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The Endgame, or Putting it All Together

1Module 6: The Endgame

Module 6: The Endgame 1

The Endgame, or Putting it All Together

Upon completion of this module, you should be able to:

• Articulate three tasks needed to operationalize an analytics project

• Explain how the four common deliverables of an analytics lifecycle project meet the needs of key stakeholders

• Use a framework for creating final presentations for sponsors and analysts

• Evaluate a data visualization and identify ways to improve it

Module 6: The Endgame 2

This module covers the following lessons:

• Operationalizing an analytics project

• Creating the final deliverables

• Data visualization techniques to support your final presentation

Module 6: The Endgame 2

The Endgame, or Putting it All Together

During this lesson the following topics are covered:

• Operationalizing a data analytics lifecycle project

• Key outputs needed for a successful analytic project, by stakeholder role

• 4 core deliverables to meet most stakeholder needs

Operationalizing an Analytics Project

Module 6: The Endgame 3

This lesson covers putting a data analytics lifecycle project into action, the outputs of a successful analytic project and the core deliverables.

Module 6: The Endgame 3

Data Analytics Lifecycle

Module 6: The Endgame 4

Discovery

Operationalize

Model

Planning

Data Prep

Model

Building

Communicate

Results

Do I have enough information to draft an

analytic plan and share for peer review?

Do I have enough good

quality data to start building the model?

Do I have a good idea about the type of model to try? Can I refine the

analytic plan?

Is the model robust enough? Have we

failed for sure?

This is a graphic portraying the data analytic lifecycle that we explored in Module 2. In this Endgame module, we will focus on the final phase of the cycle, “Operationalize”. In this phase, the project team will deliver final reports, briefings, code, and technical documents. In addition, the conclusion of this phase includes running a pilot project, and implement your models in a production environment.

As stated in Module 2, you can perform a technically accurate analysis, but if you cannot translate the results into a language that speaks to the audience, people will not see the value and much of your time will have been wasted. For this reason, we will spend time in this End Game module to show you how to put together a clear narrative summary of the work and convey it to key stakeholders.

Module 6: The Endgame 4

Data Analytics Lifecycle Final Deliverables

Module 6: The Endgame 5

Discovery

Operationalize

Model

Planning

Data Prep

Model

Building

Communicate

Results

Do I have enough information to draft an

analytic plan and share for peer review?

Do I have enough good

quality data to start building the model?

Do I have a good idea about the type of model to try? Can I refine the

analytic plan?

Is the model robust enough? Have we

failed for sure?

• Run a pilot

• Assess the benefits

• Provide final deliverables

• Implement the model in the production environment

• Define process to update, retrain, and retire the model, as needed

As mentioned in Module 2, the Data Analytic Lifecycle, the final phase of the lifecycle is focused on operationalizing the project. In this phase, you will need to assess the benefits of the work that’s been done, and setup a pilot so you can deploy the work in a controlled way before broadening the work to a full enterprise or ecosystem of users.

Your ability to quantify the benefits and share them in a compelling way to the stakeholders will determine if your work will move forward into a pilot phase and ultimately be run in a production environment. For this reason, it is critical that you identify the benefits and state them in a clear way in the final presentations. In the subsequent lesson, we will introduce you to a framework to share your work with the key stakeholders in a clear and concise way, to help illustrate the work that was done and share its potential value.

As you scope the effort of a pilot project for your model, consider running the model in a product environment for a discrete set of single products, or a single line of business, which will test your model in a live setting. This will allow you learn from the deployment, and make any needed adjustments before launching across the enterprise. Keep in mind that this phase can bring in a new set of team members – namely those engineers who are responsible for the production environment, who have a new set of issues and concerns. They want to ensure that running the model fits smoothly into the production environment and the model can be integrated into downstream processes. While executing the model in the production environment, look to detect anomalies on inputs before they are fed to the model. Assess run times and gauge competition for resources with other processes in the production environment.

