Category: Technology

Escaping the Day Two Trap: How a Robust Data Strategy Can Reignite Your Day One Mindset

In his 2016 letter to shareholders, Amazon founder and then-CEO Jeff Bezos introduced the concepts of Day One and Day Two. Day One is a metaphor that includes perpetual innovation, urgency, and a zealot-like customer focus, while Day Two is a metaphor for stasis, irrelevance, decline, and the eventual failure of an organization. Bezos argued that for an organization to stay vibrant, energetic, and relevant, it must embody a “Day One” mindset.

The Day Two mindset is dangerous for organizations because it symbolizes stasis, irrelevance, and ultimately, the death of an organization. It manifests in slow decision-making, bureaucratic processes, prioritizing internal politics over customer satisfaction, and reluctance to take risks and explore new opportunities. When an organization is stuck in Day Two, customers suffer the most.

Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. Jeff Bezos

The Blockbuster/Netflix saga has been used so many times to highlight the incumbent losing to the startup that it verges on becoming a cliche. It has mainly been used to support Clayton Christensen’s “The Innovator’s Dilemma” thesis, and I agree it does, but I also think it serves as a simple heuristic for the Day Two mindset. One of the reasons Blockbuster was unwilling to expand their streaming service was the dependency of their profits being linked to customer late fees. According to a 2010 NBC News article, “In 2000, Blockbuster collected nearly $800 million in late fees, accounting for 16 percent of its revenue.” By the time of the article’s posting, those numbers had dropped to $134 million. Slow decision-making, bureaucratic processes, prioritization of internal politics over customer satisfaction, and reluctance to take risks and explore new opportunities led Blockbuster first into stasis, then irrelevance, and ultimately, its death via bankruptcy.

Bureaucracy – Prompt by George Estrada, Image by MidJourney

So, how does an organization get out of Day Two if they are already there? An organization will need to create a mechanism that will allow it to generate enough escape velocity to propel itself back into a Day One mindset. One such mechanism is developing a leadership team (C-suite) with a robust data strategy.

A symptom of the Day Two mindset is siloed data. Where there is siloed data, there is a bureaucracy that justifies the need to keep it siloed, followed closely by internal politics that prioritize ownership over customers. See how it is all woven tightly together? If you are stuck in this Day Two mindset, you most certainly do not have a robust data strategy. While organizations can operate and function with siloed data, bureaucracy, and politics, with every day that passes, they risk not being able to anticipate, adjust, and remain relevant to their customers.

To escape the Day Two mindset, organizations must develop a robust data strategy. It has to be organized and led by the C-suite and complement and support the organization’s overall strategy and mission. A robust data strategy will be customer-centric, focused on innovating for the customer. It will improve the organization’s agility by providing its leaders and staff with the information they need to make quick decisions. It will take outside trends and help identify new opportunities while allowing for continuous improvements measured in efficiencies, increased productivity, and reduced costs, all while improving the organization’s posture on compliance requirements and risk to the business.

Data can be a real game-changer in helping drive change in an organization. It has the power to give you insights that make you rethink what you know, identify new opportunities, and guide you in developing strategies. By making data your starting point, you can:

  • Understand your customers’ needs
  • Improve operational efficiencies
  • Innovate new products and services
  • Increase speed to market

But here’s the catch: for your data strategy to be effective, you need your leadership team to be fully committed. Your C-suite needs to be the biggest cheerleaders for a data-driven culture. Just wishing or saying you want to be a data-driven organization doesn’t make you one. Leadership has to put the resources behind your data initiatives and ensure that everyone in the organization treats data as a strategic asset.

When it comes to building up your data capabilities, you must cover all your bases:

  • Governance and management of your data is imperative
  • Identifying the right infrastructure and technology platforms
  • Developing your staff’s data skills and fostering a data-driven mindset
  • Cultivating a culture that embraces data at every level

Remember, your data strategy isn’t a one-and-done thing. It’s a continuous process of improvement and iteration. You must regularly review and refine your strategy to ensure it’s always aligned with your changing business needs and new technological developments.

Finally, an effective data strategy requires collaboration and alignment across all the different functions and departments in your organization. You need cross-functional teams working together to ensure your data initiatives are in lockstep with your overall business objectives and that data is being shared and used effectively across the board.

For organizations that have ambled into a Day Two mindset, I am proposing that they use data as a catalyst for change by developing a robust data strategy that will provide insights and challenge existing assumptions.

Remember, a Day One mindset welcomes challenges to conventional thinking and established norms and is contemptuous of bureaucracy and anti-customer politics.

Previously posted on LinkedIn.

