Search through blog

Business growth – How to use data analytics the right way?

Table of Contents

Business growth – How to use data analytics the right way? eCom Ops Podcast with Aron Clymer, the founder and CEO of Data Clymer

It’s not about collecting data; it’s about what you can do with it! What answers can you get from all the information you obtain, and how can you improve business growth with data analytics? Aron Clymer, the founder and CEO of Data Clymer, shares his thoughts, comprehensive knowledge, and best practices on data analytics and data warehousing in our new eCom Ops podcast episode!


Data Clymer is a professional data services company helping businesses from various industries to build modern cloud data systems, get actionable insights and a new competitive advantage, and fully leverage their data.

Data Clymers’ data engineers implement a modern cloud data stack that enables data teams to own and control their entire system. These modern data systems expedite analytics, allowing teams to perform analytics on any data available and improve business decision-making.

Aron Clymer, Founder of Data Clymer, gain a comprehensive knowledge of the full data stack, data modeling, automation, data science methodology, analytics, and product management through his impressive career.

He has managed the roll-out of major business intelligence and analytics systems such as Looker, Amplitude, and Tableau and cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake.

He’s been a data science team leader with a proven track record of building exceptional teams and leading them to success. He spent seven years building the data team at Salesforce and was Head of Data at PopSugar.


Key Takeaways

[ 00:00:02 ] Introducing Aron Clymer, the founder and CEO of Data Clymer.

[ 00:00:27 ] How did Aron come into the topic of data warehousing?

[ 00:00:30 ] “… I’ve just been in high tech for 25 years, and oh, I don’t know how about halfway through that so career I just fresh started to specialize in data.”

[ 00:01:46 ] “… My vision was to build what I would call a full data stack engineering team. So we do data engineering specifically. It’s all around data warehousing use cases in a lot of the exciting companies and verticals that you just mentioned.”

[ 00:03:56 ] What are the most common pain points when combining data?

[ 00:04:30 ] “… using raw data and getting just data in your system is almost impossible. So it doesn’t end there. There are months and months of work really to get a system that’s working really well for you.”

[ 00:06:06 ] How can you create a strong data culture in an organization?

[ 00:06:28 ] “That’s a multi-year process that’s just not a fast process…”

[ 00:06:44 ] “… so, you want to bring your data into day-to-day business processes for everybody in the company.”

[ 00:07:50 ] “For a product company, we want product managers to use it every day or every time they come to a meeting, you know. Bring your data to your meeting; show us why you want to do something.”

[ 00:08:14 ] What are the major points eCommerce businesses should measure and monitor?

[ 00:08:53 ] What answers can ad-hoc data analysis provide?

[ 00:10:58 ] The pain point in the eCommerce business, just like in any other, is that they don’t have complete visibility into the business, products, and customers.

[ 00:14:50 ] Having a single place to manage all collected data from various tools makes data analysis much easier.

[ 00:14:48 ] Over the past two years, technology has become so advanced that you can analyze the data using the evolved data modeling approach.

[ 00:15:50 ] With adequate data modeling, you can find out in a matter of seconds what are the top 2 things buyers do before they make a purchase, or what are the top 4 things they do on a website, etc.

[ 00:17:00 ] Are data warehousing and big data the same, or is there a difference?

[ 00:17:25 ] “… A warehouse is designed for analytics. So it’s designed to put a lot of data into the big tables and then to be able to query it fast.”

[ 00:18:10 ] ETL (extract, transform, load) or ELT (extract, load, transform) means getting data from all your sources into a central data warehouse. So build a pipeline of data in, and then you model it.

[ 00:18:25 ] What is data modeling

[ 00:18:41 ] What is reversed ETL?

[ 00:19:00 ] “… what we see happening is that the data warehouse, then, is becoming more and more important as an operational tool; not just analytics, not just internal analytics, not just backward-looking sort of analytics, but an operational tool.”

[ 00:19:48 ] Today, you have applications now being built just on the data warehouse and specifically for eCommerce.

[ 00:22:29 ] What data warehousing brought that the world had never seen before is the ability for different warehouses to share data without moving them from one warehouse to another.

[ 00:22:29 ] Who has Aron learned from everything he knows about data analytics?

Aron’s No.1 eCom Operations hack

“You’ll never get out of the operational hell if you don’t automate everything.”

Subscribe to the eCom Ops Podcast

Recommended episodes



Get news about integrations and apps, every week

Try SyncSpider free for 14 days!

Create integrations using any app in our
catalog — no limitations.