Why Switchboard, Why Now?
Recently, I joined forces with Switchboard Software to run all things marketing. Switchboard is a data automation/unification platform helping revenue operations and growth marketing leaders make sense of all the terabytes of data converging from disparate sources to drive timely business decisions.
There's a sophisticated under-the-hood data platform that powers this, and there's a world-class team coming together to build and manage it.
There are a few specificities though that made Switchboard the next big challenge of choice to build my marketing career.
1) The data conundrum and tapping an underserved market – no denying that there's been an explosion in data since the digital movement took off. Every digital organization now has to sieve through and make sense of large volumes of complex data to arrive at intelligent hypotheses about their customers' buying patterns and behavior.
While building the BigQuery Data warehouse at Google, Ju-kay and Michael had an epiphany on this widening gap between the Line of Business and engineering leaders. In speaking with many RevOps and marketing analytics teams, they uncovered that these teams had been suddenly exposed to complex feeds from disparate sources like Salesforce and Marketo (as simple examples) and expanding to many other sources like social media and ad platforms. They had to converge all this 'chaotic' data and give it a 'shape' to make business decisions without overburdening the already busy engineering teams.
As we see it, the problem has gone through a 'multiplier effect' with a continuously evolving list of data sources and data types and changing business requirements, making digital success a complex problem to solve.
But Switchboard's approach to building an enterprise-grade data automation platform with domain expertise in showing the line of business leaders on how actually to use this data, and then combining that with a customer success support team (a consultative angle) has made them stick and thrive.
2) The founders' mindset, tech chops, and a customer-first approach – having been in the marketing and the startup world for a bit, I've seen startup teams drink their cool-aid - understandably. Sometimes, the warped vision or the "we are like this only bias" deters them from listening to the voice of the customer or understanding strategic moves from their competition.
In meetings with Ju-Kay and Michael and their team, I saw a different energy. As self-aware founders, they constantly question their assumptions and receive and absorb new information from advisors and their ecosystem to pivot to their customer wants. I will not deny that their experience as foundational members of Google's BigQuery Data warehouse also skewed my judgment in their favor:).
Jokes apart, this grounded approach from the duo has helped them build an impressive line-up of many large digital-first customers like Fandom, Orangetheory Fitness, Spotify, Financial Times, Giving Assistant, Pearson, Ranker, and others. They figured that just selling this as a SaaS license and asking customers (especially business leaders) to run the data operations would not cut it. They ensured that every Line of Business leader was supported by a dedicated customer success team from Switchboard.
Switchboard would also be their 'fractional data team' responsible for understanding complex requirements, configuring the platform to absorb and understand the 'data noise,' convert them to signals and build reports to enable these leaders to drive effective decisions.
3) The future of data – the data dilemma is here to stay, especially as we see digital enterprises traversing through the technology adoption and maturity curve. This will become even more important for specific verticals like e-commerce, healthcare and finance.
Added to this will be the need for data governance and other compliance type layering (which we already see) due to the nature of this data. And with so much data, applying machine learning models to create intelligent and automated approaches becomes obvious to solve this problem at scale. I am eager to see how that future will look like for us.
I see the world of data converging. Call it data ops, data automation, data management, data unification, data as a service – it will all boil down to bringing that order in chaos. And a combination of technology and people that helps to decode this in the best possible way will be the winner.