How To Succeed In Analytics If You're A Non-technical Beginner


The growing importance of business analytics

Analytics allows businesses to take a data-driven approach to achieve their goals. By leveraging technology, data modeling, and statistics, businesses can develop new insights that can help them in developing or marketing their products better (better is measured by increased sales or user engagement).
The growing trend is that most of today’s companies, big and small, are investing heavily in their technology stack to supercharge their analytics — both for product (i.e., data scientists) and sales/marketing (i.e., marketing scientists). The way we do business has changed over the years, in that technology products themselves, like iPhone apps, are the core product/services of many businesses. This allows for real-time monitoring and insights, and delivering marketing and product experiments in real-time to increase sales and engagement, which ultimately results in so much data that insights can only be mined using heavy duty technology stacks that were traditionally developed and used by engineers.

How can non-technical students fit into this role?

With the demand for technical skills in all departments in a company, there’s always a need for someone that cannot only think in a data-driven way but also operate and execute analytically. Non-technical professionals, I would argue, hold a better understanding of the business that often needs a holistic understanding of the industry, competitive advantage, high-level strategy, and tactics that drive the company. If only they can mine through all the data, they would be the unicorn all employers seek.

Three things I find important when learning analytics

1. Keep things simple
I believe that platforms should not require software installation. Companies would never have you install your own software -- nor would you have the permission to do so. When was the last time you installed a database locally on your laptop at work? Being able to work with tools and data accessible via a web browser is basically a requirement if you want to start. Basically, look for a SAAS platform to learn. Don't install any software, it's an unnecessary step in trying to learn analytics.

2. Specific content designed for marketing and business professionals
The course content you choose should be focused on edifying business and marketing students. This sounds obvious, but there are few, if any, course curriculum for business and marketing students interested in learning analytics.

In addition, I never rely on textbooks. It’s too static and rarely has an interactive component to it. Those that do have an interactive component often requires you to install databases and other tools to get hands-on. I try to use platforms, where the tools used, are common industry tools. SAAS platforms like Strata Scratch are built for non-technical marketing/business students and deploy common analytical tools often used in the industry.

This makes sense because marketing and business analytics professionals aren’t interested in developing new machine learning algorithms. They’re probably more interested in designing and executing on marketing experiments.

3. Technical expertise is not mandatory to become good at analytics, but a technical mindset is
You don’t need to know much about analytics or technology, but you do need to change your mindset. The mindset of learning, breaking down a problem, developing a solution is different between technical and non-technical people. Non-technical professionals often have a difficult time approaching and attacking a problem. Most often, I’ve found that they struggle because (1) they can’t seem to find an example in the notes that exactly match the problem at hand and (2) they can’t seem to break down the problem into smaller workable pieces so they become overwhelmed. Problems are like puzzles. Most often, engineers and scientists don’t know how to exactly solve problems or build the desired models, but through research and iterating through the problem and various approaches, they often are able to build something that achieves their goals. You must be comfortable not knowing what to do and you must be comfortable struggling along the way. Changing this mindset is done through practice and reinforcement. Technical people were once novices too, and they struggled with the exact same problems.


Become a data expert. Subscribe to our newsletter.