Why a shifting marketing landscape requires a people-first mindset

    The following is a guest piece by Brady Brim-Deforest, co-founder of Media.Monks. Opinions are the author’s own.

    Over the last decade, digital transformation has been joined by the common trope, “Every company is a tech company.” But now, a new business imperative has emerged as companies feel the pressure to become experts in artificial intelligence (AI), driven by the recursively self-improving technologies behind generative AI and headcount pressures invoked by a dreary economic outlook.

    Add to that the fallout of Silicon Valley Bank, whose collapse has shaken tech startups who relied on the bank for access to liquidity and precious networking opportunities. Startups will need to begin raising capital by the end of this year, or risk running out of runway.

    Whatever happens next, these events will significantly reshape the talent ecosystem, and therein lies a golden opportunity for the tech and marketing industry. Those that have already built innovation-driven cultures will have the best chance of survival.

    Embrace controlled risk

    True innovation starts by creating an environment where teams aren’t afraid to fail. Businesses deal with failure in different ways, and usually fall into one of two categories: those that optimize for success and those that optimize to reduce failure. While reducing failure seems like a sure path to success, it’s a slow and arduous path to the finish line. Those that optimize for success, ironically, are those who fail fast and often — shortcutting to innovation wins.

    I understand that embracing failure is a tough sell in a difficult economy, but I’m not advocating for teams to be flippant and wasteful. Rather, I advise businesses to work strategically to limit the impact of controlled failure. When it comes to decision making, walk through as many two-way doors as you can — it’s when failure is irreversible that you run into problems. You can also reduce the window for downside risk by opening yourself up to failure days, weeks or months down the line. Rather than seeking to avoid failing altogether, mitigate the risks that will only become apparent a year from now and focus on failing fast.

    When teams aren’t afraid of failure, they’re more likely to take risks that have a chance to drive meaningful upside. By building resilience, teams are better able to bounce back from setbacks. Both qualities translate into greater innovation and success.

    Set an integrated data baseline

    Once you’ve accustomed your team to the merits of failure, it’s time to break down silos that inhibit innovation at scale. The data backbone of your business is the first place that you should look to de-silo, because a strong foundation for core business data ensures the integrity, and often value, of the business systems you build on top of it.

    Despite this, I’ve found that a lot of data strategies lack a truly integrated enterprise data layer. Instead, there’s a hodge-podge of discrete and siloed data sets. Many companies today seek to embrace technology like AI in order to shortcut to success, but even if you’re quick to implement such technology, your efforts will be stymied by data that is inaccessible, poorly structured and too disorganized for AI to leverage. 

    Brands can solve this by prioritizing data governance and establishing clear policies and procedures to ensure data quality, consistency and security. This includes quality checks, data lineage tracking and data access controls that ensure data integrity. The most important aspect is investing in data cleansing and normalization tools to achieve consistency and accuracy. This way, brands can ensure that their data strategy is not only optimized for success, but also serves as the basis for automation and AI, which will unlock powerful efficiencies down the line. 

    More broadly, de-siloing data has a positive impact on business culture as well, fostering a more collaborative and transparent environment. Teams can work together more effectively when augmented by accessible data, leading to better decision making and problem solving.


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