What’s New in Business Intelligence, From Mobile to Big Data
Click to enlarge this example of a row-stored database.
Click to enlarge this example of a column-stored database.
Companies have long leveraged customer data to increase engagement, drive sales and add to the bottom line. Typically, historical data was used to determine what actions had already taken place and the factors that contributed to those actions. A company would then build its next campaign or marketing strategy based on these historical factors.
Today, business intelligence and data analytics are much more forward-looking and predictive. Rather than focus on the past, we can use data to forecast what will likely happen in the future and how customers may act. The variety and volume of data being generated in rapid fashion require a shift in how the data is stored, handled and analyzed in order to leverage it effectively. In this article, some of the newest features of data storage and analysis will be discussed, as well as why these tools are important for helping you create targeted marketing efforts that drive sales.
1. In-Memory Analytics: This is an important concept that is critical to understand in an era of big data and fast analysis. Where and how you store your data is important. Analyzing big data that is stored on a hard disk is time-consuming, slow and does not support the rapid-fire decisions needed for today’s marketers. In-memory analytics means the data is stored on a computer’s RAM (random access memory) rather than a hard disk. This allows the system to run algorithms more quickly and get results faster. Due to the availability of cheap memory, it is now possible to perform analysis in near real-time. For example, when a customer is purchasing an item and using a loyalty card, in-memory analytics can help the marketer run an analysis to quickly determine which product offers to attach to the receipt for the customer’s next visit. Faster analysis means fewer missed opportunities to capitalize on customer behavior as it happens.