siliconindia | | March 20199and operationalizing data science, leaders need to build and nurture an environment where these two distinct types of people can work together seamlessly.2. Lead from the Top Down: As an executive, you must understand that change comes from the top. Without active leadership on your part, it's highly unlikely that projects will move in the most productive di-rection. You need to be responsible for aligning the entire organization with your vision and guiding projects toward operationalization and the re-alization of ROI. It's all about your people, the culture you cultivate, and the processes that will help your or-ganization to best respond quickly to this game-changing technology. 3. Allow Enough Time to Adopt Changes: Adopting new initiatives takes time. Many organizations fail to allow enough time to undertake such major changes successfully. To implement a data science project ef-fectively, the typical enterprise must start working with their IT team and software vendors eight months prior to implementation. This is why you should think about the change-man-agement and processes required for implementation at the beginning of the project.4. Get Strategic about Opera-tionalization: To ensure that projects reach implementation, it is vital to stay nimble and create strategies that allow for unexpected scenarios. Too many organizations start-out unpre-pared for the system enhancements and other operational factors that can derail a project. When these ob-stacles occur, they are too willing to drop initiatives entirely, rather than adapting to the new circumstances. Always think about these anecdotes and create short term strategies for implementation.Implementation Isn't a Data or Technology Prob-lem, It is a People ProblemAt this stage in the progression of the Fourth Industrial Revolution, stand-ing back and waiting is no longer a good option. It's important to start taking advantage of data science and become a data and a model-driv-en organization. If you engage in a new data science initiative however you want to do so successfully. You don't want your project to be one of the 60 percent that never makes it to implementation. You already have access to a tre-mendous amount of data. It may need to be cleaned and labeled, but it ex-ists, ready to be put to use. Chances are that funding for large technology initiatives is readily available if ROI can be proven. Indeed, technology up-dates can be highly cost-effective be-cause they move organizations from large expensive providers to open source providers.The real issue preventing or-ganizations from embracing the technology and becoming data and model-driven is management. In the 21st century, change management involves more than business lead-ers and executives. Technical people now have a prominent and integral role in the process. Fourth Industri-al Revolution change-management processes involve both business and technical teams.Most non-technical business leaders are not familiar with tech-nical personas, their vocabulary, or their way of working. Realizing the ROI from implementation of ma-chine-intelligence models requires a paradigm shift and an understanding that the work of the technical team is integral to the implementation of machine learning initiatives. When you as a leader understand this, you will be in a position to take the other steps described above. Instead of al-lowing projects to drift, you will lead. Instead of rushing adoption, you will allow enough time. Instead of allow-ing operationalization issues to catch you unawares, you will expect them. And you will put your organization in a position to thrive, not wilt, in the Fourth Industrial Revolution. At this stage in the progression of the Fourth Industrial Revolution, standing back and waiting is no longer a good option. It's important to start taking advantage of data science and become a data and a model-driven organization
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