Many people used to think big businesses could only benefit from using big data. They are very wrong. Businesses of all sizes can effectively use technology and big data to help boost their sales and become more successful. In the past 10 years or more, marketing has changed dramatically. Before big data and advancements in technology, if a company wanted to change their advertising, they would have to sift through mounds of sales, general behavior and click-through data from their audience.

Big Data Changed Everything

The use of big data and enterprise data catalog scanning has changed everything. It has forever changed how businesses market their products and services to their customers. According to a recent report, businesses who put into use big data technology saw profit increases of up to 10-percent. Not only were many businesses seeing an increase in their profits, but they were also seeing a reduction in the cost of production.

There are many ways businesses can adjust their business model using big data. Below are some of the top ways big data is used in this modern era to boost sales, increase profits and reduce costs.

Customer Purchase Predictions

One great benefit of using big data is being able to make predictions about what products customers might want to buy. Some of the factors included in making these predictions are past purchases, how customers rate things they have bought and the purchase habits of other customers in similar situations. Big data pertains more to making predictions using similar purchasing habits of other customers.

Gain Edge Over Operational Risks

Before the entire world was practically connected via technology, fraud was something that was rare to deal with. Within financial institutions, operational risks can be quite high. Scammers and hackers are constantly finding ways to evolve their schemes to take advantage of companies and their customers. Through the evolution of big data, however, many financial institutions are coming to terms with how the data they collect is able to stop these scammers and hackers before they're able to cause harm.

Use Data Collected To Influence Purchases

Another vital use of big data businesses use is finding out how to influence their customers' purchases once they land on their website. To successfully do this, companies need to learn how to use data to pinpoint their customers' behavioral patterns. These patterns are then used to determine what types of products or services each customer is more likely to purchase. Specifically, the data will analyze everything from keystrokes and mouse movements to what areas of the website are visited and how many times.

Top Technologies Businesses Use To Gather Big Data Analytics

• Predictive Analytics- The most popular tool businesses use to gather big data analytics is predictive software. This is used for evaluation, discovery and deployment of predictive scenarios for the processing of big data.
• NoSQL Databases- The NoSQL databases are often used for efficient and reliable data management across numerous storage nodes.
• Knowledge Discovery Tools- These discovery tools help businesses gather big data that is both structured and unstructured. The data is pulled from a variety of sources. Sources may be from a similar platform, APIs, file systems and DBMS systems.
• Stream Analytics- In some cases, the data a business collects needs to be stored on separate platforms and in separate formats. The stream analytics software will allow aggregation, filtering and analysis of these different varieties of stored information platforms.
• In-Memory Data Fabric- This type of big data technology will help a business distribute vast quantities of data across multiple system resources such as Flash Storage, Dynamic RAM and Solid State Storage device drives. In turn, this will allow low latency accessibility and the processing of data within the connection nodules.
• Distributed Storage- These storage devices maintain replicated data in the event of independent node failures. In some cases, the data is also duplicated for periods of low latency on larger computer networks.