Designing a Data Architecture to Move Business Forward

Virginia Green, a locally owned comprehensive commercial and residential lawn care company, was positioning itself to achieve aggressive growth goals. To do so, they needed a data-centric strategy that could turn valuable information into meaningful stories and actionable results. However, a manually intensive system made capturing their data cumbersome and the capabilities incomplete. To achieve their growth vision, employees needed a more efficient way to pull and view crucial business information. With an eye on scalability, Virginia Green turned to UDig to help revamp their systems — and position them for the future.

STRATEGIC SNAPSHOT

Challenge

Vast amounts of business-critical data was stored in large, external third-party Customer Relationship Management (CRM) systems, making capturing, digesting, and interpreting data across different markets a struggle. 

Strategy

Through a two-phase process, identify and implement a strategy for designing and building a scalable data architecture that could connect to the CRM and extract valuable data.

Outcome

Scalable and flexible architecture, automations, and dashboards that allow for data digestion, sharing, and visualization.

"The amount of time we’ve saved while gaining a more thorough, accurate view of our data as result of your work is remarkable."
- David Hanny, COO

The Challenge: Fragmented Data Lost in Manual Processes

Over the years, Virginia Green had increasingly relied on technology and data to boost its competitive advantage. With multiple business markets to oversee, efficiently managing its data had been integral to their sales success. So, as executive leadership positioned the organization for their next growth stage, they needed to invest in their data capabilities. The existing system required employees to spend many hours manually pulling reports to review how each market was performing, wasting valuable time. The process wasn’t sustainable and was hindering their growth goals. 

Their current system had multiple challenges: 

  • Vast amounts of business-critical data were stored in a third-party CRM. 
  • No data architecture or storage existed in their system. 
  • The process for capturing, digesting, and interpreting the data was manually intensive and cumbersome for their small team. 
  • The existing data output used an Excel spreadsheet, which limited how they could view and interpret critical information. 

In order to enhance their capabilities, Virginia Green needed to create a flexible, reliable data architecture to analyze and manipulate the data.   

The Strategy: Design and Build a Data Architecture That Supports Automation

In order to overhaul their data systems and build on what they had, we implemented a two-phase process. Our goal was to identify business critical processes and design a solution that could provide powerful insights and scale with them as their business evolves.  

TWO-PHASED APPROACH

Phase 1 – Assess and Analyze
Before we designed their solution, we needed to know what we were working with. So, our team gained access to Virginia Green’s source systems and got to work analyzing their capabilities and challenges. Our goal was to understand the environment and landscape, so that we could identify a plan of action that healed their pain points and positioned them to move forward.  We also leveraged tools that the team already knew how to use in order to streamline learning and implementation. 

Roadblock Identified
During our analysis, we discovered that Virginia Green’s current Customer Relationship Management (CRM) tool was limited in flexibility. The CRM did not have a formalized data sourcing capability that aligned with the company’s desire to automate its system. As a result, the original solution became obsolete. In order to meet their goals, we needed a new strategy.  

Phase 2 – Build and Implement
With our plan in place, we began building their data architecture. First, we created a process to capture the data. We designed the CRM workaround and used a Robotic Process Automation (RPA) to export the data.   We created an automated process to load the data into a scalable cloud data warehouse that Virginia Green could access whenever they needed. Finally, we built a robust dashboard that enabled the team to gather analytics and view and interact with data. 

Consulting and Guidance
Along the way, we helped to guide our client in conversations with the CRM provider as we identified its areas for improvement. This guidance enabled them to have the correct conversations as a data consumer while also advising the vendor to improve their solution for all their customers.  

Training and Documentation
Our goal is to leave our clients capable of standing on their own. So, we trained their team to understand the data architecture mechanics, which deepened their employees’ tech capabilities. We also laid out the current state process and aspects of its future design in both a detailed process definition document (PDD) and solution design document (SDD). 

Our collaborative approach and solution kept the project on track and allowed our team to complete the project on time and within budget.   

The Outcome: Time Reclaimed Matched with Powerful Analytics

With their end-to-end solution in place, Virginia Green is achieving its vision of being a data-centric organization positioned for growth. Their data architecture enables their team to inspect performance consistently and accurately in relation to their goals. They have drill-down capabilities that allow customized reporting to each level of management across their business divisions. And, their employees receive emailed data reports captured overnight through automation — freeing them of over 1,000 hours in manual efforts each year. 

Further, with our team’s post-production support, they have a resource to turn to as they continue to monitor and support performance. Ready for tomorrow, Virginia Green has scalable, flexible technology that can evolve with their needs and growth every step of the way. 

How We Did It

Data Engineering
Interactive BI Application
Robotic Process Automation

Tech Stack

  • Power Automate 
  • Python 
  • Snowflake 
  • Tableau