Apra’s Data Analytics Symposium: Analytics that Work for Me!

This year’s Data Analytics Symposium, held during Apra’s Prospect Development 2017, offers a wide variety of sessions for analytics professionals, regardless of your organization size (from startup to enterprise), where your organization falls along the analytics spectrum (from as basic as descriptive to the most advanced state: cognitive), or where you are in your analytics career progression. In addition, the offerings are geared toward content that you can easily apply to your day-to-day work.

Having spent almost 20 years in the analytics space, a lot of people ask me about my perspective on analytics and what it is. So I thought I’d share my journey with analytics – including my lessons learned – and how the symposium can help you with yours.

I studied economics in college and finance for my master’s degree. Sunk cost, marginal cost, opportunity cost and optimization are in the fibers of my everyday life. I’m already thinking about the opportunity cost of writing this article when I could be walking in South Beach. I was able to justify it by writing this article while at the same time enjoying the sun in South Beach. I’m also optimizing the impact of this article at different audience segments at a very specific length: just over 1,500 words.

While an education in economics and finance prepared me to enter a career in analytics, I’ve been fortunate to be around talented people as well. These talented people always challenged me to work on solving difficult business problems. There is nothing cooler than when you can solve a problem through analytics. What’s even cooler is leveraging analytics for nonprofits. It’s especially cool when the (analytic) insights I share influence and shape conversations and decisions that serve specific causes: finding a cure for autism, funding scholarships to produce leaders who will shape the future, feeding the hungry. My ultimate high is when a leader tells me that the analytic product I produced helped them shape future dialogues and conversations. To me, this is the true essence of analytics.

I started my career as someone who sounds very “techie,”— a geek or a nerd, however you define it. Bayesian inference, decision trees, stochastic modeling — and more — were all part of my daily vocabulary. Also, I’m in love with new, cool tools like IBM Watson Analytics and Python. Sometimes I wish I could stay in the basement for four months and just code. While all these tools, jargon and technology are essential ingredients to building an analytics practice, I quickly learned what analytics is not. It’s not about tools (I drive the tools). It’s not a report or a data pull. It’s not about the new buzzwords that get thrown at me every day, and it’s not about Sir Ronald Aylmer Fisher, whom I am forever grateful to for having invented modern statistics. It’s all about insights – insights that speak to the audience. In addition, I also learned (and am still learning) that strategic and careful visualization is key to telling a story and guiding the audience to a conclusion. The human brain can process information 10,000 times faster through effective visualization. Consequently, the most optimal set of visuals get us to decisions and outcomes faster.

My analytics career progressed from being a techie to someone who influences/feeds decisions to key decision makers in an organization. To be an effective analyst requires a good mix of technical and soft skills, no matter where the organization is in the evolution of analytics. On the technical side, technology continues to evolve at lightning speed. Terms like data science, big data and machine learning are all products of this continuous evolution. All these technical tools are exciting to learn, play with and apply to our work. While technical skills (and tools) are important, soft skills are critical to moving analytics to decisions and outcomes. Soft skills are not built in days or months. It takes years to be good at soft skills. To this day, having spent almost 20 years in the analytics space, I find soft skills to be the most critical part of my job. So if I were to summarize my day-to-day analytics schedule, here’s what it would look like:

Pie chart

Conversations


This is key to any analytics practice. Understanding the strategic and tactical landscape is your first step. You can’t skip this one and jump through your spreadsheets/tables (and play with your analytical tool sets) without understanding this first. You’ll be confused or over-informed, and you’ll get overwhelmed or underwhelmed. Essentially, you’ll get stuck and ask yourself, “How did I get myself in this mess?” It’s part of the learning process, so just let it happen. You’ll find that as you have more of these conversations, you are in a wonderful place to make a unique contribution and make a difference. You are a powerful and brave analyst who can guide these conversations to help the organization raise more money and fund causes that make a difference in this world. This is not easy, but out of these conversations, you will come to specific objectives, specific business questions and a specific set of directions that will guide your analytical work and set you up nicely to do the fun analytical stuff and lead to the adoption of your analytical product.

Exploratory analysis and storytelling


This is where you get to think 30,000 feet above the data and weave data points, insights and a compelling business story for decision makers. This work answers a specific business question. Transitioning into 30,000 feet is very difficult, especially when you’re in the deep end doing a lot of data wrangling and cleansing. A few tips: take a lunch break, take a walk, read “People” magazine or watch a movie. What I also find helpful is asking, “What does the data tell us? So what?” My exploratory and storytelling products are usually delivered in a deck with compelling visuals, key takeaways, headlines, outcomes and next steps presented to decision makers and stakeholders. I am grateful to my boss for always being my center in balancing different opinions and insights in order to get to a common next step.

Writing/developing models


After the exploratory analysis to answer a business question, modeling enables the business to make decisions better and/or derive better outcomes. The renaissance spirit in me loves modeling because I see the world as a collection of data points (hard value and soft value). More often than not, these data points are not random; and more often than not, these data points can be written into a model that predicts/optimizes an outcome and guides decisions. When you think of the world this way, the possibilities are endless for you to contribute. Also, you’re not just contributing to something. You’re contributing to causes that shape the future of this world. If you haven’t figured it out yet, this is my favorite part of the analytical process – the fun stuff where I get to play with statistical concepts and tools. These days, regression, clustering, decision trees, gradient descent, R, SPSS and QlikView are my “BFFs.”

Writing/developing reports


When you’re just starting a research shop, you want to know all the data points, and reports help a great deal. Your next step then is to harness all these reports in summary views (a good balance of tactical and strategic, not too shortsighted and not too forward looking that you can’t execute) to support the decision-making process. While looking at so many data points is helpful, there is no way on earth you need all these data points/metrics to make a decision. Judgment and decisions are key here. So focus on the top 7-10 metrics that will enable you to make a decision and move forward. The developer in me loves writing reports because this is where I get to automate what I modeled and deliver the report to my users in an intuitive way.

I know this is a lot to take in. If after having read this article, you think you’re not doing analytics, be brave. Go out there and influence, educate, have conversations, continually improve until you get to the true essence of analytics. To help you get there, visit the Prospect Development sessions page for an overview of this year’s Data Analytics Symposium offerings. (To view the sessions, select the track “Data Analytics Symposium” and click “search sessions.”)

Apra has been such a wonderful platform for me to learn about the nonprofit sector. I hope to learn, listen to your stories and collaborate with you as I try to understand the wonderful world of nonprofits, so please say hello. See you at the conference!


 

Glenda Carnate is a senior data analyst for Harvard University Alumni Affairs & Development. She leads the analytics practice. This article above represents Carnate’s 20 years of experience in the analytics space (as a report developer, analyst, an influencer and a manager/leader in analytics) and is strictly her opinion. She can be reached at glenda_carnate@harvard.edu or 508-341-1715.

 

 

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