- Substitution represents basic IT modernization; such as leveraging new consumption models (e.g., cloud, “as a Service”) to directly replace functions and costs that already exist in an enterprise. Shifting from on-premises to cloud can generate tangible cost savings for an organization; however, it does not have a large impact on how an organization goes to market, better serves their customers or optimizes their key business processes.
- Extension is where disruptive technologies (e.g., data science, artificial intelligence, machine learning, data lake, IOT, blockchain) are folded into an environment to provide an organization with capabilities not available otherwise. For example, analytics frameworks are folded into existing applications to enhance the velocity and visibility of data to managers and deliver recommendations that facilitate increasingly rapid and defensible decision-making.
- Transformation is the Holy Grail; hence it represents the overarching goal of Digital Transformation. Truly transforming one or more business processes is a complex effort that requires a process centric to transforming an organization’s business models, coupled with scale-out and elastic foundational technologies.
I think this categorization is spot on. Today, the vast majority of companies are stuck on the “Substitution” phase, where IT organizations are spending considerable attention and effort “paving the cow path,” which provides marginal cost savings, but misses the bigger business opportunities offered by the “Extension” and “Transformation” phases. And I believe the problem starts with the below understanding:
Digital Transformation is about creating or enabling new business models; it’s not about improving existing business processes.
As the title of the TBR research paper highlights, to get out of the “Substitution Quagmire” requires a business-centric, business-accountable process that provides a roadmap for moving organizations from the Substitution to the Extension and Transformation phases.
Dell EMC Consulting created the Big Data Business Model Maturity Index as a guide for helping an organization understand how to become more effective at leveraging data and analytics to power its business models; to move beyond just optimizing the organization’s key operational and business processes (see Figure 1).
We use the term “Metamorphosis” as the final phase of the Business Model Maturity Index because it more accurately reflects the cultural, management and business model changes that organizations must address to successfully digitally transform their organizations. Metamorphosis is a complete change in form and function from one stage to the next in the life of an organism, as from the caterpillar to the pupa to the adult butterfly.
Figure 2 provides more details on each of the phases of the Big Data Business Model Maturity Index.
The TBR Digital Transformation phases of Substitution, Extension and Transformation map very well to the Big Data Business Model Maturity Index, which hopefully means that we can leverage our learnings from using the Business Model Maturity Index with customers to help guide organizations’ digital transformation initiatives (see Figure 3).
Let’s review what we learned from the Big Data Business Model Maturity Index engagements that we can re-purpose for organization’s embarking on their digital transformation journey. The blog “Big Data Business Model Maturity Index Guide” provides recommendations on the steps required to transition from one stage to the next in the Business Model Maturity Index. However, I’ve copied a summary of that blog below because it’s a lot easier for me to cut and paste than it is for you to click back and forth between the blogs.
Steps to Progress from Monitoring to Insights
The Insights stage is about coupling the wealth of internal and external data with predictive analytics to uncover insights about the organization’s key business and operational processes.
- Identify Key Business Decisions. Identify and understand the decisions that the key business stakeholders need to make to support an organization’s key business initiatives
- Create Analytics Sandbox. Provide an analytics environment that allows the data science team to rapidly ingest data, explore the data, and test the data for its predictive capabilities in a fail fast environment.
- Deploy Predictive Analytics. Leverage predictive analytics to uncover individuals’ relevant behaviors (e.g., tendencies, propensities, preferences, patterns, trends, interests, passions, affiliations, associations).
- Deploy Right-time Analytics. Create “right time” analytics capabilities that can flag anomalies and behavioral changes that might be worthy of analysis.
- Train Business Users. Train business users to “Think like a Data Scientist” in identifying variables and metrics that might be better predictors of business performance.
- Capture Analytic Insights. Capture and catalogue analytic insights that are being uncovered about your key business entities.
Steps to Progress from Insights to Optimization
The Optimization stage applies prescriptive analytics to deliver recommendations to customers, front-line employees, and partners to improve effectiveness of the organization’s key business processes.
- Evaluate Insights Business Relevance. Assess the potential business value of the Analytic Insights captured in the Insights phase using the S.A.M. (Strategic, Actionable, Material) methodology.
- Deploy Prescriptive Analytics. Build prescriptive analytics to deliver actionable recommendations to the key business entities that support key business decisions and use cases.
- Deploy Data Lake. Build a Data Lake that supports the capture, refinement and sharing of the organizations data and analytic digital assets (collaborative value creation platform).
- Leverage Application Development. Operationalize the recommendations by leveraging modern application development techniques to integrate the results into web sites, mobile apps, dashboards, and reports.
- Measure Decision Effectiveness. Tag the recommendations in order to determine their effectiveness. Use the results of the effectiveness measurements to fine-tune the analytic models.
Steps to Progress from Optimization to Monetization
The Monetization stage leverages the analytic learnings from the Optimization phase to create net new revenue opportunities; that is, leveraging the analytic insights to identify unmet customer and market demand that provide new revenue opportunities for new customers, products, services, audiences, markets, channels and partnerships.
- Operationalize Analytic Insights. Capture, catalogue and operationalize the captured customer, product, operational and market insights in analytic profiles (stored in the data lake) that can then be shared across multiple business use cases.
- Identify Monetization Opportunities. Run envisioning exercises with key business stakeholders to identify and assess the value of insights as they relate to new revenue opportunities.
- Prove ROI. Conduct a Proof of Value where the data science team can collaborate with the business stakeholders to determine if the analytics can be turned into new revenue opportunities.
- Operationalize New Products/Services. If there is a compelling ROI and the analytic models can generate the necessary lift, then push the new revenue opportunities to market. Instrument the rollout to monitor the monetization effectiveness and make right-time course corrections.
Steps to Progress from Monetization to Metamorphosis
The Metamorphosis stage exploits the organization’s cumulative knowledge about their data and the resulting customer, product, service, operational and market analytics to metamorphosize the organization’s business model including rewards, hiring, promotions, culture and management structure that embraces the economic value of the organization’s data.
- Create New Business Models. Consider your customers’ reasons for doing business with you; that is, what are they trying to accomplish from a more holistic perspective – retirement, health, funding college, vacation, meals, entertainment, buying a house, transportation, etc. Leverage your customer, product and operational insights to transform your business model to more easily integrate or embed into the life, or business model, of your customers and partners.
- Create Analytics Platform. Extend your analytics platform to incorporate customer-facing interactivity where customers and partners can develop new apps that integrate into their business operations.
- Enable Third-Party App Developers. Determine how to enable, scale and secure the analytics platform so that third-party application developers can develop, market, sell and support new value-added applications.
But now let me add a new task:
Operationalize the Economic Value of Data. Yea, this is a new one because at the time of creating the original Business Model Maturity Index, we just didn’t know what we know today about the economic value of data. However, digital-centric organizations are realizing that the digital assets of data and analytics exhibit economic characteristics unlike that of any other organization assets; that these digital assets can be used across an infinite number of use cases and never wear out and never deplete.
Figure 4 summarizes the tasks required to transition from Monitoring (Substitution) through Insights and Optimization (Extension) to Monetization and Metamorphosis (Transformation).
The TBR report closes by stating:
We expect the next 12 to 18 months will be critical in establishing leaders and laggards in the market, with successful vendors defining their value proposition by transitioning the conversation from an ideological discussion to a targeted road map outlining specific benefits to transforming a given business process.
Yea, we’re ready to help our clients to ensure that “journey” does not become an “ordeal”!