Ask enterprises how they feel about their data warehouses, and a high percentage express dissatisfaction. They struggle to load data. They have unstructured data but the data warehouse can’t handle it, etc. These aren’t necessarily problems with the data warehouse, however. I’d hazard a guess that usually, the dissatisfaction arises from trying to force the data warehouse (or analytical database if you prefer) to do something for which it’s not well suited. Here’s one way the error starts, according to Sammer: By now, everyone has seen the rETL (reverse ETL) trend: You want to use data from app #1 (say, Salesforce) to enrich data in app #2 (Marketo, for example). Because most shops are already sending data from app #1 to the data warehouse with an ELT tool like Fivetran, many people took what they think was a shortcut, doing the transformation in the data warehouse and then using an rETL tool to move the data out of the warehouse and into app #2. The high-priced data warehouses and data lakes, ELT, and rETL companies were happy to help users deploy what seemed like a pragmatic way to bring applications together, even at serious cost and complexity.
Protecting privacy while allowing the economy to flourish is a data challenge. AI, machine learning, and neural networks have already transformed our lives, from robots to self-driving cars to drug development to a generation of smart assistants that will never double book you. There is no doubt that AI can power solutions and platforms that protect privacy while giving people the digital experiences they want and allowing businesses to profit. What are those experiences? It’s simple and intuitive to every Internet user. We want to be recognized only when it makes our lives easier. That means recognizing me so I don’t have to go through the painful process of re-entering my data. It means giving me information — and yes, serving me an ad — that is timely, relevant, and aligns with my needs. The opportunities within the “personalization economy,” as I call it, are vast. McKinsey published two white papers about the size of the opportunity and how to do it right. Interestingly — and tellingly — the word “privacy” isn’t mentioned a single time in either of those white papers. That oversight is remarkable and overlooks the tension between privacy and personalization.
Cybersecurity is not a service or product; it is prudent to show how protecting an organisation from losses is the only way for any financial benefit to be gained. Try to communicate to the board in numbers, for example, show that a £1 investment would stop a security event that could potentially cost £10 to the company. That way, it should be possible to get the board to vote on your side by demonstrating the business case and return on investment in security measures and protection. In order for the board to determine their investment decision in security, you should give them data that focuses on any threat vectors that are already evident, such as inadequate services for security awareness and employee training, processes and policies that are not adequately applied and recorded or a lack of data backup practices and patching updates. Formulating a risk/reward equation using a tiered security approach is a good way forward, as you can then direct investments towards incident response and detecting compliance. Once you have created a robust and compelling business case for your organisation, you need to share the proposal with the board.
Data projects are doomed when the people who plan and the people who execute don’t have the same tools, the same access, or even the same goals. Data scientists are really good at asking the right questions and running exploratory models, but they don’t know how to scale. Meanwhile, data engineers are experts at making data pipelines that scale, but they don’t know how to find the insights. We’ve been using tools that require such a high level of specialist expertise that it’s impossible to get everyone on the same page. Because data scientists only ever touch small subsets of the data, there’s no way for them to extrapolate their models to function at scale. They don’t have access to production-grade data technology, so they have no way of understanding the constraints of building complex pipelines. Meanwhile, data engineers are being handed algorithms to implement with the barest context of the business problem they’re trying to solve and with little understanding of how and why data scientists have settled on this solution. There may be some back and forth, but there’s rarely enough common ground to build a foundation.
Often people assume that Scrum is just a work management approach that helps us increase efficiency by organizing our tasks. Instead, it is intended to enable people to work in focused, collaborative, autonomous teams that use empiricism, creativity, and innovation to pursue opportunities to deliver value to customers by solving complex problems. To be creative in solving challenging problems, the Scrum Team must feel safe enough to experiment, fail, and learn through empiricism. They need to view each backlog item, interaction, and piece of data as an opportunity to learn and optimize. If these things are not possible, the team will not thrive. How do we, as Scrum Masters, build an environment where this is possible? To help groups of people form into high-functioning teams, they need ownership, inspiring purpose, and self-accountability. These traits inspire curiosity and will encourage them to take responsibility for their own work, how they work as a team, and how they work with those outside of the team. How do we, as Scrum Masters, build an environment where this is possible?
