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If you managed to attend our LondonR in-person event in January, you will have noticed that the events team has gone purple as the baton officially passes from Mango Solutions to Ascent in supporting all R community events, from Bristol, Manchester and LondonR to the annual flagship EARL conference.

The Enterprise Applications of the R Language Conference (EARL) is a cross-sector conference focusing on the commercial use of the R programming language, and this year it’s taking place in person on the 6th – 8th September at the Tower Hotel in London.

With Mango founder Rich Pugh at the helm as Ascent’s Chief Data Scientist, we’re super proud to be continuing the legacy and picking up the organisation of industry favourite EARL, an event that has built an enviable reputation since it was introduced in 2014. Attracting presenters from some of the world’s leading brands and an audience of some of the most influential people in R, EARL has developed into a must-attend conference for many, and the source of innovative new solutions to real-world commercial challenges.

 

“EARL The conference for data scientists who use R.”

 

Real commercial R use cases.

As one of the world’s most widely adopted analytic languages, R has gained tremendous traction in data science projects over the years and today remains the programming language of choice for most statisticians. R’s vibrant package ecosystem and strong community and resources gives data scientists the ability to tackle any analytical challenge, from economic risk management to customer behavioural analysis. Across the pandemic, we saw R play a pivotal role on the global stage in informing effective, data-led decisions across an evolving landscape. The EARL conference attracts the same diversity of applications: past talks feature a wide variety of topics from journey planning to preventing human trafficking and technical talks on app development in R-Shiny and DevOps. Here’s some examples from previous years:

  • Using data to help reduce station overcrowding – Transport for London
  • Data to Deployment: Overcoming the challenges of embedding R models in Production – Royal London
  • Using data to flex analytical muscle: How data science, culture and commercial rigour comes together to drive better ROI – The Gym Group
  • Using data to determine how much milk cows produce – Arla Foods
  • Using data to drive better decisions – Hiscox

EARL is renowned for making delegates feel inspired, both by the creative work they see and in conversation with other R users. If some of these use cases have inspired you to tell your story – our call for abstracts is now open and the most appreciate and attentive audience in the industry is waiting for you…!   Submit your abstract here.

 

“EARL has given me things to change TODAY and other things I’ll be thinking about for a long time.”

 

5 reasons to attend EARL 2022.

EARL attracts a huge following and is supported by companies year after year. It offers great opportunities to learn technical skills and gives you the chance to explore solutions to the common issues facing the community. In conversation with your team, here’s some of our top reasons EARL 2022 needs to be on your list of must-attend conferences:

  • Compelling keynotes: We’ll be revealing our headliners in the next few weeks but rest assured they’ll be unmissable…
  • Deep dive workshops delivered by leading R consultants: EARL’s ever-popular Day 1 agenda features a variety of workshops for 2022 designed to enhance your skills in Explainable Machine Learning, Time Series Visualisation, Shiny, Purr and Plumber, backed by a library of resources.
  • Custom agenda: Build your own conference programme from the 60+ sessions on offer, based on relevance to your industry or use case (or just because!)
  • Expand your network: Invaluable networking opportunities throughout the conference. Meet like-minded professionals and enjoy a cocktail with the best in the business.
  • Creative playground: Get inspired by the latest solutions and explore ideas that you can take back to your business.

We came away inspired by some of the sophisticated work and creative ideas we saw and a new perspective on issues facing the community. EARL is a highly recommended event for anyone using R to support their business.

Whether you’re coding, wrangling data, leading a team of R users or making data-driven decisions, EARL 2022 offers insights you can immediately action across your company. We can’t wait to see you there.

Tickets go on sale in May 2022.

 

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Our first speaker at London R is Giles Heywood who works as Chief Data Scientist at Seven Dials Fund Management. As an alternative property specialist, he uses model-driven strategies to support residential property investment – and as a user of R for 20 years, and author of ‘its’ package for irregular time-series (published on CRAN), he naturally turns to R for all analysis.  

