Our goal is to help companies make smarter business decision about their data.
Whether you cultivate, aggregate, analyze, or exploit it, data has become an increasingly valuable asset in today’s business world. Unfortunately, the value of this asset has also become increasingly difficult to pin down, as most data transactions occur in isolation, without the benefit of objective benchmarks. Highland Math strives to bring clarity to a company’s data ledger by putting all data transactions on the same playing field. We elucidate the context and underlying nature of company data via global benchmarking, analytics, and custom insights.
The first step to maximizing your data’s value is getting to know it.
Each data set comes with a unique set of challenges. Missing data, a lack of organizational structure, and the assumption of knowledge from an external data set can render your data set incomprehensible. Highland Math makes it easy for you to understand your data by reconciling, consolidating, and standardizing disparate and messy data.
Truly knowing your data requires an understanding of the ecosystem in which it lives.
Once the picture of your own data comes into view, the next step is to understand the global context in which it lives. How effectively do you monetize key data segments relative to other companies? What is the global demand for the type of data you buy or sell? Highland Math brings transparency to the data economy by providing industry wide benchmarks for metrics ranging from revenue growth to data segment popularity.
We provide clear vision into your data along with insights to take action.
Highland Math layers next level analytics on top of the global view of a company’s data. We forecast the value of data segments, find trends and anomalies in the flow of data, and uncover the key factors that underlie change in the ever expanding data economy. We leverage these analytics in the context of specific client profiles to deliver the actionable insights that a business needs to get the most out of its data.