siliconindia | | March 20198IN MY OPINIONe're all familiar with the concept of the In-dustrial Revolution, but change has never stopped. We're now well into the Fourth Industrial Revolution and it's transforming the way business-es function and grow. Indeed, companies that fail to transform into data and model-driven organizations are doomed to failure. This is especially true of incumbents that risk being left in the dust by disruptive upstarts, compa-nies comfortable with a data and model-driven approach from their inception.As a brief primer, the First Industrial Revo-lution was about steam and railroads, the Second about electricity, and the Third brought about by the Internet. The Fourth Industrial Revolution is based on ar-tificial intelligence (AI). The transformation it brings will be bigger than that any previous revolution has brought about. There are three fundamental pillars of AI adoption: data, technology, and people/culture/process. In my experi-ence working with many of the largest organizations in the world, I see a consistent pattern: they are will-ing to invest in cutting-edge projects with the pow-er to revolutionize their organization, but they strug-gle to operationalize these projects. Approximately 40 percent reach implementation, while 60 percent stall or flame-out.Why is this? It's because business leaders do not pay attention to crucial change-management processes that need to be considered from the very begin-ning. If you don't think about how to oper-ationalize machine-intelligence at the start of a project, you won't be able to transform your business or realize ROI. As an execu-tive and a leader, a big part of your role is managing the change that must, by necessity, take place if your organization is to embrace data science applications. Change manage-ment is the vital step in operationalizing data-science projects and transform-ing your organization into a data- and model-driven enterprise.Four Ways to Solve Change Management ProblemsChange management can be daunting. Fortunately, the issues I see are consistent, as are the solutions. In this section, I'll give you a brief summary of the most com-mon steps organizations need to take to solve their change management problems:1. Business & Technical Teams Must Work Togeth-er: These areas of a company are usually home to very different types of people, with different talents, prior-ities, and background. For successfully implementing WHY 60 PERCENT OF MACHINE LEARNING PROJECTS ARE NEVER IMPLEMENTEDWBy Nir Kaldero, Head - Data Science, Galvanize Nil is a tireless advocate for transforming education & reshaping the field of data science. His vision & mission is to make an impact on a wide variety of communities through education, science, and technology.Nir Kaldero
< Page 7 | Page 9 >