Once a proof of concept, his robust and optimised product readily models district, area and regional property trends, cycles and risks. 

How can the right data support property choice?  

There is a growing appetite among investors for real estate alternatives – including student accommodation, senior housing, build-to-rent residential and hotels, which can offer better prospects for income growth. It also offers risk-adjusted returns than the traditional commercial real estate segments.  

The 7-strong team at Seven Dials Fund management takes a structured and systematic approach to direct real estate investment and also indirect investment through funds in this way.   

Whilst commercial real estate lacks comprehensive open data on transactions, residential property benefits from transparent and complete data on crucial variables of transaction price, floor area and income data to model the dynamics of affordability.  

Our approach is a meticulous analysis of the systematic drivers of return and the regular and often predictable patterns generated in long cycles. For a first-time buyer that can choose a property between small and expensive and larger but cheaper, the right data could help the most appropriate choice and its impact on the future property ladder progression.    

Is modelling in a property-related application fairly unique?   

Although Seven Dials primarily advises institutional clients on large portfolios, some of the most exciting opportunities are in delivering quantitative insights to homebuyers and in particular high net worth investors. We see important synergies or at least significant overlaps between institutional and retail.   

For many, buying their property is one of the most significant financial decisions they will make. Imagine if data science could be used to support decision making in line with their mortgage in the future. The housing market has generally gone up since the key ‘Price Paid’ dataset appeared in 1995, however in 2008 we saw falls of 15-20% nationwide, and in some areas prices have only recently regained 2007 highs.  Both the relative returns and risks can be tracked, modelled and managed. Of course institutions have models for property risk and return, and had sophisticated models back in 2007 which to some extent failed in the crash.  Technology has moved on considerably, aided by t-copulas, non-parametric bootstrap and stress-testing. What our team has done is not to copy others, but to start from the ground up with the best repeat sales indices we can construct, factor risk models, and forecasting consistent with those foundations.   

And how are data science models used for residential property investors now?   

There are some prototypical models on the major portals, and one of the most popular is automated valuation models (AVM).  We don’t do that, for all kinds of reasons, but it’s very appealing for individuals to get updated valuations on their homes and maybe on others, including those that are not on the market.     

What will your talk focus on and what might be the key take aways from your talk? 

My specific contribution to modelling at Seven Dials is projecting relative return within sectors of residential real estate to an investment horizon, using factors. The first factor is the overall market direction and is the sort of macroeconomic variable that is quite hard to predict, so for example an unforeseen pandemic did not hold back the market – to the surprise of many. However the relative price performance is more foreseeable since it is essentially driven by microeconomic forces, and in particular by affordability.   

In addition to the models’ straightforward price-forecasting applications for homebuyers the same analytical framework will be familiar to institutional investors and lenders, and can provide strategies for risk-controlled portfolio management.    

I’ll take you on a highly focused and structured trip through a stack of three models and show how they relate both to familiar ideas like the ‘ripple effect’ but also give precise insights into a long cycle driving relative returns both locally and nationally.  Everything is in R, and I’ll link it to some of my package choices for getting both coding and analysis done fast and accurately, or at least I can answer questions about that.  

Using R to Model UK Residential Property by Giles Heywood 

Will you be joining us at LondonR ? Giles Heywood who works as Chief Data Scientist at Seven Dials Fund Management uses model-driven strategies to support residential property investment. In his talk, discuss how both the relative returns and risks in property investment can be tracked, modelled, and managed.  Join us at LondonR  

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CIO’s globally ranked analytics and business intelligence as one of the most critical technologies to achieve the organisation’s business goals, with data and analytics skills topping the list as the most sought-after talent. As we embrace digital transformation, it’s clear that the need to upskill and resource data science teams has become far more pronounced.

Building a successful and lasting data community can often be one of the most significant hurdles to overcome, which takes time and effort. Often establishing and maintaining a thriving collaborative approach to analytics, with the right ecosystem for your community, can be a challenge.

