Are You a Data Analyst? Get These Top Tips on Effective Communication When Working Across Disciplines
💎 As a data analyst, you may already understand the importance of getting your message across when working with less technical disciplines. But do you know how you could improve your workflow to avoid mishaps? Don’t miss these tips on effective communication that will help your day-to-day work life!
đź“Ľ Play this video to get three top tips from data analyst Rosa Colom Teruel, Manager, Data Science and Analytics at Zynga, on effective communication when working with other disciplines.
đź“Ľ Tip #1: Focus On Potential. The first of these data analyst tips on effective communication is to focus on potential when pitching an idea. Before implementing a model, it's impossible to know how well it will perform or how much impact it will have on business KPIs. As scientists, it can be tempting to say, "We can't know," which is true. However, you often need to provide estimates to prioritize projects and decide which ones you believe in the most. In these cases, it's helpful to focus on potential. Instead of a definite number, you can provide a projection for the worst-case / best-case scenario, even if it requires some guesswork. Business units and product managers use estimates all the time, accepting that they could be proven inaccurate once implemented in the real world for multiple reasons. The important thing is to understand why and then take these learnings into the next project.
đź“Ľ Tip #2: Not All Technical Details Are Relevant. Next up for data analyst tips on effective communication: Science needs to be objective, detailed, and reproducible. Processes and results must be delivered in order, so peers can follow and review completed work. This fact is essential when sharing projects within your team. But if you are presenting to other disciplines, Rosa encourages you to focus on what's relevant for business decisions. For example, how will the project improve the business? What can we learn about players or customers? And what's the plan for the future? Everything else can go in a technical document or an appendix, optional to those interested in additional details.
Data Analyst Tips on Effective Communication - Last Tip!
Tip #3: Communicate Conclusions First. In school and university, you learned to present conclusions last, starting with a problem statement, showing methodology, discussing results, and finally drawing conclusions. Research shows that this is not the way that our brains consume information. It is more effective to present your findings first, followed by results and methodology last (or in an appendix, if not relevant to the audience). This approach may seem counterintuitive because it goes against the chronological order of the work. However, by starting with conclusions, you're going to grab the audience's attention, and they're more likely to remember the takeaways afterward. If you're communicating in writing, listing conclusions first will also help. Even if not everyone reads the whole document, they will still get those takeaways, and conversations will continue to move forward.
📨 Are you interested in joining Zynga? They have open positions! To learn more, click here.
Get To Know Rosa
Rosa is a Data Scientist with 8+ years of experience in the gaming/tech industry and 4+ years leading data science teams. Rosa has vast experience in defining a data science roadmap and analytics strategy that meet product needs.
More About Zynga
Zynga is a global leader in interactive entertainment with a mission to connect the world through games.
To date, more than one billion people have played Zynga’s franchises including, CSR Racing™, Empires & Puzzles™, Merge Dragons!™, Words With Friends™, and Zynga Poker™. Zynga’s games are available in more than 150 countries and are playable across social platforms and mobile devices worldwide.
Founded in 2007, Zynga is headquartered in San Francisco with studios in the U.S., Canada, U.K., Ireland, India, Turkey, and Finland.
How to Become a Data Analyst (Without Going Back to College)
Supply and demand… we all know that as job seekers, high demand and low supply work in our favor. It's a booming job market already, but even more so for data analysts.
The 2018 KMPG CIO Survey found that 46% of Chief Information Officers see "big data and analytics" as the area most in need of additional talent to support growth.
While this impacts careers in data science and data analysis alike, this article focuses strictly on a career as a data analyst, as it's a bit easier to pursue without an advanced knowledge of (read PhD in) Statistics.
Not sure what the difference is between a career as a data scientist and data analyst? Read this first.
Alright, now let's dive in - how can you become a data analyst?
What does a data analyst do?
In simple terms, a data analyst takes data that's been gathered and uses it to help their company or companies make informed business decisions. A data analyst gathers data on specific topics or metrics, often called Key Performance Indicators (KPIs), analyzes that data, and then prepares and presents their findings.
They also work with business development and product teams to use past data in order to set KPIs and track performance over time.
How can you become a data analyst?
Now that you know what a data analyst does, how do you develop the skills necessary to become a data analyst?
First off, let's take a look at what you need to know from a technical perspective.
