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In this edition
Years ago, GitHub reinvented the category of source code management and turned that model on its head – not only making code easy to access and more social, but organizing projects around people, rather than code. Those seemingly small product features had huge implications for the way people and companies built technology and collaborated around all their work – both within and across organizations. People who were previously only users could become producers; organizations of all sizes could coordinate work in new ways; software became social again and supported significant community building around it.
That was the decade of code. Now we are moving into the decade of design, one where design, not just code, is at the center of product development and successful organizations. The interface no longer reflects the code; rather, the code reflects the design. In much the same way engineering and software development went from closed, siloed systems to more open, collaborative systems, design and designers are going through the same kind of revolution.
More people today – not only designers – need to be fluent in communicating around design. If products and companies would live or die by code before, they now live or die by their product design and design literacy… that’s why I’m calling it the decade of design. And it’s been a revolution a decade in the making.
We’ve seen a rise in the use of cloud data warehouses and, with it, the emergence of a new paradigm for handling data that changes how we architect data infrastructures.
Traditionally, data was extracted, transformed, then loaded – ETL, in short – into a data warehouse. For ETL, complex transformation pipelines were built at the data source. However, cloud data warehouses have finally made it cost-effective to store all of a company’s data in a central location: we no longer need to transform data before we load it into a data warehouse. Transformation can be done when running analytics in a data warehouse.
In short, ETL has become ELT – extract, load, transform. This matters because organizations that rely on data can get data much faster and more reliably without transformation. Historical data can not only be preserved in case the transformation formula is changed down the road (something that happens quite often!), but the transformations themselves can be far more sophisticated and draw on data from diverse sources because all of the data now sits in the same location.
While IT buyers are reducing their budgets, I’ve observed that government, nonprofit, telecom, and education have maintained or even increased their budgets. Companies are still investing in security software, web conferencing, collaboration tools, and remote desktop tools. Meanwhile, cloud initiatives have also changed focus, with many IT teams putting the “lift and shift” migration on hold and instead refactoring applications already in the cloud.
For startups, it’s important to not only focus on the customer experience for self-service and onboarding, but to understand your customer base (and their needs) beyond just segmenting them by vertical. The impact of the COVID-19 pandemic depends more on who a customer is and how essential they are than on what vertical they’re in.
The scope of the Chief Security Officer (CSO/CISO) role has expanded, moving from technical IT to the boardroom. The pandemic has also accelerated this – sometimes with little planning! – as more and more companies shift to remote work. As a result, some companies have actually broken out the CSO role across multiple functions. In this podcast (recorded last year at our annual innovation summit), we discuss how CSOs can prepare for and respond to a crisis; the responsibility cloud and SaaS vendors (not to mention the government) should have in security and data breaches; and how the role of the CSO will evolve in the next five years… or if it will even exist.
Meanwhile, at a macro level, governments, policymakers, and employers are all figuring out how to reopen the economy. Contact tracing – which includes identifying and warning contacts of exposure in order to stop chains of transmission (as defined by the CDC) – is a key strategy for preventing the further spread of a disease like COVID-19.
While approaches like Google’s “privacy-safe contact tracing” dominate the headlines, what are the actual security and privacy concerns around contact tracing? Technology is not the biggest part of this discussion; it’s also about rights, cultures, and values… and the questions around what happens when people are “transformed into cell phone signals.”