Welcome to the third edition of our new blog series: Hazards on the Road to Self-Service Analytics. A couple weeks back, I introduced this series and requested our readers input to inform the topics we’d address. Well, we heard from you and we’re back with another post about a commonly cited self-service analytics hazard – Data Siloes.
For this, we talked with Jubail Caballero, a delivery architect here at InterWorks. He spent seven years in various data-centered roles with a large retailer, focusing on developing enterprise reporting systems and managing global customer insights and analytics. We decided to conduct this as an interview, which we formatted below with the bold text representing the questions and the plain text containing Jubail’s answers.
The Interview
What was a commonly encountered data preparation “hazard” you faced during this work?
My previous employer oversees massive amounts of data. With so much data, silos were an inevitable organizational challenge. For a corporation with thousands of employees and operations across the globe, data silos are something you expect to encounter and it’s important to have a plan in place to address them efficiently.
What’s a quick story and/or some helpful hints about how an organization can address this “hazard?”
In my experience, data silos would frequently pop in regional corporate offices, particularly with data that was less frequently updated and reported. Our data warehouse held lots of data, but with such a large company, it just wasn’t feasible for every file or report to go through ETL and end up in our centralized warehouse. This resulted in siloed data, manually intensive reporting processes and was particularly problematic when that data needed to be integrated with data from other parts of the company.
Fortunately, when these issues did arise, we had a great tool on hand to deal with them: Alteryx. Alteryx is a powerful data preparation and analytics platform. It allows you to build documented, repeatable workflows that join, clean, transform and analyze data. In our case, Alteryx allowed us to build efficient workflows to handle data living outside our warehouse. This had several benefits, including visibility into data and workflows, documented data transformations, partial automation of time-intensive/manual reporting and the ability to prepare data for simplified integration with other data sources. This situation was nearly a decade ago, but I still see the same things happening today. At large companies, there will always be data that doesn’t make it into the data warehouse. Using Alteryx or another data prep tool is a great way to ensure you still have visibility into what’s happening that data and it makes it much easier to extract that data from its silo and integrate it down the line.
Wrapping Up
We hope that this post was helpful in giving you a glimpse into a data “hazard” faced by an organization in the retail sector. If you want to request another look at some hazards faced in different sectors, you can fill out this anonymous survey here so we can tailor this series to you!
