Today's Tech Digest - Jul 21, 2020

Why You Should Consider A Career In Data Science.

Another thing that makes data science so popular is that it accepts people of all sorts, regardless of their background and domain. People in literally any industry can move into data science and still do amazing work in their industry with the help of data science. People from the banking and finance industry, food and health sector, arts, climate science, engineering, and physics can all couple their domain knowledge and expertise with data science and make ground-breaking progress. You do not necessarily need to have a BSc or MSc in computer science or engineering in order to start a career in data science but rather couple data science with whatever career you currently have, find a problem you can solve with the combination of both and do something. Data science in combination with Artificial Intelligence, Machine Learning, Robotics, and the Internet of Things has the power to literally automate anything in order to make lives easy. Automation of tasks can also bring huge progress to companies since work can now be done faster. Also, when work is done by humans, there is a natural tendency to be inconsistent and make human-related errors. Automating tasks handles these problems and gives us better results in a shorter time.

Top CEOs Agree That Culture Is The Key Before, During And After Crisis

“The One Carnegie approach, starting with myself and our executive team, really means coming together around common values. It doesn’t matter the country, language, race or religion, we wanted people to come together around our strong values. Just like Dale Carnegie would say, ‘Try honestly to see things from the other person’s point of view.’ “The essence is that we want to value each person as an individual and as part of an organization. People value transparency and openness. When the pandemic hit, our One Carnegie foundation helped us tremendously. From a cultural standpoint, there was transparency, and we communicated very clearly what was happening and how we were responding to it. We shifted our entire global in-person training business to live online trainer delivery, and this could not have happened without a culture of working together and moving fast. Our strong culture created alignment in all 86 countries. People felt safe asking questions and working together. The results we are seeing are extraordinary.” CEO Gary Terrinoni of Brooklyn Hospital, founded in 1839 and cited as the number one safety net hospital in America, shared, “We had to move people around to be able to support the issues that we had with COVID-19, and people just stepped up. 

What to look for when modernizing the Data Lake

Whether a company is born into the digital world or has a more traditional business, they must invest and excel in tech advances such as mobility, cloud computing, and most importantly, advancedanalytics and data science. Doing so will equip them with the right tools to innovate their existing operations and deliver a seamless experience to customers. However, it isn’t that easy to achieve this goal. To realize the benefits of advances in technologies, organizations must leverage all their data. This requires modernizing their data architectures. In other words, organizations must unlock andmigratetheir data from multiple, heterogeneous systems including legacy mainframe systems and enterprise applications, and quickly process and refine it for consumption in AI and ML initiatives. Modern, cloud-based data lakes provide enterprises the agility and flexibility they need to store and process massive volumes of diverse data. Things to keep in mind when architecting a modern data lake. Data architectures are constantly evolving. Companies are adding new sources of data, offloading data to new target systems for processing and refining, and adding new analytical tools and solutions to their technology infrastructure.

If software architects' soft skills fail, so does the business

The history of software development contains rich lessons, both good and bad. We assume that current capabilities (like elastic scale) just appeared one day because of some clever developer, but those ideas were often born of hard lessons. Pets.com represents an early example of hard lessons learned. Pets.com appeared in the early days of the internet, hoping to become the Amazon.com of pet supplies. Fortunately, they had a brilliant marketing department, which invented a compelling mascot: a sock puppet with a microphone that said irreverent things. The mascot became a superstar, appearing in public at parades and national sporting events. Unfortunately, management at Pets.com apparently spent all the money on the mascot, not on infrastructure. Once orders started pouring in, they weren't prepared. The website was slow, transactions were lost, deliveries delayed, and so on … pretty much the worst-case scenario. So bad, in fact, that the business closed shortly after its disastrous Christmas rush, selling the only remaining valuable asset (the mascot) to a competitor. What the company needed was elastic scale: the ability to spin up more instances of resources, as needed. 

Successful innovation doesn’t have to be disruptive—it’s often small, incremental, and fast

The tension between breakthrough and incremental approaches can be found in most settings, not just online businesses. For example, medicine has had a long tradition of searching for interventions that have transformative outcomes on patients. But perhaps, as surgeon and researcher Atul Gawande argues, success “is not about episodic, momentary victories, though they do play a role. It is about the longer view of incremental steps that produce sustained progress.” That, Gawande continues, “is what making a di?erence really looks like. In fact, it is what making a di?erence looks like in a range of endeavors.” One endeavor, manufacturing, has known and practiced this approach for decades. In Toyota’s renowned production system, for example, real-time experiments by its factory workers to eradicate problems are an integral part of its continuous improvement system. Even there, people are expected to form clearly articulated, testable hypotheses and explain their logic for each attempted improvement. Of course, breakthrough and disruptive innovation will continue to play an important role in driving growth, as there are limits to incremental approaches.

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