Big Data: Hype or Help for Your Business

Big Data is different than typical data solutions, such as dashboards and business intelligence (BI), in place in business today. Big Data is a fundamentally different data technology. Analytics for Big Data are also fundamentally different, creating challenges for those who develop data analytics. Getting from Big Data hype to help requires skill and experience.

Real-Time Business Understanding

Healthcare services, financial transactions, insurance claims, retail transactions, hospitality and entertainment reservations, and energy usage are captured today. From the application that captures the transaction, data is processed into financial, reporting, and BI systems for consumption and analysis.

A core concept behind real-time business understanding is that all types of data, or data variety, needed by the business must be captured and analyzed quickly. This requires a parallel data path, one aimed at providing analytics and insight as data comes into the business, to existing processes that prepare data for finance, reporting, BI, and operational use. It also requires advanced presentation techniques such as data visualization, to facilitate quick analysis.

Inferential / Predictive Analytics and Insight

Real-time business understanding requires analyzing data as individual transactions or data events occur. This requires inferential / predictive analytics that use statistical models as the foundation of analysis rather than a set of data processed as the foundation of a descriptive analysis.

A core concept for inferential / predictive analytics is that speed provides competitive advantage, such as quickly assessing the cost of a healthcare service, acknowledging a financial transaction, identifying a fraudulent insurance claim, managing inventory and pricing of a retail item or of a hospitality and entertainment reservation, and responding to energy usage.

Data Flexibility

Real-time inferential / predictive analytics do not wait for data to be processed and structured for use. This requires rapid capture of transactions and data events without concern for related data or relationships. Where descriptive analytics are structured, because they use defined data elements and aggregates, Big Data analytics are unstructured because they do not require a predefined data structure or schema. It is this unstructured characteristic that provides flexibility, allowing the data to be used for real-time and inferential / predictive analytics and insights immediately.

A core concept for data flexibility is schema-less data structures, such as those used in Hadoop and NoSQL technologies. These allow data content to evolve without requiring changes to schemas or database structures before data can be captured and used.

Lower IT Costs

Based on open source software and massively parallel commodity hardware, Big Data technologies provide IT with opportunities to lower the cost of data capture, data transformation, and analytics development, especially for very large sets of data.

The concept behind using Big Data technologies to lower IT costs is to provide flexibility, speed, and ease of implementation to basic data management and transformation functions as well as to real-time business analytics.

Big Data Strategy and Roadmap

A path to success requires a roadmap. The RCG Big Data Strategy and Roadmap determines how Big Data can best support your business goals and objectives, which Big Data technologies best fit into your IT environment, the Big Data capabilities that you need to develop, and a sequence of projects to deliver business value with Big Data.

Big Data Information Management

Incorporating Big Data into an enterprise creates challenges to existing data governance, master data management, information lifecycle management, data quality management, and KPI and metric management programs. RCG Big Data Information Management incorporates Big Data into these programs and adapts them to the particular requirements of managing Big Data information.

Big Data Information Transformation

Data transformation is a staple of processing data for finance, reporting and BI. The data flexibility of Big Data does not mean that information architecture to accommodate unstructured data such as emails, images, videos, and schema-less data structures is unnecessary. A Big Data solution requires understanding the objects being captured, designing a structure for holding these objects, structuring MapReduce and other aggregating structures for them, and other architectural tasks. RCG Big Data Information Transformation incorporates Big Data into these core structural and transformational needs for Big Data.

Big Data Analytics and Insight

The purpose of Big Data is to provide real-time business understanding using inferential / predictive analytics. RCG Big Data Analytics and Insight develops Big Data analytic models and results to meet the specific needs of the business.