Trust is the accumulation of positive experience in the data systems you use every day. In organizations of all sizes, data-driven culture is extremely elastic to small fluctuations in confidence. It only takes a few incidents of poor data quality to unravel the relationship between business and technology teams: CRM campaigns go to an inaccurate segment, behavioral metrics don't arrive in time to seize an opportunity, or executive communication is obstructed by discrepancy and deviation.

Modern competitive advantage is densely correlated with analytic capability, so exasperation can set in quickly when data quality and performance falter in the data platform. Just like in economics business teams will seek readily available substitutes resulting in shadow partnerships with vendors, a proliferation of data silos, and ultimately a decrease in shared knowledge and learning across the organization. We focus on the two key drivers of trust: data quality and performance.

Simplicity is a cornerstone of our practice, and we find that upholding this tenet is actually really hard in practice. Adding complexity is easy, and humans tend to layer it in planning, design, and implementation scenarios because business problems are amorphous, time is precious, and the exact talent you need is not in the company directory. Most days the organization is not affected by added complexity, but it's when a key employee leaves, systems fail, innovation accelerates, or scale is required that complexity escalates into a serious impediment.

Information systems are supposed to give you an edge, so they need to be easy to wield. To us that means clarity from start to finish. We don't create black boxes and turn over the keys as we leave; our solutions are meant to accelerate your own talent. We don't mix in new technology because it's popular; we leverage what makes your team productive. You can expect clear communication, precise delivery, and a sense of urgency.