Greg Council's blog

When the Future You Expect Never Arrives

When the future you expect never arrives and business predictions fall short of their mark, the culprit is—more often than not—bad or missing data. Procurement staff must ensure their data is accurate from start to finish so that their forecasts have the desired outcomes. 
Idioms abound about how to tackle future challenges such as “past results do not guarantee future performance” or conversely, “those who do not learn history are doomed to repeat it.” We have all seen or heard these intuitive phrases. On the surface, they would seem to be at odds. In reality, they address two different concepts associated with using the past (data) to understand, predict and influence the future.  
Similarly, when it comes to projects that involve the need for data, whether it is to predict sales to manage inventories or to train a system to automate a process, success hinges on having the right set of data to use as input to the decision making process. Today, where machines are often making decisions, the notion of “right set of data” becomes a lot harder to understand. This is because machines learn in a different way and the rationale for the output they produce is difficult to reconstruct. 
Machines do not have the intuition or the critical reasoning that can help to elevate or discount one data point over another. Input data must be accurate, representative, and free from bias so here are some key guidelines about your data to help ensure successful projects:
1. Accurate Data. Having accurate data is essential because a machine can learn on both accurate and inaccurate data, but only accurate data provides the desired results: a machine that provides output, which is reliable.
Greg Council, Vice President of Product Management, Parascript

Artificial Intelligence Spawns the Next Largest Divide

Artificial Intelligence (AI) is creating the next largest divide not only between people, but also between organizations. Taking full advantage of AI requires a two-pronged approach by any enterprise. First is to identify the business processes that can gain most from the introduction of AI. Second is to treat AI as a key component in any reengineering effort with quality data as one of the highest priorities.  

Since one key beneficial attribute of AI is that it can replace tedious, low-value human tasks, it is important to target processes that enable staff to focus on other higher-value areas. The perspective of pragmatically tackling routine processes first is echoed in research presented by Harvard Business Review, which provides a useful construct by defining three types of AI: one applied for automation, another for delivering insight, and a third for customer engagement.  

Data Science: the Key to Successful AI 

Greg Council, Vice President of Product Management, Parascript