Covid infection rate
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Two years ago the Public Health Evidence & Intelligence team at Hertfordshire Country Council numbered 5, fast forward to today and the team have built their capability to 32 competent R users. 

For Manager Will Yuill, it’s been an extremely busy few years as the urgency of the COVID-19 crisis took hold. The team’s workload doubled overnight, leading to extensive data sharing and analysis of daily infection rates to a range of partnership organisations. Within a week of infection rates hitting the UK, they were being asked to reprioritise workloads, model the pandemic and make recommendations locally on how to limit the spread of infection. 

With a team largely using Excel or a desktop version of R, Will knew changes had to be made to keep up with the quantity and speed of data and to maintain efficiency across the team. Met with these challenges, the team knew they had to consider an outsourcing partnership to meet their immediate and long-term objectives.     

In this blog, Will Yuill, Manager of the Public Health Evidence & Intelligence team at Hertfordshire Country Council, informs us of his challenges and just how his team have dramatically developed their remit, developed their internal capability whilst strengthened stakeholder collaboration across vital health partnerships to actively reduce the spread of the virus. 

The teams’ blockers:  

  • Software – R was not supported internally 
  • Hardware – only able to use R on low power VM’s 
  • Skills – predominantly an academic team , R skills were not applied 
  • Culture – limited opportunity to try new things with IT 
  • Capacity – limited time to try new things   

 How I built my case for R  

“Some of the team members were more familiar with using R, having exhausted the capability of Excel, so in our search for an immediate solution – an R environment seemed a logical solution. Our IT teams were Windows focused and didn’t have the capacity or skills required to support an internal Linux environment. Their priority was to support the council’s migration to home working.  

Before committing to an outsourced partnership, the team had tried high specification laptops to help resolve the immediate challenges of managing data sets but were hindered by with IT corporate policy and firewalls. However, they simply could not meet our sharing analysis needs or compliance with data governance.  With Mango’s Managed RStudio, core stakeholders could have a reliable, secure enterprise environment for data collaborating within days. There was no need for an infrastructure, and it meant we could have 24x7x365 outsourced application monitoring, performance alerts and support. Negating any dependency internally on an already stretched resource.  

With the Managed RStudio, the team have successfully developed their own Shiny applications, both public and private. The public application is currently receiving c20,000 hits a month for detailed analysis around public health services. Internal more data sensitive applications, allow effective dissemination of data trends by location and services, such as environmental health and NHS”. 

Through the use of  RStudio Teams, the team is benefiting from a go-to tool which is empowering their statisticians to manage and develop their code. The ability to provide these tools on a centralised server, accessible from anywhere and without computational constraints of a laptop, has been highly conducive to team productivity, success, and stakeholder engagement.  The data is significantly more secure with improved data governance and infinitely presents less work for the team, allowing them to focus on providing analytic value. 

The lessons learnt  

 “Over the last 2 years and working across an R-environment we have transformed our procedures, implemented best practice and significantly enhanced our stakeholder communications. Here’s some lessons I learnt along the way.  

Take your changes and run with it – if your team is working ineffectively, lacking processes and delivering value, then I strongly recommend investing in a modern data analytics enterprise. This means striving to do more with less resources, which involves pushing productivity to the max to gain the best value.  

Show ROI early – Our team were able to show the impact of our investment. Our data is effectively shared to partnership organisations daily – it is relevant, complete, timely and consistentGone are the days where organisations operate in silos, Managed RStudio has been vital for critical communications with key stakeholders.  

Know what you are looking for  – Sometimes an independent view point prevents you from wasting significant amounts of time, when thinking about data in terms of your objectives is a good place to start.   

Start small and scale – With RStudio Teams, my team is benefiting from a go-to tool which is empowering statisticians to manage and develop their code. The ability to provide these tools on a centralised server, accessible from anywhere and without computational constraints of a laptop, has been highly conducive to team productivity, success, and stakeholder engagement.    

Deployment is hard – RStudio Connect and Pins have been invaluable for production and deployment.  When we got R locally we thought we were set and then realised we could only share analysis via R Markdown and email.  RStudio Connect has allowed to share and publish interactive analysis across partnership organisations.  

Use Git – Git allows an abundance of team collaboration and help manage version control. Utilising Git provides the security, collaboration and certainty required to create and reproduce code and analysis across the team”.  

Will Yuill be joining the NHS R Community as a guest speaker on xxx where he will expand on his case for R and the impact it has had on the local authority.  He will be joined at Matt Sawkins, Product Manager of Mango. 

managed service
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In a recent webinar, we provided an overview of our Managed RStudio platform and demonstrated how modern technology platforms like RStudio gives you the ability to collect, store and analyse data at the right time and in the right format to make informed business decisions.

