Organizational agility — the ability to continuously improve, iterate, and adapt to fast-changing technology developments and customer expectations — has long set apart corporate leaders from laggards. The pace of change and innovation has never been faster and technology developments and digitalization set the pace as much as they demonstrate the impact of change for businesses and individuals alike.
If the pace of change, (and need to keep up to remain competitive) wasn’t already fast enough, the current environment, shaped by a global health crisis and the related economic uncertainty, is recognized as a (digital) change accelerator extraordinaire. A number of data points emerge that confirm the extraordinary pace and magnitude of change that is occurring. In a survey by Fortune, for example, 77% of CEOs say that their company’s “digital transformation was accelerated during the crisis.” And of the 100 CIOs, CTOs, and CDOs who attended the Metis Strategy Digital Symposium this summer, 72% said that the pace of their organization’s digital transformation accelerated since the pandemic started.
The crisis is expected to further shape the competitive landscape and likely widen the gap between organizations on the path to a successful future and those fighting for survival in a post-Covid “new normal” world.
Of the factors that will determine success or failure, two feature prominently on essentially all executives’ agendas: digital readiness and organizational agility. Both of these are tied to an organization’s organizational change management capabilities.
It may appear logical that organizations and their business and technology leaders would focus relentlessly on making sure that organizational change management (OCM) capabilities are mature and ready to be deployed at a moment’s notice, especially since change initiatives will remain an integral part of business operations and are widely expected to increase. However, despite the widely recognized need for more organizational agility, OCM is still an underdeveloped, underutilized, and underappreciated competence, even in organizations that are otherwise recognized as being high-performing and successful in their core competencies.
Most companies have some change management capabilities within their organizations, but often these efforts start too late or are haphazard in their implementation. This can lead to frustration among employees and customers and may ultimately lead to higher cost and/or risk. Digital transformation, for example, requires a great deal of change, and 70 percent of digital transformation efforts do not fulfill the promises made. Sometimes, change management may be viewed as a “soft” topic that is difficult to explore and even harder to influence in the pursuit of “hard” business results.
This presents a significant opportunity to improve operational performance and shape more favorable business outcomes by applying well-established change management approaches differently and adopting a more strategic and data-driven approach to change leadership.
Bestselling author, thought leader, and Harvard Business School professor John Kotter and his 8-Step Process for Leading Change are widely regarded as the authority on change management and leadership. The 8-Step Process, which ranges from “Create a Sense of Urgency” to “Institute Change,” provides a useful framework for a number of change initiatives. Metis Strategy has used it as a starting point for the change efforts we are involved in and built upon it to address individual situations.
As robust and proven as Kotter’s 8-Step process is, it doesn’t guarantee success; it’s thoughtful execution and careful tailoring to the unique organizational context will make the difference.
In order to build upon the power of Kotter’s framework and to address the issues we have encountered in our OCM and organizational agility work with technology and business leaders across industries, we have identified five “Moments of Truth” in change management. These work in concert with Kotter’s eight steps and make the approach more powerful and more likely to produce the desired business outcomes. We will explore each Moment of Truth below:
The delineation between continuous evolution, which ideally is part of business-as-usual, and a significant change event is gray. Where the line is crossed will depend on how much change is “normal” within an organizational context. As soon as an action or development falls out of the norm, leaders should communicate that change is taking place even if the extent and impact of any such change is still not entirely known. Doing so will allow organizations to begin managing that change and reduce the potential costs and risks associated with not addressing it early enough.
After significant change is identified but before Kotter’s “burning platform” has been identified or the sense of urgency created, firms should determine the outcomes that the change process is expected to deliver. Companies should list desired outcomes of the change initiative, as well as consequences that may occur if the change is left to occur without significant oversight. Think of this as the risk/return calculus or scenarios analysis of change. If the outcomes of freely occurring change pose a risk of being costly or distracting to the organization, active management may be necessary.
If active management is necessary, firms should assess their change readiness and put an explicit plan in place. These plans can be brief, corresponding to the magnitude and implications of the change and the desired business outcomes. At this point, the needs for change management (such as risk avoidance or mitigation), as well as related opportunities (such as faster realization of benefits) will begin to emerge. These will help make the case for – as well as increase the likelihood of – successful change management. At this stage, leaders should also gather and gain alignment among relevant stakeholders, as well as have initial conversations about change management activities and success metrics.