Naturally there’s a growing need to ensure analytic teams have access to the best tools and latest methodologies to perform their analysis and find business wisdom. Alongside identifying the analytics skills already in place, a great place to start is also to identify the best tool for the job.

Nurture and aligning members of the community

Pulling together existing disparate data science resources into a single, connected community of practice, creates a secure foundation to grow analytic talent. Having such a community means the business will have a better understanding of the skill sets that exist within the organisation already, as well as best practice examples for approaching different scenarios and a better awareness of the tools and solutions that can be used.

Defining the right tools for the community

R and Python are still the two most popular and adopted programming languages. Both tools are open source, free to use and cover pretty much everything data science-related.

R was developed specifically for statistical analysis, so naturally is the popular language choice for statisticians. R has a large user community and an actively developed large library of packages which enables effective analytics. However, R can require a steeper learning curve and people who do not have prior programming experience may find it difficult to learn.

Python on the other hand, is considered the easier of the two most popular languages to learn. Its domination in machine learning is well-known. With an increasing community base, Python is commonly taught in Computer Science lessons in Schools and therefore the rated language of choice in academia. However, Python can be considered to have its limitations especially around speed and memory, so best practice use should be applied when considering Python.

It’s not a debate as such on which language to use, but more a conversation around empowering a team to become multilingual and multiskilled, so they can use the best language for the application.

Up-skilling of analytic talent

For an organisations analytics function to thrive, it’s critical to continually attract, develop, and retain key data skills & capabilities. Understanding the mix of skills within a data science team, as well as identifying gaps to unify skills & knowledge, is vital to drive analytic value. Establishing the support of a dedicated Learning and Development partner, who provides live, instructor led, data science training programmes, designed to equip and enthuse a data team with the latest approaches, can help address this challenge & unlock business gold.

Enabling training at all levels of data awareness will be critical, and this should even include training on how to use information, to guide decision-making.

Building a successful community provides a solid basis for working out where the talent pool needs to be extended, unifies talent across the business and enables quick wins towards embedding the right culture and building the required capability.

After 20 years of experience, we are a trusted data science L&D partner to leading brands worldwide. We train thousands of data science and analytical teams every year from a range of industries and backgrounds.

 

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There continues to be a lot of hype around data science (and it’s evolving world of connected buzzwords such as AI, ML etc).  This is driving unprecedented investment in the field, driving the race for data & digital talent into overdrive. But there is scarce data available on the return on investment in data science.  This can make it difficult to make a good business case for investment, and can hamper good discussion around how to size your data team. 

To resolve this, in November we released a report, Delivering Value Through Data Science, conducted by Data IQ, the only membership business focused on the needs of data and analytics professionals from large and midsized businesses. That’s an important start because if you want expert outcomes, we believe you should ask the experts… 

Before we get into the results, I want to sound a note of caution (well, I’m a statistician after all – no figures without caveats from me!).  Whilst the data we got back is interesting, this is a relatively small sample size and more research is needed in this area.  And of course, surveying companies associated with Data IQ will also introduce bias around the maturity of the organisation and the leadership qualities around the data teams. 

That being said, here’s an eye-popping but very debatable figure: The successful application of data science could lead to a 17.9% uptick in revenue for organisations with a “mature” data science capability. 

Ambitious but not impossible, perhaps? Previous successful generations of major IT change such as ERP or e-commerce could justifiably be said to have significantly inflated revenues at many companies (think of Tesco in online retail, for example). But we need to acknowledge that it’s very hard to pin success down precisely to a specific action; growth comes in many ways. How, for example, do you measure quality of execution, committed leadership, motivated employees? These challenges do not, as yet, have ready answers within the data science community. 