The Public Health Evidence & Intelligence team at Herts Country Council demonstrated how they have benefitted significantly from the Managed RStudio – enabling collaborative development, empowerment and productivity at a time when they needed it most. In turn, they have been able to scale their department.

Many of the questions from the webinar focused on the governance and security aspects of Managed RStudio. In this blog, we’ve taken all your questions and have for further clarity attached a document that can help with any further questions regarding architecture, data management and maintenance.

Many of the questions asked were aligned to the management of data in the platform from the process of working with data on local drives, user interfaces to the management of large datasets.

There are several methods of getting data in and out of the Managed RStudio. These methods will largely depend on the type and size of the data involved.

For data science teams to work productively and deliver effective results for the business, the starting point is with the data itself. Data that is accurate, relevant, complete, timely and consistent are the key criteria against which data quality needs to be measured. Good data quality needs disciplined data governance, thorough management of incoming data, accurate requirement gathering, strict regression testing for change management and careful design of data pipelines. This is over and above data quality control programmes for data delivery from the outside and within.

Can you please elaborate on getting data into and out of the Managed RStudio platform?

Working with small data sets (< 100Mb)

For smaller data sets, we recommend using RStudio Workbench’s upload feature directly from the IDE. To do this, you can simply click on ‘upload’ in the ‘file’ panel. From here you can select any type of file from either your local hard disk, or a mapped network drive. The file will be uploaded to the current directory. You can also upload compressed files (zip),. which are automatically decompressed on completion. This means that you can upload much more than the 100Mb limit.

Working with large data sets (>100Mb)

For larger data sets or real-time data, we recommend using an external service such as CloudSQL or BigQuery (GCP), Azure SQL Database or Amazon RDS. These can be directly interfaced using R packages such as bigrquery,  RMariaDB or RMySQL.

For consuming real-time data, we recommend using either Cloud Pub/Sub or Azure Service Bus to create a messaging queue for R or python to read these messages.

Sharing data between RStudio Pro/Workbench, connect and other users

Data can easily be shared via ‘Pins’, allowing data to be published to Connect and shared with other users, across Shiny apps and RStudio.

Getting data out of Managed RStudio

As with upload, there are several methods to export data from Managed RStudio. RStudio Connect allows the publishing on Shiny Apps, Flask, Dash and Markdown. It also allows the scheduling of e-mail reports. For one-off analytics jobs, RStudio also allows you to download files directly from the IDE.

The Managed Service also allows uploading to any cloud service such as Cloud storage buckets.

Package Management

R Packages are managed and maintained by RStudio Package Manager giving the user complete control of which versions are installed.

RStudio Package Manager also allows the user to ‘snapshot’ a particular set of packages on a specific day to ensure consistency.

The solution to disciplined data governance

Data that is accurate, relevant, complete, timely and consistent are the key criteria against which data quality needs to be measured. Good data quality needs disciplined data governance and thorough management of incoming data, accurate requirements gathering, strict regression testing for change management and careful design of data pipelines. This all leads to better decisions based on data analysis but also ensures compliance with regulation.

As a Product Manager at Mango, Matt is passionate about data and delivering products where data is key to driving insights and decisions. With over 20 years experience in data consulting and product delivery, Matt has worked across a variety of industries including Retail, Financial Services and Gaming to help companies use data and analytical platforms to drive growth and increase value.

Matt is a strong believer that the combined value of the data and analytics is the key to success of data solutions.

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Big Data LDN (London), Mango Solutions’ big event takeaways: The ideal forum for sharing data expertise  

What better glimpse of the 2020’s data and analytics community than at the UK’s largest data event, Big Data LDN – and how great to be back! With 130 leading technology vendors and over 180 expert speakers, the London show on 23/24 September generated thousands of data and technology-led conversations around effective data initiatives, with real world use cases and panel debates. Everything from leadership, data culture and communities to the ethics of AI, data integration, data science, adoption of the data cloud, hyper automation, governance & sovereignty, and much more…

With a captive group of decision-makers, Mango Solutions conducted a Data Maturity floor survey of Big Data LDN participants. Respondents were asked to identify where their organisations are on their data journey, including the opportunities for data science strategy articulation, data communities, and what their biggest challenge is; with even the most data-advanced organisations modestly commenting in some areas that they had ‘room for improvement’. The fascinating results of this survey will be revealed shortly.

Conversations were approached with eagerness, around ‘harnessing data’, ‘getting more of it’, ‘data platforms’, ‘monetising data’, ‘kicking-off some AI’ – Big Data LDN being the first in-person analytics event since Covid-19 up-scaled our worlds of work and data consumption. From bright computer science graduates, to startlingly young data analysts, through to a surprising amount of MD and C-suite representation at the other end of the scale. Companies from Harrods to UBS, University of St Andrews – the best of the best of brands came out to share insight and learn – buzzwords aside – the most investable routes from potential to advantage.

