Transformation in uncertain times: Tackling both the urgent and the important A sprint-based transformation approach can help organizations achieve full potential.
By Darius Bates, David Dorton, Seth Goldstrom, and Yasir Mirza – In ordinary times, successful leaders continually strive to master the balance between the urgent and the important, both in their organizations and their daily schedules. But today’s CEOs face unprecedented financial, health and safety, and operational challenges. For them, the problem isn’t balancing the urgent with the important. It’s that most everything is both urgent and important and, given the ongoing uncertainty about COVID-19 and its aftershocks, that’s not likely to change anytime soon.
To address these challenges in the present and in the next normal, some leaders will instinctively pick two or three top priorities. Then, on the assumption it’s better to focus an already-stressed organization on must-win battles, they will launch major efforts to realize such goals.
Choosing your priorities is a good idea, but that’s just the starting point. To sustainably transform an organization’s trajectory, leaders will need to efficiently implement improvements across the whole of the organization. Our research has shown that bold programs focused on a granular set of initiatives achieve more than limited efforts do: for example, our analysis of 100-plus transformations shows that 68 percent of their initiatives are worth $250,000 or less and that, on average, each initiative owner manages no more than two of thousands of initiatives. In our experience, the best-performing transformations focus on driving change by moving pebbles, not just boulders.
So how does a company tackle the urgent and the important while also delving into sufficient detail to achieve a step change in performance and value creation? In recent years, we’ve seen several organizations achieve these goals through a structured, sprint-based approach we refer to as “road-mapping.” more>
Posted in Business, Economy, Education, History, How to, Net, Technology
Tagged Business improvement, Internet, McKinsey, Skills, Technology
Breaking through data-architecture gridlock to scale AI
Large-scale data modernization and rapidly evolving data technologies can tie up AI transformations. Five steps give organizations a way to break through the gridlock.
By Sven Blumberg, Jorge Machado, Henning Soller, and Asin Tavakoli – For today’s data and technology leaders, the pressure is mounting to create a modern data architecture that fully fuels their company’s digital and artificial intelligence (AI) transformations. In just two months, digital adoption vaulted five years forward amid the COVID-19 crisis. Leading AI adopters (those that attribute 20 percent or more of their organizations’ earnings before interest and taxes to AI) are investing even more in AI in response to the pandemic and the ensuing acceleration of digital.
Despite the urgent call for modernization, we have seen few companies successfully making the foundational shifts necessary to drive innovation. For example, in banking, while 70 percent of financial institutions we surveyed have had a modern data-architecture road map for 18 to 24 months, almost half still have disparate data models. The majority have integrated less than 25 percent of their critical data in the target architecture. All of this can create data-quality issues, which add complexity and cost to AI development processes, and suppress the delivery of new capabilities.
Certainly, technology changes are not easy. But often, we find the culprit is not technical complexity; it’s process complexity. Traditional architecture design and evaluation approaches may paralyze progress as organizations overplan and overinvest in developing road-map designs and spend months on technology assessments and vendor comparisons that often go off the rails as stakeholders debate the right path in this rapidly evolving landscape. Once organizations have a plan and are ready to implement, their efforts are often stymied as teams struggle to bring these behemoth blueprints to life and put changes into production. Amid it all, business leaders wonder what value they’re getting from these efforts.
Data and technology leaders no longer need to start from scratch when designing a data architecture. The past few years have seen the emergence of a reference data architecture that provides the agility to meet today’s need for speed, flexibility, and innovation (Exhibit 1). It has been road-tested in hundreds of IT and data transformations across industries, and we have observed its ability to reduce costs for traditional AI use cases and enable faster time to market and better reusability of new AI initiatives. more>
Posted in Business, Economic development, Economy, Education, History, How to, Net, Technology
Tagged AI, Business improvement, Data architecture, Internet, McKinsey, Skills, Technology
Derisking digital and analytics transformations
While the benefits of digitization and advanced analytics are well documented, the risk challenges often remain hidden.
By Jim Boehm and Joy Smith – bank was in the midst of a digital transformation, and the early stages were going well. It had successfully transformed its development teams into agile squads, and leaders were thrilled with the resulting speed and productivity gains. But within weeks, leadership discovered that the software developers had been taking a process shortcut that left customer usernames and passwords vulnerable to being hacked. The transformation team fixed the issue, but then the bank experienced another kind of hack, which compromised the security of customer data. Some applications had been operating for weeks before errors were detected because no monitors were in place to identify security issues before deployment. This meant the bank did not know who might have had access to the sensitive customer data or how far and wide the data might have leaked. The problem was severe enough that it put the entire transformation at risk. The CEO threatened to end the initiative and return the teams to waterfall development if they couldn’t improve application development security.
This bank’s experience is not rare. Companies in all industries are launching digital and analytics transformations to digitize services and processes, increase efficiency via agile and automation, improve customer engagement, and capitalize on new analytical tools. Yet most of these transformations are undertaken without any formal way to capture and manage the associated risks. Many projects have minimal controls designed into the new processes, underdeveloped change plans (or none at all), and often scant design input from security, privacy, and risk and legal teams. As a result, companies are creating hidden nonfinancial risks in cybersecurity, technical debt, advanced analytics, and operational resilience, among other areas. The COVID-19 pandemic and the measures employed to control it have only exacerbated the problem, forcing organizations to innovate on the fly to meet work-from-home and other digital requirements.
McKinsey recently surveyed 100 digital and analytics transformation leaders from companies across industries and around the globe to better understand the scope of the issue. While the benefits of digitization and advanced analytics are well documented, the risk challenges often remain hidden. From our survey and subsequent interviews, several key findings emerged:
- Digital and analytics transformations are widely undertaken now by organizations in all sectors.
- Risk management has not kept pace with the proliferation of digital and analytics transformations—a gap is opening that can only be closed by risk innovation at scale.
Posted in Business, Economic development, Economy, Education, History, How to, Science, Technology
Tagged analytics, Business improvement, Digital transformation, Internet, McKinsey, Skills