Developing a Code of Conduct for the Data Science and Analytics Sector
Developing a Code of Conduct for the Data Science and Analytics Sector The Data Science Foundation is reviewing its Code of Conduct and the services it delivers to members. A six-month consultation period will commence January 2018 with both internal and external stakeholders from industry, education and government. In advance of this we are inviting members to participate in a fact-finding exercise that will help to shape the debate. Below this introduction you will see five questions, we would be grateful if you would send your responses by email. Looking back the Foundation has had a tremendous 2017; increasing membership numbers, developing contacts, offering members more online functionality including the development of Personal Profile Pages, Published By Pages, Messaging Facilities and the development of the Discussion Forum. We will be launching a major new initiative with a partner at Big Data LND on 15th November and will follow this up with the launch of the Data Science Writer of the Year Awards 2018. It is now time to look forward, if you are a member of the Data Science Foundation, please participate in the debate to develop a Code of Conduct for the Data Science and Analytics Sector, to help form the services we deliver and to have your say in how the sector is represented. If you are not yet a member but have a professional interest in data and advanced analytics, become a member and join the debate. Membership is free for individuals: https://datascience.foundation/joinus Code of Conduct Initial Questions Please email your answers to email@example.com Use the subject line Code of Conduct. Please answer the following questions: Would you support a Code of Conduct for the Data Science and Analytics sector? Should the code focus on professional, ethical or moral standards or all three? What should be included in the Code of Conduct? Please provide a short description What services would you like the Data Science Foundation to provide? Would participate in the debate? The debate will initially be conducted by email questionnaire and then online discussion Background Information About the Data Science Foundation The Data Science Foundation is a professional body representing the interests of people working in the data science and advanced analytics sector. Our membership consists of both users and suppliers of data services as well as universities offering data science courses and their students. The foundation aims to create an active community of data scientists, to provide a platform to share ideas and to support professional development. All members of the foundation are provided with an online Profile page and a Published By page, which showcases all articles and papers published for peer review. Aims The primary aims of the Data Science Foundation are to: Create a community of qualified and highly skilled data science professionals Provide the data science community with a forum to share ideas and support professional development Develop approved professional standards that differentiate members from others working in the data science and advanced analytics sector The Data Science Foundation is working to: Raise the profile of data science in the UK, to educate business people about the benefits of knowledge-based decision making and to encourage firms to make optimal use of their data. Launch an education programme which includes seminars covering topics such as; ‘Helping business people understand big data’ and ‘Helping data scientists communicate with business people’. An ‘Introduction to data science’ talk will be offered to schools. Improve the way organizations use their data; by helping organizations form partnerships with universities and consultancies. The website The foundation’s website has been built as a communications platform, a publishing tool and as a means for members to procure expertise or obtain employment. The site is a source of information for the media and for those interested in learning about data science. We will ensure that the website: Contains extensive and accurate information about big data and data science education Displays accurate records of corporate, supplier, individual and associate members Is the ideal place to find a data science provider and to start a data science project Creates employment for members via the CV board Publishes the most sought-after job opportunities from leading firms Becomes a platform that allows individuals to gain recognition for their expertise and become leading figures within the industry Code of Conduct The Data Science Foundation Code of Conduct applies to all members. Promotion of Good Practices We adhere to the fact that data scientists should follow best practices at all times, and never encourage or suggest a business or client take actions that are or could be construed as criminal or unethical. All members will strive to provide competent services at all times. Integrity and Honesty We believe in the value and importance of integrity and honesty in all interactions and transactions, whether providing informal advice, a formal project plan, or visualised data for information dissemination. Capability and Expertise The Foundation advocates for more formal guidelines in terms of professional capability and expertise, and works with leading education centres and universities to develop degree courses to this end. Transparency The Data Science Foundation advocates for transparency at all levels, both within the Foundation and within partner organisations, educational institutions and government agencies. We believe in being forthright and direct, with no hidden agenda. Confidentiality Confidentiality is an essential consideration, particularly with sensitive business data. We adhere to the strictest confidentiality stipulations to safeguard these vital business and organisation assets. Information created, developed, used or learned in the course of employment with a particular client, business or organisation is considered completely confidential. Security All members must ensure that data is secure at all times, safeguarded from all threats, including but not limited to viruses, malware, internal and external hacking attempts, theft, and accident. All members must utilise industry-standard software/hardware to ensure data security at all times. Professional Standards We require all members of the Foundation to adhere to strict professional standards regarding integrity, honesty, quality, confidentiality and more. We also enforce professional standards in terms of working practice, data quality, and standards of evidence. Misuse or misrepresentation of data is not permissible. The Foundation practices strict enforcement of professional standards, and repercussions can include expulsion from the Foundation. General Membership Policy Our general membership policy applies to all members, including corporate members. Members are granted several key benefits, including access to Foundation publications, the ability to join discussions and forums, to network with others in the data science industry and more. The Data Science Foundation offers different membership options, including corporate membership and associate membership, each of which delivers unique benefits and advantages.