A high-level change readiness assessment will help firms understand the costs and benefits of OCM efforts in its fundamental terms. It will also provide an opportunity for companies to solicit perspectives and perceptions from teams or employees affected by the change. People respond to change – or even the prospect of change — in different ways, and it’s important to acknowledge the different types of perspectives and reactions that play a role in change management. This may include enthusiasts, skeptics, those who are complacent, and others.
Most importantly, leaders should identify key change agents and potential change inhibitors, as both of these groups should be engaged with care and diligence. The former can serve as advocates and accelerators of change, while the latter may need to be proactively engaged to limit the emergence of negative sentiments or inaccurate information that will be difficult to remedy after the fact. Ideally, a broad set of perspectives from all relevant levels and functional areas will be represented as the change management work kicks off in earnest. Now that the need for change management has been identified and the fundamentals are in place – with the opportunity to iteratively enhance or scale them as efforts progress – companies are ready to identify the burning platform and create the sense of urgency that will launch the change efforts in a more public and open manner.
As organizations prepare to share their change management plans widely (in the transition between Kotter’s “Create a Vision for Change” and “Communicate the Vision” steps), it is important to assess the organizational context that is relevant for the change at hand. Many change efforts often fall short because they fail to consider organizational realities, such as the circumstances of an organization’s operations or the perceptions and feelings of its employees.
At this point in the change management process, the desire for and commitment to the change efforts should be clear. This may cause employees’ natural fears and anxieties to flare up. Unless the change management plan, specifically the communication and engagement efforts, reflect specific thoughts and concerns, there’s a risk that the change efforts will meet resistance from a growing number of people affected by the change.
In order to manage this critical juncture effectively, empathy and transparency become important tools in the change leader’s toolkit. Employees are looking for leaders and colleagues to listen and to understand. They want to be heard and see their concerns and expectations addressed. The more clearly and specifically senior leaders can relate to employees of all functions and demographics, the more likely it is that their message will resonate.
A practice that I have seen work well to express empathy and ensure that team members feel heard and understood is cross-functional communication at the executive level. For example, if the chief marketing officer can explain the move towards DevOps and a microservices architecture, the CIO references the newest design standards, or the Chief Data Officer shares the vision for the customer engagement campaign, employees will take note and recognize the shared commitment to the change objective.
In our experience, honesty and plain-spokenness go a long way. Simple language and basic concepts coupled with real-world examples will be more effective than a well-drafted and well-polished presentation.
A key recommendation relative to this step is to embrace the ideas of “servant leadership” and the role of clearing obstacles in the way of change, no matter what they are. Empowering employees, delegating responsibilities, and providing space for creativity will instill and strengthen trust that is likely to yield benefits well beyond an individual change effort.
As you begin to implement your change management initiatives and realize a few “quick wins,” change leaders will transition to ensuring the sustainability and institutionalization of change, as Kotter outlines in the last two steps of his 8-Step Change Process. This is the time to not only scale the change management initiatives, but also to document and scale lessons learned so that they can be applied to other change management efforts, even those that are not the focus of the original project.
The opportunity to scale, repeat, and improve what has already worked is both difficult and valuable. If the change efforts can be broken down into individual components, the organization has the opportunity to iterate on each component in pursuit of different change objectives and business outcomes. If, for example, the original change effort focused on developing a Scrum product team, the organization could consider taking the dedicated or capability team concept to other parts of the business; explore whether other parts of the organizations are ripe for a project to product operating model shift; improve demand and capacity planning practices; or apply minimum-viable-product (MVP) principles to general operations.
The scalability and repeatability of change will be both a source and an indicator of change management maturity and the culture of change at an organization. In many cases, the success of change can be attributed to heroic efforts of individuals or teams or is the result of extensive deployment of resources to effect the change. While changes achieved under these circumstances are still commendable and, and many will be deemed successful, they may not be repeatable or scalable, either because motivation or resources cannot be replicated, or other contributing factors are no longer at play (e.g., a crisis or emergency, a regulatory deadline).