- Statistical modeling
- Experience using statistical packages to analyze datasets (Excel, SPSS, SAS, R)
- Knowledge of and experience with reporting packages, databases, and programming
- Strong analytical skills and the ability to organize, understand, and identify trends in data
- The ability to share findings with others
- Data visualization skills and familiarity with programs like Tableau
Lots of sources will tell you you need a degree in a related field to get hired, but with large companies like Google and Apple ditching their degree requirements, what you really need is demonstrated experience in the field.
You can start off with free resources to gain exposure to the field, and if it's really a passion of yours, you can check out cost-effective online options on Udemy and other platforms.
Consider Coursera's statistics courses and remember that regardless of your major, if you took any stats courses in college, they're great things to flag on your resume when applying to data analyst roles.
Any quantitative academic research (be it in sociology, psychology, or chemistry) will also be highly transferable, so be sure that's listed as well. (You don't need to have studied economics to understand modeling, but recruiters don't always know that, so do what you can to make it obvious to them!)
Lots of the tools you'll need to use as a business analyst offer free tutorials and certifications, so be sure to check them out as well:
Once you've got the basics down, look at job descriptions for data analysts at some companies where you'd like to work. Read each item on the list and ask yourself if you've done any work to prove that you're capable of the listed task. If you have, jot down what you've done and why it's relevant.
If you haven't, think of projects you could do independently to demonstrate that experience. Consider applying for internships, asking your current employer to allow you to take on data analyst tasks (companies love actionable data, so this should be an easy pitch), or working on a passion project with friends who already have experience in the field.
Within a year or two, you'll easily have enough experience to score an entry-level data analyst role, or maybe even pivot into (or create) one at your current company.
So, recap - how can you become a data analyst... as quickly as possible?
- Understand what the role entails
- Explore free resources to gain exposure
- Review the list of required technical skills
- Identify gaps in your existing skill set
- Take tailored cost-effective and free courses to address the gaps (be sure to make use of free software-specific trainings)
- Start looking at job postings at your target companies - what skills are you still missing?
- Design projects (at your company, with friends, or independently) to give yourself the opportunity to demonstrate your knowledge in those areas
- Get an internship, if feasible/necessary
- Apply to full-time roles! (And don't forget, if you work at a small company that doesn't yet have any data analysts, you can always pitch the role to them!)
Follow these tips and you'll be on your way to scoring a data analyst role (and avg. salary of $67k) in no time.
Remember, you can always go back to school for an advanced degree to unlock even more earning potential later on, but why put two years of time and money into a degree when you could dive straight into the career first?
Good luck!
Data Analyst vs. Data Scientist: What's the Difference?
Understanding the Key Differences & Similarities So You Can Decide Which Role is Right for You
You love numbers, you absolutely crushed your last Stats class, and you're an Excel wiz. Now you're thinking a career in data analysis/data science might be perfect for you. But which career should you pursue? And is there a difference between them, anyway?
The answer to the second question is a resounding yes, though the exact distinction often varies by company. To help you answer the first question, we interviewed experienced Data Scientists and Analysts at Attune Insurance, Cloudflare, Expedia Group, Pluralsight, and WW. They explained the unique skills needed to succeed in each role, as well as differences and similarities between them.
For starters, you should know that both roles are in high demand. The demand for Data Scientists is particularly high, and research indicates that the number of qualified Data Scientists is not growing at the same rate as the number of available data science positions.
Average pay for Data Analysts in the U.S. is $67,377 annually, while average pay for Data Scientists is $117,345.
But don't let these numbers be your only criteria — both roles can be quite lucrative as you advance your career, so read on to learn which role suits you best!
Data Analyst vs. Data Scientist: Defining The Roles
To kick things off and clear the air about the Data Analyst vs. Data Scientist roles, let's define the terms.
What They Have in Common:
As Carl Anderson, Director of Data Science at WW, explains, both Data Analysts and Data Scientists "tend to be numerate, methodical, and curious data lovers who love digging into numbers to ask and answer questions, tell compelling stories, and generate impactful insights for the business. There is no formal definition for these roles, and a role that might be termed Analyst in one organization might be termed a Data Scientist in another."
That said, all five companies we interviewed shared the following core tenets in their definition of each role:
Data Analyst Definition:
Data Analysts use data to define and measure success metrics or Key Performance Indicators (KPIs). They work closely with business development and product teams to provide insights on why these KPIs are or aren't being achieved.