Hey Chris,Interesting piece. I have done some similar work myself time ago, but I have always wondered whether we should simply indicate how to build a code of conduct (and making it collectively afterwards) or rather drafting something that we hope people will adopt in their daily jobsF
The new CxO gang: data, AI, and robotics
The new CxO gang: data, AI, and robotics Hiring new figures to lead the data revolution It has been said that this new wave of exponential technologies will threaten a lot of jobs, both blue and white-collar ones. But if from one hand many roles will disappear, from the other hand in the very short-term we are observing new people coming out from the crowd to lead this revolution and set the pace. These are the people who really understand both the technicalities of the problems as well as have a clear view of the business implications of the new technologies and can easily plan how to embed those new capabilities in enterprise contexts. Hence, I am going to briefly present three of them, i.e., the Chief Data Officer (CDO), the Chief Artificial Intelligence Officer (CAIO) and the Chief Robotics Officer (CRO). Sad to be said, I never heard about a ‘Chief of Data Science’, but for some strange reasons, the role is usually called either ‘Head of Data Science’ or ‘Chief Analytics Officer’ (as if data scientist won’t deserve someone at C-level to lead their efforts). Let’s see then who they are and what they would be useful for. The Chief Data Officer (CDO) A slide taken from one of the speakers at the CDO Summit in London illustrating business drivers and capabilities and how they related to the CDO job. Apparently, it is a new role born in a lighter form straight after the financial crisis springing from the need to have a central figure to deal with technology, regulation and reporting. Therefore, the CDO is basically the guy who acts as a liaison between the CTO(tech guy) and the CAO/Head of Data Science (data guy) and takes care of data quality and data management. Actually, its final goal is to guarantee that everyone can get access to the right data in virtually no time. In that sense, a CDO is the guy in charge of ‘democratizing data’ within the company. It is not a static role, and it evolved from simply being a facilitator to being a data governor, with the tasks of defining data management policies and business priorities, shaping not only the data strategy, but also the frameworks, procedures, and tools. In other words, he is a kind of ‘Chief of Data Engineers’ (if we agree on the distinctions between data scientists, who actually deal with modeling, and data engineers, who deal with data preparation and data flow). “The difference between a CIO and CDO (apart from the words data and information…) is best described using the bucket and water analogy. The CIO is responsible for the bucket, ensuring that it is complete without any holes in it, the bucket is the right size with just a little bit of spare room but not too much and its all in a safe place. The CDO is responsible for the liquid you put in the bucket, ensuring that it is the right liquid, the right amount and that’s not contaminated. The CDO is also responsible for what happens to the liquid, and making the clean vital liquid is available for the business to slake its thirst.” (Caroline Carruthers, Chief Data Officer Network Rail, and Peter Jackson, Head of Data Southern Water)” Interestingly enough, the role of the CDO as we described it is both verticaland horizontal. It spans indeed across the entire organization even though the CDO still needs to report to someone else in the organizational chart. Who the CDO reports to will be largely determined by the organization he is operating in. Furthermore, it is also relevant to highlight that a CDO can be found more likely in larger organizations rather than small startups. The latter type is indeed usually set up to be data-driven (with a forward-looking approach) and therefore the CDO function is already embedded in the role who designs the technological infrastructure/data pipeline. It is also true that not every company has a CDO, so how do you decide to eventually get one? Well, simply out of internal necessity, strict incoming regulation, and because all your business intelligence projects are failing because of data issues. If you have any of these problems, you might need someone who pushes the “fail-fast” principle as the data approach to be adopted