How to build a data architecture to drive innovation—today and tomorrow
Yesterday’s data architecture can’t meet today’s need for speed, flexibility, and innovation. The key to a successful upgrade—and significant potential rewards—is agility.
By Antonio Castro, Jorge Machado, Matthias Roggendorf, and Henning Soller – Over the past several years, organizations have had to move quickly to deploy new data technologies alongside legacy infrastructure to drive market-driven innovations such as personalized offers, real-time alerts, and predictive maintenance.
However, these technical additions—from data lakes to customer analytics platforms to stream processing—have increased the complexity of data architectures enormously, often significantly hampering an organization’s ongoing ability to deliver new capabilities, maintain existing infrastructures, and ensure the integrity of artificial intelligence (AI) models.
Current market dynamics don’t allow for such slowdowns. Leaders such as Amazon and Google have been making use of technological innovations in AI to upend traditional business models, requiring laggards to reimagine aspects of their own business to keep up. Cloud providers have launched cutting-edge offerings, such as serverless data platforms that can be deployed instantly, enabling adopters to enjoy a faster time to market and greater agility. Analytics users are demanding more seamless tools, such as automated model-deployment platforms, so they can more quickly make use of new models. Many organizations have adopted application programming interfaces (APIs) to expose data from disparate systems to their data lakes and rapidly integrate insights directly into front-end applications. Now, as companies navigate the unprecedented humanitarian crisis caused by the COVID-19 pandemic and prepare for the next normal, the need for flexibility and speed has only amplified.
For companies to build a competitive edge—or even to maintain parity, they will need a new approach to defining, implementing, and integrating their data stacks, leveraging both cloud (beyond infrastructure as a service) and new concepts and components. more>
- How chief data officers can navigate the COVID-19 response and beyond, Kevin Buehler, Holger Harreis, Jorge Machado, Satyajit Parekh, Kayvaun Rowshankish, Asin Tavakoli, and Allen Weinberg
- Designing data governance that delivers value, Bryan Petzold, Matthias Roggendorf, Kayvaun Rowshankish, and Christoph Sporleder
Posted in Business, Economic development, Economy, Education, History, How to, Net, Technology
Tagged Data architecture, Digital transformation, Innovation, Internet, McKinsey, Productivity, Skills
An operating model for the next normal: Lessons from agile organizations in the crisis
Companies with agile practices embedded in their operating models have managed the impact of the COVID-19 crisis better than their peers. Here’s what helped them cope.
By Christopher Handscomb, Deepak Mahadevan, Lars Schor, Marcus Sieberer and Suraj Srinivasan – For many companies, the first, most visible effects of the COVID-19 pandemic quickly created a challenge to their operating and business models. Everything came into question, from how and where employees worked to how they engaged with customers to which products were most competitive and which could be quickly adapted. To cope, many turned to practices commonly associated with agile teams in the hope of adapting more quickly to changing business priorities.
Agile organizations are designed to be fast, resilient, and adaptable. In theory, organizations using agile practices should be perfectly suited to respond to shocks such as the COVID-19 pandemic. Understanding the experiences of agile—or partially agile—companies during the crisis provides insights around which elements of their operating models proved most useful in practice. Through our research, one characteristic stood out for companies that outperformed their peers: companies that ranked higher on managing the impact of the COVID-19 crisis were also those with agile practices more deeply embedded in their enterprise operating models. That is, they were mature agile organizations that had implemented the most extensive changes to enterprise-wide processes before the pandemic.
That suggests implications for less agile companies as economies reopen. Should they set aside the agile practices they adopted during the pandemic and return to their traditional operating models? Or should they double down on agile practices to embrace the more fundamental team- and enterprise-level processes that helped successful agile companies navigate the downturn?
We analyzed 25 companies across seven sectors that have undergone or are currently undergoing an agile transformation. According to their self-assessments, almost all of their agile business units responded better than their nonagile units to the shocks associated with the COVID-19 pandemic by measures of customer satisfaction, employee engagement, or operational performance.
