Appendix

 

THE DELTA TRANSITIONS

IN CHAPTERS 2-6 we discussed what the analytical journey looks like for each of the DELTA elements—data, enterprise, leadership, targets, and analysts. Here, for your convenience, we put that information together into a complete picture. In table A-1, we outline what conditions are typically in place at each stage of progress in deploying analytical business applications with impact. Its two dimensions are the DELTA success factors and the five-stage journey to being an analytical competitor. The combination is a kind of map, a high-level assessment tool for analytical capability. Take a few minutes to study it, and notice how the DELTA elements align with any given stage, and how each element evolves across the stages.

Companies have found this mapping handy for a variety of tasks:

• Assessing where you are—what are your analytical capabilities, strengths, and weaknesses?

• Recognizing where to go next—what strengths can you capitalize on, and what gaps should you try to close?

• Setting reasonable ambitions—what can you hope to accomplish and when?

• Monitoring progress—how fast and how far are you traveling on the journey to capitalize on analytics?

• Perhaps most important, discussing all these things with executive leadership and everyone else with an interest in succeeding with analytics—how can you come to mutual understanding about your capabilities and commitment to a plan of action?

TABLE A-1

The DELTA transitions

	From Stage 1 Analytically Impaired to Stage 2 Localized Analytics	From Stage 2 Localized Analytics to Stage 3 Analytical Aspirations	From Stage 3 Analytical Aspirations to Stage 4 Analytical Companies	From Stage 4 Analytical Companies to Stage 5 Analytical Competitors
Data	Gain mastery over local data of importance, including building functional data marts.	Build enterprise consensus around some analytical targets and their data needs. Build some domain data warehouses (e.g., customer) and corresponding analytical expertise. Motivate and reward cross-functional data contributions and management.	Build enterprise data warehouses and integrate external data. Engage senior executives in EDW plans and management. Monitor emerging data sources.	Educate and engage senior executives in competitive potential of analytical data. Exploit unique data. Establish strong data governance, especially stewardship. Form a BICC if you don’t have one yet.
Enterprise	Find allies for small-scale analytics projects that nonetheless suggest cross-functional or enterprise potential. Manage data risk at local level. Partner with IT on common tool selection and data standards.	Select applications with relevance to multiple business areas. Keep scope manageable, but with an eye to future expansion. Establish standards for data privacy and security. Begin building enterprise analytical infrastructure incrementally.	Develop analytics strategy and road map for major business unit, if not the enterprise. Conduct risk assessments of all analytical applications. Establish enterprise governance of technology and architecture for analytics.	Manage analytical priorities and assets at the enterprise level. Implement enterprisewide model review and management. Extend analytics tools and infrastructure broadly and deeply across the enterprise.
Leadership	Encourage the emergence of analytical leaders in functions and business units.	Create a vision of how analytics will be used in the organization in the future, and begin to identify the specific capabilities necessary.	Engage senior leaders in building analytical capabilities, particularly in the areas of data, technology, and analytical human resources.	Encourage leaders to be visible with their analytical capabilities, and to communicate with internal and external stakeholders about how analytics contribute to success.
Targets	Work wherever there is sponsorship and some decent data. Target “low-hanging fruit.”	Work with business areas that are already somewhat analytical or can benefit greatly from analytics. Target business process or cross-functional applications. Start taking systematic inventories of analytical opportunities by business area.	Work with major business processes and their owners. Focus on high value and high impact targets. Take an enterprisewide approach to finding and evaluating targets. Formalize the process of targeting as a collaboration among business executives, IT and analytics leaders.	Work with the executive team. Focus on strategic initiatives, value creation, and building distinctive capability that will enhance competitive differentiation. Infiltrate the strategic planning process so analytics can shape (not just respond to) business strategy.
Analysts	Identify pockets of analysts and skills. Offer analytical skills training. Encourage analytical components of systems projects. Enlist managers to appreciate and engage analytical employees.	Define analytical positions and use specialty recruiting sources to fill them. Encourage knowledge sharing among analysts of all types. Promote rotational deployment of analysts. Provide coaching and support, especially for analytical professionals.	Evaluate analytical expertise of all information workers, develop relationships with universities and associations, and provide advanced training for analysts. Focus on developing business acumen in analysts and analytical expertise in business executives. Integrate the development and deployment process. Form communities of analysts.	Hire analytically minded employees in all business roles. Formalize an analyst-role/business-role rotation program. Organize and deploy analysts centrally. Regularly recognize analytical employees in all roles, and ensure that analysts are constantly challenged in their work.

 

Study this table with your current condition and analytical ambitions in mind. What do you need to do to leverage your strengths, shore up your weaknesses, become more DELTA ready, and increase the business impact and value of analytics? As you consider your course of action, be sure to avoid the most common pitfalls:

• Focusing too much on one dimension of analytical capability (most often technology and data) at the expense of the others.

• Devoting too much time, energy, and money on analytical initiatives that have low business impact (even if that’s what the business is asking for).

• Attempting to do too much at once.