Data Scientist Definition:
Data Scientists leverage their understanding of statistics and software engineering in order to build predictive models.
So what does this mean in practice?
Consider this example from the the data team at Cloudflare (composed of Head of Data & Analytics, Sirisha Gorantla, Lead Data Analyst, Nikole Phillips, and Head of Data Science, Katrina Riehl):
If a business wanted to know how many customer sign-ups there were in the last quarter, Data Analysts would be responsible for finding and sharing this information. They might also then compare that quarter's performance to the previous one, and share some reasons for the growth or decline in sign-ups.
If the data analysts determined that sign-ups were down, then Data Scientists could help address the problem by building a predictive model to help identify prospects that have a higher likelihood to convert as customers.
Summing It Up
Katelyn Tolbert, current Product Manager and former Data Scientist at Pluralsight, summed it up well:
Data Analysts use descriptive statistics to understand KPIs and make sense of past data. Data Scientists use advanced statistics and modeling to make predictions for the future.
What You Need to Succeed: Skills of Data Analysts vs. Data Scientists
Now that you understand the basic definitions of each of these roles, let's take a look at the personal qualifications, characteristics, and skills you need to succeed in each role.
As with the roles themselves, there is significant overlap in the skills you need to succeed in either of these roles. Martha Dreiling, Head of Analytics and Corporate Operations at Attune Insurance, explains it like this:
"In any data-centric role there are three really critical skills someone needs to have. The first is being statistically savvy, the second is data engineering skills and the ability to manipulate data, and the third is having a strong business sense and the ability to communicate findings to less technical individuals. Data Scientists are generally more indexed on the first and second skills. Great Data Scientists are great at all three. Similarly, Data Analysts are typically great on skills 2 and 3, but great ones also have a solid understanding of statistics."
While all data-centric roles require this basic suite of skills, there are some specific skills/areas of knowledge you should have depending on the type of role you'd like to apply for.
For instance, because Data Scientists need to have an advanced understanding of statistics in order to understand and analyze big data, those with a PhD in a data-oriented field may find themselves well-suited to a career in Data Science.
That's exactly how Soma Bhattacharya, who holds a PhD in economics, ultimately landed a job as a Data Science Manager at Expedia Group. In her words, "The only thing that changed when I shifted gears from academia to health policy to industry, was the kind of questions I was solving for." So if you have an academic background in statistical modeling and are ready to make the switch to industry, a career in data science is likely a great fit for you!
Alternatively, if you have less experience with advanced stats or prefer working more directly with business stakeholders, you might be better suited to a career as a Data Analyst.
Consider which of these skills you already have and which ones you'd like to develop as you decide whether a career in data analysis or data science may be a better fit for you:
Data Analyst Skills
- Storytelling
- Strategic thinking
- Business acumen
- SQL
- Data Visualization Tools (Tableau, Power BI, etc.)
- Basic skills in R/Python
Data Scientist Skills
- Highly competent in scripting languages like Python and/or R
- Machine learning
- Statistical modeling
- Data mining concepts
- SDLC (Software Development Life Cycle)
The Cloudflare team also rightly points out that both Data Analysts and Data Scientists "need to be proficient in data discovery and analysis to have a strong understanding of the data. Both roles also need to have a fundamental understanding of data stores/databases because these roles require consistent collaboration with Data Engineers."
(By the way, if you're wondering about the difference between Data Scientists and Data Engineers, a data engineer develops/maintains databases and large-scale processing systems, while Data Scientists are the ones who make sense of all that data.)
Responsibilities of Data Analysts vs. Data Scientists: What You'll Do Each Day
Wondering what you'll likely do at work each day? These tasks are commonly within the scope of job descriptions for data analysts and data scientists.
Data Analyst Tasks/Responsibilities
- Build dashboards
- Report daily/weekly reporting on KPIs
- Complete one-off analyses/queries
- Visualize data (often in Tableau)
- Communicate findings/collaborate with key internal stakeholders
Data Scientist Tasks/Responsibilities
- Build predictive models & algorithms
- Mine and analyze data to optimize business outcomes
- Communicate findings/collaborate with key internal stakeholders
- Data analysts and data scientists work on similar business problems. They both engage with data engineers and business stakeholders on a regular basis to do their job.
- Data Analysts work with business to define success metrics, build dashboards, perform exploratory analysis, and present key insights and takeaways to help measure the progress and drive actionable strategic outcomes. This role requires more human intuition than the Data Scientist role.
