Data Science for Business – 7 Major Implementations of Data Science in Businesses

Many popular companies are using Data Science for easing their regular processes. We will explore a use case of Walmart to see how it is utilizing data to optimize its supply chain and make better decisions. We will also learn the core implementations of Data Science in businesses.

In this article, we will see how data science is a must for every business. Businesses today have become data-centric. This means that the businesses of the world utilize data to make decisions and grow their company in the direction that the data provides.

Without wasting any more time, let’s jump to the importance of Data Science in business.

Importance of Data Science in Business

Did you check? – Data Science Applications in Education

Importance of Data Science for Business

There are many ways by which Data Science is helping businesses to run in a better way:

1. Business Intelligence for Making Smarter Decisions

Traditional Business Intelligence was more descriptive and static in nature. However, with the addition of data science, it has transformed itself to become a more dynamic field. Data Science has rendered Business Intelligence to incorporate a wide range of business operations. With the massive increase in the volume of data, businesses need data scientists to analyze and derive meaningful insights from the data.

The meaningful insights will help the data science companies to analyze information at a large scale and gain necessary decision-making strategies. The process of decision making involves the evaluation and assessment of various factors involved in it. Decision Making is a four-step process:

Decision Making Process

  1. Understanding the context and nature of the problem that we are required to solve.
  2. Exploring and quantifying the quality of the data.
  3. Implementation of the right algorithm and tools for finding a solution to the problems.
  4. Using story-telling to translate our insights for a better understanding of teams.

Through this way, businesses need data science for facilitating the decision-making process.

2. Making Better Products

Companies should be able to attract their customers towards products. They need to develop products that suit the requirements of customers and provide them with guaranteed satisfaction. Therefore, industries require data to develop their product in the best possible way. The process involves the analysis of customer reviews to find the best fit for the products. This analysis is carried out with the advanced analytical tools of Data Science.

Furthermore, industries utilize the current market trends to devise a product for the masses. These market trends provide businesses with clues about the current need for the product. Businesses evolve with innovation. With the growth in data, industries are able to implement not only newer products but also various innovative strategies.

For example – Airbnb uses data science to improve its services The data generated by the customers, is processed and analyzed. It is then used by Airbnb to address the requirements and offer premier facilities to its customers.

Check out more Data Science use cases of companies like Amazon, Facebook & Uber

3. Managing Businesses Efficiently

Businesses today are data rich. They possess a plethora of data that allows them to gain insights through a proper analysis of the data. Data Science platforms unearth the hidden patterns that are present inside the data and help to make meaningful analysis and prediction of events.

With Data Science, businesses can manage themselves more efficiently. Both large scale businesses and small startups can benefit from data science in order to grow further.

Data Scientists help to analyze the health of the businesses. With data science, companies can predict the success rate of their strategies. Data Scientists are responsible for turning raw data into cooked data. This helps in summarizing the performance of the company and the health of the product. Data Science identifies key metrics that are essential for the determination of business performance. Based on this, the business can take important measures to quantify and evaluate its performance and take appropriate management steps. It can also help the managers to analyze and determine the potential candidates for the business.

Using data science, businesses can also foster leadership development by tracking the performance, success rate, and other important metrics. With workforce analytics, industries can evaluate what is best working for the employees.

For example – Data Science can be used to monitor the performance of employees. Using this, managers can analyze the contributions made by the employees and determine when they should be promoted, managing their perks, etc.

Have you explored Data Science Applications in various sectors?

4. Predictive Analytics to Predict Outcomes

Predictive analytics is the most important part of businesses. With the advent of advanced predictive tools and technologies, companies have expanded their capability to deal with diverse forms of data. In formal terms, predictive analytics is the statistical analysis of data that involves several machine learning algorithms for predicting the future outcome using the historical data. There are several predictive analytics tools like SAS, IBM SPSS, SAP HANA, etc.

