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Work is essential for digital transformation success
Digital alteration, or DX, is the process of integrating numerical technology into all aspects of a business to create new opportunities for innovation and growth. This means looking at processes, products and people with new eyes, imagining how technology can change the way work is done, the tools used, and the quality of service for employees and customers. While DX focuses on technology to be successful, companies also need to focus on people. One of the key roles that companies must play in order to achieve their DX goals is the data scientist.
What is a data scientist?
A data scientist is the person who views the collected data,
interprets it, and extracts information that can be used to improve business
results. They look for patterns and irregularities, make predictions and
recommendations, and create predictions. Without data scientists, all the data
collected is just noise.
Data scientists must have some important skills and
abilities. Probably the most important skill is communication. Existence able
to explain data analytics to a non-technical business audience is critical. In
addition, they must be well versed in the business in order to provide useful
and valuable advice. And, of course, they must be specialists in using data
sources and statistical models.
Lack of data scientists
Since so many companies are adopting DX initiatives, data
scientists are in high demand. It is predictable that there will be over 4,000
job openings at DS by 2019. Unfortunately, there are not enough trained staff
to fill these roles. So how can businesses get the expert advice they need?
To fill the gaps in the rankings of data scientists,
companies are recruiting people with some of the required skills and training
them to meeting their DX needs. This includes looking at belongings like
internships to promote on-the-job learning, and examining employees in
departments who have skills like software engineering, communications, or
domain knowledge. Many companies create peer-to-peer learning teams by creating
a large number of internal data scientist-type roles tailored to the needs of
the company.
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