Posted on by George

Speed

Speed is not preordained. Speed is a choice. You’ve got to set up a culture that has urgency and wants to experiment. You can’t flip a switch and suddenly get speed — Andy Jassy

For organizations to stay resilient and competitive, they need to master two interconnected capabilities: speed and innovation. 

That’s the key insight from new research by MIT’s Center for Information Systems Research (CISR). They surveyed over 700 companies worldwide to understand what drives top performance. Their findings reveal that the highest growth and profitability come from companies excelling at both accelerating time-to-market and continually introducing new products and services.

Here are three major takeaways from the MIT CISR study:

1. Speed alone provides minimal boost — racing to market faster doesn’t improve results. The companies with real differentiation couple speed with ongoing innovation. The top performers launched more revenue-generating innovations while also getting to market faster than competitors.

2. Digital capabilities are foundational — leading companies leverage cloud platforms, APIs, automation, real-time data, and other digital technologies. These tools allow them to tap ecosystems, enable collaboration, quickly respond to changes, and rapidly deliver innovative offerings. 

3. Leadership and organization evolve — the top companies shifted from hierarchical control to empowered teams. Leadership focused on communicating strategic vision and coaching, not commanding. Shared dashboards provided transparency on value creation goals and guided data-driven decisions at all levels. Work was organized around outcomes rather than tasks.

The researchers identified four drivers (a framework) for these high-performing organizations:

1. Empowering leadership, while simplifying work

2. Adopting leading-edge technology

3. Using data as a guide

4. Nurturing an innovative ecosystem

The MIT CISR framework provides a blueprint for management practices and digital technologies that enable top performance. I recommend reading the complete research article. It offers an in-depth look at the approaches, leadership styles, and organizational models that allow some companies to continually adapt and lead their industries. And the highlighted case study on Mercedes-Benz is a model to be mimicked.

Note: Originally published as a LinkedIn article

Posted on by George

AI and Copyright Law: Finding the Right Balance 

The recent lawsuit filed by The New York Times against OpenAI and Microsoft over its artificial intelligence system has brought the issue of copyright infringement by AI into focus. As someone who has fielded questions on this topic before, I believe this is fundamentally more a matter of copyright law than technology. This lawsuit will probably draw clearer distinctions between the two domains. 

Personally, I have never been a proponent of expansive copyright laws that seem to benefit large media companies at the expense of consumers and up-and-coming artists (writers, filmmakers, visual artists). The notion that any inspiration derived from published works constitutes infringement is particularly problematic to me. As a society, we are constantly exposed to and influenced by information around us, often in ways we don’t even realize or remember. Should fleeting inspiration really be grounds for legal consequences years later?

“But the use of this technology also presents a possible existential crisis for the news industry, which has struggled to find ways to replace the revenue it once generated from its profitable print products. “

Washington Post: New York Times sues OpenAI, Microsoft for using articles to train AI – https://bit.ly/3RXsGuO

I do believe AI developers should pay licensing fees to access certain content, just as any other business would. However, news publishers face revenue challenges tied to numerous industry changes, not just technological advancements. Declining public trust in polarized news coverage deserves as much scrutiny as any technological disruption when analyzing the media’s struggles — for an in-depth assessment of such problems, I recommend James Bennet’s piece in the Economist 1843 Magazine, “When the New York Times Lost Its Way.” 

Additionally, while publishers demand recompense when AI systems summarize their content, the same publishers do not pay any form of royalties to the victims featured in their true crime reporting, which certainly drives traffic to their sites. If we consider copyright principles comprehensively, such inconsistencies stand out. 

Questions around derivative works also illustrate the complexities of this debate. If fiction authors build on themes and characters from myths and legends in the public domain, should their works also lose copyright protection? Inspiration has murky borders that our legal framework is ill-equipped to navigate. 

There is no doubt AI systems raise copyright concerns that technology companies must address responsibly. However, as we balance those interests, we should be wary of arguments that may limit access to information mainly for those unable to pay. The early promise of the internet as an open repository of human knowledge relies on getting this balance right. Legal outcomes here will shape much more than profits.

Note: originally published as a LinkedIn article

Posted on by George

What the Substance Cycle Looks Like

This is an exciting time to be a technologist, but even more so if you are a business leader, decision maker, or someone with big ideas for improving processes, member or customer experiences, or simply someone with an idea to improve society.  Technology is becoming accessible to nontechnical stakeholders in ways only one could dream of a few years ago.  

Last month Amazon Web Services (AWS) announced several new artificial intelligence (AI) products and capabilities that will help organizations operate more efficiently and effectively. If you are wondering how these emerging technologies could apply to your organization’s work.  I will briefly break down the key announcements and their potential nonprofit(and commercial) use cases in quick and simple terms/ideas.

