How To Make Confident Decisions Using Data Science?
We call
this period as Digital Era because people are digitally more
active than ever before. As a result, data is growing in an exponential way.
According to a report, IDC predicted, the data will grow by almost six folds by
2025 compared to 2018. Thus
However,
the demand for the Data Scientists is outstripping supply. To compensate this,
many organizations are trying to improve the skills of their employees through
various ways to data science to their workforce.
But, what
other things should the organizations do to compensate for this? Here are the
five ways to make the most use of Data Science.
Quality of Data is always important than Quantity
If the
quality of the input is of low quality, even the best predictive model could
show the worst results, which are not reliable. So first, we need to
understand, the quality of data is more important than gathering more data. To
evaluate the data whether it is accurate, complete and consistent, we need to
know from where and how it came together.
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Learn Skills For Tomorrow
Data literacy is one of those skills, which is
necessary for almost everyone in the digital age. The employees of an organization should be prepared for tomorrow by improving their skills of
understanding, analyzing, and most importantly questioning data.
Some
employees may fear for change of a cultural shift but they need to be educated
about the importance of learning data literacy. The importance of including
Artificial Intelligence (AI) in the workplace to support their role has to be
made clear. Once implemented and we start seeing changes, organizations need to
take feedback and be ready for the open challenges.
Learn And Upgrade By Doing
Although
organizations train their employees to hone skills, they need to participate in
other ways of learning as well such as blogs, webinars, video playlists, etc.
These fill them with confidence in the new technology.
The
technology should be learned to use as a part of their work culture but should
not feel like an extra burden by the employees. The aim should not be to turn a
company’s marketing team or accountants into Data Scientists but enhance their
skill set with the technology in their particular role.
With the
knowledge of Data Science or Artificial Intelligence, an employee can analyze
and interpret enterprise data avoiding any ambiguity. Natural language
processing helps users in finding answers through a search-engine like an
experience.
The new
technology might be of use in little things but very useful things that reduce
the work. It could be a small machine-generated alert, but once the user gets
the correct forecast and benefits from the insights, his confidence in the
technology grows and curiosity to learn increases.
Start Small Go Big
Small things
matter a lot. Even a small feature in AI can give greater insight. A small and
innovative project in AI could turn into success and can then scale it up
across the entire enterprise. For this to happen, the right employees in the
team are much necessary. A person with the right mindset to identify problems
and test hypothesis are better than with a good understanding of the algorithm.
A curious,
self-motivated who like to try and fail frequently can do the work better in
data science technology. Such employees would decipher the mystery of data
science and help enhance the projects in the organization.
Go With The Right Approach
Data
Science techniques are complex and most time-consuming. Therefore, buying
pre-trained AI models that suit your needs and could automate complex processes
could be a better idea. Choosing the right solution will help users about the
data science process and the factors considered during the model creation. It
creates transparency about the reliability and accuracy to the users and helps
them in understanding the steps in the process that fail and how to rectify
them. In such a process of corrections, users will adopt the best practices for
the implementation.
Even though
Data Science is based on logical thinking and mathematics, there is never a
single right answer since there are many approaches to a single problem or a
question. The key is to find a solution that fits everyone by conducting
various experiments and improving continuously. The entire process is to create trust between machine learning models and users by delivering valuable
information to the business.
Cheers,
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