A low-key event, but highly intelligent, Big Data LDN gave us a feel for the new ambitious, hybrid workers. Back to face-to-face networking, sizeable audience sessions, hot-desking in the cafe; there was a positive hungry-to-learn feel right through the event. Everyone, whoever they were, seemed on a mission to share intelligence and opinion on data and advanced analytics. No one job title or company outclassed the other – just a mutually supportive community of technology experts; less ego and more mutual respect.

There was the ability to attend a suitable meet-up with practical hands-on use case examples. Mango hosted a data science meet-up with guest speakers from The Gym Group and The Bank of England as well as our own Mango Consultant sharing best practice, which was well attended.

Chief Data Scientist at Mango Solutions, Rich Pugh’s talk on finding fantastic data initiatives was well-received by the audience. This focused on prioritising high impact data initiatives with buy-in from across the business. In particular how the role of data domains, supported by data and literacy, can create bridges between business ambition and data investment. In our experience, too often pitfalls exist on selecting the right data initiatives or even trying to answer the wrong question with data; so keeping a focus on achieving business goals and objectives is critical.

But as well as finding out about the latest advanced data services and technologies, this event was a great opportunity to step back from the day-to-day and be reminded of what needs to come first in any data strategy – achieving company goals, and measuring progress, in the form of clear KPIs which we can impact with data and analytics, before jumping in to make hasty data decisions. In this way, with a clear link between business goals and data, we can measure the return from data investments.

My main ‘don’t forget’ take-aways from the event:

  • It’s about aligning data initiatives with business aims. At Big Data LDN, there was consistent messaging around an ‘outcome-first mindset’. To make sure you start with business outcomes and objectives before considering the data, software and platform. It’s a valuable mantra to operate by, to make sure you always deliver the right data science, BI or software.
  • Focus on dis-benefits as well as benefits. As much as we focus on what things we should do to get value from data, we mustn’t lose focus on stopping doing the things we shouldn’t keep doing, or considering the opportunity and benefits lost when we overlook or don’t implement a particular data capability.
  • A robust data strategy must show the benefits for each stakeholder. Different people need different things from a data strategy. To gain buy-in and approval from all stakeholders, you must clearly show each of them the value they will get. In return, you will get not only their sign-off, but also, more valuably, their advocacy.

A parting word

We all learned many things from Big Data LDN 2021 – For me, I’ve learned our industry collaboration and community of data experts is unquestionable. We know remote working works, but you can’t beat the energy of this in-person event to confirm why you love doing what you do. For me, sharing our data science insight, and learning more about our peers’ technologies and services was truly rewarding.

Big Data Challenge – Our survey respondents were asked their no.1 post-Covid data priority, and we received a real mixture of enlightening and some more predictable responses. We will share these challenges with you and explain how we can support in our analytic leadership series.

As a relatively new member of the Consulting Team, working alongside Rich Pugh, Jon will be working with our clients and supporting them through their data journeys. He brings significant expertise to the team from his experience in data maturity, data & analytics, data visualisations and implementing strategies that inform successful business change and improvement, from his previous roles at Oracle, EY, HP and IBM.

If you would like to speak to a member of our consulting team about your data-driven journey, please email us.


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Mango’s ‘Meet-Up’ at Big Data London on 22nd September features guest speaker Adam Hughes, Data Scientist for The Bank of England, whose remit involves working with incredibly rich datasets, feeding into strategic decision-making on monetary policy. You can read about Adam’s incredibly interesting data remit and his team’s journey through Covid-19, in this short Q&A.

Can you tell us about your interest in data and your role at Bank of England?

Working at the Bank it’s hard not to be interested in data! So much of what we do as an organisation is data driven, with access to some incredibly rich datasets enabling interesting analysis. In Advanced Analytics, we leverage a variety of data science skills to support policy-making and facilitate the effective use of big, complex and granular data sets. As a data scientist, I get involved in all of this, working across the data science workflow.

What’s the inspiration for your talk  – effectively data science at speed?

As with so much recently – Covid. With how fast things have been moving and changing, traditional data sources that policymakers were relying on weren’t being updated fast enough to reflect the situation.

Can you tell us about your data team’s journey through covid-19 and the impact it has had?

In a recent survey, the Bank of England sought to understand how Covid has affected the adoption and use of ML and DS across UK Banks. Half of the banks surveyed reported an increase in the importance of ML and DS as a result of the pandemic. Covid created a lot of demand for DS skills and expertise within the Bank of England too. Initially this led to some long hours, but it was motivating and generally rewarding to work on something so clearly important. Working remotely 100% of the time was a challenge at first, but generally the transition away from the office has been remarkably smooth in terms of day-to-day working (though there are still disadvantages due to the lack of face-to-face contact). As outputs have subsequently been developed and shared widely in the organisation, they have been an excellent advert for data science, showing the value it can add. In particular, it’s been great to see the business areas we worked with building up their local data science skills as a consequence.

