How to Punch Above Your Weight with Analytics as a Service Stack
My success running data and analytics at Reliant Funding is based on the following 4 principles:
- Assemble a team of people with a purpose.
- Focus on outcomes and answers, not technology and tools.
- Experiment and try things with clear proof of value knowing they could fail.
- Create foundational building blocks and standards that will stand the test of time.
Build a Team with Purpose
Your analytics success/failure depends on your team’s ability to connect the dots between day-to-day business happenings, initiatives, and market forces to create data influenced insights for each line of business. When we built the data / analytics team we looked for individuals with an insatiable appetite to learn and grow beyond their existing skill set. When analytics has a seat at the table at the onset of new business initiatives instead of order taking, it sets a precedent of a data driven, problem solving team. As a result, rarely does the data and analytics team get caught flat footed tackling problems without context.
Focus on Outcomes and Answers
This age old conundrum where analytics or business intelligence teams are treated as technology development is a tough habit to break. The line between data management/stewardship and business intelligence can get blurred, so we have to be relentless in seeking and challenging how data will be used to drive action and decisions. The continuous feedback loop and chronicling of business decisions and outcomes is how we prove ROI for investing and growing data/analytics teams.
Experiment and Fail Fast
The technologist in me loves finding better ways to streamline, simplify and automate, but never at the expense of spreading a small team too thin. A couple of the technology failures I experienced were the result of not having firm proof of value and jumping in head first to a new shiny object thinking we would figure it out. The key learning/takeaway is to partner with vendors who demonstrate they are vested in customer success with dedicated success managers and top notch tech support.
Create Rightsized but “Fureproof” Foundation
At Reliant Funding, we opted for a low/no code footprint designed for a small analytics team where we catered to our existing SQL competency. Reliant Funding data and analytics stack has paid dividends allowing the team to focus its efforts extracting value from our data platform instead of burning capacity managing it. The perfect data platform for us was Snowflake, Azure Data Factory pipelines, and Datameer for data prep. This software as a service stack provides Reliant Funding with un-matched speed to acquire, validate, prepare, and deliver data insights the same day while still adhering to lifecycle management, tracking and governance structure.
What’s next?
Our analytics as a service foundation is paving the way for faster delivery of features for data science and embedding analytics directly within the business process.
What tactics and platforms are you using to shift to analytics as a service? I would love to hear from you!
About the Author
Ryan Goodman
