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Conventional wisdom dictates you need a CRM, an ERP, and marketing automation tools. Next, staff up your Sales Ops, Marketing Ops, FP&A, and Data Analytics teams. Teams work independently, and handoffs occur at each phase of the customer lifecycle.
This fragmented approach worked for nearly a decade. Teams stayed in their own systems and workflows seldom overlapped.
The lines between Sales, Marketing, Customer Success, and Analytics are becoming increasingly blurred. With the proliferation of sales and marketing automation tools, teams are routinely tasked with integrating systems and managing data. The result is a dizzying web of integrations with no obvious owner, and no source of truth. Ask any RevOps analyst which system has the "right" value for a given KPI, and they'll undoubtedly delve into all the data hygiene issues they face.
Our experience confirms that as much as 75% of operations workloads consist of manual system management, data cleansing, and reporting. The vast majority of this work could be consolidated into a single team, but rarely is.
Moreover, McKinsey & Company found that 80% of operations and analytics effort is spent on manual data processes instead of value-add work -- a result of outdated organizational structures.
DataOps solves this problem, but what is it? DataOps is a systematic approach to data management in which the end-to-end data chain, from data collection to curation and reporting, is managed at scale by a single team.
In this model, a data warehouse sits at the center of the business, ingesting raw data from across systems. Once ingested, analytics engineers employ automated jobs to clean, transform, and stage curated data for use in the business. Even moderately technical analysts can then leverage the resulting data to power vast swaths of back office processes - in a fraction of the time, and with far greater accuracy.
After years of observation, we've discovered that most RevOps tasks are actually poorly executed DataOps in disguise. With a formal DataOps structure, those work streams can be automated, subjected to DevOps-style controls, and deployed at scale.
Once deployed, DataOps can manage everything from round-robin lead routing to marketing automation and financial modeling directly from the data warehouse. It serves as a single node in your tech stack that orchestrates processes across the business simultaneously -- the possibilities being nearly limitless. As a result, the vast majority of traditional RevOps responsibilities can be passed to a small DataOps team, and a leaner RevOps function can be redeployed to higher value-add work.
Businesses that are early to adopt this model will have a major competitive advantage thanks to a leaner team, faster insights, and fewer tools.
With Modern Ops, you'll have thought leaders in the emerging field of DataOps, and industry veterans in Sales, Marketing, Customer Success, and Data on your team.
You'll see more sales, less churn, and lower operating costs. Guaranteed.
