American Landscape Partners 101
American Landscaping Partners assists partner companies in achieving objectives by providing financial and investment services, overseeing operations, offering back-office support, managing business operations, and providing consultation services. Founded by former US military Special Operators, they apply unique strategic planning, team building, and execution skills to the US landscaping industry.
American Landscape Partners operates in Tennessee, Ohio, Pennsylvania, and Florida, with plans to expand into two more States in the immediate future. The company employs about 550 people, 70-75% of whom are in the field and engage in thousands of projects annually.
The company’s experience with Precog is an excellent use case for optimizing analytics using our AI-powered, custom data connectors. Precog’s Sami Haddad interviewed Sam Giampapa Jr, Vice President of American Landscaping Partners, to talk about the company’s success using Precog to optimize his company’s ETL/data analytics workflow and, ultimately, enable rapid growth.
Sam Giampapa On the Limitations of Data Integration with Traditional ETL/ELT
Sami Haddad: Can you provide context about what you did when you realized your analytics processes needed improvement?
Sam Giampapa: I built our ETL programs on the backend using Python. It worked, but any changes required a lot of tedious updating and dumping of the database. It took a lot of time — every time we onboarded new software programs, we had to learn the API, not to mention build it out, test it, and maintain the integration. Then, once you’ve got the data in the database, someone has to build out the dashboards. We didn’t have time for that.
Sami Haddad: What sources were you working with at the time, and how many?
Sam Giampapa: We were using five to seven different sources.
Sami Haddad: You were building custom software to manage extraction and flattening. What motivated you to find a better way to accomplish your goals?
Sam Giampapa: We were using Stitch and got to the point where we had to write code to fix the code. Any changes or updates with the API caused issues, which was impeding how fast we could scale.
Sami Haddad: Did you review other ETL/ELT vendors before you found Precog?
Sam Giampapa: We looked at an extensive list of providers. They all claimed to do good things. But the most significant drivers for us to go with Precog was your ability to build new connectors at a reasonable cost — because as we complete new acquisitions, we’ll need new connectors to get all our data where we need it.
Sami Haddad: What was the first new connector?
Sam Giampapa: Aspire — which is field service software.
Sami Haddad: Was anybody else offering a connector for that?
Sam Giampapa: No.
Sami Haddad: How long did it take Precog to prepare that for you?
Sam Giampapa: About a week.
Sami Haddad: What is the destination?
Sami Haddad: What are the other sources used with Precog?
The ROI of Business-Ready Data from Every Source
Sami Haddad: How would you calculate ROI on switching to Precog?
Sam Giampapa: Precog allowed our team to focus on higher priorities instead of continually monitoring and fixing connectors. Precog doesn’t break. So, we can now rapidly scale. We’re already talking about new connectors. In the landscape industry, nine times out of 10, you will run into three significant ERPs: Aspire, Boss, or Element. We want to build connectors for those. The name of the game is data quality and speed of getting the data where you need it. Precog adds value for many reasons. We’re building a fully integrated system with data quality testing and analytics.
Sami Haddad: How many people did Precog free up?
Sam Giampapa: About two full-time people.
Sami Haddad: What aspects of Precog’s value proposition were most compelling?
Sam Giampapa: Affordability, predictability, & flexibility in the connectors come to mind. The update frequency is big too.
Sami Haddad: Can you describe one of your key dashboards?
Sam Giampapa: We’ve got our organic growth initiative dashboard, tracking where employees are located. It’s monitoring revenue and identifying areas where we can save money.
Sami Haddad: What was the first kind of data that you mapped?
Sam Giampapa: Financial.
Sami Haddad: Do you have another example of a multi-source report you’re doing in Power BI?
Sam Giampapa: Customer ranking based on profitability, ticketing efficiency, and other vital metrics. It’s not purely financial because given the way our system works, you’ll have the earned revenue for that ticket, but you’ve also got time-material-labor efficiency. So when we bid a job at a certain number of hours, we see if that crew is doing it in the bid number of hours. There’s more to it than just data.
Sami Haddad: Have you given up using Stitch? Did you try any other ones?
Sam Giampapa: We tested a few other ones and they were okay but Precog was the best, in my opinion.
Sami Haddad: How did you find us?
Sam Giampapa: It may have been word of mouth at first and then I found you on Google.