What is Data Science? 5 Applications for a Business

The volume of data in the world is increasing at a rate of 1.5 Megabytes per person per second. The world needs to collect, organise, clean, and analyze that information to draw useful meaning. Data science refers to all activities between collecting and analyzing information for insights. 

Before scientists collect data, they need to develop hypotheses. They then decide on data collection methods, data cleaning techniques, and data storage. Today, there are powerful data science programs such as SQL and R, used to write algorithms to automate the process of collection and analysis of data. It makes it possible to go through large volumes of data efficiently. Through these tools, data science becomes a reachable decision tool for both big and small firms. 

To show this, here are X applications of data science for a business.

1. Better Understanding of Customers Using Data Science

Understanding individual customer behavior and motivation is critical for proper messaging and re-targeting efforts. Today, a business can track users who visit their websites, the products they browse, the purchases they make and so forth. They can then combine this information with a customer’s social media activity based on their social profiles. A combination of customers’ information from their online activity can reliably help businesses predict products that might interest specific customers.

Through data science, businesses can rapidly improve their ROI on marketing efforts by highly refining the messaging to clients. For instance, based on past purchases, a business can recommend other complementary products. If a customer has stayed too long before returning to a restaurant, a discount voucher might prompt them to return. Gaining such an understanding of customers can help a business maximize the lifetime value of its customers. 

2. Improve Internal Financial Management in Organizations

By integrating data science in their internal processes, organisations can gain so much insight into the level of efficiency in their financial management efforts. For instance, it’s possible to track each category of expenses month-to-month. Management can easily find out the biggest expense categories and the trend for each. If a certain category is rising, it can investigate the reason.

The availability of information from external sources also allows businesses to compare their financial position with competitors and the industry at large. For instance, it can compare its ratio of revenue to assets with that of other companies in the same sector. Is the company making enough revenue from its assets? Is it over-invested in certain assets? Such questions continually help to assess and improve the financial position. 

3. In Improving Manufacturing

Manufacturing enterprises can apply data science techniques to assess and improve the efficiency of their operations. For instance, if a certain machine is experiencing frequent breakdowns, engineers can test different hypotheses to find out the reason. Doing so helps make objective repair or replacement decisions.

Preventive maintenance in manufacturing heavily depends on data collected through IoT devices. The data is crunched to determine the best time to perform routine maintenance. The health of various parts on the machine is also assessed so that they are replaced at just the right time to minimize the downtime of operations.

Manufacturing today is such that a high level of preciseness is required to fulfil quality demands. As such, data is the best way to determine quality levels. The manufacturing enterprise can collect relevant information on each product in the manufacturing line. Whenever unacceptable deviations occur, the operations can be halted and adjustments made so that the organization does not lose money producing items that will not pass the quality standards tests. 

4. Making Market Trends Predictions using Data Science

Organizations today have to keep in-step with trends that apply to their industry. Companies have to keep up with conversations consumers are having on social media, to tell when interest in a product is growing or declining, and react accordingly. Social media listening tools are available today and they can help a company tell the public sentiment towards the company. Tracking the mentions of your company on social media today is extremely important in reputation management.

Being able to predict demand months ahead also informs producing and stocking decisions to avoid overstocking or stock-outs, both of which are detrimental to the firm.

Demand today is a factor of many factors that often have little correlation with one another. Data scientists build algorithms that can take in many data points to help come up with realistic predictions of what might occur soon. 

5. Improving Security Through Anomaly Detections

Companies in the financial services and e-commerce sectors have a challenge to prevent fraud. The sophisticated nature of fraud today and the numerous number of transactions companies handle make it difficult to detect fraud through manual reviews. However, through data science techniques, it’s possible to detect anomalies and zero-in on potentially fraudulent activity.

For instance, if a customer whose credit card has little activity is suddenly attempting numerous transactions in quick succession, the credit company might block the card temporarily until the person confirms they are the ones attempting such transactions. The same goes when the card is being used in an unusual location. Insurance companies also use data science techniques to identify unusual patterns in claims and conduct relevant investigations to prevent fraud. 

Real-time collection and analysis of data in achieving value in data science investment in security efforts. Detecting possible credit card fraud and automating real-time corrective action gives customers the assurance they need. 

Other Areas of Application

The application of data science in business is limitless. As long as data scientists can develop a hypothesis and collect the right data to test it, businesses can draw value from such data. E-commerce websites can recommend the right items to visitors based on profiles built. Companies can set up autonomous systems to improve manufacturing through self-correcting production lines. Data powered machine learning algorithms can help chatbots understand human language and automate customer service. 

If your company would like to start leveraging data science for efficiency, Transcendent Software can be your data science partner. We will help you identify areas where to collect data and use it to create value. If you are already collecting data, we can help with the cleaning, organizing, and analysis.