throughout the entire organization, who considers data as a company asset and wants to set the fundamentals to allow fast trial and error experimentations. And above all, someone who is centrally liable and accountable for anything about data. A CDO is then the end-to-end data workflow responsible and it oversees the entire data value chain Finally, if the CDO will do his job in a proper way, you’ll be able to see two different outcomes: first of all, the board will stop asking for quality data and will have clear in mind what every team is doing. Second, and most important, a good CDO aims to create an organization where a CDO has no reasons to exist. It is counterintuitive, but basically, a CDO will do a great job when the company won’t need a CDO anymore because every line of business will be responsible and liable for their own data. A good CDO aims to create an organization where a CDO has no reasons to exist. In order to reach his final goal, he needs to prove from the beginning that not investing in higher data quality and frictionless data transfer might be a source of inefficiency in business operations, resulting in non-optimized IT operations and making compliance as well as analytics much less effective. The Chief Artificial Intelligence Officer (CAIO) If the CDO is somehow an already consolidated role, the CAIO is nothing more than a mere industry hypothesis (not sure I have seen one yet, although the strong ongoing discussions between AI experts and sector players— see here and here for two opposite views on the topic). Moreover, the creation of this new role highlights the emergence of two different schools of thought of enterprise AI, i.e., centralized vs decentralized AI implementation, and a clear cost-benefit analysis to understand which approach will work better is still missing. My two cents are that elevating AI to be represented at the board level means to really become an AI-driven company and embed AI into every product and process within your organization—and I bet not everyone is ready for that. So, let’s try to sketch at a glance the most common themes to consider when talking about a CAIO: Responsibilities (what he does): a CAIO is someone who should be able to connect the dots and apply AI across data and functional silos (this is Andrew Ng’s view, by the way). If you also want to have a deeper look at what a CAIO job description would look like, check out here the article by Tarun Gangwani; Relevance (should you hire a CAIO?): you only need to do it if you understand that I is no longer a competitive advantage to your business but rather a part of your core product and business processes; Skills (how do you pick the right guy?): first and more important, a CAIO has to be a ‘guiding light’ within the AI community because he will be one of your decisive assets to win the AI talent war. This means that he needs to be highly respected and trusted, which is something that comes only with a strong understanding of foundational technologies and data infrastructure. Finally, being a cross-function activity, he needs to have the right balance between willingness to risk and experiment to foster innovation and attention to product and company needs (he needs to support different lines of business); Risks (is a smart move hiring a CAIO?): there are two main risks, which are i) the misalignment between technology and business focus (you tend to put more attention on technology rather than business needs), and ii) every problem will be tackled with AI tools, which might not be that efficient (this type of guys are super trained and will be highly paid, so it is natural they will try to apply AI to everything). Where do I stand on that? Well, my view is that a CAIO is something which makes sense, even though only temporarily. It is an essential position to allow a smooth transition for companies who strive for becoming AI-driven firms, but I don’t see the role to be any different from what a smart tech CEO of the future should do (of course, supported by the right lower management team). However, for the next decade having a centralized function with the task of using AI to support the business lines (50% of the time) and foster innovation internally (50% of the time) it sounds extremely appealing to me. In spite of all the predictions I can make, the reality is that the relevance of a CAIO will be determined by how we will end up approaching AI, i.e., whether it will be eventually