Strategies and Functions
This section describes capabilities and functions that enable a Digital Operating Model to achieve an outcome or set of results. Some of these are well-known functions likely to already exist within most technology organizations, while others reflect new thinking to support digital operations. Whereas a function is largely the responsibility of a single team or domain of expertise, strategies reflect a set of practices shared across multiple teams that are equally critical to the success of the Digital Operating Model.
Note that each of these areas is the subject of a larger body of knowledge and rapidly evolving thinking and solutions that should continue to be evaluated by business and technology leaders. The following sections strictly highlight how these strategies and functions serve the Digital Product-Centric Digital Operating Model.
Value Management represents both a core strategy and critical function of the Digital Operating Model. The previous section highlighted the need to communicate a common strategy for performing and qualifying the value management of Digital Products to create consistency in management across the business. It also protects against value degradation and risk exposure as a Digital Product is operated over time. A central Digital Value Management Office can take the lead in developing, communicating, and improving this strategy. The centralized office will also publish processes, provide templates, and evaluate and administer tools and platforms specific to executing the digital value management strategy.
The strategy for value management must be adopted across the entire business. To achieve this successfully, it should drive a well-rounded common approach to qualifying value management, ensuring Product Managers do not over-focus on some areas (for example, costs), while ignoring others (for example, customer experience), and ensuring all stakeholder requirements are facilitated (from CIO, CISO, business execs, etc.), in addition to delivering on the primary consumer value requirements. While driving common requirements for product value qualification, the strategy should also never restrict Product Management from acting on product-unique value contributors. It should also not require effort on the part of the Product teams that does not create visibility or is not ultimately leveraged for further value creation. Elements of Product Value Management shows the primary areas of a digital value strategy from the Digital Product perspective.
Management of any given Digital Product will involve monitoring an extensive range of metrics unique to the product. Even Digital Products closely similar in architecture may involve significant ranges of unique metrics. Poorly organized reporting of these metrics can make the analysis of a given Digital Product, much less entire product portfolios, virtually impossible for anyone outside of the Product team.
An ideal solution is to establish common product performance areas and require Product Managers to aggregate and align their metrics with these common categories. Best practices for the designation of common performance areas dictate they should be well-rounded, ensuring a holistic consideration of the product ecosystem, and resonate with business leaders. In practice, this means the business leaders can assess common performance areas even while they may not fully understand each of the technical metrics that have been aggregated within the performance area. For some areas, the value management strategy can define common Key Performance Indicators (KPIs) while also allowing Product Managers to elevate and report on product-specific KPIs within each performance area.
The strategy may also designate product performance areas, as well as their common KPIs, by portfolio. The implicit Digital Product types detailed in Funding Models, as well as customer-base, or other inputs may also be leveraged to inform the ideal strategy.
Improving and transforming customer experience represents a powerful value proposition for many Digital Products, but can also reflect highly subjective concepts that can be difficult to consistently quantify, qualify, or assess over time, especially across different products. While there is likely to always be an element of subjectivity as well as specificity for a given product, it is important that the value management strategy requires all Product Managers to dedicate effort to understanding and qualifying this critical value element. The Digital Value Management Office should take the lead on education and the sharing of best practices, conducting research of emerging practices, and canvassing the industry for platforms and tools to measure and qualify customer experience.
Failing to understand and manage the technology supply chain of a given Digital Product can result in a wide range of negative outcomes from delivery delays, decreased quality, unexpected costs, and even overlooked risks. Failing to accurately forecast future demand, as well as prioritizing and shaping demand, is likely to lead to similar outcomes. Another concern is if delivery far exceeds consumption, and whether the overall delivery and support of the product is right-sized. Finally, target adoption rates should be weighed against current operating costs and future investment.
A well-rounded value management strategy should require qualification by Product Managers that demand management is being performed, the technology supply chain is healthy, and that the leveraging and adoption of available delivery is fully optimized.
Traditionally, technology costs across the business were distributed to a list of approved projects and to operational budgets aligned with organizational silos rather than to the Digital Products they supported, making it difficult to demonstrate the value received by the business as a result of these investments. Distributing costs to Digital Products and Digital Product lines across the business linked to clear value propositions resolves this, but also demands that Product teams take on responsibility for cost management, which in turn requires an understanding of costs and finances across a broader community of Digital Practitioners.
Understanding, managing, and communicating Digital Product costs are likely to represent a significant and potentially complex set of responsibilities for Product teams going forward. The Product Manager is explicitly accountable not only for reporting these costs, but for ensuring all of these costs are continuously optimized throughout the entire product lifecycle.