What’s the talk about and what are the key takeaways?

The talk will cover some of the techniques we used to get, process and use new data sources under time pressure, including what we’ve learnt from the process. The key takeaways are:

  • Non-traditional datasets contain some really useful information – and can form part of the toolkit even in normal times;
  • Building partnerships is key;
  • A suite of useful building blocks, such as helper packages or code adapted from cleverer people helps speed things up;
  • Working fast doesn’t mean worse outcomes.

We look forward to seeing you at Mango’s Big Data London, Meet Up, 22nd September 6-8pm, Olympia ML Ops Theatre. You can sign up here.

Guest speaker, Adam Hughes is one of The Bank of England’s Data Scientists,

flexing analytical muscle
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Mango’s ‘Meet-Up’ at Big Data London on 22nd September features guest speaker Premal Desai, Head of the Data & AI function for The Gym Group.

For the Gym Group, advancing customer retention and pricing strategies have been critical drivers to success. Their focus over the last few years has been to deliver value from their analytic investments and to develop a stronger analytical culture. You can read about their data journey in this short interview with Premal – the key successes and challenges they have overcome.

 Introducing Premal Desai, Head of the Data & AI

Premal currently leads the Data & AI function for The Gym Group where he oversees a high performance team covering Engineering, Analytics and Data Science. Prior to this, Premal spent almost a decade with Thomson Reuters, leading Strategic Marketing & Analytics functions globally. He has previous experience across Europe working for Orange, and spent several years with PA Consulting in their Strategy & Marketing Practice. Premal is a 2021 Board Advisor for the Business of Data, a Seed Investor for a number of start-ups and outside of work, Premal is a passionate Liverpool FC fan + renowned for his desire of fast German automobiles!

Can you tell us a bit about your role at The Gym Group?

I’ve been fortunate to have a varied and interesting career over the past 20 years – spanning both client side and consultancy roles. I’ve worked across different sized organisations, covering Telecoms, Financial Services and most recently Health & Leisure. The two common passions for me are getting to grips with understanding numbers and customers! I currently look after the Data & AI team at the Gym Group which allows me to combine these passions – I have a small, but very capable team of Engineers, Analysts and Data Scientists who support the business to uncover insights and drive growth.

Can you tell us a bit about your journey with data at The Gym Group?

The Gym Group are generally a data-oriented business, which is fortunate given my role! Over the past 3 years, we’ve been on a mission to grow our capability across a number of areas – the depth and breadth of data used across the organisation, to improve the level of granularity and sophistication of data & analytics and to turn data into more of a strategic asset. As part of this, we’ve also expanded the range and skills of our people, how we consume data and grown the range of insight we’re able to provide.

How important has being data driven been as part of your recovery post covid?

During COVID, data was absolutely central to give the business confidence across a number of areas during a unprecedented period of time. For example, we wanted our customers to feel safe using our facilities once they opened – and so the company developed a capability showing live Gym Busyness via mobile – and we used various models to help us determine appropriate levels of Gym occupancy. As a business, it’s actually helped us to see data use cases more strategically and we’re delighted to have seen really positive business recovery since Gyms re-opened in April 2021.

Can you tell us about your talk ?  what should we expect and what are the key takeaways?

The talk will be focused on a couple of really important commercial drivers for our business – Customer Retention and Pricing – and the data / data science journey we’ve been on in these areas over the past couple of years. For balance, we’ll talk about both successes and some practical challenges. In addition, we’ll also speak to the importance of culture and ROI, and some factors that I think have been important on our journey. In terms of key takeaways – I encourage you to attend the talk!

Keen to join us? Premal’s talk at the Data Science Meet-Up is featured as part of the Big Data LDN line up at 6-8pm, 22nd September , AI & MLOPs Theatre. Register here.

We hope to see you there.


Data Science Competency for a post-COVID Future
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Written by Rich Pugh, Chief Data Scientist, Mango Solutions (an Ascent company)

The COVID-19 pandemic disrupted supply chains and markets and brought economies around the globe to a standstill.

Overnight, governments, public sector agencies, healthcare providers and businesses needed access to timely and accurate data like never before. As a result, demand for data analytics skyrocketed as organisations strived to navigate an uncertain future.

We recently surveyed data scientists working in a variety of UK industry sectors, asking them about:

  • how their organisation’s reliance on data changed during the pandemic
  • how their teams are having to re-align their skills sets to deliver the intelligence that’s needed; and
  • top trends on the horizon as organisations pursue a data-driven post-COVID recovery.

