Why Data Science Matters and How It Powers Business

What Does a Data Scientist Do?

Most data scientists in the industry have advanced 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 some advantages of data science in business:

• Mitigating risk and fraud. Data scientists are trained to identify data that stands out in some way. They create statistical, network, path, and big data methodologies for predictive fraud propensity models and use those to create alerts that help ensure timely responses when unusual data is recognized.
• Delivering relevant products. One of the advantages of data science is that organizations can find when and where their products sell best. This can help deliver the right products at the right time—and can help companies develop new products to meet their customers’ needs.
• Personalized customer experiences. One of the most buzzworthy benefits of data science is the ability for sales and marketing teams to understand their audience on a very granular level. With this knowledge, an organization can create the best possible customer experiences.

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Why Data Science? 8 Ways a Data Scientist Can Add Value to Business

1. Empowering Management and Officers to Make Better Decisions

An experienced data scientist is likely to be a trusted advisor and strategic partner to the organization’s upper management by ensuring that the staff maximizes their analytics capabilities. A data scientist communicates and demonstrates the value of the institution’s data to facilitate improved decision-making processes across the entire organization, through measuring, tracking, and recording performance metrics and other information.

2. Directing Actions Based on Trends—which in Turn Help to Define Goals

A data scientist examines and explores the organization’s data, after which they recommend and prescribe certain actions that will help improve the institution’s performance, better engage customers, and ultimately increase 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 insights and drive action. Once the staff understands the product capabilities, their focus can shift to addressing 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. Data scientists create models using existing data that simulate a variety of potential actions—in this way, an organization can learn which path will bring the best business outcomes.

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, most companies will have at least one source of customer data that is being collected. But if it isn’t used well—for instance, to identify demographics—the data isn’t useful. The importance of data science is based on the ability to take existing data that is not necessarily useful on its own and combine it with other data points to generate insights an organization can use to learn more about its customers and audience.

A data scientist can help with the identification of the key groups with precision, via a 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

Reading through resumes all 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 scientists can work their way through all these data points to find the candidates who best fit the organization’s needs.

By mining the vast amount of data that is already available, in-house processing for resumes 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 add value to any business who can use their data well. From statistics and insights across workflows and hiring new candidates, to helping senior staff make better-informed decisions, data science is valuable to any company in any industry.

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