The TCO for a given product can best be understood as a set of costs directly associated with developing, delivering, operating, and supporting the product over time. These direct costs can be further sub-divided as the ongoing costs of delivering and operating the product in its current state (Continuous Delivery (CD)) and, separately, the costs of scaling, optimizing, and transforming the product over time (Continuous Improvement (CI)).
In addition to these costs, many Digital Products today leverage a technology supply chain in which they consume other supporting Digital Products for their own delivery and operation, and it is typically beneficial to view and manage these costs separate from product-direct costs as either chained or indirect product costs.
One of the most significant areas of these chained or indirect costs for many Digital Products is likely to be consumption of cloud services. These services play a significant role in the delivery of many Digital Products, and the specific selection of compute, storage, and other services can have an outsized impact on the ongoing costs of delivery. Even the high-level selection of vendors and environments across hybrid and multi-cloud environments can have a significant impact. Also consider that the best mix of these environments and services to optimize product value will likely continue to change overtime, requiring regular re-evaluation by the Product teams. The FinOps Foundation, which provides a framework for cloud financial operations, is a well established body of knowledge dedicated to the expansive and evolving practice of managing cloud costs. More information can be found at finops.org, a community program under the Linux® Foundation.
Below this highest-level view of costs lie added layers of complexity that will have to be managed by the Product teams. Differentiating between Capital Expenditure (CAPEX) and Operating Expenses (OPEX), fixed versus variable costs, depreciation reporting, accurate forecasting, and many other elements play an important role in product cost management and continuous cost-optimization. It is important that the Product Manager and/or members of the Product team have the appropriate level of expertise and support to manage these responsibilities.
Failing to effectively manage Digital Product costs can have significant consequences for the long-term value of the Digital Product, and even to the financial performance of the business itself. The costs of operating internal, business-enabling Digital Products can be reflected in the companies’ Costs of Goods Sold (COGS), or even have a major effect on product-pricing for external-consumer Digital Products. In fact, the potential impact on final product pricing resulting from underlying cost management, both in margin opportunities and in lost revenue, cannot be understated. Even poorly managed costs for internal end-customer Digital Product types and digital foundation product portfolios can result in a significant drain on business funding.
Managing costs at the product-level is critical to product value management, but it is also part of a broader strategy to fund value creation, innovation, and the Digital Operating Model itself. These portfolio and enterprise-level considerations and solutions are the focus of Funding Models, which provides details on funding models to enable the Digital Operating Model.
Here, again, the Digital Value Management Office will play an important role in standardizing a common process for modeling the total costs of product ownership and making supporting platforms and processes available. Support will need to cover all areas of Digital Product cost-management; for example, maintaining a small practice and supporting platforms for performing FinOps. The Digital Value Management Office will also need to partner closely with Procurement and Finance teams to develop and optimize all of these processes and, over time, ensure supporting platforms and templates are widely available and leveraged, and drive appropriate levels of education around finance and costing to Product teams.
Proactively understanding and managing the risks associated with product delivery, improvement, and ongoing operation is a major element of value management. It is critical that Product teams, stakeholders, and consumers recognize that decisions relative to risk management will almost always be associated with tradeoffs in other areas of the products value management (costs, performance, experience, etc). Product Managers own the ultimate accountability for managing overall risks associated with development, delivery, and ongoing operation of their products. While they may delegate some responsibilities to select Product or Delivery teams and rely on a central digital security practice for advisory support, final accountability always ends with the Product Manager.
Digital security, while significant, is only one element of a holistic risk management strategy for a given Digital Product. A complete strategy for risk management should consider risk to areas including business performance and conformance, strategic and operational, regulatory and legal, internal compliance, health and safety, and even risks associated with social and company perception. Across each of these areas, Product Managers must assess the current and appropriate levels of compliance, exposure, supportability, and resiliency. Product Managers need to regularly evaluate each of these areas to identify risk, measure and assess those risks, monitor and report, and associate each risk with plans to avoid, mitigate, and respond to risks. Finally, these considerations will need to be made within each phase of the Digital Product lifecycle. To be successful, Product Managers will need to work closely with central Digital Value Management and/or the Risk/Control & Audit office to develop and maintain their risk strategy. A central Digital Security team will establish the process and architectural governance that Product Managers will need to incorporate throughout the product lifecycle, and they should also be leveraged as advisory partners to develop a digital security strategy purpose-fit for a given Digital Product.
The business-wide product value management strategy does not represent an all-encompassing set of responsibilities for Product Managers. It should, instead, only be presented and thought of as a common starting point.
