Category: Tech History

Tech Exec - leadership lessons from history Welcome to the Tech History category on tech2exec. History is a valuable teacher, and it has much to offer in terms of insights for tech executives. By looking back at past events, we can gain a better understanding of how certain decisions and actions have shaped the technological landscape we see today.

In this section, we will explore various historical events that have had a significant impact on the tech industry. We will delve into the stories behind these events and extract valuable lessons that can inform our present-day decisions as tech leaders.

Lessons for Leadership – 118th US Congress

The US Congress and tech industry may seem like polar opposites, but there are valuable lessons that can be learned by both parties. In particular, the misfunctioning of the Congress can provide important insights for a tech executive when it comes to collaboration and gaining consensus.

Partisan gridlock is a key issue in the 118th US Congress.

With a two-party system and divided ideologies, finding common ground can be challenging. This leads to legislative stalemates and a lack of progress. Similarly, in the tech industry, differing perspectives can hinder decision-making. Bridging these gaps is crucial for success.

In the Congress, special interest groups often influence lawmakers and impede progress on important issues. Similar challenges may arise in the tech industry, with stakeholders or investors prioritizing their own agendas, hindering collaboration and consensus. Effective communication and compromise are key to bridging gaps and achieving shared goals. This requires actively listening to diverse perspectives and finding ways to compromise.

Having a clear decision-making process helps prevent gridlock and promotes collaboration.

Like Congress passing legislation, tech companies should have structured processes for important decisions. This ensures all voices are heard and progress is made. Diversifying perspectives and promoting diversity within the team leads to effective decision-making. In Congress, representatives from different states and backgrounds bring diverse viewpoints to the table, good and bad. In the tech industry, a diverse team with individuals from different backgrounds brings fresh ideas and avoids groupthink.

Prioritizing effective communication, structured decision-making, and promoting diversity within teams helps tech companies overcome obstacles and achieve common goals. Tech executives must recognize the significance of these practices and implement them to drive progress and success. By incorporating these principles into their company culture, tech execs pave the way for a brighter future of technological advancement. Collaboration remains crucial for companies to stay competitive and have a positive impact on society as technology evolves rapidly.

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Building Consensus in a Politically Charged Climate

Yogi Berra the Leader

Last night, I watched a captivating documentary on the legendary Yogi Berra. Yogi was the esteemed catcher for the New York Yankees from 1946 to 1965. Throughout his remarkable career, he achieved an impressive record, being selected as an All-Star for 15 consecutive seasons, while also playing a pivotal role in 10 World Series victories.

“90% of the game is half mental.”
Yogi Berra

Despite his talent, Yogi faced criticism for his height and unconventional appearance, not fitting the typical “Yankee Look.” However, he became known for his famous “Yogi-isms” – witty phrases with paradoxical or nonsensical elements. Examples include “It ain’t over ’til it’s over,” “90% of the game is half mental,” and “When you come to a fork in the road, take it.” These sayings have permeated popular culture, injected humor and offering wisdom. Yogi Berra, a beloved figure, known for his fun-loving personality and ability to bring people together.

In 1964, Berra managed the Yankees, leading them to the World Series but falling short against the Cardinals. Yankee leadership didn’t respect Yogi and fired him. Despite doubts about his managerial readiness, the players enjoyed playing under him. Then, in 1969, he coached the New York Mets, a weak team. Against all odds, he led them to win the World Series, a remarkable feat hailed as a miracle.

In 1973, George Steinbrenner acquired the Yankees, a team that hadn’t won the World Series since Berra’s last tenure as manager. When Berra was appointed as the team’s manager once again in 1977, the Yankees were in last place. However, under Yogi’s leadership, the team experienced a remarkable turnaround, culminating in a triumphant World Series win. This achievement showcased Yogi’s exceptional abilities, proving his impact extended beyond his prowess as a player.

Despite facing ridicule, Yogi Berra consistently proved his value to any team. His achievements remind us not to judge based on appearances. Those overlooked often make the most impact. Yogi’s ability to connect with players and align their strengths made him a respected leader who inspired loyalty. Tech execs can learn from Yogi. Finding joy in the pursuit of excellence is crucial. In the fast-paced world of technology, lightheartedness and humor boost morale and foster a positive work environment. Yogi’s playful attitude and clever remarks remind us to savor the journey as much as the destination.

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Today’s AI Code Generators vs. Tools from the Past

I enjoy reflecting on past technology and how it has evolved. In the past, tech execs could leverage coding tools like Pacbase that assisted in generating code. This piques my interest in comparing and contrasting today’s AI code generators with tools from the past.

AI code generators differ from previous tools like Pacbase in their higher level of automation. Pacbase, although advanced at its time, heavily relied on human input and decision-making for code generation. In contrast, AI code generators utilize machine learning algorithms to analyze data and automatically produce efficient, task-specific code. This automation saves time and resources while improving the accuracy and reliability of the generated code.

Another difference lies in the scope of capabilities.

While tools like Pacbase primarily focused on generating standard code structures, AI code generators have the capacity to create intricate and innovative solutions that transcend traditional coding patterns. This allows developers to concentrate on more creative and high-level tasks, while leaving the monotonous and repetitive coding work to AI.