Like all business capabilities, an organization’s change management competencies should not be static. They should be subject to continuous review and iterative improvement. As you learn which change management activities work and which ones don’t, you will develop the type of change management capabilities that best meet your organization’s needs. To the extent possible, it can be useful to create a data-driven after-action review. Identify and document the insights, and work with change management and functional leaders to turn the lessons learned into concrete improvements.
A well-written OCM playbook will enable an organization to leverage the advantages of similar or even repeatable change management efforts while also building capabilities that can successfully manage new or uncertain challenges. While there are universal OCM “best practices,” what is right for your organization will largely be driven by the prevailing organizational culture.
In addition to these five Moments of Truth in Change Management, two critical ingredients to OCM and Organizational Agility must be called out. First is recognizing that organizational culture will not only make or break any particular change effort. It will also determine the sustainability and repeatability of change.
Secondly, data and analytics in change management is becoming an increasingly powerful tool to enhance the effectiveness of an organization’s change management capabilities and counter the false yet common perception that change management is “soft.” These powerful tools at change leaders’ disposal should be used throughout the change effort and beyond.
A data-driven approach to change management includes:
These two critical success factors of culture and data and intrinsically linked. An organizational culture that embraces changes and develops a data-driven mindset is a rare but powerful combination that all organizations and leaders should aspire to attain.
Throughout this article, we have emphasized the symbiotic relationship between organizational agility, organizational culture, and change management capabilities. In this setup, culture and agility will likely continue to shape change management capabilities more than the other way around. Ultimately, the general maturity of the organizational change management capability, as well as the success of each individual change management effort, should be judged by the organization’s universally accepted measures of business outcomes, business impact, and business value.
Chris Davis co-authored this article.
Companies continue to face implementation challenges as they rush to comply with data privacy regulations such as Europe’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This is due largely to a mismatch between their management of data and the stringent requirements set by the regulations.
Organizations can address the complexities of privacy regulations via a well-defined data governance framework, which leverages people, processes and technologies to establish standards for data access, management and use. Such a framework also enables companies to address elements of privacy, including identity and access management, consent management and policy definition.
As leaders implement data governance models with privacy in mind, they may face challenges, including lukewarm executive buy-in, lack of a cohesive data strategy, or diverging opinions about how data should be used and handled. To address these obstacles, leaders should consider the following actions:
While a Chief Data Officer or CIO may lead the implementation of a data governance framework or model, data governance should be a shared responsibility across a company. At a minimum, the IT department, privacy office, security organization, and various business divisions should be involved, as each has an important stake in data management. Bringing in a variety of stakeholders early allows firms to establish key data objectives and a broader data governance vision. This collaboration can take the form of a dedicated task force or may involve regular reporting on data governance and privacy objectives to the executive board.
Data privacy, similarly, is also a shared responsibility. All employees have a part to play in maintaining data privacy by following accepted standards for data collection, use and sharing. Indeed, implementing a successful data governance model with privacy in mind requires educating employees on governance concepts, roles and responsibilities, as well as data privacy concepts and regulations (e.g. the definition of “personal information” vs. “consumer information”).
After establishing a governance vision and driving employee awareness, organizations can define their desired data governance roles – such as data owners, data stewards, data architects and data consumers – and tailor the roles to their needs. Some companies may distinguish between data stewards and data owners, for example, with the former responsible for executing daily data operations and the latter responsible for data policy definition. For one client with a large and complex IT department, Metis Strategy established a governance hierarchy with an executive-level board, combined data steward/owner roles, and other positions (e.g. data quality custodians). This structure facilitated ease of communication and enabled the client to scale its data management practices.
In the long term, firms should incorporate data governance and management skills into their talent strategy and workforce planning. Given the expertise required and the shortage of qualified people for some data-intensive roles, organizations can consider enlisting the help of talent-sourcing firms while focusing internal efforts on talent retention and upskilling. As companies’ strategic goals and regulatory requirements change, they should remain flexible in adjusting their data governance roles and ownership.
To respond adequately to consumer privacy-related requests for data, organizations should establish standardized procedures and policies across the data lifecycle. This will allow companies to understand what data they collect, use and share, and how those practices relate to consumers.
