Back in 2010 when I started as the “analytics guy” at Demandware (which later became Salesforce Commerce Cloud), I worked with brands & retailers to help them identify growth opportunities using data. Back then, several of these organizations were still early in their e-commerce journeys. Traditional retail thinking had a heavy influence on the way digital leaders thought about business growth. This seeped into analytics and all things data. But Digital had this big advantage that everyone knew, but weren’t quite sure of how to leverage effectively — . data This led to organizational leaders behaving a bit like kids in a candy store - excited and acting “grabby” at any and all the data they can get. E-Commerce platforms were a firehose of customer behavior and transaction data, something that brick&mortar retail always struggled to collect. This created “fat data”. Data that accumulated in dashboards, emails, and slides that bogged leaders down. They were wading through “data muck” and more often than not, it sapped their energy instead of propelling them forward. Fast forward to 2023, as brands (are forced to) migrate over to (aka GA4), this “capture everything possible” mindset is making a comeback, albeit in a nuanced manner. It seems like Digital leaders are looking at this as an opportunity to refine their data capture strategy and fix things that they’ve seen break in their recent experience. Google Analytics 4 So I found myself asking - how do we help brand leaders understand what to track and get comfortable with that decision? Today, the attitude towards data has shifted a bit (for the better). E-commerce teams love data. Numbers, reports, and charts get emailed around and shared in collaboration tools on a regular basis. As a result, these organizations have internal “data factories” that churn out charts on a regular basis, for one-time studies as well as scheduled reporting. But is some amount of data collection unnecessary, and perhaps even damaging to the organization by creating complexity and adding confusion? Digital leaders want their decisions to be based on as much hard data as possible. Some still look at it as a source for reinforcing their intuition but some like to be challenged. In all cases, though, managers would do well to ask themselves about their data as a way to ensure that data moves the needle for the organization and doesn’t create an “analysis muck” for the leadership to wade through. 4 questions Are we asking the right questions? Many organizations just collect what’s available instead of focussing on the information needed to help make decisions and run the business. Instead, managers should make a list of questions they want data to answer. These questions should help them run the business and make operational decisions. Then focus their data collection efforts around those than everything else that’s possible. Does this data tell a story? Data in its is almost never usable. It needs to be put together into coherent information. This, then, needs to be connected to the current context of the business to tell a “story”. Data platforms and analytics systems are getting very good at the former. It seems like we still need a human for the latter. Generative AI may change that, but not in the summer of 2023. Managers should then focus on the data that’s needed to convey the story they want to tell. Stories sell. raw form Does this data help us look prepare by predicting probabilities? Data can help react to recent events, and it can also help prepare for potential events in the near future. Almost all tools these days provide some level of forecasting and predictive capabilities. As a last resort, doing simple regression in a spreadsheet tool of your choice is getting easier by the day. In this brave new world, managers should focus on collecting data for the metrics they want to be able to predict and prepare for. Does it help you answer the “why” behind events? We have gotten better are answering “what” questions. But not so much in answering “why”. Answers to “why” require some level of intuition and judgment. It also requires qualitative data. Managers would serve their organizations well by thinking in advance about qualitative data that can help answer the “why” behind the key questions they need to be answered. Collecting and processing data is only getting easier. But E-commerce leaders and managers need to be mindful of the operational burden and cognitive overload that data overload can create. Stay lean - move fast. Avoid data muck.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

48 Hours of Meta Threads

For more insightful posts, find me on Wordpress (I know… old school)

Read My Stories

Too Long; Didn't Read

Make resilience your competitive advantage

Do You Need All This Data?

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

48 Hours of Meta Threads

10 Great Articles On Data Science And Programming!

10 FinTech Trends in 2021 [Part II]

10 Financial Predictions for 2021 to Protect Your Wealth

13 API Metrics That Every Platform Team Should be Tracking

48 Hours of Meta Threads

10 Great Articles On Data Science And Programming!

10 FinTech Trends in 2021 [Part II]

10 Financial Predictions for 2021 to Protect Your Wealth

13 API Metrics That Every Platform Team Should be Tracking

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps