571 new websites are created every minute of the day.  There are 100 terabytes of data uploaded daily to Facebook. On YouTube, users upload 48 hours of new video every minute of the day.

According to estimates, the volume of business data worldwide, across all companies, doubles every 1.2 years.

Advertising is about communication as it seeks to inform consumers about a business’s product and services. But different consumers want to hear different messages. Big data can refine those messages, predict what customers want to hear with predictive analytics, and yield new insights in what customers want to hear.

According to a 2015 research from Chief Marketing Office Council and Ebiquity, only 30 percent of the survey participants said they were doing well managing the data explosion. Less than 40 percent of them said they were doing well overcoming financial restraint and demonstrating ROI, and just 29 percent said they were performing ok with data analysis in order to create personalized experiences.

As budgets for digital campaigns and more personalized customer engagement strategies increase, advertisers need additional expertise in data analytics, content creation, and channel proliferation to improve ROI.

A recent  report conducted by Rubinson Partners, in conjunction with Viant and Nielsen Catalina Solutions demonstrated that if an advertiser is selling a product that is subject to repeat purchase, an advertisement sent to a consumer at the right time in the purchase cycle will generate up to 16x more  Return On Advertising Spending (ROAS) than if an advertisement is sent at other times. By knowing which consumers have bought which products at which time, a marketer who has aggregated big data can use that information together with information about specific consumers in order to create more effective advertising.

Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights.  And with today’s technology, it’s possible to analyze your data and get answers from it almost immediately.

To better understand the big data analytics process, you need to know the steps and technologies involved in big data analytics: data acquisition and data mining.

The first step is data acquisition. The acquisition process has two components: identification and collection of big data. Identification of big data is done by analyzing the two natural formats of data — captured through a digital medium (Cookies, Web Analytics and GPS tracking) and information related to physical elements of our world (in the form of pictures, videos and other such formats)

The second step in the data acquisition process is collecting and storing data sets identified as big data through MAD — magnetic, agile and deep- process.

Another element of the big data analytics is the technology that stores these massive data sets. JavaScript Object Notation or JSON is the preferred protocol for saving big data.  IMDB systems store the data in the RAM of big data servers, drastically reducing the time taken by traditional databases to access and process information.  Apache Spark is an example of IMDB systems. 

Data mining is a recent concept which is based on contextual analyzing of big data sets to discover the relationship between separate data items. The objective is to use a single data set for different purposes by different users.  

Big data can be used to help create targeted and personalized campaigns that ultimately save money and increase efficiency by targeting the right people with the right product. Gathering information and learning user behavior is the key.