Big Data Solution


Within a single platform, Aspire Tech solution provides big data tools to extract, prepare and load your data, plus the visualizations and analytics that will change the way you run your business.

Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights to better business decision. Using Big Data technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions. Big data analytics is the technique that automates the process of collecting, processing, contextualizing and analyzing large sets of data, commonly referred to as big data, to uncover patterns that help a business make better decisions. Big data analytics differs from traditional data analytics because it has the ability to capture and analyze data sets that are very large, move fast and lack a common structure.

Why is big data analytics important?

Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. In his report Big Data in Big Companies, IIA Director of Research Tom Davenport interviewed more than 50 businesses to understand how they used big data. He found they got value in the following ways:

1. Cost reduction. Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.

2. Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.

3. New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.














BLENDED BIG DATA ANALYTICS




A tightly coupled data integration and business analytics platform accelerates the realization of value from blended big data.

  • Full array of analytics: data access and integration to data visualization and predictive analytics
  • Empowers users to architect big data blends at the source and stream them directly for more complete and accurate analytics
  • Seamlessly switch or combine data processing engines with in-cluster execution to maximize existing processing capacity
  • Ability to spot check data in-flight with immediate access to analytics, including charts, visualizations, and reporting, from any step in data prep
  • Supports the broadest spectrum of big data sources, taking advantage of the specific and unique capabilities of each technology
  • Open, standards based architecture makes it easy to integrate with or extend existing infrastructure



BIG DATA DEPLOYMENT FRAMEWORK

There are several factors that influence the decisions to deploy big data technologies in an organization.



HOW IT WORKS AND KEY TECHNOLOGIES

There’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most value from your information. Here are the biggest players:

Data can come into many different formats (structured data and unstructured data) and can be collected from many different formats from different systems. So is very important to create platforms that can capture unstructured and structured data in meaningful formats. We can leverage MPP systems and Hadoop for high speed performance to process this massive volume of data. With the help of this big data appliance data can be modeled to help predictive analytics, spatio-temporal analysis, data mining and text mining.



Data management: 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. Once data is reliable, organizations should establish a master data management program that gets the entire enterprise on the same page.

Data mining: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. With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.

Hadoop:This open source software framework can store large amounts of data and run applications on clusters of commodity hardware. It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open source framework is free and uses commodity hardware to store large quantities of data.

In-memory analytics: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. This technology is able to remove data prep and analytical processing latencies to test new scenarios and create models; it's not only an easy way for organizations to stay agile and make better business decisions, it also enables them to run iterative and interactive analytics scenarios.

Predictive analytics:Predictive analytics technology uses data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It's all about providing a best assessment on what will happen in the future, so organizations can feel more confident that they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations and marketing.

Text mining:With text mining technology, you can analyze text data from the web, comment fields, books and other text-based sources to uncover insights you hadn't noticed before. 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.


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