Fulcra

→ something that we advise our clients on all the time, and is a major portion that I think takes people by surprise sometimes, is that most organizations is that their default is to treat their data science projects like software engineering projects that they’re currently running at the organization Nov 22, 2018 highlights & Science something that we advise our clients on all the time, and is a major portion that I think takes people by surprise sometimes, is that most organizations is that their default is to treat their data science projects like software engineering projects that they’re currently running at the organization. So if they want their data scientists to be filling out Jira tickets and have Sprints. Not only the data scientists, but data engineering is not a similar task like that either. And the platform architecture too, is similar. They all share something in common. in data science, data engineering, and platform architecture, it’s one of those things where you can spend forever on something and it won’t be done. So, it’s all about, “When do I feel like stopping?” Or, “When do I run out of money?” Rather than, “Okay, this application is done. I’ll ship it, it’s in a box. It’s all good to go. We release it to the world and we sell it. It’s great.” On the data science side it’s hard to tell how long something’s going to take until you do it. So there’s this chicken and egg problem. I can’t write the Jira ticket it’s going to take two weeks, until I actually spend the two weeks to do it, and realize it’s actually going to take four weeks. And so when you try to apply these traditional software engineering project management things on these projects it doesn’t work. It actually causes harm in a lot of cases….there’s actually a new discipline that needs to arise. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ I’ve actually heard a project manager say, “You know, any line of code that my developers write to audit what they’re doing, to put stuff in a database, is a line of code that they’re not putting in developing the application Nov 21, 2018 highlights & data & Science I’ve actually heard a project manager say, “You know, any line of code that my developers write to audit what they’re doing, to put stuff in a database, is a line of code that they’re not putting in developing the application.” And so they frequently encourage a huge technical debt as they’ve got this great application now, but when it comes time for phase two of the project, to do something interesting with the data that this application should have stored somewhere but didn’t, we’re kind of left holding the bag because the application developers were kind of short sighted. And to my mind this is the kind of short term thinking that hinders really good data science. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ [it] is a symptom of really bad project management Nov 21, 2018 highlights & Science [it] is a symptom of really bad project management. It seems to me that the way to solve this problem is to have everybody in the room when the project is being designed … It’s sort of like life insurance. You know, you don’t really need it until you need it, but you’ve got to keep having it, even when you don’t need it. The projects that I’ve seen that have been most successful are the projects in which the data scientists, the data engineers, and… the application developers are all there in the room from the beginning, with the customer talking about what the problem is they want to solve, what a minimal product is, what the final solution should be, what the users expect out of this. And if you start from that place you’re much more likely to get empathy. …That’s the first thing. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ I don’t think that data scientists and data engineers at most organizations that I’m working with have figured out how to communicate with anybody Nov 21, 2018 highlights & Design & Science I don’t think that data scientists and data engineers at most organizations that I’m working with have figured out how to communicate with anybody. So, not even with each other, but how does a data scientist and a data engineer fit into, a modern one, that’s building some new systems, how are they interacting with different lines of business? How are they interacting with marketing, sales? How are they interacting with product design? ….even this at a fundamental level, there’s major problems in the industry. And how they’re interacting with each other? — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ in our consulting engagements, and also two other data science consulting companies that I know and work with, if we have a pure play data science project, meaning that the data engineering’s not in scope, the customer said that they were going to take care of it, we won’t start work until we have proof that the data’s been loaded Nov 20, 2018 highlights & Science in our consulting engagements, and also two other data science consulting companies that I know and work with, if we have a pure play data science project, meaning that the data engineering’s not in scope, the customer said that they were going to take care of it, we won’t start work until we have proof that the data’s been loaded. We’ve been burned so many times by them saying like, “Oh, you know what? You guys can start on Monday. We’ll get the data loaded sometimes next week.” We’re not even going to start until that data’s there….that’s the other issue too with the data engineer. I actually ran into this issue….on the younger side of the data engineers, one of the issues that we run into is that they don’t have the seniority to stand up to some ancient Oracle DBA that’s not willing to play nice. …it’s a really hard role to fill because, you’re right,… the interpersonal skills, and the political navigation skills are really important for the data engineer. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ I’ve never heard of anybody having a data engineering undergrad class, but you’re starting to hear data science classes pop up Nov 20, 2018 highlights & Science I’ve never heard of anybody having a data engineering undergrad class, but you’re starting to hear data science classes pop up. … I have some ideas about why that is, but I think where we’re at right now is data science is a pretty fairly well defined career path and profession. People generally know what that means.…there’s a lot of impact from hype still that’s starting to wear down a little bit. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ There is a seemingly myriad of terms to describe people who interact with models Nov 19, 2018 highlights & Learning & Science There is a seemingly myriad of terms to describe people who interact with models. Just a few terms that are currently in usage include researchers, data scientists, machine learning researchers, machine learning engineers, data engineers, infrastructure engineers, DataOps, DevOps, etc. Both Miner and Presser commented upon and agreed that before any assignment of any term, the work itself existed previously. Presser defines data engineering as embodying the skills to obtain data, build data stores, manage data flows including ETL, and provide the data to data scientists for analysis. Presser also indicated that data engineers at large enterprise organizations also have to be well versed in “cajoling” data from departments that may not, at first glance, provide it. Miner agreed and indicated that there is more thought leadership around the definition of data science versus data engineering which contributes to the ambiguity within the market. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ Over the past five years, we have heard many stories from data science teams about their successes and challenges when building, deploying, and monitoring models Nov 19, 2018 highlights & Science Over the past five years, we have heard many stories from data science teams about their successes and challenges when building, deploying, and monitoring models. Unfortunately, we have also heard that many companies have internalized the model myth, or the misconception that data science should be treated like software development or data assets. This misconception is completely understandable. Data science involves code and data. Yet, people leverage data science to discover answers to previously unsolvable questions. As a result, data science work is more experimental, iterative, and exploratory than software development. Data science work involves computationally intensive algorithms that benefit from scalable compute and sometimes requires specialized hardware like GPUs. Data science work also requires data, a lot more data than typical software products require. All of these needs (and more) highlight how data science work differs from software development. These needs also highlight the vital importance of collaboration between data science and engineering, particularly for innovative model-driven companies seeking to maintain or grow their competitive advantage.
Yet, collaboration between data science and engineering is a known challenge. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ Over the past five years, we have heard many stories from data science teams about their successes and challenges when building, deploying, and monitoring models Nov 19, 2018 highlights & Science Over the past five years, we have heard many stories from data science teams about their successes and challenges when building, deploying, and monitoring models. Unfortunately, we have also heard that many companies have internalized the model myth, or the misconception that data science should be treated like software development or data assets. This misconception is completely understandable. Data science involves code and data. Yet, people leverage data science to discover answers to previously unsolvable questions. As a result, data science work is more experimental, iterative, and exploratory than software development. Data science work involves computationally intensive algorithms that benefit from scalable compute and sometimes requires specialized hardware like GPUs. Data science work also requires data, a lot more data than typical software products require. All of these needs (and more) highlight how data science work differs from software development. These needs also highlight the vital importance of collaboration between data science and engineering, particularly for innovative model-driven companies seeking to maintain or grow their competitive advantage. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/

