Part 1 - Identity Assurance by Our Own Volition and Memory

Part 1 – Identity Assurance by Our Own Volition and Memory

In an earlier article we discussed what technology can displace the password. The proposition of Expanded Password System (EPS) that we advocate is now acknowledged as a ‘Draft Proposal’ for OASIS Open Projects that OASIS has recently launched as a new standardization program. We have
How prepared are you to overcome the misuse of AI

How prepared are you to overcome the misuse of AI

Overcome the Misuse of AI Have you ever considered that the AI system integrated into your organisation's computing infrastructure could possess a threat? What if it is indeed true? Will it wreck your entire organisation and cause massive breaches of sensitive information? We can only
Bill Schmarzo

It’s Not Digital Transformation; It’s Digital “Business” Transformation – Part III

Digital “Business” Transformation

This is the third part of the series on the “Customer Journey Digital Transformation” methodology for helping organizations to become more effective at leveraging their digital assets to disrupt traditional business models and disintermediate customer relationships. As defined in the blog “What is Digital Transformation?”:

Digital Transformation is application of digital capabilities (data, analytics and intelligent applications) to processes, products, and assets to improve efficiency, enhance customer value, manage risk, and uncover new monetization opportunities.

In Part I “It’s Not Digital Transformation; It’s Digital “Business” Transformation – Part I” we introduced two fundamental digital transformation concepts:

  • We introduced a process for identifying the sources of value creation.This customer-centric approached (think “outside-in”)identified, validated, valued and prioritized the sources of customer (and market[1]) value creation by leveraging a Design Thinking technique called the “Customer Journey Mapping.”
  • We also introduced a process for identifying the engines of value capture.This production-centric approach (think “inside-out”) leveraged Michael Porter’s Value Chain Analysis methodology to identify and prioritize the organizational capabilities necessary to capture the sources of value creation.

In part II “It’s Not Digital Transformation; It’s Digital “Business” Transformation – Part II”, we introduced the “Customer Journey Digital Transformation” methodology to identify the digital assets (data, analytics, apps) necessary to support the organization’s digital transformation.  The key digital assets that are uncovered in the methodology include:

  • Key Decisions or Outcomesin each stage of the customer journey.
  • Metrics or Key Performance Indicators(KPIs) against which progress and success in each stage will be measured.
  • Predictions and Recommendations we want to deliver to the customer in support of key decisions and desired outcomes.
  • Key Business Entities around which we want to capture analytic insights in order to improve the effectiveness of customer’s key decisions.
  • Intelligent Apps (AppDev) requirements (think Market Requirements Document) for creating an application that delivers an actionable customer experience.

Customer Journey Digital Transformation – Part III

In part 3 of the Customer Journey Digital Transformation series, we will introduce a 4-step process for operationalizing the results of parts I and II:

  • ·Step 1: Identify and Monetize Customer High-value Outcomes. Identify the customer (or market) high value decisions or outcomes, and then identifying what data and analytics are necessary to capture those customer (market) those high-value outcomes.
  • Step 2: Identify and Mitigate Customer Low-value Inhibitors. Identify the customer (or market) low value inhibitors, and then identifying what data and analytics are necessary to mitigate or eliminate those customer (market) low-value inhibitors.
  • Step 3: Map Outcomes to Value Chain.  Map the data and analytics that monetize high-value outcomes and mitigate low-value inhibitors back to the organization’s internal value capture functions (Value Chain Analysis).
  • Step 4: Identify Intelligent App Requirements.  Capture the application Market Requirements (MRD and create a Minimum Viable Product (MVP) concept in order to create a more actionable, compelling user experience.

We will explore these steps in light of a Customer Journey Digital Transformation exercise that we ran at the University of San Francisco (USF) and at the National University of Ireland – Galway (NUI-Galway). The focus of these exercises with the students sought to identify the data, analytics and intelligent application needs necessary for USF and NUI-Galway to digitally transform their universities.

