Data management

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Cardus Inc. provides Big Data Engineering services to conceptualize and execute carefully planned big data strategies across multiple industry areas. We aid businesses in developing big data engineering capabilities to drive their vision.

Big Data is emerging technology used to refer to data sets that are colossal in size and can handle the complex data-processing that comes with mobile application development.
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Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s 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.
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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.
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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.
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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.
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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:

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There’s no single technology that encompasses big data analytics. Of course, advanced analytics 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.

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[av_toggle title=’Machine Learning’ tags=”]
Machine learning, a specific subset of AI that trains a machine how to learn, makes it possible to quickly and automatically produce models that can analyze bigger, more complex data sets and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
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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 you have reliable data, organizations should establish a master data management program that gets the entire enterprise on the same page.
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[av_toggle title=’Data mining’ tags=”]
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.
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[av_toggle title=’Hadoop’ tags=” av_uid=’av-rcu5v’]
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.
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[av_toggle title=’In-memory analytics’ tags=”]
By analyzing data from system memory (instead of 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.
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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.
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[av_toggle title=’Text mining’ tags=”]
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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