1. Amazon Bedrock makes advanced AI models easily accessible. 

Bedrock offers pre-built foundational AI models from top providers like Anthropic and Meta. This means your developers don’t need deep AI expertise to tap into powerful natural language processing. Possible use cases include building a virtual assistant to answer donor questions or automating tasks like sorting and tagging incoming donations.

2. Amazon CodeWhisperer can suggest code tailored to your needs.

CodeWhisperer is like autocomplete for coding. The new customization feature allows it to make suggestions based on your organization’s own codebase and conventions. This will boost productivity for developers working on your nonprofit’s custom software and websites.

3. Amazon QuickSight Q — turn text into data visualizations.

QuickSight’s new natural language capabilities let analysts generate charts and calculations by describing what they want in plain English. Your data team could quickly visualize important metrics like program enrollment or fundraising progress through conversational commands.

4. Multiple model options for different needs.

With new models like Titan Embeddings and Llama 2, Bedrock offers variety to suit different use cases. This flexibility allows you to test and find the best AI approach for each task, from donor segmentation to grant application screening.

The above services will provide the building blocks and organizations will combine to create customized solutions – organizations of all sizes will benefit. These innovations and productivity boosts will unlock the potential to better achieve social impact missions.

As I noted above, this is a great time to be in technology.  

Note: originally published as a LinkedIn article

Posted on by George

Regulating Crypto

Photo by Ivan Babydov on Pexels.com

In a Wall Street Journal oped titled, Why Warren and Sanders Object to Crypto Rule, by Brian P. Brooks and Charles W. Calomiris, the authors, make a compelling argument for bringing the cryptocurrency sector into the supervised national banking system. Interestingly, while taking a jab at how risky the already regulated, supervised national banking system is. They theorize that the objection of staunch regulators like Senators Bernie Sanders and Elizabeth Warren to doing so is:

Here’s our theory: Crypto developers are trying to build a financial system where users have more control. In that system, credit is allocated by algorithms rather than loan officers, payments are settled instantly on blockchains rather than slowly inside the Federal Reserve, and customer funds are secured by cryptographic keys rather than by hackable debit-card PINs. A user-controlled financial system threatens the vision of a government-controlled system for which Sens. Warren and Sanders continue to advocate.

Why Warren and Sanders Object to Crypto Rules – WSJ: https://on.wsj.com/3CKD3uX

The authors go on to say that they agree with the current administration’s opinions on cryptocurrencies. And that the purpose of having a regulatory system is to “take risky financial activities for which there is high customer demand, and make them less risky.”

So, on the one hand, the system is not beyond risk, and regulations don’t eliminate, or for that matter, reduce risk — see 2008. And on the other hand, the system is there to help make these sectors “less risky.”

I read this as advocates for cryptocurrencies looking for legitimacy from the government. A legitimacy that would open the floodgates for major, “too big to fail” institutions to go wild with speculation and productizing for customers while hiding behind the safety net of the federal reserve.

For all the virtues and promises of decentralization, the cryptocurrency sector remains controlled by a very few. A few who will benefit immensely from the legitimacy regulation would lend it. The same few will have the funds and influence to use said regulation to lock out any potential newcomers or disruptors.

Perhaps banks and the federal reserve, in particular, may be taken out of the central role or, at a minimum, set to the sidelines. But all that data will need to run over something; those who control the network will control the economy.

Posted on by George

Hyperloop

This 11-minute video explains how the hyperloop would work

I’ve been curious about how the hyperloop would work and found this video. Overall it makes a lot of sense and would make the world much smaller and more accessible. It’s unfortunate that bureaucracy will kill this before it ever gets started.

The video is from 2018, and while I suspect the pandemic put a lot of the momentum on hold, there are too many established industries at risk of disruption with technology like the hyperloop. Couple that with unimaginative policy and lawmakers, and you end up with pretty graphics and promises with no deliverable.

You can find Elon Musk’s whitepaper, Hyperloop Alpha, here.

Concept drawing from Elon Musk’s Hyperloop Alpha whitepaper
Posted on by George

Machines Risen

Photo by Somchai Kongkamsri on Pexels.com

Washington Post article on the use of AI in weapons systems. Well written and timely. But I feel that the concerns come too late. It is unlikely that the US or any other power will walk away from using AI in their weapons. Given the proliferation of AI systems, anyone that does will be at a disadvantage.

In March, a panel of tech luminaries including former Google chief executive Eric Schmidt, then-chief of Web services, now chief executive of Amazon Andy Jassy and Microsoft chief scientist Eric Horvitz released a study on the impact of AI on national security. The 756-page final report, commissioned by Congress, argued that Washington should oppose a ban on autonomous weapons because it would be difficult to enforce, and could stop the United States from using weapons it already has in its arsenal.

