In 2019, global venture capital investment in Fintech totaled at least $33.9 Billion. For years, the incumbent players had been warning the sector that when the next recession hit, or when fintech faced a “real” crisis, that the sector would, at worst, collapse, and, at best, see a whole bevy of fintech players disappear
Today’s business leaders face an uncertain economic landscape. In the aftermath of unprecedented business disruption in 2020, organizational decision-makers are turning their focus to new concerns. According to McKinsey research, supply chain disruption, inflation, and a growing labor shortage are now top concerns for the C-suite.
It’s the ultimate Catch-22. As businesses grow, executives need to keep a close watch on operations — but that growth itself can blur visibility. Multiple teams, data sources, and silos of information can create challenges in determining who is doing what, where inefficiencies exist, and what improvements should be made.
Data Science tools, algorithms, and practices are rapidly evolving to solve business problems on an unprecedented scale. This makes data science one of the most exciting fields to be in. As exciting as it is, practitioners face their fair share of challenges. There are well-known barriers that slow down predictive modeling or application development. Finding the right data and getting access to it are two of the top pain points we hear from our customers.
I’ve been blogging for about a year about the power of misinformation and our obligations as data professionals to combat it. In a March 2021 blog post, titled “The Power of Misinformation,” I outlined some of our biological instincts that make us susceptible to misinformation and how tricksters exploit them.
Doing more of the work you want, rather than must, is always the dream. And it’s just plain good business. However, many financial professionals feel that they get bogged down trying to find and connect data, rather than focusing on strategic insights.