So while impressive, a figure of 17.9% feels unachievable for an organisation starting on their data journey.  Too high to be believed, perhaps (but clearly a figure many of the leading data-enabled organisations will stand behind).But, working in the space and seeing data science in action across organisations, the average figure of 6.7 per cent revenue growth (across all organisations surveyed) seems plausible. So, let’s agree in principle that careful investment in an ability to interrogate data and (crucially) act on these results and good things will happen for your business. 

We have definitely seen plenty of anecdotal evidence through our 19 years delivering data science projects, that back up at least this 6.7% figure: 

  • We’ve seen successful numerous data-led customer retention project reduce churn by at least 8% in our customers;  
  • Our higher ROI single data science project (a next best action engine in finance) was directly responsible for an 8 figure uptick in revenue;  
  • Deploying analytic solutions in areas where subjectivity has previously reined will routinely yield incremental gains of over 30% in target metrics. 

In our experience, the scale of the return is typically driver as much by change and culture as it is by algorithms and code.  Blocking the road to change are the familiar foes of inertia, cost justification, change management and skills. Ultimately, leaders must lead and drive their organisations forward to become data-enabled and data-driven because, as ever, business alignment is key.  

The DataIQ survey found a 4x increase in value where strategy is aligned with investments. Again, this matches well with our experience – doing data for the sake of doing data is very unlikely to yield results, and walking around your business with an AI-shaped-hammer looking for something to hit is not the way forward.  Data and analytics needs to be focused on, and deliver on, your business ambitions. 

Those that don’t lead from the front and pursue long-term data science programs will see rivals make better, more auditable decisions faster and will become cannon fodder for internet giants edging into their markets and for startups with no legacy. 

Today, pundits queue up to tell us becoming a data business is essential, and of course they’re right. We can’t all be Google but we can learn about data, harness it and apply it. Goldman Sachs hired more software engineers than Facebook, Gousto applies data to “little touches” that delight customers, Black and Decker reengineered production and dispensed with what one executive called “lazy, rusty asset syndrome”. These are all companies applying data to understand customer behaviour, improve service and drive competitive differentiation. This is not all about Tesla, Netflix and Amazon: data science needs to be pervasive and ubiquitous across all sorts of organisations. Start small to convince with data science quick wins by all means, but leaders also need to be persistent and not expect overnight gains. As with other game-changing capabilities such as AI, they need to be in it for the long term and learn as they evolve. 

“Data that is loved tends to survive,” said Kurt Bollacker and companies need to learn to love their data. The opportunities (and the risks) are too high to act it any other way. “Culture eats strategy for breakfast,” said Peter Drucker, so build a culture of continuous business improvement supported by data (and data science) now that will lead to data maturity and better, more consistent results over time. 

Start now. Download the research paper, compare and contrast against your own experiences and start thinking about your next steps. You may not see a 17 per cent revenue hike anytime soon, but you’ll be on the right path. 

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2021 has been a year of inspiring and enabling businesses to be more digitally strong and data-driven. Following our acquisition by Ascent in November 2020, we’ve been able to support customers with integrated software and data capabilities, at a time when many European businesses have had to pivot to stay relevant in an increasingly digital marketplace.

Mango’s data science proposition is now fully embedded at the heart of Ascent, enabling our customers to build more intelligent, efficient and engaging organisations and create sustained competitive advantage. We’re focused on helping customers embrace digital transformation, enhance their digital capabilities and leverage opportunities at the intersection of data, software and platform.

Looking ahead to 2022, the new shape of our business will allow us to continue to deliver value to more and more organisations who are balancing purpose and profit.

Impact – Access to new talent communities and a wider geographical reach.

We’re now part of a 400+ strong team that’s growing – fast – in a vibrant market. Ascent’s HQ is in London, with a data science community located in the South West, specialist engineering hubs in Malta, Bulgaria and Spain, and smaller local teams in 14 countries worldwide.

