Choosing the Right Big Data Analyzer

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FICO Big Data Analyzer eliminates Hadoop complexity, placing powerful analytics in the capable hands of an expansive array of industry users and analysts. With its recent acquisition of Adaptive Software, Dataflow, andqi, and IBM Worklight, IBM is poised to usher in a sea change in big data analytics. This shift promises to dramatically alter how businesses manage and utilize enterprise data and intelligence.

Enterprises are leveraging Hadoop to analyze enterprise data that previously would have been handled by various applications, such as SQL, Oracle, and HD Analytics. However, managing large data analytics requires a significant investment of resources, and a large datacenter hosting provider can quickly mount a challenge against cloud providers. The recent acquisition of IBM Worklight simplifies the challenge, by giving IBM access to a comprehensive portfolio of big data analytics tools. While some of IBM’s existing solutions, such as the Dataplane andcade, already address some of the challenges associated with large data analytics, additional solutions from IBM Worklight give the company access to the full capabilities of its new Data Center architecture. The acquisition marks the next step in expanding what is already a massive cloud infrastructure.

Data scientists from industry verticals such as finance, supply chain management, and e-commerce are rapidly finding ways to leverage big data analytics software. These business users need models and dashboards to maximize their productivity, while also reducing their environmental footprint. A well-designed analytics software package for a data scientist will enable business users to express and control the information they collect, while allowing them to make informed decisions about their business. A particularly useful example of this is finance software. Finance data scientists need dashboards that show their most important metrics at a glance. Similarly, supply chain management scientists want to see visual representations of their product activities and progress, both in time and cost. You can get more information about orlando data recovery

Other types of business users also use big data analytics software in order to gain a competitive advantage. Surveyors and land managers may wish to compare demographic data or property boundaries to obtain relevant insights about trends in customer buying patterns. Geologists can analyze satellite images to detect surface-level dynamics that could impact mineral exploration. All of these types of data exploration require robust and flexible analyzers that can be integrated with other applications.

The decision to acquire a big data analyzer and associated tools should be motivated by business users’ needs to improve productivity and cut costs. An analyzer does not have to be customized; however, it often is. After all, some owners and managers want to reduce their IT budget by utilizing analytical tools specifically designed for the business. Businesses that are unable to customize an analyzer to meet their unique needs may be forced to spend hundreds or thousands of dollars on outside talent to perform the analysis. This cost savings would, in turn, help to justify the acquisition.

If you have been contemplating purchasing a big data analyzer, or similar analytic tool, but are unsure of its suitability for your company, consider how data science tools have transformed scientific research. Analysis of large, complex data sets such as those generated by today’s online business has been developed as a specialty’s area of scientific research. Thanks to emerging technology and a rapidly expanding computing market, analysts and scientists are able to leverage large sets of data in a way that was impossible just a few years ago. Data science tools are an ideal fit for data scientists who are seeking to apply emerging technologies in their own organizations.

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