What they told us offers some interesting insights into the fast-evolving world of data science.

Decision intelligence gets real

Our findings highlight how the sudden disruption of the COVID-19 pandemic brought the importance of data analytics sharply into focus for business leaders and decision makers across the enterprise.

Almost two-thirds (65%) of those surveyed said that demand for data analytics rose across their organisation. The top request areas for problem-solving and enabling informed strategic decisions included:

  • Immediate crisis response (51%) – risk modelling, digital scaling and strategy as organisations looked to make near-term decisions to address key operational challenges.
  • Informing financial/cost-efficiency decisions (33%).
  • Logistics/supply chain (26%).

As reliance on data became mission-critical, data scientists in some industry sectors were at the nerve centre of COVID-19 response efforts as organisations looked to solve real-life problems fast.

Data scientists are adapting their skills sets quickly

As organisations beef up their data strategy to better prepare for future disruptive events and thrive and survive in the new normal, data scientists are having to adjust to new ways of working and adapt their skills sets fast. Indeed, 49% of data scientists say their organisation is now investing in building their internal capabilities through learning and development programmes, with 38% actively recruiting to fill gaps.

Now part and parcel of the enterprise decision-making team, data scientists confirm they are having to hone their business and communication skills to ensure they are able to support business leaders across the organisation better. Indeed, an impressive 34% identified working more effectively with business stakeholders was now a top priority. With data now being used more broadly across the organisation, one-third (33%) of the data scientists confirmed that they plan to boost their own communication and business skills so they can interact more cohesively with business leaders – and collectively identify the right problems to solve for their organisation.

Top data trends for 2021

As organisations continue to push ahead with operationalising their data and analytics infrastructures to handle complex business realities, data scientists are scaling up their deployment of machine learning algorithms to automate their analytical models.

According to our poll, upskilling their machine learning (ML) skills was identified as the #1 priority for 45% of data scientists as they look to accelerate their AI and ML computations and workloads and better align decisions throughout the organisation.

Similarly, big data analytical technologies (such as Spark, Storm and Fink) was the top priority for 39% of UK data science teams, as was getting to grips with deep learning (39%) as analytics teams look to jointly leverage data and analytics ecosystems to deliver coherent stacks that facilitate the rapid contextualisation decision-makers need.

Finally, with more people across the organisation becoming increasingly dependent on data-driven decision making, data scientists are having to find new ways to present data in ways that business teams will understand.

In a bid to democratise data and support faster decision making on the front line, they’re working on increasing their skills in areas like data visualisation (27%) and modelling (23%) so they can tease out trends, opportunities and risks in an easily digestible way that makes it easy for decision-makers to consume and engage.

New opportunities on the horizon

In a post-COVID world, organisations are looking to tap into an increasing number of data sources for the critical insights they’ll need to tackle emerging challenges. In response, data scientists are having to extract and analyse data quickly – even in real-time – and in the right way. Integrating data-driven insights into the decision-making process.

In response, data scientists are having to upgrade their technical and business skills as organisations look for efficient and innovative ways to use the big data at their disposal.

In summary, the research highlights both how important it is to align central data communities in order to boost and demonstrate value across the business, while ensuring that investment in L&D programmes is fully aligned with developing trends and business objectives.



NHS-R Community
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The NHS is one of the UK’s most valued institutions and serves as the healthcare infrastructure for millions of people. Mango has had the pleasure of supporting their internal NHS-R community over the last few years, supporting the initiative from its inception and sharing our knowledge and expertise at their events as they seek to promote the wider usage and adoption of R and develop best practice solutions to NHS problems.

According to a recent survey by Udemy, 62% of organisations are focusing on closing skills gaps, essential to keeping teams competitive, up to date and armed with the relevant skills to adapt to future challenges.  For many institutions, an important first step is connecting their analytics teams and data professionals to encourage the collaboration and sharing of knowledge. With ‘Data literacy’ fast becoming the new computer literacy, workforces with strong data skills are fast realising the strength and value of such skills across the whole organisation.

As the UK’s largest employer, comprising 207 clinical commissioning groups, 135 acute non-specialist trusts and 17 acute specialist trusts in England alone, the NHS faces a particularly daunting task when it comes to connecting their data professionals, a vast group which includes clinicians as well as performance, information and health analysts.

The NHS-R community was the brainchild of Professor Mohammed Mohammed, Principal Consultant (Strategy Unit), Professor of Healthcare, Quality & Effectiveness at the University of Bradford. He argues,  “I’m pretty sure there is enough brain power in NHS to tackle any analytical challenge, but what we have to do is harness that power, promoting R as the incredible tool that it is, and one that can enable the growing NHS analytics community to work collaboratively, rather than in silos”.

