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.
 

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:

• 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

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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.

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.
 

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. 

Data Science Certification Training - R Programming