Big Data Tool
Philip Morris Ukraine

Big Data Tools are software applications or platforms developed to process, store, and analyze large volumes of complex data. After all, there is a big challenge for businesses to investigate unstructured information, such as in-store transactions for retail and traffic patterns for logistics. The problem lies in the volume, velocity, and complexity of Big Data. On the other hand, this information can be valuable for businesses to make advanced predictions about different insights.

In such a way, Big Data Tools help organizations to handle massive amounts of structured and unstructured data that traditional databases and analysis tools cannot easily manage. Such solutions enable businesses to gain insights into customer behavior, market trends, and operational efficiency, and support decision-making processes.

Big Data Tools are frequently used by enterprises to improve consumer demand forecasting. They allow you to get valuable insights into customer behavior, preferences, and market trends.

For example, Coreteka developed its Big Data Tool for a retailer that wanted to predict the most frequented time in its outlets, allowing the marketing department effectively plan its campaigns. To make it possible, the Coreteka team has leveraged different information sources like weather, social, and cellular networks.

In general, Big Data Tools can analyze vast amounts of data from various sources, e.g., social media, online transactions, and customer reviews. This makes it possible to identify patterns and trends in consumer behavior. The analysis can help businesses understand what products and services customers are interested in, what factors influence their purchasing decisions, and how their preferences change over time.

By combining these insights with traditional forecasting methods, businesses can improve their accuracy in predicting consumer demand. For example, retailers can use Big Data Tools to analyze sales data, foot traffic, and social media activity to forecast demand for specific products and adjust their inventory levels accordingly. Similarly, logistics companies can use Big Data to predict demand for their services and optimize their operations accordingly.

Together with Machine Learning technology, Big Data Tools can be great solutions for real-time suggestions implementation. For example, an eCommerce website can use Big Data Tools to track a customer’s browsing history, search queries, and past purchases in real time. Based on this data, it’s possible to make personalized product recommendations, suggest complementary products, or offer promotions and discounts to the customer.

Customer-oriented businesses should strive to provide a personalized and seamless experience for consumers. This can be done with real-time suggestions based on collected and analyzed information from Big Data Tools. Of course, each company can find it useful. However, real-time suggestions are common in:

• eCommerce. Online retailers can use real-time suggestions to offer personalized product recommendations, promotions, and discounts based on a customer’s browsing and purchase history. This can help increase customer engagement and sales.
• Banking and finance. Real-time suggestions enable providing personalized financial advice, investment recommendations, and fraud detection alerts based on a customer’s financial behavior and history.
• Healthcare. Healthcare providers can use real-time suggestions to offer personalized treatment recommendations, medication reminders, and health monitoring based on a patient’s health data and history.
• Travel and hospitality. Thanks to real-time suggestions, companies can provide personalized travel recommendations, booking options, and promotions based on a customer’s travel history and preferences.
• Transportation and logistics. Transportation and logistics companies can use real-time suggestions to optimize delivery routes, reduce delivery times, and improve overall efficiency based on real-time data on traffic patterns, weather conditions, and delivery times.

If you’re eager to explain more examples of how Big Data works for logistics, read our article in the blog.

There are many Big Data tools available in the market, and the choice of the best tool depends on the specific use case and requirements. However, here are five of the most popular and widely used Big Data tools:

1. Hadoop — an open-source Big Data processing framework that allows distributed processing of large data sets across clusters of computers. It is widely used for batch processing, data warehousing, and machine learning applications.
2. Apache Spark — an open-source, in-memory Big Data processing engine that can perform real-time processing, batch processing, and machine learning. It provides APIs for programming in Java, Scala, Python, and R, and is widely used for data processing, streaming, and analytics.
3. Apache Flink — an open-source stream processing framework that provides both batch and real-time processing capabilities. It is used for building high-performance, distributed, and fault-tolerant data processing pipelines.
4. MongoDB — a NoSQL database that is widely used for storing and retrieving large volumes of unstructured and semi-structured data. It provides scalability, high availability, and real-time analytics capabilities.
5. Tableau — a business intelligence and data visualization tool that can connect to multiple data sources, including Big Data tools like Hadoop and Spark. It provides interactive and easy-to-use dashboards and visualizations for data exploration and analysis.

It should be noticed that the above-mentioned solutions, apart from Tableau, are suitable for technical professionals, not companies. In most cases, such business applications are custom. They are developed from scratch, depending on your goals and requirements.

Custom Big Data Tool costs can vary widely depending on the specific software vendor, the features and functionalities included, and the scale of the business using the software. However, if we’re talking about intelligent data visualization solutions like Tableau, the cost can be formed from monthly or annual subscriptions.

It is important to note that while consumer demand forecasting software can be a valuable investment for businesses, it is not the only factor to consider in improving demand forecasting. Other factors such as data quality, data integration, and expertise in data analysis and modeling can also play an important role in the accuracy and effectiveness of demand forecasting.

As a custom IT solutions provider, Coreteka provides Big Data Tool development services. Since our solutions are made from scratch according to specific business goals and requirements, you will get the tech product that completely resolves your personal challenges. We already successfully developed the Big Data Tool for our client and have extensive expertise in Machine Learning.

Are you ready for implementing innovations in your business? Contact us to learn how we could help.