Published: July, 2020
For the July 2020 edition of our newsletter we asked John Loury, President of CAUSE+EFFECT Strategy to help answer this question:
In my experience the answer to that question is to first understand the impact it will have on people, process, and then technology. Accomplishing the challenging task of understanding those factors along with evaluating and inventorying your data sums up the initial phase of every consulting engagement we lead. During initial consultations with customers, we spend multiple hours collaboratively working through what is required to create a data driven strategy that fully delivers based on established expectations and determine if there are the processes in place to support and scale.
Many times, they are aware that data analytics is important, and they have the buzz words down like AI and machine learning. And at this point who doesn’t know what Watson can do… but their understanding of the impact on the people-those developing, implementing, and benefiting from the insight data science creates-is usually much more fairy tale like and lacking a comprehensive road map.
That lack of planning and finer detail is why many initial data science initiatives fall flat and in some cases are abandoned altogether. By investing a small amount of time working through these concepts themselves prior to a consulting company coming in, an organization can accelerate building, developing, and executing a strategy that can provide them with deep customer insights, a competitive edge or even a new product line.
We understand that hiring a data scientist is prohibitive for many smaller organizations and we can help with that. Our company can help build a strategy and provide resources to execute. But it is imperative that stakeholders understand the value of their data, and how data science will impact their people, process and technology in order to be successful.
Today, we are at an inflection point with the popularity of data and the tools to wield it. Organizations don’t need to develop internal data scientists, just help us bridge the gap between what resources they currently have (including people, process, and technology) and how they would like data to help revolutionize their business.