Successfully owning accountability for a Digital Product will require the continuous evaluation of its evolving value proposition and ecosystem and engagement with consumer and stakeholder groups, which is likely to dictate efforts and activity not surfaced by the initial value management strategy. In fact, in some cases, these requirements may represent the sole determining factor for whether value was received by the consumer or not. Continuously searching out, identifying, and then prioritizing these elements within the broader value management strategy is an important activity for the Product teams.
One of the stated goals for the reference Digital Operating Model was to continuously manage the risks of operating Digital Products across the entire business, and to be able to scale this capability no matter how rapidly or extensively the business digitizes. This goal is primarily accomplished by decentralizing accountability for the risk management of Digital Products alongside a 3-pillar strategy for managing risk and digital security across the business, as shown in 3-Pillar Digital Enterprise Risk Management Strategy.
Risk management relative to a given Digital Product was detailed earlier in this section. The business-wide strategy for digital risk management leverages end-to-end Digital Product lines and the explicit accountability for managing risk aligned to the Product Managers as its mechanism for ensuring risks are managed for all Digital Products at all times.
Here, again, it warrants highlighting that the Product Manager holds final accountability for any risk related to the development, delivery, and ongoing operation of the product. Digital security represents only one element of that strategy, and Product Managers will need to leverage a centralized digital security practice as advisors to develop a purpose-fit security policy, including assurances that the product adheres to all applicable digital security standards set by the practice. The Product team will also need to collaborate closely with the Control & Audit team to identify controls for managing risk and allowing the team to perform audits, which can supply critical feedback to Product teams and provide key data in qualifying the broader value management of the product.
A centralized function for Digital Security is responsible business-wide digital security governance; for example, standards and practices for secure coding. They will act as advisors to Digital Product teams across the business, and will need to engage regularly with a separate Control & Audit function to identify risk controls.
It is also important not to forget that in end-to-end Digital Product lines the digital security practice will host a number of Product teams of its own, responsible for the delivery and operation of a practice-specific set of Digital Products. These digital security products may include firewalls, intrusion detection systems, or company-wide architectural solutions such as Zero-Trust (refer to: G21f). As designated Digital Products, each is assigned a Product Manager who then owns accountability for the long-term value of the product in the same manner as Digital Products delivered across the business.
A separate function and team dedicated to identifying controls and performing audits must be established, with full independence from both Product teams and the central Digital Security function. Control and audits must now be applied at the business/enterprise level, to individual Digital Products, and across the central Digital Security function. The explicit designation of Digital Products end-to-end across the business creates a significant opportunity to identify new control and audit points that are capable of more directly impacting business performance goals and conformance requirements.
Auditing of the development, delivery, and operation of Digital Products from the central Digital Security team must also be audited by this independent function. For example, as Zero-Trust architectures are deployed by the central Digital Security practice to support the enterprise strategy, this office will be responsible for regularly auditing those solutions.
This central group may alternatively own the common strategy for the risk management of Digital Products and drive relevant education and training for Product teams.
Resourcing of human talent including employees, contractors, external partners, and even Managed Service Provider (MSP) teams represents an often overlooked element of technology supply chains. It also increasingly reflects a need for a specialized set of considerations that is distinct from talent resourcing activities already performed in traditional business organizations. In a digital business, workflows and demand will be much more dynamic going forward, alongside potentially significant impact across multiple products when digital talent resourcing becomes a bottleneck in a technology supply chain. This will require talent resourcing strategies supporting Digital Product development, delivery, and operation to be equally Agile and flexible. These new challenges and requirements will be highlighted again in the discussion on supporting large-scale digital delivery (see Re-Purpose the Central IT Organization), as well as the organizational models needed to support the Digital Operating Model (see Organizational Models).
Supporting this capability in a digital business will require new thinking and practices unique from traditional talent resourcing functions. Evolving the classification of resources, leveraging of multi-discipline resources, supporting new organizational structures, and the adoption of new processes and platforms for work management, reporting, and engagement of resources are all important aspects of a resourcing strategy specific to the Digital Operating Model. Given the rapidly developing challenges and requirements in this area, considered alongside the potential impact of disruption to technology supply chains, establishing a central team dedicated to understanding, owning, and optimizing this function of the Digital Operating Model could represent a differentiator in digital value creation across the entire business.
Change will be constant in a digital business. Changes significant enough to be considered transformative, with the potential to impact broadly leveraged Digital Products or even major business operations, will also occur more frequently. While most change will be directly linked to increased value or decreased risk it is equally important to realize that change itself carries inherent risks. This may come in the form of resistance to change, which can impede the adoption of changes and realization of planned value. Worse, justification for a change could be poorly communicated or the impact of a change misunderstood, resulting in a significant product abandonment by the consumer-base to whom the change was intended to deliver increased value. The capacity to comprehend potential ripple effects of change, anticipate support or resistance to that change, understand people and cultures across different organizations and customer-bases, and to proactively communicate the reasoning and impact of change to stakeholders and consumers now represents a critical capability of a Digital Operating Model.