Case Study: USF/NUI-Galway Digital Transformation Exercise

We asked our USF and NUI-Galway students to apply the Customer Journey Digital Transformation methodology to digitally transform their universities. By digitally transforming its operational and educational models, USF and NUI-Galway will be better prepared to serve the holistic and lifetime educational needs of its customers (students). As the first step in the exercise, we asked the students to address the following questions with respect to their “Education” journey:

  • What are the student’s personal, financial and career educational objectives?
  • What success looked like from the perspective of the student?
  • What are the likely impediments or inhibitors to educational success?

We then asked the students to identify the data, analytic and application needs across the following 5 stages of the “Student Education” journey:

  • Stage 1 (Epiphany): Selecting a College
  • Stage 2 (Pre-execution): Starting College
  • Stage 3 (Execution): Attending College
  • Stage 4 (Post-execution): Post graduation
  • Stage 5 (Expiration): Continuing Education & Legacy

The students then applied the worksheet in Figure 1 to each of these 5 stages.

Figure 1:  Customer Journey Digital Transformation Worksheet

Here are the digital transformation operationalization next steps the students uncovered.  See Appendix A for the combined results from the two workshops.

Step 1:  Identify and Monetize Customer High-value Outcomes

In step 1, we want to identify, validate, value and prioritize the sources of customer or market value; that is, identify those outcomes which are of the most value from the perspective of the customer.  Once we identify these sources of customer value, we want to determine what predictive and prescriptive (recommendations) analytics we need to create to capture those sources of customer value.

In Table #1, we identified a sample high-value outcome for each of the 5 stages of the “Student Education” journey map.  We then identified the analytics necessary to capture those sources of customer value.

Table #1: Monetizing Customer High-value Outcomes

Note: I use the concept of Scoresfrequently in my teaching because it is an analytics concept that most folks, especially in the United States, can understand thanks to the prevalence of credit scores.  Scores help to predict likelihood of certain actions or outcomes, such as how a credit score predictsthe likelihood of a borrower to repay their loan.  Scores are analytic-based metrics that support the key decisions your organization is trying to make.

Step 2:  Identify and Mitigate Customer Low-value Inhibitors

In step 2 we want to identify the inhibitors of customer value; that is, identify those outcomes which hinder customer value creation and encumber a successful customer journey.  Once we identify these inhibitors of customer value, we want to determine how we can leverage analytics to mitigate or eliminate those inhibitors (see Table #2).

Table #2: Mitigating Customer Low-value Outcomes 

Step 3:  Map Outcomes to Value Chain

Next, we want to map the high-value outcomes and low-value inhibitors, and the supporting analytics, to the organization’s internal value capture (value chain) processes.  As an example, we have mapped two high-value outcomes – Getting College Acceptance Notification and Graduating on Time – to the organization’s value chain processes in Table #3.

Table #3: Mapping High-value Tasks to Business Functions

Homework assignment:  Map the low-value inhibitors and the supporting analytics to the organization’s internal value capture (value chain) processes in Table #4 for two low-value outcomes: Getting College Rejection Notification and Graduating Late.

Table #4: Mapping Low-value Outcomes (Inhibitors) to Business Functions

Customer Journey Digital Transformation Part III Summary

The blog is already too long, so I’ll cover Step 4: Identifying Intelligent Apps Requirements in my next blog in the Customer Journey Digital Transformation series.

For folks interested in a deeper dive on Identifying sources of customer value, check out the “Digital Transformation Introduction” training video that I created around “Going Out to Eat.”  I used a common experience to show the process of identifying and quantifying the high-value outcomes and the low-value inhibitors.

Watch this space for the next episode on the Customer Journey Digital Transformation methodology (see Figure 2).

Figure 2: Digital Transformation Value Creation Mapping

Blah…

By Bill Schmarzo

Bill Schmarzo

CTO, IoT and Analytics at Hitachi Vantara (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”. 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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