Business perspective: strategy and outcomes

Strategy management – Leverage cloud to accelerate your business outcomes. Consider how cloud can support and shape your long-term business goals. Identify opportunities for retiring technical debt and leveraging cloud to optimize your technology and business operations. Explore new cloud-enabled value propositions and revenue models. Consider how new or improved cloud-enabled products and services can help you reach new customers or enter new market segments. Prioritize your strategic objectives and evolve your strategy over time in response to technological developments and changes in your business environment.

Portfolio management – Prioritize cloud products and initiatives in line with strategic intent, operational efficiency, and your capacity to deliver. Delivering the right cloud products and initiatives at the right time will help you operationalize your strategy and accelerate your business outcomes. Leverage automated discovery tools and the seven common migration strategies for moving applications to the cloud (known as the 7 Rs) to rationalize your existing application portfolio and build a data-driven business case.

Balance your cloud portfolio by considering short-term and long-term outcomes as well as low-risk (proven) and higher-risk (experimental) opportunities. Include migration, modernization, and innovation initiatives, and consider financial (lower costs and/or increased revenue) and non-financial (for example, improved customer and employee experience) benefits. Optimize the business value of your portfolio in line with your resource, financial, and schedule constraints. To reduce your time-to-value, consider increasing the frequency of your planning cycles or adopting a continuous planning strategy.

Innovation management – Leverage cloud to develop new, and improve existing, processes, products, and experiences. By enabling you to instantly provision and shut down resources, cloud can help you reduce your time-to-value and innovation-related cost and risk. To fully take advantage of the potential for increased business agility that comes with cloud adoption, develop an innovation strategy that includes a mix of incremental innovation initiatives focused on optimizing your existing products, processes, and experiences, as well as disruptive innovation initiatives focused on enabling new business models. Create mechanisms for soliciting and selecting ideas in line with your strategic priorities, and develop an end-to-end process for scaling successful innovation pilots.

Product management – Manage data- and cloud-enabled offerings that deliver repeatable value to internal and external customers as products through their lifecycles. Organizing your teams around data- and cloud-enabled products will help you become more agile and customer-centric:

Strategic partnership – Build or grow your business through a strategic partnership with your cloud provider. If you offer cloud-hosted software solutions, cloud-integrated products, or cloud-related professional, consulting, or managed services, strategically partnering with your cloud provider can help you build your cloud expertise, promote your solutions to customers, and drive successful customer engagements.

As you progress along your partnership journey, leverage promotional credits, funding benefits, and co-selling opportunities to help you build or grow your business. Leverage your cloud provider’s marketplace channel to expand reach, and technical resources to help you mature your cloud-based products and services. Publish joint case studies to highlight success in solving specific business challenges.

Data monetization – Leverage data to obtain measurable business benefit. Cloud facilitates the collection, storage, and analysis of vast amounts of data. To obtain measurable business benefits, develop a comprehensive and long-term data monetization strategy that’s aligned with your strategic intent. Identify opportunities for leveraging data and analytics to improve operations, customer and employee experience, and decision-making, as well as to enable new business models.

For example, consider leveraging customer behavior insights to drive hyper-personalization and localization, micro-segmentation, subscriber retention, loyalty and rewards programs, and the like. Focus on transactional value that helps you understand and complete business transactions, informational value that helps you describe past performance and infer conclusions, and analytical value that helps you automate activities, guide decisions, and predict outcomes. First monetize data internally within your organization before considering opportunities for external monetization (for example, selling data via a marketplace).

Business insights – Gain real-time insights and answer questions about your business. Near real-time descriptive insights can help you complete your data monetization strategy by enabling you to track business performance, improve decision-making, and optimize operations. Establish cross-functional analytics teams with a good understanding of the business context. Focus on technical (such as statistics) and non-technical (such as visualization and communication) skills. Align your analytics efforts with business goals and key performance indicators (KPIs). Leverage the Data Catalog to locate relevant data products, and visualization tools and techniques to discover trends, patterns, and relationships in the data. Focus on the “big picture” first and drill down into the details as required.

Data science – Leverage experimentation, advanced analytics, and machine learning to solve complex business problems. Predictive and prescriptive analytics can help you complete your data monetization strategy by enabling you to improve operational effectiveness and decision-making as well as customer and employee experience.

Once you’ve identified opportunities for business process transformation, ensure that your Data Catalog contains the data products required to support the building, training, and testing of your machine learning models. Leverage continuous integration and continuous delivery (CI/CD) practices to improve operational resilience and reproducibility of your machine learning workflows. Understand how your models make predictions and identify any potential biases. Deploy suitable models to production and monitor their performance. To mitigate risk, delegate low confidence predictions for human review.