Executives emphasized that the agile teams have continued their work almost seamlessly after the shock, without substantial setbacks in productivity. In contrast, many nonagile teams struggled to transition, reprioritize their work, and be productive in the new remote setup. The alignment between agile teams’ backlogs and their business priorities allowed them to shift focus quickly. more>
Posted in Business, Economic development, Economy, Education, Healthcare, History, How to, Net, Technology
Tagged Agile management, Business improvement, Health, McKinsey, Pandemic, Productivity, Skills
Redefine the Line: How automotive trends are changing the ways we move from point A to B
By Tarun Tejpal – The automotive industry has been one of the most dynamic and exciting incubators of technological and product innovation in the modern world. A unique mix of investment, consumer interest, and industry competition has driven this dynamism with a constant search for the next feature, style, or capability to capture the public imagination. At the 1964 New York World’s Fair, General Motors (GM) hoped to capture such interest with the Firebird IV concept car. GM explained, then, that the Firebird IV “anticipates the day when the family will drive to the super-highway, turn over the car’s controls to an automatic, programmed guidance system and travel in comfort and absolute safety at more than twice the speed possible on today’s expressways.” (Gao, Hensley, & Zielke, 2014).
GM’s vision of the future was striking and exciting, but the technology did not yet exist to make it a reality. Ford took a different approach to generating buzz in the market, focusing on the present. Instead of forecasting a future of self-driving cars and super highways, Ford launched a car for “young America out to have a good time”: the Mustang (Gao et al., 2014). It engaged the new generation by providing both transportation and personal expression in a stylish, highly configurable, and inexpensive package. Ford estimated it would sell 100,000 Mustangs, but one year after the launch it had sold over 400,000 (Gao et al., 2014).
Vehicles are now a central feature of everyday life. Since 1964, global vehicle sales have grown by nearly 3 percent on average each year, nearly double the rate of population growth, resulting in one billion vehicles on the road today (Gao et al., 2014).
However, large-scale trends, such as a surging Chinese automotive market, electrification, and urbanization, are beginning to affect the form and function of vehicles and personal mobility systems. more>
Posted in Business, Economic development, Economy, Education, History, How to, Nature, Net, Science, Technology
Tagged Auto industry, Business improvement, Internet, PLM, Productivity, Program Lifecycle Management, Siemens, Skills
How to restart your stalled digital transformation
Most digital initiatives sputter before they take full effect. A new survey finds that organizations stand a good chance of recovering lost momentum because slowdowns typically happen for reasons within their control.
McKinsey – At organizations pursuing digital transformations, more than seven in ten survey respondents say the progress of these efforts has slowed or stalled at some point. In the latest McKinsey Global Survey on the topic, we set out to understand what organizations can do to prevent burnout or to restart their engines if burnout occurs during these transformations, which previous research has found have a lower success rate than do more traditional transformations. The good news is that in most cases, organizations can prevent or overcome a loss of momentum.
More than 60 percent of respondents who report stalled digital transformations attribute the problem to factors that—with the right discipline and focus—organizations can control in the near term to medium term. This finding runs counter to widespread assumptions that external pressures, such as market disruptions or regulatory changes, pose the biggest threats to digital initiatives. More commonly reported sources of derailed progress include resourcing issues, lack of clarity or alignment on a company’s digital strategy, and poor quality of the digital strategy to begin with.
If a digital transformation stalls, the results suggest that organizations can regain momentum by implementing rigorous change-management and internal-communications programs and clarifying the transformation’s projected impact, which can help build alignment and commitment. For scaling digital programs beyond the pilot phase—the first stumbling block in a transformation’s execution—clarity on the time frame and expected economic impact is important, as is partnering with operations. Should an intervention be needed to reenergize a transformation, having the CEO step in appears to be advantageous. Further lessons come from respondents at companies that avoid stalling in the first place. They often say their organizations maintain momentum by obtaining strong alignment and strategic clarity before a transformation gets under way. more>
Posted in Business, Economic development, Economy, Education, How to, Net, Science, Technology
Tagged Business improvement, Capital, Digital transformation, McKinsey, Productivity, Skills