Power Point Presentation

Big Data And Business Intelligence

Business Value With Big Data

For business to survive in a competitive environment, organizational change requires improved governance, sponsorship, processes, and controls, in addition to new skill sets and technology all work in harmony to deliver the benefits of big data. See Fig. 13.2

Data science has taken the business world by storm. Every field of study and area of business has been affected as companies realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de fac to programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for generating business intelligence

For IT Leaders

Making sense of terabytes of data is a daunting, complex task. To explain advanced analytics algorithms in succinctly to upper management for their support is just as challenging. IT leaders must understand the tasks at hand and ask the correct questions such as:

How can we attract, and retain employees with the skills we will need? What data do we need and what is the optimal way to collect and manage the massive amounts of structured and unstructured data involved. How can we best support varied and dynamic business needs for information more rapidly?

Turning Data Into Business Intelligence (BI)

It is vital that organizations provide cost-effective and rapid access to BI if they are to stay in business in this millennium. The solution is a BI system capable of providing a set of technologies and products for supplying users with the information they need to answer business questions and make tactical and strategic business decisions.

Early, first-generation system is known as host-based query and reporting. The second-generation system is known as data warehousing. The third-generation system is what we know now as BI for a more complete solutions to the needs of business users.

BI systems focus on improving the access and delivery of information to both the providers and consumers. This is achievable by providing advanced graphical and Web based online analytical processing (OLAP) and mining tools, and prepackaged applications that exploit the power of those tools. Due to the need to process and analyze large volumes of information, a BI system must provide scalability and be able to support and integrate products from multiple vendors.

Business Driving Forces

1. The need to increase revenues, reduce costs, and compete more efficiently. BI systems are focused toward end-user information access, delivery and provide packaged solutions in addition to support the technologies.

2. The need to manage and model the complexity of today's business environment. Companies of today are providing and supporting a wider range of products and services to a broader and more global customers than ever before.

3. The need to reduce IT costs and leverage existing corporate business information. IT like corporate intranets, cloud computing, and subscription driven information delivery help to reduce the cost of BI to a wider user. BI systems also broaden the scope of the information that can be processed to include not only operational and warehouse data, but also information managed by office systems and corporate Web servers.

Five Main BI Requirements

The main requirements of a BI system are:

1. Support for prepackage applications solutions.

2. A cost-effective solution that provides an expedient ROI to the business and enables an organization to compete more effectively.

3. Quick access to an organization's BI data warehouse for a wide range of end users, including information providers and information consumers.

4. Continue support for new technologies such as OLAP and data mining.

5. An open, extensible, and saleable operating environment.

Big Data & BI Industry Examples:

IBM Security and Intelligence Extensions to enhance traditional security solutions to prevent crime by analyzing all types and sources of big data.

Three requirements are needed to enhanced intelligence and surveillance insight to analyze data-in-motion and at rest to:

· Find associations

· Uncover patterns and facts

· Maintain currency of information

Real-time cyber-attack prediction and mitigation to analyze network traffic to:

· Discover new threats sooner

· Detect known complex threats

· Take action in real-time

Crime prediction and protection to analyze telco and social data to:

· Gather criminal evidence

· Prevent criminal activities

· Proactively apprehend criminals

Industry Examples:

Government threat and crime prediction and prevention for TerraEchos.

TerraEchos uses streaming data technology to support covert intelligence and surveillance sensor systems for the needs of deployed security surveillance system to detect, classify, locate, and track potential threats and highly sensitive national laboratory such as the Oak Ridge National Laboratory.

The benefits of this system is able to reduce time to capture and analyze 275MB of acoustic data from hours to one-fourteenth of a second which enables analysis of real-time data from different types of sensors and 1,024 individual channels to support extended perimeter security which in term to be able to response more intelligently to any threat.

 

Operations analysis for CISCO Corporation:

· Intelligent infrastructure management: log analytics, energy bill forecasting, energy consumption optimization, anomalous energy usage detection, presence-aware energy management

· Optimized building energy consumption with centralized monitoring; automated preventive and corrective maintenance

· Utilize IBM product of InfoSphere Streams, InfoSphere BigInsights, IBM Congos

References

Simon Jeggo. (2013). IBM Big Data Platform - Turning big data into smarter decisions