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Predictive Analytics Solution Offering

From MIKE2.0 Methodology

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This article is a stub. It is currently undergoing major changes as it is in the very early stages of development and is only a placeholder. Please help improve MIKE2.0 by adding to this article.
Hv2.jpg This Solution Offering currently receives Minor Coverage in the MIKE2.0 Methodology through the Overall Implementation Guide and SAFE Architecture, but Activities are missing and there are no detailed Supporting Assets. In summary, the Solution Offering has not yet been formulated to the point where it should be used.
A Creation Guide exists that can be used to help complete this article. Contributors should reference this guide to help complete the article.

Contents

Introduction


The Predictive Analytics Solution Offering provides an approach for organizations to gain a deep understanding of their current information assets through existing data sets by looking for trends that are not obvious without detailed analytical processes and technologies. This discovery-driven analysis is complemented by the development of “what if” scenarios that can help predict future behaviour. Key enablers for this offering are detailed techniques related to data mining, model development and model simulation.

Executive Summary

Solution Offering Purpose

This is a Core Solution Offering. Core Solution Offerings bring together all assets in MIKE2.0 relevant to solving a specific business and technology problem. Many of these assets may already exist and as the suite is built out over time, assets can be progressively added to an Offering.

A Core Solution Offering contains all the elements required to define and deliver a go-to-market offering. It can use a combination of open, shared and private assets.

Solution Offering Relationship Overview

This Solution Offering is part of the Business Intelligence Offering Group

MIKE2.0 Solution Offerings provide a detailed and holistic way of addressing specific problems. MIKE2.0 Solution Offerings can be mapped directly to the Phases and Activities of the MIKE2.0 Overall Implementation Guide, providing additional content to help understand the overall approach. The MIKE2.0 Overall Implementation Guide explains the relationships between the Phases, Activities and Tasks of the overall methodology as well as how the Supporting Assets tie to the overall methodology and MIKE2.0 Solutions. Users of the MIKE2.0 Methodology should always start with the Overall Implementation Guide and the MIKE2.0 Usage Model as a starting point for projects.

Solution Offering Definition

Relationship to Solution Capabilities

Relationship to Enterprise Views

Mapping to the Information Governance Framework

Mapping to the SAFE Architecture Framework

Mapping to the Overall Implementation Guide

There are several considerations when implementing an analytics capability. Extra focus needs to be placed across Phase 1, 3, 4, and 5 of Overall Implementation Guide.
The key areas that will need to be thought out are:
• Defining an enterprise strategy
• Defining an appropriate infrastructure to support desired objectives
• Setting stakeholder expectations
• Development and on-going maintenance of the analytics solution

Mapping to Supporting Assets

Logical Architecture, Design and Development Best Practices

Product-Specific Implementation Techniques

Product Selection Criteria

Relationships to other Solution Offerings

Predictive Analytics are by definition highly speculative. "What if" analyses extrapolate future trends and events based upon current ones. In order for these predictions to be remotely accuate, the data upon which these trends are based needs to be as accurate, complete, and detailed as possible. To this extent, organizations cannot "walk before they can run." Organizations that are immature, undisciplined, and reactive in terms of their data management will likely find themselves lost unless and until they have implemented other aspects of the MIKE2.0 Framework. These include:

In order to achieve the desired results, organizations will have to use a multifaceted and distinct approach. In other words, they will not be able to follow a "recipe." 

Finally, getting to a point at which the organization can produce meaningful analytics does not mean that other information management (IM) initiatives can case. On the contrary, solid data management practices need to continue indefinitely if the organization expects to make meaningful decisions based upon different scenarios. 

Extending the Open Methodology through Solution Offerings

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