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As publishers of desktop Project KickStart and online Webplanner, we are pleased to provide free, high quality project management templates and tools based on project management best practices. Bring your marketing strategy into the 21st century using social media, blogs, news releases, and viral marketing. Inspired by the cold winter weather at the end of 2010, this template will help plan your next ski trip, so you don't forget anything. Predictive Analytics is a data-driven extension that complements other BI capabilities, such as querying and reporting, OLAP and data visualization. Combining BI with Predictive Analytics, in what is becoming known as Business Intelligence + Predictive Analytics , can produce new levels of insight that weren’t possible before, but successful implementation requires establishing a repeatable process. To start, consider the Predictive Analytics and BI pieces as two projects, a notion that will be relevant through the rest of this article. Know the criteria that define success and failure, which is vital to understanding the success of the projects and knowing when to go back to previous steps. The CRISP-DM methodology (as shown on the diagram) is recommended for Predictive Analytics and is summarized in the following six steps. Data Preparation: Data specification, data cleaning and variable transformation are all a part of this step. Now, here is a sample of a summarized project plan to show how a BI + Predictive Analytics project would work.
When models are ready to deploy, consider this a deliverable of the Predictive Analytics Project.
Understand that each modeling technique may require data that is different from other techniques.
Benchmarking modeling output is difficult; it varies by industry, the data used, the values used, the techniques used and the business cases.
It’s important to understand each step and keep in mind you are dealing with an iterative process.
A business plan tells potential investors who you are, your professional goals, what you plan to do to achieve those goals, and how that achievement can generate income to be used to pay back the investor and future finance your career.
Below you will find many helpful key points to cover and what not to cover in your business plan. Summary of your project, including the money you need to successfully launch the project and reach your market.
Description of your industry and marketplace you are operating within (market size, trends, and competition). Marketing plan (estimated sales and market share, strategy, pricing, sales card distribution, and publicity and advertising. A project justification document evolves from a project business case and concept definition document (CDD) and will detail the specific reasons why the project needs to be done. You can think of a project justification document as being akin to a mini project initiation document or even project charter.

Now as you can probably tell, many of these sections can be completed by copying and pasting out the relevant information which was included in the concept definition document and project business case. In fact looking at the index there are only a few additional sections which you will need to write up from scratch.
Now you might think that something extremely detailed is required in this section along the lines of what would be included in the project scope statement. This will relate to any major projects ongoing at the sametime which could impact on this project in terms of project resource allocation or software testing life cycle. Further you need to detail these carefully because you need to ensure the project sponsors are aware when they need to make the key business decisions to ensure the milestones youa€™ve documented are achievable. This is an extremely important section particularly if you are utilising external suppliers.
Further you need to be clear to write down what assumptions you have made when detailing the milestone plan, and also the budget for the project. Sometimes project manager's are so keen to get the project started that they rush through this document which causes huge problems in the future. These templates can be used with very little editing, or they can be customized for your organization, depending on your project management needs. Then download a project template below, and begin modifying it to suit your project's needs. Use this template to keep your main characters on task and overcoming obstacles in the nick of time.
That’s why Predictive Analytics has grabbed the attention of the Business Intelligence (BI) world. Predictive Analytics provides a synergy of technologies that let users analyze past and current performance to make predictions. With that in mind, I am going to share some best practices that we have derived from current and past BI + Predictive Analytics implementations. Not knowing what direction you want to go is a recipe for failure, with failure in this case being incorrect modeling output, missing deadlines, and veering outside of project scope and budget.
Buy-in is important for any project, but with Predictive Analytics, this means committing stakeholders to aggressively applying results to decision making.
For example, Logistic Regression technique requires a binary target, and Survival Analysis requires two targets (time and status).
Sometimes there are no benchmarks, since the business case and solution have not been done before. This means sometimes more than one model output is needed to handle multiple business cases.
Models do not come out perfect on the first try; in fact a perfect model should be questioned as well as the data that was used to create this “perfect” model. It actually has been around since 1930 when Fisher and Durand created the first credit score model.

Tools cannot provide everything; mentors should select the technique based on business context. With that in mind, we will revisit this topic in a future issue of the WebFOCUS Newsletter.
Information Builders: Business Intelligence and Integration - Transforming Data into Business Value. A business plan can help you find financing for your project, help determine when the project will make a profit, and is the map that shows how to get from an idea stage to project completion and profit. The reason being, that whilst you are creating a project plan, it will be a milestone plan ie high level. The reason being that any project time and cost quotes which they provide will be heavily caveated because the business requirements documentation wona€™t even be documented yet.
Whilst you might think that no-one could possibly hold you accountable for the information in the project justification document (PJD) and delivery of it, the reality is that despite the project risks being all too apparent and the project management requirements being vague, if you do not properly document this in the assumptions section you will find yourself in a world of pain all too quickly. The primary benefit of using Predictive Analytics to guess the future is that it gives you the ability to take appropriate actionable steps.
Both share dependencies on each other, though the BI project encompasses the Predictive Analytics piece. If they are not already on board, bring them in before the modeling (Predictive Analytics) project, so that they can guide in the data collection, model selection, etc. However you will quickly find that your project management stakeholders expect those milestones to be met. Hence they need to ensure that when they are estimating project costs, should the defined project scope change dramatically, that they are not still held to their initial estimate.
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