Change leadership, however, represents a strategic, holistic, and proactive consideration for how changes could impact company resources, cultures, and even existing tools and processes; for example, anticipating resistance to adoption of a new product and driving proactive communications to position the need for change. It represents an established body of knowledge with various models, methodologies, and domains of expertise populated by an industry of professionals and specialists. The goal is to merge this body of knowledge and its experts with Digital Practitioners as a core capability for the Digital Operating Model.
Most technology organizations already leverage basic change management processes matured through years of standardized ITSM practices. These activities primarily deal with standardizing advance planning, review and approvals, and stakeholder notification and engagement. Basic change management processes are still essential and will largely remain the responsibility of Product teams. The Digital Change Leadership Office acts as a proactive advisory and support resource for Product teams to support high impact and transformational changes especially when the consumer base is involved. The office will also perform research, conduct surveys, provide assessments and recommendations, and obviously provide change leadership support for strategic products, portfolios, and throughout major transformation efforts.
In practice, Enterprise Architecture has been a crucial function of successful technology management for far longer than most businesses have leveraged it. It represents an even more critical function in a digital business, where scale, complexity, impact, and rate of change in technology and business alignment are multiplied.
To best enable the Digital Product-Centric Reference Operating Model, Enterprise Architecture acts as an advisory function, and not as a central approver for digital innovation, which was sometimes the case in traditional management models.
The overriding goal for Enterprise Architecture teams is to maintain alignment between business strategy and digital systems, helping business and technology leaders and Product teams balance wide-scale innovation at speed with architectural integrity. The goal is not to achieve 100% enforcement at all times; in fact, allowing acceptable levels of deviation and even duplication amongst Product teams will be the only way to avoid slowing critical innovation. The Enterprise Architecture function ensures the enterprise-wide perspective is maintained to help align activities across portfolios, foster collaboration between teams, and prevent large-scale duplication or deviation from architectural standards.
At a high-level, supporting the Digital Product-Centric Operating Model requires Enterprise Architecture teams to partner closely with Digital Product teams, Digital Delivery teams, and business and technology executives.
Many popular standards and practices for Enterprise Architecture in place today were not developed to support the decentralized nature of continuous, parallel innovation needed to support end-to-end Product teams, creating a major challenge and risk for the overarching businesses. An ideal solution does exist, however, in combining several standards published by The Open Group. For example, The TOGAF® Standard, 10th Edition [C220] provides a complete solution for designing, implementing, and governing Enterprise Architecture and, from there, The Open Agile Architecture™ Standard (also known as the O-AA™ Standard) [C208] provides guidance, tools, and techniques for executing Enterprise Architecture in an Agile manner.
The Digital Operating Model and underlying Product teams accountable for value and risk management will rely heavily on a successful business-wide strategy for data and information. Data and information play a critical role in every aspect of value management, and must be made available at the point of authority where decisions are being made. This means ensuring information is accessible to the decentralized Product teams. Without making the right data and information available at the point of authority, value management of the product is likely to be compromised.
At the same time, Product teams managing the entire product-lifecycle will generate significant levels of new and meaningful data, which might benefit other Product teams, Delivery teams, and even business operations. At times it can be difficult to preemptively determine when specific data or information can create value opportunities, so determining when and how best to make this data and information broadly available without introducing risk is another priority in supporting the goals of the Digital Operating Model.
The data itself may be the product. A data product provides a set of explicitly defined and easy to use data sharing contracts, and is discoverable, addressable, understandable, trustworthy, truthful, intrinsically valuable, and secure. Data as a product is one of the four principles of Data Mesh. Refer to: Data Mesh Learning (https://datameshlearning.com/), an open, user-focused community of practitioners, for more information. Data products are digital products, and the Digital Product-Centric Reference Operating Model applies to their creation, their use within an enterprise, and their delivery to exernal customers.
Finally, data and information play a key role in development of business intelligence, automation, Machine Learning (ML), and Artificial Intelligence (AI) – all of which are likely top priorities for Product teams seeking to innovate. In practice, this increases the likelihood of resources specializing in data being embedded with business-internal and external-customer Digital Product teams. This increases the need for a centralized strategy and governance that can drive standards and best practices while simultaneously enabling the success of these teams specific to their products. A centrally-managed Center of Excellence (CoE) (detailed in Re-Purpose the Central IT Organization) for the delivery of data engineering is likely to also be vital to supporting the unpredictable but time-sensitive demand spikes in data specialist resources.