Each element should be followed by the punctuation mark shown here. Earlier editions of the handbook included the place of publication and required different punctuation such as journal editions in parentheses and colons after issue numbers. In the current version, punctuation is simpler only commas and periods separate the elementsand information about the source is kept to the basics.
An IBM view of the structured data analysis landscape: Thus, business analyticsis a category of computing rather than a specific method, application or product.
In many ways, business analytics is the next competitive breakthrough following business automation but with the goal of making better business decisions, rather than simply automating standardized processes. This new computing category leverages the wealth of data being produced on a daily basis.
In the near future tremendous amounts of additional data will become available, including both structured data, such as from sensors and unstructured data, such as from cameras, social media and sentiment from the social network. Business analytics centers around five key areas of customer needs: This first segment is foundational to business analytics.
Gaining a deeper understanding of why things are happening, for example, gaining a full view of your customer transaction history, segmentation, sentiment and opinion, etc.
Leveraging the past to predict potential future outcomes so that actions and decisions are computed in order to meet the objectives and requirements of the organization.
This segment of the market is about strategically aligning everyone in the organization — from strategy to execution. It is about enabling enterprise and operational visibility. It is about documenting the preferences, priorities, objectives, and requirements that drive decision-making.
This article focuses on one slice of the business analytics space that is based on leveraging structured data to achieve improved business outcomes.
Our thesis is that structured data analytics includes three categories of increasing complexity and impact: These three categories support the customer needs described above. We further refine the analysis of structured data into three categories of analytics: A set of technologies and processes that use data to understand and analyze business performance Predictive Analytics: A set of mathematical techniques that computationally determine a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance.
Organizations that undertake a journey into the applications of business analytics must begin with an information management agenda that treats data and information as a strategic asset. Once information is treated as an asset, then descriptive, predictive and prescriptive analytics can be applied.
Typically an organization begins this journey by examining the data generated from its automation systems: The organization may also have unstructured data, such as contracts; customer complaints; internal e-mails; and, increasingly, image data from facility monitoring systems, as well as unstructured data from various Web sources, such as Facebook, Twitter and blogs.
This article addresses only the analysis of structured data but recognizes that structured data, such as time and location, author, topic and other semantic information, is attached to unstructured data.
Together structured and unstructured data establishes a degree view of information to improve decision-making. Many of the business analytic techniques used for structured data can be applied to unstructured data as well.
Descriptive analytics can be classified into three areas that answer certain kinds of questions: Standard reporting and dashboards: How does it compare to our plan?Contemporary Metaphilosophy. What is philosophy?
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