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Writer's pictureDimitris Adamidis

The evolution of Sales Ops and the basic DataOps framework can elevate your Revenue Operations to the next level.

Updated: Jul 1


DataOps
DataOps & RevOps

RevOps Venture's mission focuses on the significance of processes, measurement, and technical alignment. We enable effective management and optimization of GTM strategy through methodologies, tools, and best practices. Our focus on success criteria and technical fit guides decision-making and ensures solutions align with business goals.

 

As the scope of sales operations (SalesOps) expands, SalesOps professionals report dedicating 73% of their time to supporting non-sales functions, up from 39% in 2019, according to a survey by Gartner, Inc. 


Two underlying trends explain why non-sales activities are taking a bigger share of operations time. First, there is growing pressure on the sales teams to be more productive, effective, and efficient as their world gets more complex. Second is the expectation that when you work in operations, you are on the hook to bring solutions while your team is unlikely to grow. At least not substantially. This means you, the ops teams, must be more deliberate or selective where they invest their time, literate in metrics or data-driven, and empowered to make tactical decisions about priorities to achieve organizational targets. This last one is the hardest to achieve in traditional organizations that rely on leaders who have learned their way when AI or data analytics is wishful thinking. 


One of RevOps' most critical roles is facilitating data-driven decision-making. With the wealth of data available today, organizations can analyze performance, identify trends, and predict outcomes more accurately than ever before. With it, we can efficiently deliberate. Let's put it into perspective. According to Statista, the amount of data created, copied, and consumed globally reached 64.2 zettabytes in 2020. Its forecast suggests a rapid increase, reaching 180 zettabytes by 2025. Historically, in 2015, we generated only 15.5 zettabytes; by 2024, this has increased by 791.94% to 138 zettabytes. This started becoming mainstream in businesses where 27 percent of executives declare they profit from their companies' significant data initiatives. From automation, edge analytics, and generative AI through data-driven product development, all these initiatives will only increase across industries, impacting everyone. The data collection process offers an opportunity for operations teams to solve prioritization and the ability to take deliberate tactical action correcting the GTM metrics trajectory. Actually, to make them fast. 


The typical cliche is that operations teams' data-driven decision-making involves collecting and analyzing data from multiple sources, including sales performance metrics, customer feedback, and market trends. Yes, it's easier said than done. The sales ops professionals can use data to generate insights that inform strategic decisions, such as identifying new market opportunities, optimizing pricing strategies, and improving customer engagement. 


Another emerging factor is that Revenue Operations professionals increasingly coordinate efforts across various departments to ensure cohesive operations and strategy execution. Several times, I've written that slides and Excel will take anything you wish for the strategy. However, the data will tell you otherwise regarding the implementation. That's the dissonance that often leaves operations teams in the dark; therefore, having access to multiple sets of data can help them assess what resources an organization will need to get things done or how drastic decisions they will need to make together with other leaders to achieve them. Although strong analytical and project management skills are necessary to facilitate this collaboration, they are critical to the existing and future success of operational execution in building the DataOps framework that helps companies operate with a democratized analytical capacity. It's the DataOps that enables operations teams to achieve agility to work closely with other functions, create more effective workflows, improve communication, and drive better business outcomes.


For instance, SalesOps can collaborate with the finance department to align sales forecasts with budget planning, ensuring that financial resources are allocated efficiently. But in many companies today, both teams for that matter, every single team, operate in a silo-designed system so that nobody else can have a preview of each other's field. While pin-pong conversations continue about "what should we do?" or "who is superior to who", decisions are pending, and organizations end up with staggering losses against their more agile competitors. Customers lose in the end because of the low market competitiveness. Yes, it sounds like we all have been there, but the real problem is the building frustration and inability to move forward. Feeling stuck in time without making much progress leads to broken cultures. I know this sounds dramatic, but we all know how it ends. The bottom line is that gatekeeping is nobody's servant in the long run. 


That's why It is crucial to start working on the DataOps framework early. Often, startup processes are asynchronous and overlap, creating a messy situation. However, this doesn't have to be acutely painful if you start small and slowly scale as the company progresses, adding more transactions, interactions, etc. We can avoid this mess by starting early and laying a solid foundation for the future. 


Traditionally, teams look at it through the lens of headcounts instead of building infrastructure or capacity. Integrating DataOps and RevOps teams is my preference, and there are many reasons for me to think this is a good idea. There are a few things we need to keep in mind while working on this framework. While starting on this journey, it's essential to identify the key goals, including improving data quality, enhancing revenue forecasting, and improving your customer retention. These could be for starters. It's also crucial to define the scope of the integration by specifying the data sources, processes, and teams involved. It makes sense while we have 200+ applications in 750 employee companies. So, what's the data we need for all these teams? Right. These must be sorted and aligned with specific teams with clear master data definitions. Yes, there will be a lot of documentation, but that's a better choice than hiring another 15 analysts to do vlookups or sitting on the bench running queries and concatenating the spreadsheets. 


