Improved Data Accessibility Using Cloud and Visualization Tools

In today’s world, organizations need to make quick, data-driven decisions to stay ahead. But as anyone in the data field knows, making sense of massive amounts of data isn’t always easy. I saw this firsthand at Tradeshift Inc., where data accessibility became a huge bottleneck. Teams across departments were struggling to access real-time, actionable data, leading to delays and missed opportunities.

As a Data Scientist and Team Leader, I took on the challenge to solve this by improving data accessibility by 75%. Here’s how I did it using cloud platforms and powerful data visualization tools like Tableau, DOMO, and Python.

The Problem: Data Siloing and Delays

At Tradeshift, we hit a tipping point where real-time data was becoming essential. However, our data was spread across different systems. The Product and Engineering teams had their setups, and the Customer Support team relied on outdated spreadsheets. So, when decision-makers needed insights, they often had to wait for manual reports or ask analysts to run queries.

A few key issues:

• Data Siloing: Teams weren’t working with a central source of truth. Each department had its own version of data.
• Lack of Real-Time Access: Reports took days to generate—if you needed something urgent, it was a serious bottleneck.
• Manual Reporting: Every department needed its own reports, so analysts spent hours pulling data and creating custom files.

These problems were not only frustrating but inefficient, slowing down critical decision-making across the company.

The Solution: Cloud and Visualization Tools

To solve these issues, I led a shift to cloud-based platforms and integrated data visualization tools to create a system where everyone could access data instantly. Here's how we did it:

1. Migrating to the Cloud

We moved our data to AWS and Azure—the cloud allowed us to create a centralized storage system where everyone, from Product to Customer Support, could access the same data at any time.

• Automated Data Pipelines: Using tools like AWS S3 and AWS Glue, we set up automated pipelines to centralize data across the company, streamlining how it was accessed and shared.
• Scalability: With the cloud, we could easily scale as our data grew without worrying about hardware or capacity constraints.

This move was crucial because centralizing data meant that everyone—across every department—could access the latest, most relevant information quickly. Before, accessing data from various systems could take days. After the cloud implementation, all the data became available instantly.

2. Visualization Tools: Bringing Data to Life

Once the data was centralized, we needed an easy way for teams to visualize and interact with it. That's where Tableau, DOMO, and Python came in.

• Tableau: We created custom, interactive dashboards that allowed different teams to visualize real-time metrics. No more waiting for reports—teams could see the data they needed at the click of a button.
• DOMO: For real-time analytics, DOMO pulled data directly from the cloud and displayed it in visually engaging formats.
• Python for Automation: I wrote custom ETL scripts in Python to automate data wrangling, ensuring that everything was processed correctly without any manual errors.

These tools empowered teams to access the data they needed instantly without always relying on the data team. By incorporating self-service dashboards, departments could log into a single source of data, check the status of ongoing projects, and update business metrics in real time.

3. Self-Service Data Access

We also introduced a self-service portal. Instead of asking analysts to generate reports, teams could go in, find the data they needed, and make decisions based on live insights.

• Real-Time Dashboards: Personalized dashboards helped different departments stay on top of their key metrics, from customer satisfaction to production rates.
• Alerts: With real-time alerts set up, teams were notified about any significant changes, enabling them to act quickly.

Before this setup, gaining access to timely reports required emails, file sharing, and waiting for analysts to process information. After implementing these self-service tools, teams no longer needed to wait for someone else’s schedule to pull up the reports.

How We Achieved a 75% Improvement in Data Accessibility

The 75% improvement was calculated by measuring the reduction in time it took for teams to access data before and after these tools and processes were in place.

1. Time Saved with Cloud-Based Solutions

• Before the cloud: Data was housed in multiple internal systems. Teams needed to request reports, leading to delays in processing times ranging from hours to several days.
• After migration: The AWS and Azure infrastructure allowed instant access to real-time data via centralized systems and automated pipelines. This drastically reduced the average waiting time for reports from several hours to just a few minutes. The time it took to access a specific data set dropped by approximately 50% for most teams.

2. Faster Reporting with Dashboards

• Before Dashboards: Teams spent an average of 3-5 hours manually preparing and sending out reports, re-checking for accuracy, or adapting reports for different stakeholders.
• After Dashboards: Thanks to the self-service Tableau and DOMO dashboards, reports could be generated by stakeholders themselves in minutes, resulting in a 60% faster turnaround time for key analytics and decision-making processes.

3. Reduction in Manual Queries

• Before Automation: Analysts manually queried data and prepared reports for each team. Each query took approximately 1–2 hours to extract, transform, and load.
• After Automation: By automating repetitive data extraction and transformation processes with Python scripts, the same tasks were completed in minutes. Automation saved up to 80% of the processing time that analysts spent on regular data tasks.

Combining These Improvements

When we combined the improvements in real-time data access, faster reporting via dashboards, and the reduction in manual tasks, we reduced the time it took to access usable data by a cumulative 75%.

Teams that once had to wait several days for updated reports or data were now accessing live, actionable data in real-time—often in less than 30 minutes from request. This dramatic improvement increased team productivity, allowed for faster decision-making and created a more agile, data-driven company culture.

The Results: A 75% Improvement in Accessibility

Once we moved everything to the cloud and added these powerful visualization tools, the improvements were almost immediate. Some of the biggest wins included:

• 75% Improvement in Data Accessibility: With real-time data access and self-service dashboards, teams could now make faster, better-informed decisions.
• 50% Boost in Efficiency: The ability to access data instantly without the need to request reports saved analysts and decision-makers countless hours.
• Faster Decision-Making: Real-time data and dashboards allowed decision-makers to act on critical information immediately, reducing time to pivot business strategies.

Key Takeaways

Looking back, here are a few lessons I learned during this project that I’d recommend to anyone facing similar challenges:

• Centralize Data in the Cloud: Migrating to cloud-based platforms makes it easy to store, access, and scale your data in one place.
• Embrace Self-Service Tools: Empower your teams with tools like Tableau and DOMO that let them access the data they need in real-time without always relying on someone else.
• Automate: Automating data processes is crucial to reducing delays and making your workflows more efficient.

With 75% better data accessibility, decision-makers had the tools to make smarter, faster choices, improving both agility and overall performance at the company.

Join the Conversation

Have you faced similar challenges with data accessibility in your team? What tools or approaches have worked best for you? Let’s talk!

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