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Importance Of Data Science For Businesses

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It is becoming clear day by day that there is a massive advantage in data processing and analysis. A recent report by LinkedIn states that Data Science is dominating the emerging job ranking; it has witnessed a 37% hiring growth over the last 3 years. Data Scientists are being called the modern-day superheroes! And for good reason – data science is proving to be crucial in the transformation of almost every industry. Wondering how data science helps business and why it is so important? This blog will answer all your questions

What Is Data Science For Business?

Data Science is a multidisciplinary field which strives to provide valuable information that can help in improving decision-making.  It deals with vast volumes of data to find hidden patterns and derive meaningful business objectives. The main purpose of data science is to provide insights that can help in bettering strategic planning, research, new product and process developments, and much more.

Components Of Business Understanding Phase In Data Science Lifecycle

The Data Science Lifecycle:

The Data Science Lifecycle

The business understanding phase in the data science lifecycle involves:

  • Understanding the business process
  • Defining the business objective
  • Defining and framing the business problem/s
  • Deciding the success criteria

In this scenario, the complete lifecycle of data science is revolving around the business objective. Figuring out a meaningful solution to a problem that the business is facing is the end goal of this entire activity. The problem can be anything – from reducing costs, or increasing profits to refining targeted marketing, improving retention rates or even predicting the rate of a commodity. Now you understand the numerous data science advantages in a business? Half the battle is won when a successful data science project can express the business problem in a way that can ensure the best data-driven solution.

How Data Science Helps Businesses?

From an operational point of view, data science has been revolutionary for businesses all across the world. Below are a few significant benefits of the application of data science for business:

Taking Better Decisions

Data science validates the fact that an organisation’s data can be used to facilitate improved decision-making processes. By incorporating Business Intelligence and through measuring, tracking and recording performance, an organisation can gain the knowledge to create essential decision-making strategies.

Adopting Best Practices

Once the data scientist identifies business challenges, the organisation can then ensure that all the employees are in sync with the business objectives.  Once everybody is on the same page, adopting best practices to drive action in order to address key business challenges becomes easy.

Making Better Products

In order for a company to attract customers, it needs to first develop products that fulfil the needs and requirements of the end user. The customer needs to know that the company can provide guaranteed satisfaction. This is where data science can help – companies can use collected and analysed data to develop products that can leave the end user impressed.

Testing New Ideas

With the help of technologies like big data analytics, artificial intelligence, and machine learning, data science can contribute towards the discovery of new market trends, generating new ideas, and creating solutions to problems that have not been resolved yet.

Bettering Marketing Campaigns

One of the data science advantages in businesses is that it helps with bettering marketing campaigns. When you know what your customers/users want, you can create much more effective advertising and branding campaigns. Without data science for business, your marketing initiatives would end up being misguided and you would spend a lot of money on possibly unnecessary campaigns.

Bug Detection And Improvements

Data science for business can also help in bug detection. By analysing user data, businesses can detect possible errors in the workflow. This information can be used to optimise the processes required to boost customer satisfaction and take brand loyalty to the next level.

Predictive Analytics

Predictive analytics makes or breaks an organisation’s data science initiative. Advanced predictive tools and technologies use historical data and implement machine learning algorithms to predict the future outcome. SAP HANA, SAS, and IBM SPSS are just a few of the several predictive analytics tools out there. Predictive analytics can be used for:

  • Customer segmentation
  • Sales forecasting
  • Risk assessment
  • Developing video games
  • Customer service

The Bottom Line:

Data science is an exciting field that is gaining popularity day by day. If you want to be a part of an industry where your expert skills and knowledge can literally make or break an organisation’s success, you should definitely upskill yourself to stay ahead of the curve. Check out this comprehensive Data Science Architect Program that GeekLurn offers in partnership with Nasscom and IBM. This 2-year course comes with 100% placement and money-back guarantee.

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.

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Neel Neeraj

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
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