priorities are and where the organization plans to go. How does data analysis fit into that vision?
So you know your job and your organization. Great! But what’s going on elsewhere? Whether you fundraise, manage programs, or make waffles, keeping an eye on industry best practices and innovation, while developing a strong network can spark ideas and give you a place to turn for help.
We’re all busy, so it’s hard to make time for a bit of leisurely reading, but do try. I like to keep one professionally-related book going at a time. A few minutes here and there adds up.
I don’t know about you, but I’m more of a web junkie. Find a few blogs, online publications, and social media sources to follow. Set aside some time to find out what people are talking about and join the conversation. Just try to set some time limits on your surfing or its quickly 2am and you’re somehow on the IMDB page of a 1980s TV show.
Whatever the source, reading and reflecting on the knowledge in your field will help you think creatively about how data is and could be used.
Work your network:
If your organization has the budget, try to get to at least one professional conference a year. Whether it’s a classic like APRA or CASE, or a newer one like DRIVE, go! Meet people and learn new ideas. Even if you end up at a less than stellar session or two, you’ll at least have time out of the office when you can brainstorm about possibilities.
Locally, look for regional events from your professional organizations and even try Meetup groups in related topics. Don’t forget to connect with people on LinkedIn and Twitter to continue the conversation.
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Those are just a few ideas for becoming an expert on your business. You’re likely doing a lot of these things anyway, so it’s a bonus that it sets you up to be a triple threat in data analysis!