Furthermore, AI code generators continually learn from their own outputs and user feedback, constantly improving and adapting to new challenges. This provides a significant advantage over traditional tools that often become outdated and necessitate frequent updates or manual adjustments.

However, one similarity between AI code generators and past tools is the need for human oversight and intervention. While AI can greatly automate the coding process, it still relies on human programmers to provide initial input, establish parameters, and ensure that the generated code aligns with the intended goals.

In conclusion, AI code generators have revolutionized the coding landscape, greatly enhancing efficiency and precision in software development. Nonetheless, they still require collaboration and supervision from human developers to achieve optimal results.

Click here for a list of AI terms that tech leaders should know.

What is Infrastructure as Code (IaC)?

Career as a Software Programmer – the world is changing

It takes an Einstein

The definition of insanity is doing the same thing over and over again and expecting different results.
Albert Einstein

Failure to adapt hinders tech executives. Repeating tasks doesn’t signify correctness. Assess, adjust, and align your approach. Effective leaders recognize the need for change and embrace it.

“Change is the only constant” – a familiar saying that certainly holds true in tech. With rapid advancements, leaders must be adaptable. It’s more than staying up to date; it’s about being open-minded and willing to pivot strategies when needed.

To create an adaptable culture, leaders must encourage teams to embrace change and be flexible. This involves implementing continuous evaluation processes, welcoming new ideas, and fostering a growth mindset within the organization.

Being adaptable involves anticipating challenges and pivoting accordingly. Tech executives must be proactive, continuously assessing the market, industry trends, and competitor strategies to stay relevant and competitive. This means being open to change, even if it means deviating from their original plans.

One major factor driving the need for adaptability in tech executive roles is the ever-evolving landscape of technology. With new innovations and disruptions emerging constantly, executives must be able to quickly grasp and incorporate these changes into their business strategies. This requires a high level of agility and flexibility, as well as a willingness to take risks and try new approaches.

In addition to technological advancements, market conditions can also shift rapidly in the tech industry. Changes in consumer behavior, economic fluctuations, and political developments can all have significant impacts on companies operating within this sector. As such, tech executives must not only be adaptable in terms of technology, but also in their overall business strategy.

Being adaptable means tackling failures and setbacks. In the tech industry, not every decision leads to success. Leaders must view failures as learning opportunities, adjusting their approach to navigate the changing technology landscape. The key is to remain resilient and open-minded, willing to pivot and try new strategies. This mindset of adaptability is crucial for both personal and professional growth in the tech world.

In conclusion, adaptability is crucial for effective leadership in the tech industry. By fostering a flexible culture, encouraging continuous evaluation and improvement, and proactively anticipating challenges, leaders can position their company for success in an evolving market. So, embrace change, stay open-minded, and approach every situation with an adaptable mindset. Thrive in the world of technology!

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Value of Project Management – you need a true Project Manager

Legacy Mainframe Environment

Today, tech execs are concerned about the mainframe computer application code. Many companies have had mainframe computers since the 70s and 80s. Large companies, particularly in insurance and finance, built applications during that time that still run on mainframes now. These applications consist of mostly COBOL code, with millions of lines.

SIDE NOTE: COBOL is the oldest still used programming language, developed in 1959. The only other language even close in age is C, which was developed in the early 70’s.

Today’s mainframe computers have powerful processors and seamlessly run COBOL applications alongside Docker containers. Tech executives face challenges with complex COBOL, PL/1, and Assembler code, as well as managing decades of data in diverse environments like DB2, MySQL, and Oracle. We’ll discuss data in a future post.

Mainframe applications have long been vital for enterprise business processing. They were game-changers, and still handle key workloads effectively. However, the drive to convert or move these applications has been slow. Today, tech execs face fierce competition in aggressive markets. Outdated systems hinder companies from keeping up with innovative rivals. Cloud computing enables competitors to invest in new systems without hardware burdens. Consequently, older companies face disadvantages and must modernize their legacy application environment. The three reasons for this transformation are:

Agility: Companies need IT systems that can be updated for functional processing requirements in a timelier manner. Shorter development cycles are a must for organizations to keep pace.
Cost: The mainframe is the costliest computer available. In many organizations, it’s also difficult and time consuming to maintain. The complexity of the code and data environments makes keeping the systems up and running difficult. Modern cloud technologies offer a significant reduction in cost of ownership.
Risk: Knowledge of legacy environments is fading away as programmers who developed this code many years ago retire. Skills in COBOL, PL/1, CICS, etc. are becoming scarce, making managing the applications and responding to major incidences more challenging.

To remain competitive, organizations must tackle legacy mainframe systems. The transformation should uncover the current state and map out an ideal future state. Develop a value proposition with a total cost of ownership analysis for transitioning to the cloud. When it comes to maintaining the mainframe and harnessing the power of the cloud, it’s worth considering strategies from industry leaders like IBM. Take into account the costs of migration and retooling, but also weigh them against the benefits of ownership. Furthermore, take the time to explore the numerous advantages that cloud computing has to offer.

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TCO and the Hybrid Cloud Environment