Discuss the specific value of the IBM Intelligent Analytics Cloud

Although businesses used to rely on intuition, they can now use business intelligence and analytics to make factual decisions and answer questions such as how to design product prices, what markets the new service targets, and how to reduce risk. In this series, we will discuss the value of applying cloud computing in business intelligence and analytics. In the first part, we introduce the reason of business intelligence and analysis. In this article, we will discuss the specific value of the intelligent analysis cloud.

Information and the development of information technology, as well as information on various ways to change the way we live and work. Business intelligence and analytics are designed to improve our decisions by translating large amounts of data into valuable insights.

Because of a better ability to analyze and understand data, from chief executives to front-line employees, they have the right to present factual analysis and may influence the development of major decisions. Companies can challenge the status quo and take bold steps to improve performance. The ultimate goal is to get the right information to the right people at the right time. But the challenge is to transform enterprise functions to provide effective business intelligence and analytics services.

In addition to dealing with the growing data, another challenge is to turn the data into relevant information. Today, information flows very quickly. The ability to access relevant data and better anticipate future results is of great value. This forward-looking ability helps companies weigh the pros and cons and make better decisions about future pursuits. Business intelligence and analytics help transform the growing volume of data into insights.

The scope of use of the computer has been expanded. With the introduction of information technology, the business is also improved by simply using the computer for repetitive tasks such as automated table handling. Current innovations can organize large amounts of data and transform information into intuitive reports and scorecards. Consider these potential impacts. In many other opportunities, you can use innovative technologies to make better decisions about where to sell new products and services, or to use medical information to provide better treatment, or to improve urban traffic conditions. The basic concepts behind business intelligence and analysis are not new. For some time, quantitative analysis methods have been used in business, such as financial market trade. The change now is the maturing of technologies and tools to provide business intelligence and analytics services. Today's tools make it easy to access all business intelligence and analysis tools across the enterprise, rather than just the designated user base, for example, IBM Cognos 8 BI, a tool with an intuitive user interface and a combination of cloud computing deployment strategies.

Imagine an organization that can effectively and timely share relevant information (possibly customer or inventory data) in an organization. Instead of storing similar information in their own spreadsheets, individual employees can generate custom reports using public services. Through this service and similar scenarios, enterprises can use business intelligence and analysis methods to improve information sharing and introduce factual analysis into mainstream business.

But technology is just the beginning. How to use technology will provide greater value-added. For example, an enterprise performs a software and hardware solution. In a typical model, a business analyst submits a request to an IT service group for Analysis and provides a report. Skilled IT staff may be aware of the business they are engaged in, but not necessarily experts in this area. If the business analyst does not need deep technical knowledge to be able to analyze the data, then it can achieve efficiency and gain insight. In this way, the speed of decision making will increase. Business analysts can access analysis and information directly without waiting for IT resources to become available. Unnecessary communication levels should be removed. By moving tasks, such as creating reports from IT service groups, you can release these resources to develop new innovations.

Cloud

Before transforming IT capabilities to provide effective business intelligence and analytics services, it is important to consider the appearance of cloud computing. You may want to simulate an IT service that is based on the current successful service model, such as water or electricity, rather than reinventing the wheel. Public facilities can serve many customers while standardizing and centralizing delivery. They use economies of scale to provide competitive prices and added value. After further consideration, you may consider these operational requirements: scalable, resilient, flexible, automated, and standardized. You can also imagine an environment in which end users request services in a simple way and have a simple process of adding, maintaining, and exiting services. Since there are many different aspects, where should we start when developing business intelligence and analytics services? First, consider the operational part of the service and determine whether they (for example, utilities) are a good choice for centralization.

The four key operational components of the service are:

Hardware Software Data Service Application

The hardware infrastructure can be centrally managed by the enterprise or managed locally by the line of business. Local management practices can cause a surge in service farms, and this can lead to reversals, as companies are taking advantage of virtualization and want to benefit from economies of scale.

Business intelligence software and middleware can be purchased on a departmental or enterprise level basis. Over time, departments may develop different preferences and skill combinations, but the savings that are achieved when negotiating a larger number of licenses for a product or a smaller series of enterprise-wide software often exceed those preferences.

Further cost reductions will be seen in terms of management and maintenance costs. But the department strongly opposes the centralization of data. Data liability is a sensitive topic. The line of business must control information because it is important for the business it is responsible for, so the line of business wants to manage the data for itself, which gives them enough flexibility to respond to market changes.

Business applications provide strategic value for each line of business. While the underlying software and middleware can be centralized, it is not feasible to use the same approach to differentiate the value of end user applications.

In an operational component, the hardware and software infrastructure components provide a centralized approach for themselves. The centralization and standardization of the infrastructure is called cloud computing. While cloud computing marks the transition from distributed state to centralized state, there is still real value in this change. Lines of business can focus on which data and business applications are more important, and can also obtain a reliable infrastructure from a designated cloud vendor.

