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Once again, we are delighted to announce a stellar line up of speakers for this year’s EARL Conference; from Retail and Insurance to Media, Manufacturing and Pharmaceutical, the range of industries now using R stats in their workflow continues to grow.

If you are interested to hear why companies such as BBC News, BMW Group, Arla Foods, GSK, Microsoft, Hiscox, Mumsnet and Gym Group have turned to R, then you can get early bird priced tickets for a limited time.

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Julia Silge is joining us as one of our keynote speakers at EARL London 2019. We can’t wait to hear Julia’s full keynote, but until then she kindly answered a few questions. Julia shared with us what we can expect from her address – which will focus on how Stack Overflow uses R and their recent developer survey.

Hi Julia! Tell us about the StackOverflow Developer Survey and your role at Stack Overflow

The Stack Overflow Developer Survey is the largest and most comprehensive survey of people who code around the world each year. This year, we had almost 90,000 respondents who shared their opinions on topics including their favourite technologies, their priorities in looking for a job, and what music they listen to while coding. I am the data scientist who works on this survey, and I am involved throughout the process from initial design to writing copy about results. We have an amazing team who works together on this project, including a project manager, designers, community managers, marketers, and developers.

My role focuses on data analysis. Before the survey was fielded, I worked with one of our UX researchers on question writing, so that our expectations for data analysis were aligned, as well as using data from previous years’ surveys and our site to choose which technologies to include this year. After the survey was fielded, I cleaned and analyzed the data, created data visualizations, and wrote the text for both our developer-facing and business-facing reports.

Why did you use R to analyse the survey?

All of our data science tooling at Stack Overflow is R-centric, but specifically, with our annual survey, we are working with a complex dataset on a tight schedule and the R ecosystem provides the fluent data analysis tools we need to deliver compelling results on time. From munging complicated raw data to creating beautiful visualizations to delivering data deliverables via an API, R is the right tool for the job for us.

Were there results from the survey this year that came as a surprise?

This is such a rich dataset to get to work with, full of interesting things to notice! One result this year that I didn’t expect ahead of time was with our question about whether a respondent eventually wanted to move from technical work into people management. We found that younger, less experienced respondents were more likely to say that they wanted to make the switch! Once I thought about it more carefully, I came to think that those more experienced folks with an interest in managing probably had already shifted careers and were not there to answer that question anymore. Another result that was a surprise to me was just how many different kinds of metal people listen to, more than I even knew existed!

Do you see the gender imbalance improving?

Although our annual survey has a broad capacity for informing useful and actionable conclusions, including about gender, our results don’t represent everyone in the developer community evenly. We know that people from marginalized groups and underrepresented groups in tech participate on Stack Overflow at lower rates than they participate in the software workforce. This means that we undersample such groups in our survey (because of how we invite respondents to the survey, mostly on our site itself). Over the past few years, we have seen incremental improvement in the proportion of responses that are from marginalized or underindexed groups such as minority genders or minority racial/ethnic groups; we are so happy to see this because we want to hear from everyone who codes, everywhere. We believe the biggest driver of this kind of positive change is and will continue to be improving the balance of who participates on Stack Overflow itself, and we are committed to making Stack Overflow a more welcoming and inclusive platform. This kind of work can be difficult and slow, but we are in it for the long haul.

What future trends might you be able to predict from the survey?

One trend we’ve seen over the past several years that I expect to continue is the normalization of salaries for data work. Several years ago, people who worked as data scientists were extreme outliers in salary. Salaries for data scientists have started to move toward the norm for software engineering work, especially if you control for education (for example, comparing a data scientist with a master’s degree to a software engineer with a master’s degree). I don’t see this as entirely bad news, because it is associated with some standardization of data science as a role and increased industry agreement about what a data scientist is, what a data engineer is, how to hire for these roles, and what career paths might look like.

Given Python’s rise again this year, do you see this continuing? How will this affect the use of R?

Python has exhibited a meteoric rise over the past several years and is the fastest-growing major programming language in the world. Python has been climbing in the ranks of our survey over the past several years, edging past first PHP, then C#, then Java this year. It currently sits just below SQL in the ranking. I have a hard time imagining that next year more developers will say they use Python than say they use SQL! You can dig this interview up next year and point out my prediction failure if I am wrong.

In terms of R and R’s future, it’s important to note that R’s use has also been growing dramatically on Stack Overflow, both absolutely and relatively. R is now a top 10 to top 15 programming language (both in questions asked and traffic). Data technologies are in general growing a lot, and there are many factors that go into an individual or an organization deciding to embrace R, or Python, or both.

Thanks Julia! 

You can catch Julia and a whole host of other brilliant speakers at EARL London on 10-12 September at The Tower Hotel London.