Module 6: The Endgame 5

Key Outputs from a Successful Analytic Project, by Role

Module 6: The Endgame 6

Role Description What the Role Needs in the Final Deliverables

Business User

Someone who benefits from the end results and can consult and advise project team on value of end results and how these will be operationalized

• Sponsor Presentation addressing: • Are the results good for me? • What are the benefits of the findings? • What are the implications of this for me?

Project Sponsor

Person responsible for the genesis of the project, providing the impetus for the project and core business problem, generally provides the funding and will gauge the degree of value from the final outputs of the working team

• Sponsor Presentation addressing: • What’s the business impact of doing this? • What are the risks? ROI? • How can this be evangelized within the

organization (and beyond)?

Project Manager

Ensure key milestones and objectives are met on time and at expected quality.

Business Intelligence Analyst

Business domain expertise with deep understanding of the data, KPIs, key metrics and business intelligence from a reporting perspective

• Show the analyst presentation • Determine if the reports will change

Data Engineer Deep technical skills to assist with tuning SQL queries for data management, extraction and support data ingest to analytic sandbox

• Share the code from the analytical project • Create technical document on how to

implement it.

Database Administrator (DBA)

Database Administrator who provisions and configures database environment to support the analytical needs of the working team

• Share the code from the analytical project • Create technical document on how to

implement it.

Data Scientist

Provide subject matter expertise for analytical techniques, data modeling, applying valid analytical techniques to given business problems and ensuring overall analytical objectives are met

• Show the analyst presentation • Share the code

As you begin to frame the final deliverables, keep in mind the interests of each of the main stakeholders and be sure to frame your presentations in a way that will address their interests and concerns. Be prepared to discuss how your work will impact end users and others in the business.

Module 6: The Endgame 6

4 Core Deliverables to Meet Most Stakeholder Needs

Module 6: The Endgame 7

1. Presentation for Project Sponsors • “Big picture" takeaways for executive level stakeholders.

• Determine key messages to aid their decision-making process.

• Focus on clean, easy visuals for the presenter to explain and for the viewer to grasp.

2. Presentation for Analysts • Business process changes.

• Reporting changes.

• Fellow data scientists will want the details and are comfortable with technical graphs (such as ROC curves, density plots, histograms).

3. Code for technical people

4. Technical specs of implementing the code

We will provide further details on how to create the final deliverables in the next lesson. Here are a few general guidelines about preparing the results of the analysis for sharing with the key sponsors:

1) The more the audience is comprised of executives, the more succinct you will need to be. Most executive sponsors attend many briefings in the course of a day or a week. Ensure your presentation gets to the point quickly and frames the results in terms of value to the sponsor’s organization. For instance, if you are working with a bank to analyze cases of credit card fraud, highlight the frequency of fraud, the number of cases in the last month or year, and how much of a cost or revenue impact there is to the bank (or focus on the reverse, how much more revenue they could gain if they address the fraud problem). This will demonstrate the business impact better than deep dives on the methodology. You will need to include supporting information about analytical methodology and data sources, but generally only as supporting detail or to ensure the audience has confidence in the approach you took to analyze the data.

2) If presenting to other analysts, focus more time on the methodology and findings. You can afford to be more expansive in describing the outcomes, methodology and the analytical experiment with a peer group, as they will be more interested in the techniques, especially if you developed a new way of processing or analyzing data that can be reused in the future or applied to similar problems.

3) Use imagery when possible. People tend to remember mental pictures to demonstrate a point more than long lists of bullets.

Module 6: The Endgame 7

The Endgame, or Putting it All Together

During this lesson the following topics were covered:

• Operationalizing a data analytics lifecycle project

• Key outputs needed for a successful analytic project, by stakeholder role

• 4 core deliverables to meet most stakeholder needs

Summary

Module 6: The Endgame 9

This lesson covered what is needed to put a data analytics lifecycle project into action, the outputs of a successful analytic project and the core deliverables.

Module 6: The Endgame 9