considered a mere instrument(AI-as-a-tool) or rather a proper business unit (AI-as-a-function) The Chief Robotics Officer (CRO) We moved from the CDO role, which has been around for a few years now, to the CAIO one, which is close to being embedded in organizational charts. But the Chief Robotics Officer is a completely different story Even if someone is speaking about the importance of it (check out this report if you like), it is really not clear what his tasks would be and what kind of benefits would bring to a company, and envisaging this role requires a huge leap of imagination and optimism about the future of work (and business). In few words, what a CRO will be supposed to take care of is managing the automated workforce of the company. To use Gartner’s words, ‘he will oversee the blending of human and robotic workers’. He will be responsible of the overall automatization of workflows and to integrate them smoothly into the normal design process and daily activities. I am not sure I get the importance of this holistic approach to enterprise automation, although I recognize the relevance of having a central figure who will actively keep track and communicate to employees all the changes made in transforming a manual activity/process into an automated one. Another interesting point is who the CRO will report to, which is of course shaped by his real functions and goals. If robotics is deeply routed into the company and allows to create or access new markets, a CRO might directly report to the CEO. If his goal is instead to automatize internal processes to achieve a higher efficiency, he will likely report to the COO or to a strategic CxO (varying on industry and vertical). My hypothesis is that this is going to be a strategic role (and not a technical one, as you might infer from the name) which, as the CAIO, might have a positive impact in the short term (especially in managing the costs of adopting early robotics technologies) but no reason to exist in the longer term. It is easier to think about it in physical product industries rather than digital products or services companies, but automation will likely happen in a faster way in the latter, so we will end up having a Chief of Physical Robotics Officer (to manage the supply chain workflow) as well as a Chief of Digital Robotics Officer (to manage instead the automation of processes and activities).
Thanks Chris - a bit speculative in some points but I think useful to at least start a conversation
Data Science Foundation will be at Big Data LND
The Data Science Foundation and Big Data LDN 15-16 November 2017 – Stand 327 Olympia London Meet us at stand 327 Big Data LDN on 15-16th November 2017! Find out more about the work of the Data Science Foundation and see how becoming a member would help you make more Data Science Connections. We are launching the Data Science Writer of the Year Awards 2018. The awards recognise the contribution made by individuals who create and share data science knowledge and understanding. All members of the Data Science Foundation are eligible to participate in the awards. Individual membership is free of charge. Big Data LDN is a free to attend conference and exhibition open to all, and will host leading global data and analytics experts, ready to arm you with the tools you need to deliver the most effective data-driven strategy. With content divided into comprehensive sections, you’ll have the opportunity to ask the big questions, share ideas with forward-thinking, likeminded peers, and learn from leading members of the Data community. Big Data LDN is back for a second year and is set to be larger than ever in 2017. The two-day event is essential for those with businesses wanting to deliver a data-driven strategy. Get the latest updates on fast/real-time data, artificial intelligence, machine learning, GDPR, deep learning, self-service analytics and much more. The event will host leading, global data and analytics experts, ready to arm you with the tools to deliver your most effective data-driven strategy. Discuss the big questions and share ideas with forward-thinking peers and leading members of the data community. Be in the vanguard of the data revolution, sign up to Big Data LDN and learn how to build a bright data-driven future for your business. Register free here and visit our stand at the event https://bigdataldn.com
The Data Science Foundation will be on stand 327 at Big Data LDN. It would be great to meet.