For example, the CCPA provides consumers with the right to opt out of having their personal information sold to third parties. If a retailer needed to comply with such a request, it would need to be able to answer questions in the following categories:
Establishing policies and standards for the above can help organizations quickly determine the actions needed to respond to customer requests under privacy regulations. Companies should communicate policies widely and ensure that they are being followed, as failing to do so can propagate the use of inconsistent templates and practices. At one Metis Strategy client, for example, few stakeholders had sufficient awareness of data management and access standards, despite the fact that the client’s IT department had established extensive policies around them.
To successfully implement data governance frameworks and ensure privacy compliance, firms may also need to address challenges posed by legacy infrastructure and technical debt. For example, data often is stored in silos throughout an organization, making it difficult to appropriately identify the source of any data privacy issues and promptly respond to consumers or regulatory authorities.
Firms also need to evaluate the security and privacy risks posed by outsourced cloud services, such as cloud-based data lakes. Those using multiple cloud providers may want to streamline their data sharing agreements to create consistency across vendors.
Some technologies can help companies leverage customer data while mitigating privacy risks. In a Metis Strategy interview, Greg Sullivan, CIO of Carnival Corporation, noted that data virtualization enhanced his organization’s analytics capabilities, drove down operational and computing costs and reduced the company’s exposure to potential security and privacy gaps.
Companies can also consider new privacy compliance technologies, which can enhance data governance through increased visibility and transparency. Data discovery tools use advanced analytics to identify data elements that could be deemed sensitive, for instance, while data flow mapping tools help companies understand how and where data moves both internally and externally. These tools can be used to help organizations determine the level of protection required for their most critical data elements and facilitate responses to consumer requests under GDPR and CCPA.
Although legacy technology overhauls can be time-consuming and costly, firms that are decisive about doing so can reduce their privacy and security risks while avoiding other challenges related to technical debt.
As the global data privacy landscape evolves, organizations should continuously adapt their data governance models. Companies should proactively address their obligations by designing data governance roles, processes, policies, and technology with privacy in mind, rather than reacting to current and forthcoming privacy legislation. Companies doing so can not only improve risk and reputational management, but also encourage greater transparency and data-driven decision-making across their organizations.
“Given a ten percent chance of a 100 times payoff, you should take that bet every time. But you’re still going to be wrong nine times out of 10.” –Jeff Bezos
Leading organizations like Amazon, Walmart, Uber, Netflix, Google X, Intuit and Instagram have all vigorously embraced the philosophy that rapid experimentation is the most efficient and effective path to meeting customer needs. In an interview with Metis Strategy’s Peter High, entrepreneur Peter Diamandis explains that the most nimble and innovative companies like Uber and Google X “are running over 1,000 experiments per year and are creating a culture that allows for rapid experimentation and constant failure and iteration.”
Traditional strategic planning taught us to study all the pieces on the chess board, develop a multi-year roadmap, and then launch carefully sculpted new products or services. Executives believed that there was only one chance to “get it right,” which often left organizations allowing perfect to be the enemy of the good.
However, in the digital era, decision velocity is more important than perfect planning.
Accelerating decision velocity through experimentation
The most successful organizations cede the hubris of believing they will always be able to perfectly predict customer or user demands, and instead let data—not opinions—guide decision making. The data that informs decision making is derived from a series of experiments that test a hypothesis against a small but representative sample of a broader population.
The experiment should examine three questions
And then lead to one of three conclusions:
Often, experiments fall into the second category, in which case organizations demonstrate enough viability to iterate on the idea to further hone and enhance the product-market fit. The key is to gain this insight early, and course-correct as necessary. It is easy to correct being two degrees off course every ten feet but being two degrees off course over a mile will cause you to miss your target considerably (+/-0.35 feet vs. +/- 184 feet).
One simple example is when Macy’s was evaluating the desire build a feature that would allow customers to search for a product based on a picture taken with their smartphone. Other competitors had developed something similar, but before Macy’s invested significant sums of money, the retailer wanted to know if the idea was viable.
To test the idea, Macy’s placed a “Visual Product Search” icon on its homepage and monitored the click-through behavior. While Macys.com did not yet have the capability to allow for visual search, tens of thousands of customers clicked through, and Macy’s was able to capture emails of those that wanted to be notified when the feature was ready.