- Data Scientists work on building predictive models to help improve on the success metrics — they have the same goal as data analysts, but their approach is different. They rely more heavily on machine learning & data processes than on human intuition.
Martha gave an example of how this works at Attune Insurance, a small-but-growing company:
"Given that we are a small but growing company, the two roles work closely together and have some overlap. Data science is more focused on predictive analytics, whereas data analytics is focused on getting data to end users (other teams internally) by cleaning it and making it available via reporting tools. Both roles are responsible for being able to communicate findings to the rest of the business, but data analytics may interact with other teams more frequently."
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Have more questions about data science, data analysis, or even data engineering? Let us know in the comments!
And if you're on the fence about whether a career in data analysis/data science is right for you, we'll leave you with a piece of advice from Soma, Senior Data Science Manager at Expedia Group:
"If solving for real-world problems using the power of big data and scientific methodology excites you, then I'd encourage you to jump in and start snorkeling into this ocean… you never know what you might discover."
Ready to dive in?
Check out these open data science and data analysis roles:
Data Analyst & Data Scientist Roles at Attune Insurance
Data Analyst & Data Scientist Roles at Cloudflare
Data Analyst & Data Scientist Roles at Expedia Group
Meet Christine Hurtubise, VP of Data Science at Stash
Christine will be a featured speaker at our event for data scientists in NYC on 6/19
Christine Hurtubise, VP of Data Science at Stash, the innovative company dedicated to simplifying investing, is spearheading how Stash uses data to drive value. We spoke to Christine about her career journey, what products Stash has in their pipeline and how she's found her own leadership style.
On June 19th, Christine will be a featured speaker at an invite-only event PowerToFly is presenting for women data scientists, engineers and analysts. If you are interested in attending, you can learn more about the event here or email hi@powertofly.com for an invitation.
How did you first become involved in data science?
I studied Mathematics and French in college, so I was always interested in an interdiscipline practice, and Data Science is a great mix of communication and quantitative work. After graduation, I became a consultant at the software company SunGard, where my team worked on developing and implementing banking risk models. At that time (2011), data science was blossoming as a field, so I moved to the FinTech company, OnDeck, as a data scientist. It was an opportunity to leverage my financial modeling skills while better understanding machine learning and the start-up business.
In November, you transitioned from being a data scientists to the VP level. How were you able to make sure that this transition was a successful one?
I was at OnDeck for three years, where I had started and ultimately led three other teams of growing size. The biggest difference between an individual contributor role and a leadership one is that your focus has to be on elevating the work of those around you. Growing the skills of my team is crucial, but I am also tasked with making sure Stash uses data in a way that drives value. I spend a lot of time with my colleagues understanding our roadmap and what we can deliver to the market.
During your time at Stash and beyond, what have you seen as the biggest change in the fin tech landscape?
FinTech companies need to develop a strong brand around client trust, as they are new entrants in a market that is tasked with a sensitive and important role of customer's financial well-being. Customer privacy and protection are central to our values at Sash. We do not share data. Our focus is using data to offer financial access at scale, and being able to customize clients' experiences across our various products.
What current or upcoming products at Stash have you particularly excited?
I'm very excited to see the evolution of our Coach feature, which guides your journey on the app by presenting dynamic and personalized challenges. This gamification is one of several places where we can use models and user-specific rule sets to make finance fun and engaging.
What advice would you have for other women who are looking to enter leadership roles?
There's no "one size fits all" in leadership; you have to find your own style and voice. I'm not loud or particularly outgoing, so I focus on developing genuine relationships and producing quality work consistently in order to give the times I do speak up the most impact. If you have a clear vision for your practice, then you should take opportunities to step up and share your ideas.
Working at a startup can be a fast paced environment. How do you maintain a strong work-life balance and how does Stash help you achieve that?
I try to approach each project with high quality focus, as opposed to grinding through arbitrary hours. Stash gives me flexibility in my hours along with a lot of creative control over my work. The work here is so interesting, that I get excited to continue. My commute is quite long, so I can never stay late in the office, but I am happy to work at home later.
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Follow Stash on PowerToFly for updates on events and open roles.
Disclosure: The views expressed in this article are not necessarily those of Stash. Stash is not providing any financial, economic, legal, accounting, or tax advice, or recommendations in this article
