Impact of Data Science on Business

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According to a McKinsey report, 35% of Amazon’s revenue comes from its referral system. Netflix saves about $1 billion a year through personalization and recommendations. These cases and so many others have one common denominator – data science. For the better part of a decade, companies have been leveraging their most valuable asset, data, and utilizing data science to stay ahead of their competitors.

Data science delivers tremendous business value, from making more informed decisions and goal setting to identifying opportunities, classifying your target audience, and selecting the right talent. Let’s take a look at how companies capitalize on industry insights using data science.

What is Data Science?

Data Science refers to the study of data, the process of collection and classification, and using it as strategic information to drive business growth.

During this process, data scientists work on analysis, data content organization, and standards research, using the latest technologies and tools for the activity. Part of this work is also devoted to scenario design and hypothesis analysis.

Drawing on their knowledge of statistics and mathematics, data scientists explore the most probable outcomes indicated by the data.

Why is Data Science Important for Businesses? 

Strategic decisions are indispensable for data-driven companies. Therefore, if your company does not apply the same management model, you will not be able to achieve results comparable to your competitors. Data science for business is essential to deliver what your audience wants. And if you don’t, your competitors will.

Inefficiencies often cost businesses up to 30% of their revenue. By using data science, businesses can avoid inefficiencies and boost return on investment. It’s expected, for example, that big data will reduce healthcare costs in the US by 20%.

Data science helps companies use advanced tools and technologies to automate the complex business processes involved in extracting, analyzing, and delivering raw data. When technology is advancing rapidly, and data is generated at an incredible speed, it is essential to stay in touch with reality in order to best anticipate future trends.

Artificial intelligence (AI) and machine learning (ML) are experiencing explosive growth as companies actively look for fast, cost-effective, and innovative ways to use big data. But to deploy these technologies effectively, teams need to stay up to date with the latest trends in data science. This covers AI, ML, the Internet of Things, deep learning, and other disciplines. It is a combination of data-driven inferences, algorithmic computations, various analyses, and technology that help solve complex business problems.

With an understanding of data science, an enterprise can deploy advanced tools and technologies to automate the complex business processes associated with extracting, analyzing, and presenting raw data. Technology is constantly advancing, and the amount of data is growing at an unprecedented rate. That’s why it is essential to be aware of the current situation and be able to anticipate trends that will occur in the future.

The Benefits of Data Science for Business Industries

Data Science for Manufacturing

Data science allows for insight-driven manufacturing. Ford’s compelling history in data science is the perfect example of how manufacturers use data. Ford uses advances in wireless connectivity with in-vehicle sensors to better understand driver behavior and reduce production times.

Manufacturers use high-quality data from sensors installed in machines to predict equipment failure rates and optimize inventory management and production space. For a long time, manufacturers have tried to solve the problem of equipment downtime. The advent of the Internet of Things has allowed manufacturers to make machines communicate with each other – the data obtained is used with the help of data science to reduce unplanned equipment downtime.

Dynamic responsiveness to market demands is another challenge facing this industry: production line changes are at the heart of ensuring dynamic responsiveness. Manufacturers now use a historical analysis of line change data and product demand to determine effective line changes. The combination of statistical models and historical data helped to anticipate stock levels on the shop floor. As such, manufacturers can now easily and accurately select the number of components needed on the shop floor.


The Impact of Data Science on the Financial Service Sector

Financial services companies turn to data science for answers – using new data sources to build predictive models and simulate market events. Businesses turn to NoSQL, Hadoop, and Storm to use non-traditional datasets and store various data for future analysis.

Sentiment analysis has become another valuable resource for several purposes. With sentiment analysis, banks can track trends, respond to issues, track new product launches, and improve brand perception. They make the most of market sentiment data to restrict the market when some unforeseen event occurs.

Data science comes to life to automate credit risk management. Take, for example, Aliloan from Alibaba. An automated online system that distributes loans to online merchants who have difficulty obtaining loans. Alibaba analyzes customer ratings, transaction records, and other information from payment data and e-commerce platforms to see if a supplier is trustworthy. Financial institutions use innovative credit scoring methods to offer automated small loans to vendors.

Real-time analytics serve the purpose of financial institutions in the fight against fraud. Banks analyze spending patterns, account balances, employment details, and credit histories to determine if transactions are fair and open. Lenders get a clear view of clients’ business operations, assets, and transaction history through credit scores that are updated in real-time.

Data science also helps financial institutions learn who their customers are, and in turn, offer customized products, run relevant campaigns, and create products that match customer segments. Where risk mitigation is imperative for financial institutions, predictive analytics does the job.


How Data Science Affects the Tourism Industry

After having moved away from a time when travel companies created customer segments for advertising, these same companies now have a complete overview of each client to make customized offers. How is this possible?

Travel companies use a combination of datasets from social media, itineraries, predictive analytics, behavioral targeting, and location tracking to get a 360-degree view. For example, a customer visiting Facebook pages in Zurich might be attracted by discounted airfare offers to Switzerland.

Delta Airlines planned to donate the Nokia Lumia phablet to 20,000 flight attendants. With this technology, flight attendants have instant access to customer preferences and previous travel experiences to ensure a more personalized experience. The key here is to achieve an all-encompassing view of the customer.

In Conclusion

Data science is one of the fastest-growing fields across all major industries. With it, you can analyze how you need to improve business processes to maximize growth and ROI when deploying new technologies.

Don’t hesitate to turn to specialists if you are planning to build comprehensive data-driven solutions. Get in touch with Unicsoft to unlock intelligent data across business processes to drive growth.