by Angela Guess
Lisa Morgan recently wrote in Information Week, “A great number of companies are investing in data science, but the results they’re getting are mixed. Building internal capabilities can be time-consuming and expensive, especially since the limited pool of data scientists is in high demand. Outsourcing can speed an organization’s path to developing a data science capability, but there are better and worse ways to approach the problem. ‘The decision to outsource is always about what the core competency of your business is, and where you need the speed,’ said Tony Fross, VP and North American practice leader for digital advisory services at Capgemini Consulting. ‘If you don’t have the resources or the ability to focus on it, sometimes outsourcing is a faster way to stand up a capability’.”
Morgan goes on, “A recent survey by Forbes Insights and Ernst & Young (EY) revealed that most of the 564 executive respondents from large global enterprises still do not have an effective business strategy for competing in a digital, analytics-enabled world. ‘Roughly 70% said that data science and advanced analytics are in the early stages of development in their organization,’ said John Hite, director, analytic architect, and go to market leader for the Global Analytics Center of Excellence at EY. ‘They said they had critical talent shortfalls, inconsistent skills and expertise across the organization’.”
She adds, “Unfortunately, data science projects and initiatives can fail simply because organizational leaders don’t think hard enough about what the business is trying to accomplish. They also need to consider what resources, if any, are already in place, and how the project or initiative will affect people, processes, technology, and decision-making.”
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