Washington Post: The U.S. says humans will always be in control of AI weapons. But the age of autonomous war is already here — By  Gerrit De Vynck

The key will be how tightly the protocols lead from one stage or escalation to another. If AI systems can make decisions that escalate into using powerful weapons of mass destruction, including nuclear, then we are fucked. There has to be a man-in-the-middle approach that buffers how far and fast the AI systems can go. But it is safe to assume that battlefield engagements will have AI systems running point.

The frustrating aspect of this subject is that the speed that technology continues to move leaves very little time for society to review and sensibly argue the ethical implications. Now, anyone who reads science fiction knows that these topics have been covered in detail by writers for decades, but our leaders and society have dismissed these stories as fantasy. Now they have come to reality.

Posted on by George

Digital Transformation’s Lessons From COVID

Among the many lessons to be learned from the COVID crisis, the one that stands out the most is the American workforce’s resilience.

One primary concern expressed by technology leaders and many stakeholders around digital transformation has been the impact these changes would have on their users’ ability to perform their tasks while adapting to new technologies. This is especially true when users are being introduced to platforms like Salesforce, Dynamics, O365, Teams, Slack, and many more.

Context Is Everything

The digital transformation approach has always been slow and steady. But while prudent, this approach can take a 3-month project and devolve it into a 3-year ordeal with frustrated stakeholders, staff, and an overworked technology team. I’ve spoken to countless CxO’s for whom the term digital transformation is a four-letter word.

The last year has shown that a narrowly scoped outcome with senior leadership’s unquestioning commitment is critical for a timely and successful digital transformation. And while everyone involved in a digital transformation project will confirm that their project has identified outcomes and complete commitment from the C suite, most of those projects have lacked the contextualization that COVID has provided; swim, or you will certainly sink.

Lessons Learned

Organizations need to start treating the lessons learned process as critical to their long-term success. Too often, this necessary process is often outright discarded to get to more “critical” work, or it is greatly declawed by not having any executive sponsor driving for actionable data. Organizations that set lessons learned from COVID as a primary objective for their organizations stand to gain a competitive advantage over those that don’t take this process seriously.

Adoption = Success

Finally, we cannot forget about the backbone of any organization, its people. On-going training programs are critical, but to drive success in any digital transformation project will require having partners and vendors with mature customer success programs that will help drive user adoption of the new tools. So when selecting a platform or a managed service provider, it is imperative that along with the tools and services, you have a good understanding of how their customer success program works and exactly how it will deliver value to your organization. And a sign of a mature program is one that continuously helps users with adoption.

On-The-Fly

After months of uncertainty and dire news reports, the American workforce has managed to deliver and exceed its productivity in many areas. I believe this is because leadership across organizations raised expectations for their staffs and their staffs answered the calls.

In 2020, for many organizations, the digital transformation happened on-the-fly, and as a result, they will be better positioned for the future. But organizations need to optimize these transformations into long-term cultural values that will allow them to build on the lessons learned and prepare them for the next unforeseen challenge. 

COVID came out of nowhere, and its disruption will be felt for years to come, but our workforce, while shaken, was not deterred, and that is an outcome that should provide us all with optimism for our future.

Posted on by George

Sailing into the Storm

The Solarwinds compromise is generating more questions than answers with every iteration of coverage. How far and deep this goes is anyones guest. The guys at Security Now do a good summary of what was known (as of mid last week).

In the following clip from Bloomberg Technology’s episode from Friday December 18th, Microsoft President, Brad Smith, does a great job in articulating what the incoming Biden Administration’s priorities should be in light of the Solarwinds hack.

Posted on by George

Apollo 11 Deepfake

Trust is the foundation of any organization or society. In a time where news, opinions, and the public are delivered with a few clicks and without context. Deepfake technology poses a sinister threat to our way of life. The In Event of Moon Disaster project by MIT is engaging and sobering. The six-minute video is excellent! But its pho-thenticity is disturbing and a warning sign of things to come.

There are excellent resources on the site for an in-depth study of Deepfake technology. Governments will need to draft sensible policies around such techniques, and news organizations will need to develop a discipline around rushing to distribute videos without verifying their authenticity.

PREMIERE OF FULL FILM & COMPLETE SPEECH! In July 1969, much of the world celebrated the “giant leap for mankind” that the successful moon landing constituted. In 2020, nothing is quite so straightforward. In Event of Moon Disaster illustrates the possibilities of deepfake technologies by reimagining this seminal event. What if the Apollo 11 mission had gone wrong and the astronauts had not been able to return home? A contingency speech for this possibility was prepared, but never delivered by President Nixon – until now. The immersive project invites you into this alternative history and asks us all to consider how new technologies can bend, redirect and obfuscate the truth around us.

You can visit and access all the resources of this project by going to: https://moondisaster.org/

Posted on by George