Three years in and the NHS-R Community has begun to address that issue, bringing together once disparate groups and individuals to create a community, sharing insights, use cases, best practices and approaches, designed to create better outputs across the NHS with a key aim of improving patient outcomes.  Having delivered workshops at previous NHS-R conferences, Mango consultants were pleased to support the most recent virtual conference with two workshops – An Introduction to the Tidyverse and Text Analysis in R. These courses proved to be a popular choice with the conference attendees, attracting feedback such as “The workshop has developed my confidence for using R in advanced analysis” and “An easy to follow and clear introduction to the topic.”

Liz Mathews, Mango’s Head of Community, has worked with Professor Mohammed from the beginning, sharing information and learnings from our own R community work and experience.  Professor Mohammed commented:

“The NHS-R community has, from its very first conference, enjoyed support from Mango who have a wealth of experience in using R for government sector work and great insight in how to develop and support R based communities. Mango hosts the annual R in Industry conference (EARL) to which NHS-R Community members are invited and from which we have learned so much. We see Mango as a friend and a champion for the NHS-R Community.”

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In 2020 the EARL conference was held virtually due to the restrictions imposed by COVID-19. Although this removed the valuable networking element of the conference, the ‘VirtuEARL’ virtual approach meant we reached a geographically wider audience and ensured a successful conference. Thought leadership from academia and industry logged in to discover how R can be used in business, and over 300 data science professionals convened to join workshops or hear presenters share their novel and interesting applications of R. The flexibility of scheduling allowed talks to be picked according to personal or team interests.

The conference kicked off with workshops delivered by Mango data scientists and guest presenters, Max Kuhn of RStudio and Colin Fay from ThinkR, with topics including data visualisation, text analysis and modelling. The presentation day both began and finished with keynote presentations: Annarita Roscino from Zurich spoke about her journey from data practitioner to data & analytics leader – sharing key insights from her role as a Head of Predictive Analytics, and Max Kuhn from RStudio used his keynote to introduce tidymodels – a collection of packages for modelling and machine learning using tidyverse principles.

Between these great keynotes, EARL offered a further 11 presentations from across a range of industry sectors and topics. A snapshot of these shows just some of the ways that R is being used commercially: Eryk Walczak from the Bank of England revealed his use of text analysis in R to study financial regulations, Joe Fallon and Gavin Thompson from HMRC presented on their impressive work behind the Self Employment Income Support Scheme launched by the Government in response to the Covid-19 outbreak, Dr. Lisa Clarke from Virgin Media gave an insightful and inspiring talk on how to maximize an analytics team’s productivity, whilst Dave Goody, lead data scientist from the Department of Education, presented on using R shiny apps at scale across a team of 100 to drive operational decision making.

Long time EARL friend and aficionado, Jeremy Horne of DataCove, demonstrated how to build an engaging marketing campaign using R, and Dr Adriana De Palma from the Natural History Museum showed her use of R to predict biodiversity loss.

Charity donation 

Due to the reduced overheads of delivering the conference remotely in 2020, the Mango team decided to donate the profits of the 2020 EARL conference to Data for Black Lives. This is a great non-profit organization dedicated to using data science to create concrete and measurable improvements to the lives of Black people. They aim to use data science to fight bias, promote civic engagement and build progressive movements. We are thrilled to be able to donate just over £12,000 to this brilliant charity.

Whilst EARL 2020 was our first such virtual event, the conference was highly successful. Attendees described it as an “unintimidating and friendly conference,” with “high-quality presentations from experts in their respective fields” and were delighted to see how R and data science in general were being used commercially. One attendee best described the conference: “EARL goes beyond introducing new packages and educates attendees on how R is being used around the world to make difficult decisions”.

If you’d like to learn more about EARL 2020 or see the conference presentations in full, click here.

Mango's success - a data conversation
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As we approach the new year, it seems an appropriate time to look back at how Mango’s 18-year history has reflected the evolving landscape of the data industry. It’s hard to believe that founders, Matt and Rich, have been sharing the data story since 2002, long before the term ‘data science’ gained popularity, and well before most organisations had begun to recognise the value of their data.  Matt and Rich have borne witness to this data revolution, via the big data era through to the current day where data is recognised as a new class of economic asset; universities routinely offer data science courses and Government departments have adopted algorithmic decision making.

Championing transition projects focussing on productivity through data science and a move towards repeatable and scalable models, Mango’s emphasis has been on ingraining data as part of a company’s DNA and supporting the creation of a data-driven culture.

It’s easy to see how the co-founders have remained at the forefront of the industry for so long, delivering data science projects to some of the world’s best-known companies. They credit their longevity to their open, honest and outcome-focused way of doing business and their deliberate shift from analytics as a reactive tool to adding value and the insights to drive decision making.