The next stage is to establish core principles for all teams, including agile, DevOps, or lean manufacturing principles, to create a seamless and continuous delivery process, foster collaboration among teams, and enhance efficiency in managing and utilizing data. I know what most of you will tell me. It is unnecessary work or meetings, but prioritizing the alignment of sales, marketing, and customer success functions to propel revenue growth is critical. You simply can't do all things at the same time with limited resources. You need to make your bets and learn quickly from them. And don't forget to include other functions. I'd assume HR will have its principles, too. 


The next one is collaborative culture. Yes, it's a culture that creates connections between people so they can work together and solve problems instead of shelling themselves out to others. This is part of the bigger picture, too. The structure of the teams must reflect the strategic objectives the company must execute. This means the company can't afford to have two or sometimes three agendas for these teams. If your group of data scientists or analysts reports under the product, you are unlikely to be at the stage where you are scaling your GTM team. However, when you shift the focus towards scaling your sales and marketing, it must be the revops team that is driving the agenda of that team with its priorities. I have often seen the setup of that team to be based on the leader's wish or personal relationship or positioned as a central "corporate team" for no reason. Nothing comes for free, and in both cases, leaders of the groups must take the lead and communicate consistently and efficiently with the rest of the teams to maintain the feedback loops and facilitate alignment across the teams. This is not a territorial game but a game of synergies. 


Progress measurement must continue and remain, to a greater extent, transparent. I don't like the phrase Agile Methodology, as many abuse it for slowing things down in endless conversations. It almost contradicts the intent here. Multi-iteration is appropriate, but please implement sprints and Iterations without losing the ability to adapt. Pace matters to agility is not an excuse but a true commitment to do things fast. Automate CI/CD for Data by implementing continuous integration and continuous delivery (CI/CD) for data pipelines to ensure that data is always up-to-date and accurate. Using automated testing to validate data quality and integrity at every stage of the pipeline will help your teams complete the work faster. 


By integrating revenue data, your teams can utilize a unified data platform that supports multiple environments (development, staging, production) and scales automatically based on workload demands. Ensure integration of CRM, marketing automation, and customer success tools with the data platform for a holistic view of revenue operations. The same will apply for other functions. To avoid confusion while working with the executives pulling data from multiple sources at different times, you must have data quality checks in place. Implement real-time data quality checks using statistical process control (SPC) to monitor and ensure data accuracy and consistency. Data governance policies must be established to maintain data integrity and compliance with regulations to avoid unpleasant conversations.


Often, leaders look at the wrong thing in the wrong order. Moving from one stage of analytical proficiency to another takes time. However, we shouldn't push to accelerate the process if it is well-planned. One way is to leverage advanced analytics like artificial intelligence or machine learning to analyze data and generate insights to drive revenue growth. However, I would only suggest rushing with this process if your organization has achieved a certain level of data-driven decision process. Once you master descriptive analytics, try to implement predictive analytics to forecast revenue and identify potential opportunities and risks.


Many things done today manually could be based on this framework or augmented within minutes instead of waiting for many days or weeks of dysfunctional processes to produce an output. Use data analytics to automate lead scoring, ensuring sales teams focus on the leads with a higher propensity to convert. You can do the same with customer segmentation or revenue forecasting, significantly improving your mid-year or annual planning process. 


Conclusion: Embracing the Evolution of RevOps through DataOps


The future of operations is firmly rooted in the power of data. The proliferation of data—projected to reach 180 zettabytes globally by 2025—presents challenges and opportunities. Any operations team must navigate this data-rich landscape to generate insights, optimize strategies, and drive business outcomes. The evolution towards a Revenue Operations (RevOps) model underscores the importance of integrating SalesOps with other functions to ensure cohesive and efficient operations. 

Ultimately, RevOps' evolution is a testament to the transformative power of data. As organizations continue to prioritize data-driven strategies, operations will play a pivotal role in navigating the complexities of the modern business environment. And it's getting even more complex with the emerging technologies that must be operated on the back end. Adding more headcounts is not the way to go from now on. Therefore, embracing these trends and developing the necessary organizational ability to make decisions quickly will ensure that Operations professionals are well-equipped to meet the challenges and seize the opportunities that lie ahead. 

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