Data analytics refers to the process used to enhance productivity and business improvement by extracting and categorizing data to identify and analyze behavioral patterns. Techniques vary according to organizational requirements.
The primary goal of data analytics is to help organizations make more informed business decisions by enabling analytics professionals to evaluate large volumes of transactional and other forms of data. Data analytics can be pulled from anything from Web server logs to social media comments.
Potential issues with data analytics initiatives include a lack of analytics professionals and the cost of hiring qualified candidates. The amount of information that can be involved and the variety of data analytics data can also cause data analytics issues, including the quality and consistency of the data. In addition, integrating technologies and data warehouses can be a challenge, although various vendors offer data integration tools with big data capabilities.
Big data has drastically changed the requirements for extracting data analytics from business data. With relational databases, administrators can easily generate reports for business use, but they lack the broader intelligence data warehouses can provide. However, the challenge for data analytics from data warehouses is the costs associated.
There is also the challenge of pulling the relevant data sets to enable data analytics from cold data. This requires intelligent data management solutions that track what data is kept and where, and enable you to easily search and find relevant data sets for big-data analytics.