The Church of Agile is being corrupted from within by institutional forces that [can’t] adapt to the radical humanity [of] collaborative, self-organizing, cross-functional teams. … Agile wasn’t supposed to be this way. … Agile is supposed to be centered on people, not processes. … But many businesses instead prioritize controlling their commodity human resources. … Companies have dressed it up in Scrum’s clothing, claiming Agile ideology while reasserting Waterfall’s hierarchical micromanagement. … Properly implemented Scrum or Kanban [should] lead to the desired outcome within finite time and budget. … Stories as mini-Waterfalls [treat] the engineer as a cog in their employer’s machine … with no understanding of the craft, creativity, and critical thinking required to solve such complex problems. … Scrumfall relies, in other words, on the product team … providing a complete and perfect specification before development begins. And it relies on the development team … planning out a complete and perfect implementation before a single line of code is written. … The invading Waterfall taskmasters hidden in Scrum’s Trojan Horse absolutely hate uncertainty.
Ecstasy’s emphasis on predictability is perhaps best illustrated via the type system, known as the Turtles Type System, because it is bootstrapped on itself. As in Smalltalk, everything in Ecstasy is an object, and all Ecstasy types are built out of other Ecstasy types. In other words, unlike in Java or C#, there is no secondary primitive type system and chars, ints, bits, and booleans are all objects. In common with Java and C# there is a single root called Object — although, In Ecstasy, Object is an interface, not a class. Technically the type system supports a long and rather intimidating-looking list of features. It is fully generic and fully reified, covariant, module-based, transitively closed, type-checked and type-safe. The majority of type safety checks are performed by the compiler and re-checked by the link-time verifier, with only those checks in which the types cannot be fully known beforehand performed at runtime — specifically to allow support for type variance. “The Ecstasy language rules automatically handle covariance and contravariance,” Purdy wrote in an email response to The New Stack.
Threat intelligence is a key component to developing an offensive mindset. That’s why proactive cybersecurity auditing can be one of the best courses of action in stopping cyberattacks before they can impact an organization. To implement the right changes to cybersecurity strategy, an organization needs to understand fully existing network vulnerabilities. This can be accomplished through a few different tactics, including penetration testing and vulnerability scanning. Penetration testing involves a person purposefully hacking into a network to identify weaknesses to an organization’s system, while vulnerability scanning consists of an automated test that looks for potential security vulnerabilities. Both tactics enable organizations to better grasp the mind of a hacker and understand the “how” behind a potential attack. Something else to be considered – under the right circumstances – is the possibility of hiring a former hacker. Their insight could prove to be extremely helpful, as aptitude in identifying weaknesses can be a useful asset. Many former hackers find roles as a penetration tester / red team member fulfills their desire to expose system flaws while doing so legally, for the betterment of security.
The past few years have made this worse. At many companies, the entire staff quickly became remote, and the days of team lunches, onsite gyms, happy hours, and chats in the hallway disappeared. Suddenly, that company culture ceased to exist. Even as some people returned to the office many weeks or months later, many others did not. As companies institute remote or hybrid working environments on a permanent basis, there are fewer opportunities to build relationships with colleagues in person. The loss of work friendships is likely one reason so many people are choosing to leave their jobs, as CNBC reported. And among those who stay, success and creativity take a hit. In a recent study, Yasin Rofcanin, a professor of management at the University of Bath in the UK, and a group of colleagues found that friendship between coworkers is the most crucial element for enhancing employee performance. The isolation takes perhaps the biggest toll on mental and emotional health. Feelings of isolation are deeply intertwined with stress and anxiety. Without other people to lean on, it can be much more difficult for colleagues to find the resiliency they need to face each workday.
Cautious users are willing to comply with new protocol changes, but just need some time to fully adjust. They may need more gentle encouragement than the typical user, as they take more of a “wait-and-see” approach to new cyber security changes. This may be due to fear that any changes could disrupt their workflow. This can pose a serious risk as vulnerabilities are more exposed during major changes to security. ... Traditionalist users are generally hostile to change and often do not trust IT help desks, thinking that the processes for asking for help are too time consuming. Because they do not engage with understanding how these new changes will directly impact their everyday workloads, some may either wait until the last minute before integrating the new security changes, or resist altogether. ... Like traditionalists, overachievers may ignore cyber training sessions, emails from IT, or avoid learning new authentication processes – seeing these as below their skill level. However, this group of users is often overlooked when an assessment is performed, as through their own experiences, they may feel that the resources within the organisation are not adequate.
Quote for the day:
"Increasingly, management's role is not to organize work, but to direct passion and purpose." -- Greg Satell