This was enough to begin pursuing the idea further. Yasir Anwar, the former CTO at Macy’s, said teams are “given empowerment to go and test what is best for our customers, to go and run multiple experiments, to test with our customers, (and) come back with the results.”
To accelerate decision velocity, we recommend that all companies develop a framework to create a “Business Experimentation Lab” similar to the likes of Amazon and Walmart. This Business Experimentation Framework (BEF) should outline how people with the right mindset, enabled by technology (though sometimes technology is not necessary), can leverage iterative processes to make more well-informed, yet faster decisions. Doing so frees organizations from entrenched, bureaucratic practices and provides mechanisms for rapidly determining the best option for improving customer experiences out of a list of possibilities.
A Business Experimentation Framework is crucial to:
While nearly every department can introduce some flavor of experimentation into their operating model, a core component and example in eCommerce is A/B testing, or split testing. A/B testing is a way to compare two versions of a single variable, and determine which approach is more effective.
At a recent meetup at Walmart’s Bay Area office, eCommerce product and test managers discussed the investments, processes, and roles required to sustainably hold A/B testing velocity while ensuring the occurrence of clean, accurate, and controllable experiments. Walmart began its journey towards mass A/B testing with a top-down decree—“What we launch is what we test”—and now is able to run roughly 25 experiments at any given time—and Walmart has grown the number of tests each year from 70 in 2016 to 253 in 2017.
To enable A/B testing at this velocity and quality, Walmart developed a Test Proposal process that organizes A/B tests and provides metrics for test governance, so teams can quickly make decisions at the end of a test. A Test Proposal defines:
To facilitate the lasting adoption of a Business Experimentation Framework, organizations must staff critical roles like test managers, development engineers, and test analysts. Walmart, for instance, has created the following roles to enable the launch and analysis of 250 tests per year:
Institutionalizing a bias for experimentation is not easy. We have seen several barriers to adopting a Business Experimentation Framework, such as:
Typically, enthusiasm for experimentation gains momentum with one beachhead department. That department develops a test-approval process that is supported by the tools and data necessary to test, analyze, learn, and make accurate go/no-go decisions.
Here is a blueprint for introducing a test-first culture:
If done well, establishing a Business Experimentation Framework will allow organizations to figure out what matters to most customers, within a limited amount of time, for a limited cost, and with a risk-reward tradeoff that will ultimately play to their favor.
As Bezos said, “We all know that if you swing for the fences, you’re going to strike out a lot, but you’re also going to hit some home runs. The difference between baseball and business, however, is that baseball has a truncated outcome distribution. When you swing, no matter how well you connect with the ball, the most runs you can get is four. In business, every once in a while, when you step up to the plate, you can score 1,000 runs. This long-tailed distribution of returns is why it’s important to be bold. Big winners pay for so many experiments.”
1/25/18
The following article is an abridged version of the Metis Strategy whitepaper on business capabilities. Click here to view the full whitepaper.
By Chris Davis and Leila Chouai for CIO.com
If you’re not thinking like a software company, you’re already behind.
Software companies focus on codifying and then scaling everything they do. To do that, business subject-matter expertise and technical expertise must become one in the same, converging once siloed disciplines.
In a recent interview with Metis Strategy, Cathy Bessant, Bank of America’s Chief Operations & Technology Officer, explained that convergence must apply to all companies, saying, “Technology has completely changed the notion of business integration. You cannot say the business is technology or technology enables the business—they are one and the same.”
Your company will not be able to compete at scale and speed if delivery teams have not gone beyond typical IT-business hand offs to true convergence. This convergence extends beyond obvious points of technology dependence, such as an eCommerce website or managing internal productivity tools; it is happening everywhere.
Still, many companies struggle with where to start on this transformation. Business function leaders often communicate high-level goals that are difficult for technology leaders to translate into concrete actions, and technology leaders often approach a problem by addressing the technology first, and the business outcome second. They end up six months into a “digital transformation” effort with a disparate collection of projects, but no cohesive sense of prioritization or interdependence to create a more tech-driven future.
The solution to bridge this gap between strategy and execution is for IT leaders to be better collaborators and communicators, and to understand the business and customer needs as well as their business partners do. But that is easier said than done.
Start by rooting your IT plans in a well-defined business capabilities map, and then transform the way that IT goes to market by driving cross-functional operating model convergence in the long term.