We have discounted early bird tickets available for a limited time – please visit the EARL site for more information, we hope to see you there!

gambling and gaming industry
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One of my favourite movies of all time is Rain Man, in which Dustin Hoffman plays Raymond Babbit, an autistic savant whose ability to count hundreds of cards at once leads to significant wins at the Las Vegas casino tables. Fortunately for gambling and gaming companies, Raymond’s counting abilities extend far beyond the normal range of human subitising and, aside from the occasional winning streak, the vast majority of us will be net losers. But in the gambling and gaming industry, it’s not just the customer who needs the statistical insight at their fingertips in order to succeed, it’s the vendors. And this insight comes in the form of data, AI/ML and the correct cloud infrastructure in order to help vendors win as big as their customers.

On the surface of it, the odds are stacked in favour of the vendors. With 2 billion gamers across the world, and the current size of the global gambling market – almost $46 billion – forecast to double in the upcoming years, it’s clear that the ‘have to be in it to win it’ player mentality is proving lucrative for vendors. Each of the billion global players will participate in multiple actions and interactions, leaving a trail of data as they go, allowing vendors to act on this information to really understand their customers. But scratch beneath the surface and a mine of potential complexities unfold. How do they prevent the loss of high value customers ? How do they optimise marketing spend? Or predict & prevent problem playing/gambling?  How do they ensure they are identifying and developing the right products? Forecasting sales accurately? Predicting churn? Or preventing & detecting fraud?

When Mango begins a new engagement with companies operating in this sector, before any data science can take place, it’s crucial to ensure that the right questions are being asked and challenges being addressed.

Let’s take a look at some of those common challenges facing the gaming & gambling industry:

Preventing the loss of high value customers

Solving the issue of customer churn is one of the biggest challenges for any online business, but the effective use of data science can help by better forecasting when customers – and particularly high value customers – look likely to leave. The more accurately you can forecast churn, the more effective you can be at preventing customer loss. Using data science to segment the customer base by any attribute, such as age, location or date they joined, means approaches can be developed that are personalized – and therefore relevant – to that customer base. For example, analytics could show a list of customers who are approaching the end of their contract or detect less activity on an account than is ‘normal’ according to historical patterns, or perhaps that new, heavily featured games are not being played. In all of these instances, data driven decisions can be made on the most effective intervention tactics and appropriate incentives to retain these customers, such as loyalty points, or reduced price play.

Optimising marketing spend

Contrary to popular opinion, marketing funds are not bottomless pits, and adopting a ‘spray and pray’ approach will likely result in little, if any, return on investment. With customer data being captured with every online transaction, however, vendors can gather huge volumes of structured and unstructured data about each individual in order to offer targeted, personalised marketing. The key word here is ‘personalised.’ Just because 100 customers might fall into the same broad segment, it doesn’t mean they should be targeted in the same way. Individuals within these groups have individual preferences, and algorithms can help determine which communication or marketing channel would have the most impact with a particular customer and deliver the highest response rate, thus optimising marketing spend.

Maximising cross & upsell through great customer experience

Ensuring that customers are happy enables vendors to cross and upsell and data science can unlock insights which help win and retain customers. These insights can help online businesses ‘know their customer’ and therefore make tailored improvements to the customer experience and measure immediate impact. Did increased spend on customer retention contribute to increased revenue? If so, by how much? What is the impact of multiple offers and communications to customers on sales, unsubscribes and retention? Excellence in customer service can be achieved by adopting a data driven, 360 degree approach which offers a thorough understanding of the audience and a means to deliver the service desired at the right time and via the most appropriate channels.

Predicting & preventing problem gambling

As the gambling and gaming industry grows, so does the problem of gambling addiction. According to a recent BBC article, there are about 430,000 people experiencing problems with gambling and, as we all know, this can impact anyone. There is no typical ‘problem gambler’ – it’s an issue that transcends all social and demographic groups. The Gambling Commission recently launched its new three-year National Strategy which focuses on prevention, education, treatment and support for problem gamblers, which is a significant step in the right direction. And data science can help this process by identifying potential candidates for such support. Using data collected on the betting patterns of every customer, including the time of day, frequency and size of bets placed, a picture can be built up of an individual’s typical behavioural patterns, so that any gradual change or deviation from this pattern can signal the onset of a potential problem. At this point, the company can decide to apply intervention strategies, such as the temporary stop of an account, or refer the player for online help.

Predicting & preventing fraud

In this sector, the list of potential pitfalls is sadly, long and sobering for customers and vendors alike. Frequent, often large volumes of credit card payments being made, free credit offered by companies as incentives to play encouraging fake accounts being created, stolen credit card details, accounts being hacked….I could go on. Fortunately, advanced analytics can be used to help create a picture of ‘normal’ account activity for individual players, and so flag any abnormalities at the earliest opportunity. With this early detection system in place, an effective monitoring programme can help protect the organisation and the individual.

