Big data analytics audit large amounts of data to uncover hidden patterns, correlations and other insights.
Big data analytics helps organizations strap their data and use it to analyze new opportunities. That, in turn, leads to smarter business moves, happier customers, more efficient movement, and higher profits.
1. Cost reduction
Big data technologies such as cloud-based analytics carry significant cost improvement when it comes to storing large amounts of data furthemore they can identify more efficient ways of doing business.
2. Better decision making.
With the speed of Hadoop an open source distributed processing framework that manages data processing and storage for big data applications running in clustered systems combined with the ability to analyze new sources of data, businesses are able to analyze information with the speed of light.
3. New products and services.
With the ability to gauge customer needs and satisfaction through analytics more organizations are creating new products to meet customers’ needs.
Key Technologies for Big Data Analytics
Hadoop
Hadoop has moved far beyond its beginnings in web indexing and is now used in many organizations for a enormous variety of tasks that all share the common theme of lots of diversity, volume and velocity of data.
Data Integration
Data integration is the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information. A complete data integration solution delivers trusted data from a variety of sources. One of the operational challenges for businesses dealing with big data is to process terabytes or petabytes of data in a useful way for customer deliverables.
Data management
Data needs to be high quality and well-governed before it can be accurately analyzed. With data continually flowing in and out of an organization, it’s important to establish repeatable processes to build and maintain standards for data quality.
Types of Big Data Analytics Tools
Big Data Analytics tools are important for organizatons because they help businesses save time and money and aid in gaining insights to inform data-driven decisions. There are various types of tools to improve the process of analyzing data: data storage, data management, data mining, data analysis, data visualization, data integration, and data quality.
Big Data Analytics Challenges
Several different difficulty can make it problematic to achieve the benefits promised by big data analytics vendors:
Data Growth
Data growth is the biggest data center hardware infrastructure challenge for large enterprises.
Unstructured Data
Such as email messages, images, audio files, videos and other types of files. This unstructured data can be very difficult to search so vendors are frequently updating their big data analytics tools to make them better at examining and extracting insights from unstructured data.
Cultural Challenges
Despite the fact big data analytics is becoming mainstream, it hasn’t infiltrated the corporate culture everywhere yet.