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  • Anantaya Pornwichianwong

How to apply data to businesses

“Data is the new oil” This saying shows that data has become a valuable asset. Also, data technologie, ranging from data analytics, and data science, to machine learning, are constantly evolving. These technologies allow us to maximize the use of data, build an intelligent machine learning model, and perform ever more in-depth data analysis.

Data technologies can be applied to almost every industry. A single technology or model can be utilized in a variety of ways and create a variety of benefits, from designing a more streamlined and efficient operating system, saving labor, and reducing costs in both time and human resources, which lead to the possibility to generate more profits.

Any industry can make use of data in its own way. Sertis would like to invite all readers to explore the core competencies of data technologies, especially, a data-driven machine learning model that can be applied to all industries and power up businesses, improve working processes, increase profitability, and drive our business forward.

1. Using data to predict the future with predictive modeling

The first benefit of data analytics and data science is predictive modeling, which is a machine learning model that analyzes historical data to detect patterns and trends and then predict future outcomes, in order to make more accurate and informed decisions and strategic planning. This model can be utilized in various ways and applied to almost all industries, whether retail, finance, or manufacturing. The insights from the model are reliable because the predictions are based on the analysis at the granular levels of large historical data sets.

There are various examples of predictive modeling applications. For instance, it predicts customer behavior by analyzing the behavioral data, and predicts consumer trends based on socio-economic environment data. In the era of COVID-19, traditional computational demand forecasting is no longer accurate due to huge and sudden changes in consumer behaviors and trends. Utilizing predictive modeling to collect all environmental and situational data to predict future trends is a much more effective solution. Another popular application is applying predictive modeling to predictive maintenance to monitor the operations of manufacturing machinery and equipment. Also, it can predict when the machine needs maintenance in order to take care of it in advance before the breakdowns cause unplanned downtime.

2. Free up manpower and eliminate human errors with classification and categorization

Classification and categorization are basic tasks that are required in every industry, such as product classification, customer classification, data classification, and manufacturing equipment classification. Even though the job sounds like a piece of cake, it is time-consuming. It eats up employees' time and, most importantly, it is also prone to errors. Applying data technologies and deep learning models to help us automatically organize and analyze fragmented data is a compelling choice. We can train models to learn the characteristics of each type of data, and automatically classifies and organizes all input data. This solution will save working time and reduce staff labor to free up employees for more value-added tasks.

The model goes through all available data and organizes them into categories as it was trained. Deep learning works well with fragmented data in a variety of formats, such as separate customer data in the forms of email, document, image, video, text, and voicemail. Even in diverse formats, the deep learning model is able to organize all types of data using much less time than humans. Also, a data classification model can be more beneficial than you think. For example, NASA has used this type of model to detect and categorize photos of objects in space to find a fascinating insight into the universe.

3. Detect every outlier with anomaly detection

Another capability of data and models is the detection of anomalies in large datasets, which normally may be able to escape our eyes. However, the anomaly detection model can detect everything, no matter how small those anomalies are. Especially with large datasets in today's Big Data era, an anomaly detection model would be even more useful, because completing the tasks accurately is far beyond the sole human capability.

This model can be applied in a variety of industries, especially in industries that require high security such as the financial or banking industry. The model, for example, detects customers' unusual spending behaviors to see if their credit card information has been hacked and suspends the card before damage occurs. American Express, a credit card provider, also uses the model for 24/7 real-time monitoring. Furthermore, in the cybersecurity industry, this type of model can be used to detect abnormalities in the system to prevent attacks in a timely manner. In addition, the anomaly detection model can be applied to general data analysis to detect and organize mismatched data to increase the accuracy of the analysis.

4. Offer the right recommendation and impress customers with recommendations and personalization

The key challenge of various industries that involves the sales of goods and services to customers is to make consumers satisfied with our offerings. This means presenting the right product at the right time. This problem often arises in the retail industry, whether it is about consumer goods, fashion, or beauty, to home and garden equipment. This is a mutual challenge for all industries involved in direct-to-consumer selling.

In addition to solving this problem, data technology also came to elevate the recommendation and presentation of products and services to the next level with a specific, personalized recommendation based on the needs of each customer. These recommendations would take a lot of time and cost and couldn't be personalized enough without the help of technology. Now, we can automate the task by importing large datasets to machine learning models to create a recommendation and personalization model. The model would analyze the purchase history and the connection of each customer to create customer profiles. Then, it would group customers with similar profiles and preferences, and provide personalized recommendations of products and promotions, or send promotional messages designed for individual customers at the right moment to encourage purchase decisions.

In addition to the retail industry and e-commerce platforms, major streaming service providers such as Netflix also use this model to help recommend content to viewers. Even the health and medical industry are also able to make use of the model to analyze and recommend personalized treatment and care for individual patients. This model provides even more exclusive services that create a better customer experience and impression toward brands across all industries.

Core capabilities of data technologies enhance operations and are applicable to all industries. But to be able to use data to its fullest and apply machine learning models to improve operations, businesses need a robust data infrastructure.

Here at Sertis, we are a data solution provider who will help you from the ground up with the data infrastructure, data preparation, database development, and data processing system to make the most of the data. We also provide intelligent machine learning modeling to help you gain accurate and practical data insight. Start today and grow by leaps with us.

Learn more about Sertis data solutions at:

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