Practical Applications of Big Data

Big data has been around for a while, but it's only just now that we're starting to see the true potential of what it can do.

Big data is a term used to describe the vast amounts of data collected daily by businesses and organizations. The data can come from many different sources, such as mobile devices, web servers, or even sensors on machinery. As technology continues to evolve, so will the amount of big data available to us. This is why businesses need to prepare now, as this will give them a considerable advantage in navigating the future of big data.

This article will discuss leveraging big data via predictive analytics and demand forecasting.

Big data and predictive analytics

Big data is a term that has been around since the late 1990s, but it is only in the last 4 or 5 years that we are beginning to understand how big it truly is and how it can be leveraged. Big data refers to datasets that are too large for traditional database software tools to analyze. The size of big data sets ranges from hundreds of gigabytes to terabytes and beyond (1 terabyte = 1 trillion bytes). 

Predictive analytics is another technology that has gained popularity over the last few years. It is used in various contexts, but generally refers to using historical data or behavioral patterns to predict what will happen in the future. Think about how often you've heard someone say, “they're going to do it this time" or “it'll never work." These statements are based on past experiences or observations that have led us to believe things will turn out a certain way. Predictive analytics takes this concept further by using historical data about events and previous outcomes for similar situations.

Predictive analytics is a subset of big data analytics that uses mathematical models and algorithms to predict trends or other events based on historical data sets. The purpose of these models is to help organizations make better decisions about how they should allocate resources or respond to events in the future based on past performance. For example, suppose you wanted to know what time of day people were most likely to book flights online. In that case, you could use predictive analytics software to analyze past booking patterns and generate an accurate prediction as a result.

Predictive analytics and demand forecasting

Demand forecasting is a critical component of predictive analytics. Demand forecasting helps companies predict how much demand they expect in the future based on current market conditions. This allows companies to plan their production levels accordingly so they don't produce too many or too few products.

Demand forecasting is also used by retailers who want to know how many items they should stock in their stores at any given time. This saves them money by preventing them from ordering more items than necessary. It also prevents customers from having long waits for items that aren't available yet due to low inventory levels.

Organizational benefits 

• Manufacturing: Demand forecasting helps manufacturing businesses predict future product demand and plan production accordingly. This helps them reduce costs and ensure enough product is available on time. It also enables companies to determine how much inventory they should keep in stock at any given time.
• Finance: The finance department is usually responsible for ensuring that the business has enough money to meet its obligations. Demand forecasting makes it easy for them to forecast the amount of cash required for inventory or in-store purchases. Your finance department can benefit from predictive analytics in two ways: better cash flow and increased operational efficiency.
• Retailers: Retail organizations use demand forecasting to determine what inventory they need to keep in stock and how much to order from suppliers. It helps retailers manage their inventory costs by ensuring they do not purchase too many items that might be left unsold.
• Sales: Salespeople use demand forecasting to determine which products will sell well during various seasons or holidays and when companies should launch new product promotions. This enables them to forecast sales volumes accurately and plan their strategies accordingly.
• Marketing: They can use demand forecasting models to identify potential trends in consumer spending patterns over time so that they can adjust marketing strategies accordingly.

Demand forecasting basics

The process of using predictive analytics to forecast demand can be broken down into three steps:

• Data collection and preparation: This step is crucial because it ensures that the historical data that is used to predict future events is accurate and reliable. In most cases, this involves collecting and integrating data from several different sources into one comprehensive database. It also includes cleaning up the data so you can use it for analysis.
• Modeling: Once your data is prepared, you can start building models based on your historical sales data. You may have one model for forecasting sales for all products or multiple models for each individual product. When choosing which model(s) to use, consider the following questions: 
  • How complex should the model be? 
  • How much time will it take to run? 
  • How accurate do you need it to be? 
  • What resources are available?
• Testing and validation: Once you’ve built your model(s), testing them using real-world scenarios before using them in production environments or making significant changes based on their output predictions is essential.

Big data has more and more to offer organizations as the technology to extract deep insights from this data become more sophisticated and readily available.  Organizations must invest in these tools and technology to stay competitive and relevant.   

If you would like to explore further how big data predictive analytics and demand forecasting can help your business, email us at intellect2@intellect2.ai. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM  develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.