Asked about the most notable transition they had seen over the years, Matt referenced how the world has changed: “You can’t have barriers of data within organisations. Siloed data and analytics teams were once the norm, but these create structural, cultural and technological obstacles, wasting resource and inhibiting productivity. Many of the biggest challenges associated with data are not so much analytic problems, but fundamental information integration issues. Technology has moved at a huge pace in the past decade and that continuum between software advances and a recognition of the importance of data grows ever closer.”

Secrets of Success

There have been many secrets to Mango’s success, starting with its name.  “We considered lots of options incorporating ‘Statistics’ or ‘Analytics’ but they all seemed rather dull or dry and, in retrospect, would have dated very quickly,” remembers Rich. “Whilst ‘Stats Entertainment’ was just one of Matt’s inspired suggestions, our decision to name the company Mango, after his cat, has allowed us to continue to evolve and stay relevant through all the technological changes of the past 18 years.”

The name aside, it’s the founders’ approach that has been the real secret of their success. “Data for us has always been a way of doing business”, says Matt. “Looking back, we were right to place the emphasis on using analytics to empower end users. Our business has always been about making sense of data science, building out the capability by finding the experience, looking for knowledge and focussing on skills transfer and developing autonomy and support.  We’ve always believed in making data science easier for organisations, working alongside them and helping to broaden the scope and skills of the inhouse teams”.

Matt and Rich are unanimous that a vital element in Mango’s success, has been its people. “We’ve been lucky enough to attract extremely talented people, whilst also having a very successful internal graduate programme,” confirms Matt.  “My father’s advice was always to surround yourself with the best people and that’s exactly what we’ve managed to achieve. It was a proud moment to see that this year’s DataIQ list of Top 100 data professionals featured not only Rich, but also two of our former colleagues.”


There have been many highlights along the way, but for Matt and Rich there have been some standout memories and high points over the past eighteen years.  “Standing on the platform at Zurich train station celebrating our first major contract win was a very memorable moment,” recalls Matt. “It was the point when we realised that we really were onto something new, securing a big customer who’d been won over by our style and attitude.”

A particular Mango achievement is their work in the R Community, including the creation of EARL (Enterprise Applications of the R Language), the first commercially focused R conference. The first EARL conference was delivered in 2014 and is now a firm annual fixture for R users across the UK and Europe.  Previous iterations have also seen EARL conferences delivered across the US. The original idea for the conference came from Rich, and the event is entirely organised and run by Mango staff. “The culture and openness displayed at EARL is fantastic, with companies keen to share their knowledge and use cases and talk frankly about their R journeys” remarks Rich. “Our work within the R community and the recognition that Mango has received for our R user groups and EARL is something we are particularly proud of.”

Lessons learned

Mango’s initial work was primarily within the life sciences and financial sectors. “A lot of our early work was in highly regulated industries and the rigour of working in those environments was massively valuable”, recalls Rich. “Everything we learned in those regulated industries we now deploy across industry ensuring a robust approach and the delivery of best data science practices and real practical advice.  Whilst much of our early work was in SAS, S- Plus and R, Mango has always been agnostic about tech, working within whichever language best meets our clients’ requirements and objectives; these days much of our work is in python.”

A phrase that resonates with Mango is ‘Give a man a fish and you feed him for one day; teach a man to fish and you feed him for a lifetime’.  “We work alongside our clients, mentoring and helping to upskill their teams, leaving them able to operate independently at the end of our involvement,” states Rich. “This approach is greatly valued by our customers, irrespective of where they are in their own digital transformation journey, who recognise the value that we add.”

Teamwork is at the heart of Mango’s work, whether it’s working in internal teams or as part of a client’s team. The introduction of the Belbin framework has been enormously useful in creating a team structure and awareness of individuals’ behavioural strengths, fostering more effective communication. “We started by employing the right people”, said Rich, “but the Belbin framework and our own Trusted Consultant programme has cemented a really productive team ethos.”

“Looking back, if there was one thing that we wished we’d done earlier, it would have been to introduce a marketing presence,” mulls Matt. “We were fortunate to grow organically and benefit from recommendations and repeat business, but in the past couple of years, the work undertaken by our marketing team to promote Mango to a wider audience has resulted in awards and recognition that have really amplified our presence and message.”

Looking ahead

“We are extremely proud of the company that we have built,” attests Rich, “and today Mango is focused on facilitating the sorts of conversations that we recognised as needing to be had some 18 years ago when we first founded.  We urge businesses to embrace methodical and pragmatic data processes before they dive in at AI/ML-level but are grateful, at least, that these latter tools have finally provoked the data conversation”.



where does digital value lie?
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Alex James, CTO at Ascent, helps us track down value in an increasingly digital age.


