Big Data

Guiding the Envisioning Process: Prioritization Matrix Worksheets

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) September 3, 2013

Now that I’ve submitted my last chapter to the publisher for my upcoming “Big Data:  Understanding How Data Powers Big Business,” it’s time to get back on the blog trail.  Book CoverThis blog covers a couple of worksheets[1] developed to assist the big data use case Prioritization Process.  You’ll remember that prioritizing your big data use cases is one of the key things we do in a Big Data Vision Workshop.

As you know from my previous blogs, one of my favorite organizational and alignment tools is the Prioritization Matrix (see Figure 1:  Prioritization Matrix below).  The Prioritization Matrix is a marvelous tool for:

  • Identifying the “right” use case to pursue with big data based upon a balance of business value and implementation feasibility
  • Ensuring that both IT and business stakeholders have a voice in discussing the relative value and implementation challenges for each identified use case
  • Enabling the capture of the key business drivers and implementation risks for each of the identified use cases
  • Summarizing the “right” use cases so that everyone can agree upon a path forward

Figure 1: Priority Matrix

Prioritization Matrix Worksheets

To drive the prioritization process, I developed two worksheets that help the Visionimages (1) Workshop participants to identify and understand the drivers behind both the Business Value and Implementation Feasibility axis of the Prioritization Matrix.  Each of these worksheets looks at the business value and implementation feasibility from a data source perspective.

As part of the Vision Workshop process, numerous internal and external data sources are identified as potentially new sources of customer, product, campaign and/or operational insights.  These two worksheets provide a guide for helping consider both the business value as well as the implementation feasibility of those data sources vis-à-vis the targeted business initiative.

downloadNote:  You will still have to make the connection between the data sources and the use cases during the Vision Workshop prioritization process.  But these worksheets will help the workshop participants to understand and quantify the business value drivers (measured on the vertical axis of the Prioritization Matrix) and the implementation risks (measured on the horizontal axis of the Prioritization Matrix) that drive the prioritization process.

The Business Value Assessment worksheet (see Figure 2: Business Value Assessment Worksheet below) assesses the potential business value of each data source that is being considered as a source of insights for the targeted business initiative.  The Business Value Assessment worksheet helps the Vision Workshop participants to identify and understand the business value drivers for each data source as related to the targeted business initiative.

Figure 2: Business Value Assessment Worksheet

The Implementation Feasibility Assessment worksheet (see Figure 3:  Implementation Feasibility Assessment Worksheet below) assesses the implementation risks or impediments of each data source that is being considered for the targeted business initiative.  The worksheet helps the Vision Workshop participants to identify the feasibility variables or implementation risks for each of the identified data sources as related to the targeted business initiative.

Figure 3: Implementation Feasibility Assessment Worksheet

Let’s walk through a real-world example to see how to apply these worksheets.

Improve Customer Up-sell Effectiveness Example

Let’s say that our targeted business initiative is to improve the effectiveness of our imagescustomer up-sell marketing campaigns.”  The Vision Workshop process would uncover numerous data sources that could be sources of new customer, merchant, and campaign insights that could be used to support the improve customer up-sell marketing effectiveness business initiative. Example data sources that could be identified as part of the Vision Workshop process include:

  • Customer sales transactions
  • Marketing campaign results data
  • Customer demographics, lifestyle and behavioral data
  • Merchant demographics data
  • Digital Marketing Platform (DMP) customer digital data
  • CRM data such as consumer comments and email conversations
  • Web analytics data
  • Social media data like Facebook and Twitter

The Vision Workshop process would then identify the key business drivers for the improve customer up-sell marketing effectiveness business initiative.  These business drivers could include:

  • Improve customer profiling – uncover new customer behavioral insights and product purchase tendencies that can be used to improve the granularity, accuracy, and actionability of the customer profiles
  • Improve merchant profiling – uncover new merchant product and product category sales insights that can be used to improve merchant profiling and segmentation
  • Improve offer relevance – leverage customer and merchant product preferences and associated insights to improve the matching of merchant offers to customer profiles and behavioral categories
  • Improve offering timing – leverage real-time location data coupled with targeted merchant offers to improve the timeliness and relevance of offering delivery

Below is an example of what the assessment of the business drivers vis-à-vis the different data sources might look like for our improve customer up-sell marketing effectiveness business initiative (see Figure 4:  Customer Up-sell Business Drivers Assessment Worksheet below).

Figure 4: Customer Up-sell Business Drivers Assessment Worksheet

The combination of business drivers and the insights that can be gleaned out of the different data sources drives the “Business Value” column.

Next we want to walk through the Implementation Feasibility assessment of the improve customer up-sell marketing effectiveness business initiative.  The Vision Workshop process would identify several implementation risks for the customer up-sell marketing effectiveness business initiative, such as:

  • Ease of acquiring or accessing the data
  • Cleanliness of the data
  • Accuracy of the data
  • Cost of acquiring the data
  • Granularity of the data

Below is an example of what the assessment of the implementation risks or impediments vis-à-vis the different data sources might look like for our improve customer up-sell marketing effectiveness business initiative (see Figure 5:  Customer Up-sell Implementation Feasibility Assessment Worksheet below).

Implementation Assessment Illustrative Figure 5

Figure 5: Customer Up-sell Implementation Feasibility Worksheet

Notice that we carried the Business Value column over from the Business Drivers worksheet to the Implementation Feasibility worksheet.  We did this so that we’d have all the data together necessary to guide the placement of the resulting use cases in the Prioritization Matrix.

Improve Customer Up-sell Effectiveness Example

The Business Drivers and Implementation Feasibility assessment worksheets can help guide the Vision Workshop participants as they look to prioritize the use cases that come out of a Vision Workshop process.  These worksheets look at the different data sources to provide additional insights that can help to determine the potential business value and implementation risks for each identified use case.


[1] Unfortunately, I wasn’t able to get this material into the book, but the associated worksheets will be available for download from the book’s website once the book is released.

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (aka “Dean of Big Data”)

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting the strategy and defining the Big Data service offerings and capabilities for Dell EMC Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power the organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill was ranked as #15 Big Data Influencer by Onalytica.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored Dell EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a masters degree in Business Administration from the University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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