Early Career Experiences
In the early stages of my career as an inside sales manager, the sales and marketing teams had developed advanced systems and processes at that time for lead generation. The quality and quantity of leads generated depended heavily on the proper balance between operations and system facilitation, particularly regarding organic leads. At that time, search engines were the primary source of lead generation, and the marketing team excelled at creating awareness and interest.
On the receiving end of leads, we implemented early-level scoring (A, B, C) and alerts for the Sales Development Representatives (SDRs). Everyone seemed satisfied with the process until one day when we abruptly lost access to our CRM due to political reasons. Within two weeks, we rolled out a new CRM that was mediocre compared to what we had been using. The following months unfolded like this:
- Marketing continued to create awareness and interest.
- Search engines kept generating leads.
- Our website remained operational with forms still in place.
- The new CRM was not properly synced; leads disappeared into the abyss (unknown to us at the time).
- Our pipeline began to dwindle.
- The funnel started to dry up.
Then came the “Oh Shit” moment: Where’s the data? Where are the leads?
After some digging and sorting, our lead generation report finally returned, showing 10,000+ leads. At that time, our Service Level Agreements (SLAs) for follow-up were set at 24 hours. We were well past that deadline. Panic set in as we faced long days trying to fix the sync issue and establish a process for lead response. From that point on, operations were never quite the same; it felt like we were constantly struggling to stay afloat, especially when lead sources were unknown and we had to rely on slow reports to inform us about new leads entering the funnel. A sync error caused a significant disruption in pipeline generation. As a result, SDRs were laid off, Account Executives (AEs) began looking elsewhere, and revenue growth started to slow. Frustration and low morale crept in for many—all due to an abrupt change in systems.
Lessons Learned
This experience marked my first encounter with an interruption in data flow and highlighted how heavily reliant we were on data to keep our sales team operational. There was no easy fix at that time; I had to hire a new team to verify and call thousands of leads that had been left unattended—many of whom eventually expired or moved on. I eventually moved on as well but soon realized that data could either nourish or starve the sales funnel.
In subsequent years, I focused on sales and marketing operations projects, helping several startups launch their go-to-market strategies. In every role I held, finding, sourcing, and managing data was crucial for facilitating company growth. While we all understand this importance, I often wondered why there wasn’t a solution to maintain clean data with one source of truth. In an ideal world, this would indeed make us all ecstatic! Thousands of tech companies emerged because data management was chaotic; there was a clear need for solutions addressing matching, enrichment, sourcing, deduplication, etc. Welcome to the Data Economy! New jobs were created alongside systems and processes designed to keep pace with the demand for data cleanliness.
The Data Economy Today
During my time at Digital Pi, we worked with clients needing assistance with their marketing automation systems—primarily focused on deployment or optimization. Each project began with a snapshot of their data. Alarmingly, 98.9% of our clients experienced data attrition. Marketing executives turned to us for solutions that would enhance their market reach. How could we perform effectively while providing our clients with support? This was nearly impossible without proper data management.
The common solution involved deploying data tools for enrichment; however, these tools only promised success for 50-70% of their databases at any given time. Sourcing additional data often resulted in duplicates and mismatches. Many marketing and sales organizations still struggle with these challenges—especially when someone has to foot the bill. Data is expensive; good data is even pricier. Thus, the Data Economy remains vibrant and is under a brighter spotlight than ever before—largely because it is essential for building and running AI operations within organizations.
Conclusion: The Path Forward
Returning to my initial point: it always comes back to the data. We have all been struck by the AI lightning bolt; executives are eager to deploy AI across their operations. However, what they may not realize is that their data quality is lacking! Also, AI is Artificial Intelligence not Automatic Intelligence, thus requiring crossfunctional collaboration. So how do we integrate everything effectively to optimize operations? Much of this challenge ties back to revenue growth; therefore, I pose some questions for department heads:
- Customer Success: What is your data strategy around AI deployment?
- Customer Support: What is your data strategy around AI deployment?
- Sales: What is your data strategy around AI deployment?
- Marketing: What is your data strategy around AI deployment?
- Finance/HR: What is your data strategy around AI deployment?
- IT: What is your data strategy around AI deployment?
Customer 360 is primed and ready; you just need to get your data right. It’s time for collaboration and integration across departments—figuring out how your strategies can align with one overarching goal: revenue generation. This is why the DataPros community has become an essential factor in helping disparate data operations and teams communicate effectively while planning for the next significant technological movement.
Let’s roll up our sleeves, get our hands dirty, and talk about data! Or simply put, Talk Data To Me.