“Companies that don’t or can’t keep up in this age of digital transformation are going to get left behind by their competition,” says everyone, all the time.

But what does that mean, and where and how should companies be investing to actually drive digital transformation going into 2021?

We are currently seeing high levels of investment in a few key areas:

  • Predictive data & analytics
  • Data warehousing & reporting
  • Smart buildings
  • Industry 4.0 smart machinery
  • Artificial Intelligence
  • Smart asset monitoring

And in some areas we have seen a downward turn of interest – such as blockchain, drones and virtual reality.


Two things that become very clear when looking at the list are that investments are being made in areas where software and data meet, with high levels of interdependency. For example, smart buildings are used for asset monitoring purposes and contribute to data warehouses, which can be used for predictive purposes to drive efficiency gains. Each link in the chain incrementally increases value.

This interconnectedness is where we see the most value creation for organisations who get it right. Looking at investments in isolation, it’s hard to see the ROI against implementation costs and risks. When you look at these investments as an interconnected web (each node driving inputs and outputs with value generation at the centre), that picture changes. Technology areas which are seen as stand-alone drivers such as VR are struggling to attract the same broad levels of investment.

However, digital transformation is a journey, not a destination, and simply investing in a web of technologies doesn’t guarantee success, or even any kind of return. CIOs need to think further ahead and carefully balance current pain with future anticipated needs. Organisations are understandably rarely in the position to take giant leaps away from models and processes that have made them successful today, so evolutionary roadmaps that typically span 3-5 years are a common approach. A strong roadmap constantly evolves, actively acknowledging obsolescence, technical debt, and the operational pain of change – balancing these against technology’s ability and responsibility to deliver radical organisational improvement.


An organisation’s ability to deliver successful change therefore depends upon the ability to execute both the technology roadmap and change management activities in sync. New capabilities in IoT or AI for example will only ever deliver value as part of a cohesive web of solutions – they are not the standalone ‘silver bullets’ some businesses expect them to be.

This is a bit of a move from some ideologies of the past. Lean practices have proven very successful in start-up technology companies and have spilled over into larger organisations. However, this approach may not be well suited to modern digital transformation projects. Focus on short-term ROI and individual projects is typically embedded in change-resistant organisations, leading to piecemeal investments without a strong roadmap and vision, which leads to poor returns as valuable data and information stay locked in siloes unable to drive or consume value from the rest of the organisation.


One of the main obstacles to overcome in forming a strong digital strategy and not falling into this trap is the acknowledgment of pace of change and obsolescence. In the world of IoT, capital investments have often been written off long before they’ve even been depreciated off the balance sheet. Why is this? Unreasonably high ROI requirements, lack of flexibility in the original solution and lack of interoperability are all key culprits.

These experiences tend to make CIOs more cautious and pessimistic about their outlooks. The landscape right now is changing in regard to data sensitivity regulations, growing data sizes, cost of staff with the skills to maintain systems and lack of interoperability between solutions. All of these if ignored can cripple a solution and turn the return negative over time as they layer on increased cost and complexity.

However, all of these challenges can be overcome with a strong strategy. Capability-driven models that outsource much of the heavy lifting to SaaS providers and place cloud-based capabilities like Azure at the centre of their architectures remove much of the risk around data management. Similarly, carefully planned integration architectures and service-oriented designs with comprehensive APIs allow for changes down the road and a plug-and-play type approach to expanding services.


While just a handful of years ago traditional hardware-centric IT skills may have been sufficient to maintain business operations, most organisations are finding themselves in a place where access to modern programming and software engineering skills are table stakes to keep their strategy on track. Over the next few years, skills such as data engineering and data science will start to move to the top of that list. So, another large part of setting a successful digital strategy is talent-focussed – not only in training and upskilling but in understanding, balancing and forecasting external vs internal expertise requirements: which capabilities belong in-house and which should be rented as a service and consumed as an Opex item.

Digital transformation doesn’t just change the technologies people use, but also how people work. We are moving into an age where cultural and process change needs to happen in step with technology change, and where an organisation’s technology proposition needs to be thought of as an interconnected web that creates value. The cost of implementing just one node or solution may not seem to create enough value in isolation, but as part of the whole value-producing web, it becomes an absolute necessity.

In summary…

Leaders and CIOs need to remember that internal capabilities are only part of the solution – limiting your ability to execute to your own domain of expertise will ultimately be restrictive. The fastest solution isn’t always the best, but well-paced solutions that take into consideration all other transformation vectors will always win in the long run. And roadmaps that directly deal with and allow for the realities of change, obsolescence and technical debt tend to be the most successful.

So, the answer to where does digital value lies is, counterintuitively, not in any particular area of investment or technology, but in the interconnected web of data, action and insight that lies between those investments, driven by a strong overarching digital strategy.