Introduction
Without professional expertise that will turn
cutting-edge technology into actionable insights, Big Data is nothing. Today, a
lot more organizations and institutions in the financial sector as well, are
opening up their doors to big data and unlocking its power, thus increasing the value
of a data scientist who
knows how to drive the value of a large amount of information that already
exists inside an institution.
It has now become a universal truth that modern businesses are awash with data. Last year, McKinsey estimated that big data initiatives in the US healthcare system “could account for $300 billion to $450 billion in reduced health-care spending, or 12 to 17 percent of the $2.6 trillion baseline in US health-care costs”. However, bad data is estimated to be costing the US roughly $3.1 trillion a year.
It is becoming clear by the day that the value lies in processing and analysis of data – and that is where a data scientist steps into the spotlight. Executives have heard of how data science is a sexy industry, and how data scientists represent superheroes, but most are still unaware about the value a data scientist holds in an organization.
It has now become a universal truth that modern businesses are awash with data. Last year, McKinsey estimated that big data initiatives in the US healthcare system “could account for $300 billion to $450 billion in reduced health-care spending, or 12 to 17 percent of the $2.6 trillion baseline in US health-care costs”. However, bad data is estimated to be costing the US roughly $3.1 trillion a year.
It is becoming clear by the day that the value lies in processing and analysis of data – and that is where a data scientist steps into the spotlight. Executives have heard of how data science is a sexy industry, and how data scientists represent superheroes, but most are still unaware about the value a data scientist holds in an organization.
What
a Data Scientist Does
Most data scientists in the industry have advanced degrees and training in
statistics, math, and computer science. Their experience is a vast horizon that
also extends to data visualization, data mining, and information management. It
is fairly common for them to have previous experience in infrastructure design,
cloud computing, and data warehousing.
Here are instances when a company can benefit from having a data scientist:
Here are instances when a company can benefit from having a data scientist:
·
When there is a need to crunch large volumes of numbers
·
When possessing lots of operational and customer data
· When they can benefit from social media streams, credit
data, consumer research or third-party data sets
The Ways a Data Scientist Can Add Value to Business
8 ways a Data Scientist can add value to any business:
1. Empowering management and officers to make better
decisions
A data scientist who is experienced will serve as a
trusted advisor and strategic partner to the management of an institution and
ensure that the staff maximizes their analytics’ capabilities. A data scientist
will communicate and demonstrate the value of the institution’s analytics
product to facilitate an improved process of decision making across the various
levels of an organization, through measuring, tracking, and recording all the
performance metrics.
2. Directing the actions based on trends which in turn help in defining goals
A data scientist examines and explores the
institution’s data, after which they recommend and prescribe certain actions
that will help improve the institution’s performance, and better engage
customers, ultimately increasing profitability.
3. Challenging the
staff to adopt best practices and focus on issues that matter.
One of the responsibilities of a data scientist is
to ensure that the staff is familiar and well-versed with the organization’s
analytics product. They prepare the staff for success with the demonstration of
the effective use of the system to extract insight and drive action. Once the
staff understands the product capabilities, their focus can shift to addressing
the key business challenges.
4. Identifying
opportunities
During their interaction with the organization’s
current analytics system, data scientists question the existing processes and
assumptions for the purpose of developing additional methods and analytical
algorithms. Their job requires them to continuously and constantly improve the
value that is derived from the organization’s data.
5. Decision making
with quantifiable, data-driven evidence.
With the arrival of data scientists, data gathering
and analyzing from various channels has ruled out the need to take high stake
risks.
6. Testing these
decisions
Half of the battle involves making certain
decisions and implementing those changes. What about the other half? It is
crucial to know how those decisions have affected the organization. This is
where a data scientist comes in. It pays to have someone who can measure the
key metrics that are related to important changes and quantify their success.
7. Identification and refining of target audiences
From Google Analytics to customer surveys,
companies will have at least one of the many bases of customer data that is
collected. But if it isn’t used well, for instance - to identify demographics,
the data wouldn’t be useful.
A data scientist can help with the identification of the key groups with precision, via thorough analysis of disparate sources of data. With this in-depth knowledge, organizations can tailor services and products to customer groups, and help profit margins flourish.
A data scientist can help with the identification of the key groups with precision, via thorough analysis of disparate sources of data. With this in-depth knowledge, organizations can tailor services and products to customer groups, and help profit margins flourish.
8. Recruiting the
right talent for the organization
Running through CVs through the day is a daily
chore in a recruiter’s life, but that is changing due to big data. With the
amount of information available on talent - through social media, corporate
databases, and job search websites - data science specialists can work their
way through this data and hunt the best of candidates that will fit the
organization’s needs.
Recruitment will thus no longer be an exhausting and time consuming human review process. Through mining the vast amount of data that is already available, in-house processing of CVs and applications, and even sophisticated data-driven aptitude tests and games, data science can help your recruitment team make speedier and more accurate selections.
Recruitment will thus no longer be an exhausting and time consuming human review process. Through mining the vast amount of data that is already available, in-house processing of CVs and applications, and even sophisticated data-driven aptitude tests and games, data science can help your recruitment team make speedier and more accurate selections.
Conclusion
Data science can definitely add value to business
by the addition of statistics and insights across workflow, be it hiring new
candidates to helping senior staff make better and informed decisions. Data
science can add value across all industries.
Interested in a career in Big Data? Simplilearn offers a wide range of courses in the subject with instructor led training from industry experts, as well as hands on experience, practice tests, and high quality eLearning content. So get out there, and get certified.
Interested in a career in Big Data? Simplilearn offers a wide range of courses in the subject with instructor led training from industry experts, as well as hands on experience, practice tests, and high quality eLearning content. So get out there, and get certified.
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