Business capabilities are an integrated set of processes, technologies, and deep expertise that are manifested as a functional capacity to capture or deliver value to the organization. They outline “what” a business does, as opposed to “how” a business does it. They are the definition of your organizational skills, best represented in a landscape map that allows you to evaluate the full spectrum of capabilities against each other.
Business capability maps are not just about technology; these tools are designed to improve an organization’s holistic ability to improve a business outcome, and in many cases, it is not the technology that is the constraint, but rather a process, skill, or policy issue.
Consider the process for onboarding a new employee. Strong onboarding capabilities make the experience seamless for the new hire. From the second an employee steps into the office, they might:
If the desired outcome for this capability is to provide a seamless employee experience where the employee is productive in less than three days, the different functional areas should integrate their strategic plans to meet that objective. This is often challenging in an organization that thinks and acts in functional silos, but a capability-driven approach will bridge that gap.
Many organizations have never formally documented their business architecture and therefore struggle to understand business priorities. To bridge that gap, IT will generally dispatch enterprise architects or business relationship managers to form bonds with functional leaders, understand their current processes, and identify the pain points. As a result, they map the business capabilities. This exercise elevates technology leaders and their business partners to common ground, on which both can add value to the conversation: one around business process improvement, and the other around technology enablement.
We generally suggest no more than four levels of cascading capabilities, with the fourth level most resembling the associated process. Keep in mind that business capability maps are not organizational charts. By definition, they are anchored by the business outcome, with many functional areas converging to realize that outcome.
Once you define your capabilities, prioritize them to help provide strategic direction to the organization. Not all capabilities are of equal importance to your ability to compete, so you need to ensure you are not boiling the ocean. While there is more nuance in practice, for simplicity, capabilities fall on a scale of achieving competitive parity through sustaining competitive advantage, and it is important to evaluate which are the most important to your business’ success. This segmentation will not change tremendously year by year, unless there are major shifts in the competitive forces at play.
Once you segment and prioritize your capabilities, you should evaluate the current state maturity for each capability, as well as the target future state. Evaluating maturity levels is as much art as it is science. As a result, the defining of maturity levels cannot be done independently, and often the conversation around why something is or is not mature is as valuable as whatever score you give yourself.
We recommend undertaking this exercise with cross-functional groups that have an understanding of the capability from different perspectives. We often evaluate capability maturity as a function of process definition, degree of automation, organizational reach, and the measurement of the business outcome. This evaluation will influence the prioritization of near-term investments and will not always coincide 1:1 with the segmentation mentioned above. For example, if you have low maturity in a “parity” capability, you would still want to invest in that capability to get it up to par.
Enhancing a capability may require investments in people, processes, or technology. Therefore, a converged team of business function experts and technology leaders should jointly identify improvement activities. IT should lead in aligning the technology services (if your organization uses an ITSM approach) and technical architecture needed to enable these capabilities—but all in the context of how the business process may change. Once you have aligned your technical architecture, IT can identify gaps and redundancies. For example, if you have multiple applications supporting your “expense management” capability, you might opt to undertake a cost-benefit analysis of maintaining all of the applications. Conversely, you might discover you have a prioritized business capability of sales forecasting without a technology architecture supporting or enabling that capability. You might identify this an area where a new technology services is needed to provide data analytics to the sales operations team.
Once developed, capability maps can bridge the gap between strategy and execution by driving organizational alignment around where investments are needed.
For example, we recently helped a growing technology company through this journey. The IT organization had been viewed as an order-taker, and it often struggled to get budget consideration for more strategic projects that would add value to the business, but the CIO was intent on evolving the organization into a more strategic partner.
The CIO knew that the convergence of business process improvement and technology enablement was key, so the team worked closely with business function leaders to develop prioritized capability maps across the organization. Then they leveraged the capability maps to identify areas in greatest need of investment, and in turn forced trade-off decisions that resulted in a meaningful prioritization of focus areas that galvanized the team. The converged business and technology teams, oriented around shared business outcomes, had threaded the needle from strategy to execution.
In the end, one of the business partners said, “We have tried to do this many times over the past six years, and this is by far the best it has ever gone.” That is how IT goes to market differently, and wins.