→ The disparity in ways that file selection is presented or obstructed in apps is bewildering and frustrating. Nov 17, 2018 highlights ”The only way apps should be doing it currently is with iOS 11 style file APIs, but many apps have either legacy file solutions, bespoke (ie, confusingly differentâââand differently-abled) file pickers or would rather pull you into their own cloud platform. The disparity in ways that file selection is presented or obstructed in apps is bewildering and frustrating. It forces me to become the expert on file picker UI and capabilities from app to appâââtime consuming, pointless knowledge that should be learnt once at a platform level. Of the legacy methods; copying/sharing from iTunes is prehistoric, duplicating files between apps is barbaric, and sharing files between apps with WebDAV is soporific. If you have better options, use them. Many apps offer some specific integration with a cloud serviceâââsome to widen your options, others to steer you to their own eco-system.” â

https://medium.com/@pixelthing/fe-webdev-on-ipad-pro-2018-c55283f01e4c

Well put. Some file organizing difficulties are so obtuse that I wonder if developers use their own apps.

→ The other major announcement: an independent oversight body to review appeals for content removals Nov 16, 2018 highlights The other major announcement: an independent oversight body to review appeals for content removals. Zuckerberg first discussed the idea of a “Facebook Supreme Court” with Ezra Klein in April; I wrote about why such a body was necessary in this space in August, during the Alex Jones imbroglio. I asked Zuckerberg today whether he thought the body should publish its opinions, creating a kind of case law; he told me that he did. The body won’t be up and running until the end of 2019 at the earliest, but when it arrives we can expect a growing body of social network jurisprudence, and it’s going to be fascinating to watch. — https://www.theverge.com/2018/11/16/18097833/facebook-definers-scandal-washington-recap

→ What struck me was the language Zuckerberg used to discuss this issue — it’s different than anything he has said before. Nov 16, 2018 highlights & People & Social

What struck me was the language Zuckerberg used to discuss this issue — it’s different than anything he has said before. And it goes to the heart of social networks’ role in creating a polarized, destabilized electorate:

One of the biggest issues social networks face is that, when left unchecked, people will engage disproportionately with more sensationalist and provocative content. This is not a new phenomenon. It is widespread on cable news today and has been a staple of tabloids for more than a century. At scale it can undermine the quality of public discourse and lead to polarization. In our case, it can also degrade the quality of our services.
Our research suggests that no matter where we draw the lines for what is allowed, as a piece of content gets close to that line, people will engage with it more on average — even when they tell us afterwards they don’t like the content.

— https://www.theverge.com/2018/11/16/18097833/facebook-definers-scandal-washington-recap

→ One point of discussion that researchers across many different strands of visualization researchers agreed upon was the visualization research is not as visible as it should be to the many people designing, critiquing, and otherwise using visualization in the world Nov 15, 2018 highlights One point of discussion that researchers across many different strands of visualization researchers agreed upon was the visualization research is not as visible as it should be to the many people designing, critiquing, and otherwise using visualization in the world. This includes data scientists, information designers, business analysts, researchers in other fields, and numerous others. Some aspects of this disconnect are institutional — our research is often buried behind paywalls. But we suspect that a large portion of it is also due to the way that visualization researchers, like many scientists, are not always motivated or skilled in communicating their research to a broader audience. — https://medium.com/multiple-views-visualization-research-explained/why-we-should-be-talking-about-visualization-research-4d28a2032de3?source=rss—-304d1be73000—4

→ There’s another thing I don’t like about these standard datasets, however, and that’s that they are missed opportunities Nov 15, 2018 highlights There’s another thing I don’t like about these standard datasets, however, and that’s that they are missed opportunities. If I’m displaying a new graph layout algorithm or a new visual analysis platform, it doesn’t really matter what data I use to illustrate it in the paper figures, so long as it’s sufficiently complex and “realistic” that it looks like my system does what it’s supposed to. That means that I have an opportunity to use that visual real estate to show something that I (and perhaps also you) care about. Why waste that space on something trivial? — https://medium.com/multiple-views-visualization-research-explained/doing-more-with-sample-datasets-d9ea622cecd7?source=rss—-304d1be73000—4

→ As impressive as the fruits of innovation have been, the pace of change seems relentless and, at times, almost mindlessly linear Nov 14, 2018 highlights & Innovation As impressive as the fruits of innovation have been, the pace of change seems relentless and, at times, almost mindlessly linear. It suggests a possible disconnect between the products or services being rolled out to the market and whether a specifically identifiable business or consumer need actually exists in sufficient degree to justify the development and investment of resources involved. — https://jfsdigital.org/2018/11/12/the-limits-of-innovation-high-techs-diminishing-returns/

→ Networks have emerged as a transformational adaptation to a community service environment characterized by complex social and economic issues, and the needs of a diverse population. Nov 13, 2018 highlights Networks have emerged as a transformational adaptation to a community service environment characterized by complex social and economic issues, and the needs of a diverse population.

