Big Data Analytics

Big Data Analytics

Big Data focuses on managing and storing the data through proficient databases.

Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.

The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence programs. That could include Web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things.

The most highly used database NoSQL used in Big Data and real-time web applications technologies, frequently correlated with unstructured data in last year’s version of trends in Big Data. NoSQL database as a leading piece of the enterprise in IT environment becomes clear as the benefits of schema-less database concepts become more pronounced.

Why Big Data Analytics

Cost reduction

Big data Analytics such as Hadoop and cloud-based analytics bring significant cost advantages.

Faster decision making

businesses are able to analyze information immediately

New products and services

customer satisfaction through analytics comes the power to give customers what they want.

How It Works and Technologies

Data needs to be high quality and well-governed before it can be reliably analyzed. With data constantly flowing in and out of an organization,
it’s important to establish repeatable processes to build and maintain standards for data quality.

Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions.

By analyzing data from system memory (instead of from your hard disk drive), you can derive immediate insights from your data and act on them quickly.

Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data.

Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.

Big Data Analytics Benefits

Big Data analytics is the answer to all data related queries, be it handling, maintaining, managing, extracting, storing data from a diversity of
structured and unstructured sources of information. Big Data focuses on managing and storing the data through proficient databases.
We provide robust and wide-ranging Big Data services, which handle the storage, transaction, analysis and maintenance of the
unstructured data in a planned and controlled way and then further utilize it for real-time analysis, visualization and foresight.

Higher the volume of data, more comprehensive will be the 360-degree view of information, thereby increasing insight and depth of information ensuring better decision making.

Velocity refers to the influx and investigation of flowing data. Increasing customer data fed rapidly into the Big Data platform leads to increased likeliness to make the right decision at the right time to achieve management goals.

Indication of certain key risk or potentially harmful areas, well ahed in time means added fortune to business

Different sources and forms of data, be it from the logs, CRM systems, social media and many more, help increase in better-informed decisions.

Value dimension talks of identification of value added information. Higher the consolidation, cleansing, consistency of information,
there are increased chances of precise decision making.