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software vendors eight months a model-driven organization. If When you as a leader understand
prior to implementation. This is you engage in a new data science this, you will be in a position to
why you should think about the initiative, however, you want take the other steps described
change-management and processes to do so successfully. You don’t above. Instead of allowing projects
required for implementation at the want your project to be one of the to drift, you will lead. Instead of
beginning of the project. 60 percent that never makes it to rushing adoption, you will allow
implementation. enough time. Instead of allowing
4.Get strategic about operationalization issues to catch
operationalization. To ensure that You already have access to a you unawares, you will expect
projects reach implementation, tremendous amount of data. It may them. And you will put your
it’s vital to stay nimble and create need to be cleaned and labeled, organization in a position to thrive,
strategies that allow for unexpected but it exists, ready to be put to use. not wilt, in the Fourth Industrial
scenarios. Too many organizations Chances are that funding for large Revolution.
start out unprepared for the technology initiatives is readily
system enhancements and other available if ROI can be proven.
operational factors that can derail Indeed, technology updates can be
a project. When these obstacles highly cost-effective because they
occur, they are too willing to drop move organizations from large
initiatives entirely, rather than expensive providers to open source
adapting to the new circumstances. providers.
Always think about these
anecdotes and create short term The real issue preventing
strategies for implementation. organizations from embracing the
technology and becoming data- and
Implementation Isn’t a Data or model-driven is management. In
Technology Problem, It’s a People the twenty-first century, change
Problem management involves more than
business leaders and executives.
At this stage in the progression of Technical people now have a
the Fourth Industrial Revolution, prominent and integral role in
standing back and waiting is no the process. Fourth Industrial
longer a good option. It’s important Revolution change-management
to start taking advantage of data processes involve both business
science and become a data- and and technical teams.
Most non-technical
business leaders are
not familiar with
technical personas,
their vocabulary, or
their way of working.
Realizing the ROI from
implementation of
machine-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.
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