The term cloud computing is used in different fields. But the use of cloud computing has a common theme. On the one hand, cloud computing is an infrastructure and service approach. On the other hand, cloud computing is also a user experience and business model.

Cloud computing is:

scalability: Can increase capacity without affecting functionality. Flexibility: Allows applications to continue to run if the underlying component fails. Resilience: You can add or change functionality without changing or interfering with the current functionality. Automation and standardization: add resources in a standardized manner and, if possible, add resources in an automated manner. Service lifecycle Support: Set up new infrastructure and software, and maintain or eliminate it. Self-Service: Provides an Easy-to-use interface that allows end users with no deep skills to request new resources. Cloud computing is not just an improvement to the data center infrastructure, but a user experience and business model. In a cloud deployment, end users see a standard service product that is easy to access and provides quickly. Figure 1 depicts cloud computing and its basic components.

Figure 1. Cloud computing Components

The basic internal components of cloud computing are data center infrastructure, service catalogs, and component libraries. The data center infrastructure includes hardware (such as System z), software (such as IBM Cognos 8 BI), and middleware (such as DB2). The component library contains the service components required for hardware, software, and delivery services. The Software catalog lists services provided to customers, such as the installation of Linux customers, Cognos licenses, and even a complete intelligent analytics cloud service.

The key roles in cloud computing are service consumers, administrators, software publishers, and component vendors. Service consumers make requests through access services with a standard user interface. The cloud Administrator monitors and manages the delivered services and resources. A software publisher may be an internal department that develops custom services. Component vendors such as IBM also provide services such as intelligent Analytics cloud.

When discussing cloud computing, it is important to distinguish between two types of clouds. Although the naming method is different, there are still major classifications, namely the public cloud and the private cloud.

The

service provider owns and manages the public cloud and accesses it by subscription. The public cloud provides a range of standardized business processes, applications, and infrastructure services based on the price per user. The benefits of the public cloud include standardization, capital retention, flexibility, and shorter application deployment time. Private cloud can only be accessed through a corporate or partner network. Private cloud provides more customization capabilities, drives efficiency, and maintains the ability to standardize and implement best practices. Other benefits are the level of availability, resiliency, security, and privacy that is determined by the level of the enterprise different from the external vendor.

For many businesses, the public cloud is not very safe and reliable. Private cloud provides greater flexibility and is used for enterprise-class solutions.

IBM Intelligent Analytics Cloud

The conversion of business intelligence and analysis capabilities can lead to changes in important decisions, help anticipate future results, and empower employees. IBM Intelligent Analytics Cloud services offerings are designed to overcome challenges to change and enable customers to successfully improve their own business intelligence and analytics capabilities.

The IBM Intelligent Analytics Cloud is:

Support Business intelligence and analysis delivered service offerings at the customer location where the private cloud is deployed. The goal of the IBM Intelligent Analytics Cloud is to make business smarter, empower businesses, and make better decisions for all employees, especially those close to customers and suppliers.

To benefit from service offerings and prove their value, IBM performs an intelligent analysis cloud in-house. The internal solution is called the Blue Insight, and the goal is to transform IBM by developing an enterprise-wide business intelligence and analytics strategy that leverages:

public services, infrastructure, knowledge, and processes in the field of analysis and business intelligence. A centralized infrastructure that authorizes an enterprise to utilize the expertise of its domain to convert plans.

The results of the implementation of the Blue Insight will have many benefits, including:

Integrated business intelligence and software PRODUCT collection an end user can tap a larger number of data sources to service more than 200,000 users the ability of hardware, software cost saving and operation efficiency enhanced elasticity

Without a challenge there will be no IBM change. Different lines of business have their own business intelligence methods and different software and hardware products. There is also resistance to centralization of data and other services. But in the end, IBM has achieved significant value.

IBM's next step is to help customers understand their own experiences and gain the benefits of cloud-based business intelligence and Analytics.

The IBM Intelligent Analytics cloud dramatically reduces the number of departmental solutions for a single BI environment, and this environment supports a large number of users across the business line. In addition, the Intelligent Analytics Cloud:

improves standardization through a single point of control of departmental business processes, enterprise security, and compliance. More efficient use of technical resources to support common business intelligence and analytical delivery tools. Reduce the capital and operating costs required to support enterprise-wide services. Supports self-service methods for allocating business intelligence and Analysis Services, and these services reduce the time, resources, and costs of delivering services to new divisions, departments, and users.

With an intelligent analytics cloud, IBM helps clients create business intelligence and analytics services. Like IBM, you can expect to see the positive results of cloud computing, such as cost savings, the ability to support a large number of users, and the simplification of a software product set. More efficient processes improve accessibility and enable service customers across the enterprise to exploit intelligence and analytics capabilities. Business lines can use common Analysis Services to organize information and make more factual decisions. Cloud based business intelligence and analytics provide great potential. Once launched, these features present unprecedented opportunities.

Concluding

The next section discusses the services included in the Smart Analytics cloud. The requirements combination presented in this article and in the next section is the basis for the following schemas: schema overview, functional architecture, and operational architecture.