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→ our review identifies several approaches that show some promise for improving the use of research in population health policy. Nov 13, 2018 highlights & Health

our review identifies several approaches that show some promise for improving the use of research in population health policy. They include the following:

A system for commissioning rapid reviews
Tailored approaches to presenting research findings to policymakers
The involvement of policymakers in research teams and networks
Interactive seminars and conferencing technology for communicating evidence
Initiatives to build capability in people and across organisations
Funded institutional-level collaborations.

— http://blogs.lse.ac.uk/impactofsocialsciences/2018/11/12/less-than-5-of-papers-on-the-use-of-research-in-health-policymaking-tested-interventions-to-see-what-worked-but-those-studies-reveal-a-number-of-strategies-for-improvement/

→ One theme was the importance of ensuring that policymakers are provided with research syntheses and summaries that match their needs Nov 13, 2018 highlights & Health One theme was the importance of ensuring that policymakers are provided with research syntheses and summaries that match their needs. This was the lesson from a study from Monash University, Australia, that interviewed 43 policymakers on a strategy aimed at supporting the use of systematic reviews in health policy. The policymakers in this study overwhelmingly agreed that research summaries were critical to increase the use of research. The study demonstrated a need for layered or “graded-entry” formats, ranging from short summaries all the way up to detailed reports. It showed the need for a mechanism to assess policymakers’ changing research needs so they could be targeted with a relevant synthesis.
This was also the message from a 2011 study from the US, which tested four different types of policy briefs on mammography screening with nearly 300 policymakers. The study authors found different types of policymakers tended to like different types of briefs, with staffers more likely to report a preference for story-focused briefs and legislators preferring data-focused briefs.
Another theme to emerge was the need for better collaboration between researchers and policymakers, and for the two to build closer relationships. One large study, involving nearly 1,000 policymakers, looked at an intervention where researchers presented their findings directly to policymakers in either traditional seminars or interactive roundtables. Policymakers agreed that such presentations stimulated their thinking, that the interactive roundtables were more relevant to their needs compared with traditional seminars, and that the new knowledge could be used in their work as policymakers.
Three of the studies under review focused on increasing organisational capacity to use research. A Canadian study looked at a scheme to improve implementation of best practice guidelines in health promotion programmes using a team of “organisational champions”, while a Dutch study explored the use of masterclasses for public health professionals and policymakers supporting a practice-based research project. — http://blogs.lse.ac.uk/impactofsocialsciences/2018/11/12/less-than-5-of-papers-on-the-use-of-research-in-health-policymaking-tested-interventions-to-see-what-worked-but-those-studies-reveal-a-number-of-strategies-for-improvement/

→ This was also the message from a 2011 study from the US, which tested four different types of policy briefs on mammography screening with nearly 300 policymakers Nov 12, 2018 highlights & Health This was also the message from a 2011 study from the US, which tested four different types of policy briefs on mammography screening with nearly 300 policymakers. The study authors found different types of policymakers tended to like different types of briefs, with staffers more likely to report a preference for story-focused briefs and legislators preferring data-focused briefs. — http://blogs.lse.ac.uk/impactofsocialsciences/2018/11/12/less-than-5-of-papers-on-the-use-of-research-in-health-policymaking-tested-interventions-to-see-what-worked-but-those-studies-reveal-a-number-of-strategies-for-improvement/

→ The first thing to note is that, although we identified over 300 papers on the use of research in health policymaking, the vast majority of these were descriptive Nov 12, 2018 highlights & Health The first thing to note is that, although we identified over 300 papers on the use of research in health policymaking, the vast majority of these were descriptive. Very few – in fact just 14 of 304 articles – actually concerned testing interventions to see whether they worked. There is a serious discrepancy, therefore, between surging interest in this area and the small number of studies actually testing strategies.
The 14 articles we did find (reporting on 13 intervention strategies) tended to be methodologically weak. Only one study used an experimental design, while one other used a pre/post-test design. The others used a range of approaches and were characterised by an absence of control groups, small sample sizes, and self-report data. Most measured outcomes related to factors that influence research use rather than actual research use. — http://blogs.lse.ac.uk/impactofsocialsciences/2018/11/12/less-than-5-of-papers-on-the-use-of-research-in-health-policymaking-tested-interventions-to-see-what-worked-but-those-studies-reveal-a-number-of-strategies-for-improvement/