With big data becoming more prevalent in the business world, a lot of data terms get thrown around, with many not quite understanding what they mean. What is data mining? Is there a difference between data mining and predictive analytics? How are the two related? All of these are great questions and discovering their answers can provide a deeper insight into using data science to benefit your company.
If you are hoping to effectively leverage web data to increase business opportunities by forecasting upcoming trends, understanding the various terms used in web data is crucial. Both data mining and predictive analytics, powerful processes that help in establishing powerful data-driven decisions, are two terms used quite often and even sometimes are used interchangeably within data science. But it’s important to note, predictive analysis goes beyond data mining from predicting what might happen next using existing data sourced from data mining.
What is data mining?
Data mining is basically the process of analyzing large sets of data to find patterns, relationships, and trends that otherwise might be missed through more traditional analysis methods. It is used to uncover shared similarities or groupings in web data that help gain insights for business decisions.
Data mining is used for a variety of different purposes, including financial research where it is used by investors to look at a start-up’s financials to determine if they want to offer funding. It is also used to collect data on sales trends to better inform everything from marketing to inventory needs, in addition to securing new leads. Data mining is used to comb through social media profiles, websites, and digital assets to compile information on a company’s ideal leads to start an outreach campaign.
What is predictive analytics?
Predictive analytics uses mathematical algorithms and machine learning to identify how likelihood something will occur in the future based on patterns of previous data. The goal of predictive analytics is to use past knowledge of what has happened to provide a better idea of what to expect in the future.
What is the difference between data mining and predictive analytics?
Well both data mining and predictive analytics use algorithms to discover new insights to find the best business solutions. The data mining process is heavily based on algorithms to analyze and extract information that automatically discovers hidden patterns and relationships within the data.
Within predictive analytics, the process uses data patterns to make predictions with machine learning. Machines take both historical and current information and it is then applied to a model that predicts future trends. The biggest difference between the two is that data mining explores the data but predictive analytics takes it a step further by telling you what will happen next.
The benefits of predictive analytics to businesses
Using predictive analytics with data sourced with data mining is a powerful way to help project what may happen later on in the business, allowing leaders and key decision makers to plan accordingly. It makes looking into the future more accurate and reliable than previous tools.
Organizations today are using predictive analytics in almost every industry. Technology like Import.io’s web data integration give companies the tools to be able to predict various types of consumer behavior and patterns, and how to appropriately prepare according to business needs. Retailers use it to forecast inventory to efficiently maximize sales. The travel industry frequently uses predictive models from past travel trends to set ticket prices and hotel rates.
With 80% of all web data being unstructured, predictive analysis tools can help make sense of all that data while providing valuable insights for businesses. This allows businesses to learn which solution to take for the best possible outcome from previous logical data that has been captured and then helping predict the future.
Predictive Analysis Is Data Mining’s Future
There is no denying it, the amount of web data is only going to continue to grow. Organizations have a greater use for data than ever before. Predictive analytics has captured the support of a large range of organizations, with a global market projected to reach approximately $10.95 billion by 2022.
The importance of web data is exponentially growing. Capturing that important web data can give your sales and marketing teams the extra intelligence needed to effectively plan and optimize their results. Import.io is assisting in the success and growth of marketing and sales by providing critical web data insights.
Getting Started with Web Data Integration
Data can be a powerful tool in a company’s arsenal, but it’s only useful when it is understood. From data collection, data mining, and through the entire process to analysis and visualization, web data integration delivers a result that is more valuable and meaningful than ever before. This allows users to make decisions and build on web data with great confidence.
Import.io is the world’s only Web Data Integration solution. We take data from the world’s biggest repository and reliably turn it from human readable to machine-ready, allowing enterprises to drive positive business outcomes by gaining deeper insights from robust alternative data sets. Import.io’s quality dashboard feature allows you to get an accurate view of your web data pipeline. This allows you to analyze the health of your pipeline and easily resolve any issues.
Import.io is the world’s leader in interacting with web data, providing a flexible, extensible platform with deep capabilities. Contact a data expert today to see how Import.io can help your organization leverage web data.