Data Science: Self learning
Can I become a self-taught data scientist? by Priyam Kakati https://www.quora.com/Can-I-become-a-self-taught-data-scientist/answer/Priyam-Kakati?share=f8b8f366&srid=trpA
Data.Science Start up in India: EdGE Networks
With artificial intelligence (AI) starting to impact many aspects of our personal and working lives, it’s only natural that it should be used in one of the most challenging areas of corporate operations — human resources.Indian startup EdGE Networks is hoping that its With artificial intelligence (AI) starting to impact many aspects of our personal and working lives, it’s only natural that it should be used in one of the most challenging areas of corporate operations — human resources.Indian startup EdGE Networks is hoping that its technology will help bring AI into the human resources sector. It has developed a system that uses AI and data science to help companies hire the best people and manage their workforce, talent acquisitions. will help bring AI into the human resources sector. It has developed a system that uses AI and data science to help companies hire the best people and manage their workforce, talent acquisitions.https://edgenetworks.in/2017/08/24/startup-profile-edge-networks/
How to become a data scientist in 2017
It is one of the hottest fields in information technology what with Glassdoor andLinkedIn citing it as the hottest jobs to watch out for in 2017. In this article, we tell youhow to become a data scientist in 2017. If you need any more validation, a recent article authored by Thomas Davenport and DJ Patil in the Harvard Business Review pegged ‘data scientist’ the sexiest job of the 21st century.http://analyticsindiamag.com/become-data-scientist-2017/
Data Science in India
How do I become a data scientist in India? by Yassine Alouini https://www.quora.com/How-do-I-become-a-data-scientist-in-India-1/answer/Yassine-Alouini?share=57a8ac84&srid=trpA
AI based Hacking
Weaponizing AILast year, two data scientists from security firm ZeroFOX conducted an experiment to see who was better at getting Twitter users to click on malicious links, humans or an artificial intelligence. The researchers taught an AI to study the behavior of social network users, and then design and implement its own phishing bait. In tests, the artificial hacker was substantially better than its human competitors, composing and distributing more phishing tweets than humans, and with a substantially better conversion rate.The AI, named SNAP_R, sent simulated spear-phishing tweets to over 800 users at a rate of 6.75 tweets per minute, luring 275 victims. By contrast, Forbes staff writer Thomas Fox-Brewster, who participated in the experiment, was only able to pump out 1.075 tweets a minute, making just 129 attempts and luring in just 49 users.http://gizmodo.com/hackers-have-already-started-to-weaponize-artificial-in-1797688425
Customer Churn Analytics
Customer churn - or attrition - measures the number of clients who discontinue a service (cellphone plan, bank account, SaaS application...) or stop buying products (retail, e-commerce...) in a given time period. Churn rate is an important business metric as it reflects customer response to service, pricing, competition... As such, measuring churn, understanding the underlying reasons and being able to anticipate and manage risks associated to customer churn are key areas for continuous increase in business value.Reducing Churn Rates Through Predictive AnalyticsEssentially, predictive churn modelling will achieve three goals : understand the key factors of client attrition, identify the clients most at risk of leaving, and provide targeted insights on which retention actions should be implemented.https://www.dataiku.com/solutions/use-cases/churn-analytics/
Data Science Resources
We have a growing list of useful data science resources, but it would be great to get further content. I'd like to grow this section to ensure that every DSF member who is interested in expanding their range of skills has access to a resource to get them started. The basic skills of a data scientist are:Programming (R, Python and others)Statistical Modelling and Statistical Inference (techniques, assumptions and applicability)Machine Learning (algorithms and heuristics)Data Cleaning and Wrangling Visualisation and CommunicationProject Management and Research MethodsI'm gathering resources to share on the website and I'd encourage everyone to share anything they've found useful. For anyone wanting to learn or improve their use of R I think Hadley Wickham's home site is the best place to start: http://hadley.nz/ - a very useful jumping off point, which can give you the ability to do useful things very quickly. It's particularly useful to look at the tools provided by the tidyverse.For practicing R I've found R-exercises to be useful: http://www.r-exercises.com/start-here-to-learn-r/ though constant practice is needed to consolidate your learning.I'll be sharing further resources and adding them to the site over the coming days.Simon