The possibilities for data science to help your business win are far reaching and, if you’re wondering how you can find out more, I’m delighted to say that next Wednesday evening, I’ll be presenting alongside Rackspace and Google to share with attendees some of the possibilities. We’ll be showing how to build a successful data science capability on Google Cloud, aligning key challenges with the ‘Art of the Possible.’ By answering the right questions through advanced analytics, we can help you create predictive models, including churn, assessing demand and customer life time value to enable effective decision making.

Don’t leave your fortune to Lady Luck. Organisations that win with data science will do so by answering the best business questions, not creating the best answers to data questions.

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While we’re aiming to try and fit in as many EARL talks as we can, we know it’s impossible to see them all! We’ve asked some of the Mango team to let us know who they’re looking forward to seeing speak. First up is Alfie Smith one of our Data Scientists – more team picks to follow!

Alfie Smith

Avision Ho’s “Why a Nobel Prize algorithm is not always optimal for business” looks to be an interesting presentation on the problems that come with translating academic research into commercial applications. As data science consultants, we have to be able to tell our clients the risks of rushing to the newest algorithm; particularly when every new research paper creates a hype-bubble.

I’m excited to hear “Promoting the use of R in the NHS – progress and challenges” by Professor Mohammed A Mohammed. As the brother of an NHS doctor, I’ve heard many stories of the NHS’ dependence on archaic tech and the bottle necks it creates. I’m fascinated to hear whether R is solving some of these problems and whether my R skills could be of value to the UK’s health service.

Lastly, I’m very intrigued by Theo Boutaris’ “Deep Milk: The Quest of identifying Milk-Related Instagram Posts using Keras”. At EARL, we’re going to hear lots of stories of R solving huge business problems. However, it’s often the smaller, wackier, stories that I remember long after the event. I’m hoping Theo’s presentation will give me an anecdote to talk about at the next Bristol Data Science meet-up.

If any of these talks sound interesting please take a look at who else is speaking – we also have early bird ticket prices available for a limited time.

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With the popularity of Internet of Things (IoT) increasing, organisations are becoming overwhelmed by the explosion of data generated from a number of different sources. Mango’s Chief Data Scientist, Richard Pugh, recently spoke to IoT-Now Magazine the safety of the IoT with so many connected devices.  In her article, entitled ‘Mitigating the Business Threats’ (May, 2nd 2019) Anasia D’Mello gives her advice about the application of data science to derive value from this data. Contributing to the article, Richard said: “data science can also help when it comes to considering the value of the data produced. Rather than simply using a tool to turn data into an actionable insight, data science is a way of blending technology, data and business awareness to extract value, not just information.”

Regularly working with business leaders to mentor them on delivering the true value of data he shares his views on why adopting a data-driven culture can help your business succeed. In the article he states that businesses stand a far greater chance of success in the Information Age in terms of increasing efficiencies, optimising revenues, and providing richer experiences for clients if a company-wide culture of data science is adopted.

If you thrive on data then we’d like to hear from you. We are hiring positions in Data Science and Data Engineering.

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Almost all companies are investing in some sort of data project – data analytics, big data, AI, data science. “The journey to data-driven maturity isn’t really about digital first and foremost”, says Richard Pugh Mango’s Chief Data Scientist, “it’s actually about the necessity of transforming your business model”.  He regularly mentors business executives on how to build a data-driven company, providing practical guidance on carrying out transformation.

CDO’s often cite low levels of data-driven maturity – however with the perfect blend of technology, data and business awareness to extract value, not just information from data is key to success. There are vital steps that can help businesses succeed with their big data and data science endeavours. The first and most vital step is to ensure representation by leadership for data-driven initiatives, educating the business at large about the possibilities of analytics.

Business leaders need to work with their data practitioners to teach the whole business a common language around advanced analytics and dispel preconceptions of what value data science can bring to the organisation. This will open up more fertile ground for working out what business questions data can and cannot help solve.

Without leadership alignment, it will nearly be impossible to instigate the culture shift required to truly become data-driven, ensuring a programme of work that aligns the leadership, business, and analytic functions around a unified vision.

For practical, real-world advice on data-driven approaches and to ensure you have these vital steps in place to deliver your data-driven transformation, please contact Lisa Sheppard at Mango Solutions for more information about our Art of the Possible series by email info@mango-solutions.com or via telephone.

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We were thrilled with the overall quality and amount of abstracts we received for this year’s EARL London Conference. Which made our job of selecting the final speakers even more difficult!

We are pleased to share with you the speakers for EARL London 2019 – we will be interviewing some of our speakers over the next few months, so you can find out what to expect from their talks. As you can see we have a wide range of topics and industries covered – so there will be something for everyone.

The final agenda with times will be released in the next few weeks – in the meantime, take a look at who’s talking and make the most of our early bird ticket offers.