Big data analytics examines large data sets, uncovering hidden patterns as well as unknown correlations, and customer preferences. The data analytics can lead to more effective marketing, new revenue opportunities and better customer service for the business. The primary goal of big data analytics is to help companies to make better business decisions, allowing data scientists, predictive modellers and other analytics professionals to study big volumes of transaction data that can’t be found by normal business intelligence (BI) programs.
Data could include web server logs and Internet clickstream data, social media content and social network activity reports. Data warehouses may not be able to handle the processing demands posed by sets of big data analytics that need to be updated frequently or even continually. Because of this, many companies are now using Hadoop and YARN, MapReduce, Spark, Hive and Pig as well as NoSQLdatabases.
In some cases, Hadoop clusters and NoSQL systems are used to hold data before it gets loaded into a data warehouse for analysis. Hadoop data lakes serve as a central storage area for a company's raw data, subsets of the data can then be filtered for analysis in data warehouses and analytical databases or analysed in Hadoop using batch query tools, stream processing software and SQL on Hadoop technologies.
Big data analytics can be analysed with analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis. Normal BI software and data visualization tools can also be used. Possible problems with big data analytics for companies include a lack of in house analytics skills and the high cost of analytics professionals.
Big data analytics is now accepted as mainstream thinking. Company leaders are now looking to use big data analytics to identify trends, detect patterns and find other valuable findings from the array of data available to them. You could go out and buy big data analytics software but big data analytics technologies on their own aren't sufficient to handle the task.
Planned analytical processes and people with the talent and skills are essential to carry out an effective big data analytics initiative. You should quantify the potential business value that big data software can offer, big data analytics tools can be used by retail, healthcare, financial services and other industries. Professionals warn against companies plunging into using Hadoop or other big data technologies before making sure they're a good fit for business needs.