There are various applications of predictive analytics in businesses such as customer segmentation, risk assessment, sales forecasting, and market analysis. With predictive analytics, businesses have an edge over others as they are able to foresee future events and take appropriate measures in respect to it. Predictive Analytics has its own specific implementation based on the type of industries. However, regardless of that, it shares a common role in predicting future events.

Predictive Analytics Process

5. Leveraging Data for Business Decisions

In the previous section, we understood how data science is playing an important role in predicting the future. These predictions are necessary for businesses to learn about future outcomes. Based on this, businesses take decisions that are data-driven. In the past, many businesses would take poor decisions due to the lack of surveys or sole reliance on ‘gut feelings’. It would result in some disastrous decisions leading to losses in millions.

However, with the presence of a plethora of data and necessary data tools, it is now possible for the data industries to make calculated data-driven decisions. Furthermore, business decisions can be made with the help of powerful tools that can not only process data faster but also provide accurate results.

Learn about the Data Science tools for small businesses 

6. Assessing Business Decisions

After making decisions through the forecast of the future occurrences, it is a requirement for the companies to assess them. This is possible through several hypothesis testing tools. After implementing the decisions, businesses should understand how these decisions affect their performance and growth. If the decision leads to any negative factor, then they should analyze it and eliminate the problem that is slowing down their performance.

There are various procedures through which businesses can evaluate their decisions and plan a suitable action strategy. These decisions revolve around their customer requirements, company goals as well as the needs of the project executives. Furthermore, in order to assess future growth through the present course of actions, businesses can make profits considerably with the help of data science.

7. Automating Recruitment Processes

Data Science has played a key role in bringing automation to several industries. It has taken away the mundane and repetitive jobs. One such job is that of resume screening. Every day, companies have to deal with hordes of applicant’s resumes. Some major businesses can even attract thousands of resumes for a position. In order to make sense of all of these resumes and select the right candidate, businesses make use of data science.

The data science technologies like image recognition are able to convert the visual information from the resume into a digital format. It then processes the data using various analytical algorithms like clustering and classification to churn out the right candidate for the job. Furthermore, businesses study the right trends and analyze potential applicants for the job. This allows them to reach out to candidates and have an in-depth insight into the job-seeker market.

Have you ever thought – How much is the salary of Data Scientist?

Now, let’s see the case study of Walmart and discuss how it is using data to modify the supply chain and understand the need of customers.

Data Science Case Study

Walmart – Leveraging Data to Make Business Better

Walmart store

Walmart is the world’s largest retailer. It is one of the many major industries that is leveraging Big Data to make the business more efficient. Walmart handles a plethora of customer data. A staggering amount of about 2.5 petabytes of data is collected from the customers every hour. This data is unstructured that is utilized through Hadoop and NoSQL. It tracks and monitors various factors that might affect the sales at Walmart stores.

Some of the ways in which Walmart is using data science are:

  • Walmart is using data science to make store checkouts more efficient. There are certain times of the day where the checkouts can become crowdy. This makes it difficult for Walmart employees to manage customers during rush hours. However, with the help of predictive analytics, Walmart can analyze data and determine the best form of checkout for each store, that is, self-checkout and facilitated checkout.
  • Walmart is using real-time analytics to analyze the purchasing patterns of the customers. This allows them to stock up on products that are in demand and also the products which will be in future demand based on several factors.
  • Walmart is managing supply chain and logistics with the help of data science. It manages its inventory and analyzes the rate of its depletion, thereby taking the necessary steps to mitigate it through efficient logistics. Walmart also analyzes the transportation lanes for the company’s trucks to follow. It specifies an optimized route using data science, thereby reducing the cost and time.
  • Walmart is personalizing the shopping experience by analyzing the preferences and behaviour of the customers. Using data science, it tracks the purchasing patterns of the customers and recommends them further products and discounts to improve their shopping experience.

Summary

In the end, we understand how data science plays an important role in businesses. We realized how data science is being used for business intelligence, for improving products, for increasing the management capabilities of companies and for predictive analytics. We also went through a use case of Walmart and how they utilize the data science to increase their efficiency.

You can also explore the future of Data Science & its career prospects.

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