Big Data

Prioritization Matrix: Aligning Business and IT On The Big Data Journey

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) January 14, 2013

One key challenge to a successful Big Data journey is gaining consensus and alignment between the business and IT stakeholders in identifying the initial big data business use cases that 1) deliver sufficient value to the business, while 2) possessing a high probability of success.  One can find multiple business use cases where big data and advanced analytics can deliver compelling business value.  However, many of these use cases have a low probability of execution success due to:

  • Availability of timely, accurate data
  • Inexperience with new data sources like social media, mobile, and sensor logs
  • Limited data or analytic people resources
  • Lack of experience with new technologies like Hadoop, MapReduce, and text mining
  • Weak business and IT collaborative relationship
  • Lack of management fortitude

We have found one tool for driving business and IT collaboration and agreement around the “right” initial use cases for your big data journey – those with sufficient business value and a high probability of success – is using the Prioritization Matrix.  Let me share how the Prioritization Matrix works to not only prioritize the initial big data use cases, but how it can foster an atmosphere of collaboration between the business and IT stakeholders.

Role of the Prioritization Matrix within the EMC Big Data Vision Workshop

The EMC Big Data Vision Workshop uses facilitation and envisioning techniques to help the business stakeholders uncover the use cases where big data and advanced analytics can power the organization’s key business initiatives.  The Prioritization Matrix is the capstone of a Vision Workshop.  The Prioritization Matrix facilitates the discussion and debate between the Business and IT stakeholders in identifying the “right” use cases to start a big data initiative – those use cases with both meaningful business value (from the business stakeholders’ perspectives) and reasonable feasibility of successful implementation (see Figure 1).

Figure 1: Prioritization Matrix

Focusing the Prioritization Matrix process on a key business initiative – such as reducing churn, increasing same store sales, minimizing financial risk, optimizing market spend, or reducing hospital readmissions – is critical as it provides the foundation upon which the business value and implementation feasibility discussion and subsequent placement on the matrix discussion can occur.  The targeted business initiative provides the key performance indicators (against which success will be measured), the critical success factors (which are the desired outcomes that need to be achieved in order to successfully complete the business initiative), and the execution timeframe (typically 9 to 12 months in duration).

The Prioritization Matrix Process 

The Prioritization Matrix Process starts by placing each identified use case (identified in the Vision Workshop) on a “Post It” note (one use case per “Post It”).   The group (which must include both business and IT stakeholders) decides the placement of each use case on the Prioritization Matrix (weighing business value and implementation feasibility) vis-à-vis the relative placement of the other use cases on the Matrix.

The business stakeholders are responsible for relative positioning each business case on the Business Value axis, while the IT stakeholders are primarily responsible for relative positioning of each business case on the Implementation Feasibility axis (considering data, technology, skills, and organizational readiness).

The heart of the Prioritization process is the discussion that ensues about the relative placement of each of the use cases, such as:

  • Why is use case [B] more or less valuable than use case [A]?  What are the specific business drivers or variables that make use case [B] more or less valuable than use case [A] (see Figure 2)?

Figure 2: Facilitating The Positioning Debate

  • Why is use case [B] less or more feasible from an implementation perspective than use case [A]? What are the specific implementation risks that make use case [B] less or more feasible than use case [A]?

It is critical to the prioritization process to capture the reasons for the relative positioning of each use case, in order to better identify the business value drivers and potential implementation risks.

The Prioritization Matrix Traps 

One of the keys to effectively using the Prioritization Matrix is to understand the discussion traps, and to guide the workshop participants around those traps.  In particular, you want to avoid use cases that fall into the following matrix zones:

  • “Zone of Mismanaged Expectations” which are those use cases with huge business value but little chance of successful execution (e.g., solve world hunger)
  • “Zone of User Disillusionment” which are those use cases which are easy to execute but provide little business value (ERP data warehouse)
  • “Zone of Career Limiting Moves” which are those use cases which have little business value and have a low probability of success (likely some senior executive’s pet project based upon the latest article they read in Forbes or Harvard Business Review)

Use cases that fall into one of these zones should be avoided as they either don’t provide enough business value to be meaningful or relevant to the business stakeholders, or are too risky to IT from an implementation perspective.

Note:  understanding where each use case falls, and the open discussion between the business and IT stakeholders about why each use case is positioned where it is, is key to avoiding surprises once into the project.  Eyes wide open!

The Prioritization Matrix:  Another Tool In Your Big Data MBA Toolkit 

The Prioritization Matrix is a marvelous tool for facilitating a conversation between the business and IT stakeholders about where and how to start the big data journey.  It provides a framework for identifying the relative business value of each business use case (with respect to the targeted business initiative), and for identifying and understanding the implementation risks.  Out of this prioritization process, both the business and IT stakeholders should know what use cases they are targeting and the potential business value of each use case, and have their eyes wide open to the implementation risks that the project needs to avoid or manage.

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 strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an 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 also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was 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 Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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