Impact – End to end approach: data, software and platform

We are now able to offer end-to-end solutions to our customers. From the development of software systems, products and applications to data platforms, engineering, consulting and advanced analytic solutions we now deliver a greater breadth and depth of linked capabilities. You can find out more about this approach here.

Impact – A new consulting & strategy team

This year we have built a phenomenal data consultancy team at the heart of Ascent. We’ve recruited some of Europe’s sharpest data minds into the team, using this talent to help our customers transform their organisations. We’ve worked with customers on data literacy programmes and right-sized data strategies that guide investment, drive value and underscore future commercial success. And once we’ve explored the strategic challenge, we can support customers with the execution experience and skills to get it right first time.

2022 in data is set to be an exciting year, as companies continue to understand the value data science offers in making their organisations smarter and more agile – and therefore more resilient in the face of change. The world has shifted, and companies who kept the pedal down on data and analytics and invested in AI and machine learning to support their data-driven business model will emerge as leaner, smarter, more engaging businesses.

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After another fantastic EARL Conference held online this September, we are delighted to share that we were able to donate £8,000 to DataKind UK.

The aim of the Enterprise Applications of the R Langauge Conference is to inform, educate and inspire, and we certainly feel inspired by the great work DataKind UK have done this year and plan for the next.

Suzy from the DataKind UK team kindly sent us the statement below to share with the community:

At DataKind UK, we are looking ahead to ensure that we continue to provide what the sector needs to make full use of responsible data science. This year, we have heard the ideas, requests, and feedback of charities, our volunteer community, our staff team, our partners, and others in the sector, and distilled these insights into a new five year strategy. This generous donation from The EARL Conference will support us in our work to engage nonprofit leaders with data, to help our social sector data communities to flourish and grow, and to provide that crucial data support to social sector organisations through our DataDive, DataCorps projects as well as our soon-to-be launched mentoring programme.

Thank you Suzy! We look forward to reading about DataKind’s work over the next year.

We’re excited to tell you that the EARL Conference will be back in-person from the 6-8th September 2022 at the Tower Hotel, London. The call for abstracts will open in January – stay up-to-date and sign up to the mailing list here.

 

 

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Data science has made huge gains for early adopters and progressive businesses in the last ten years. For organisations in this digital era, spotting new opportunities, improving processes and working with greater insight into the variables that affect business success is vital.

Mango Solutions’ research report Delivering Value Through Data Science, which was conducted by DataIQ, shows the potential revenue gains from data science and some driving factors to build organisational data maturity. The study surveyed data practitioners, ranging from heads of department to chief data officers and global directors.

Companies surveyed with ‘advanced’ data science capabilities reported incremental value equivalent to 18% of business revenues may be generated as a direct result of an advanced data science approach. Those with less mature data science capabilities were more abstemious in their projections but even organisations at the early stages of data maturity claimed a respectable 4.1 percent revenue increase.

So, whether you are still in the planning phase or practicing advanced data science, with data-driven processes embedded in decision-making, data science will boost revenue.

It’s encouraging that data science is picking up in momentum. While the more radical 5.2% of companies kicked off with data science over 10 years ago, the majority, under 40% of businesses surveyed, began developing their data science capabilities in the last 3-5 years, and almost 30% of businesses surveyed started in the last 6 months. Being somewhere on the data-science journey is a must to support wider digital transformation success.

Getting the C-suite to back the data science strategy and incorporate it into the company strategy can unlock serious potential. Among organisations surveyed at an ‘advanced’ level of data maturity, 17 out of 20 (83.3%) data science functions were created on the initiative of the board. Embedding data science in the company vision is key, as those ‘advanced’ level organisations are more than 50% more likely to recognise data science within the company vision (33.3% v 20.7% on average).

It’s key to establish clear metrics to measure success, in order to be able to prove a return on investment and potentially bid for additional funds to grow the function later on. The report shows that 66% of organisations surveyed with ‘advanced’ data maturity can define the relevant impact metrics for each data science initiative.

Continual championing of data science by company chief executives can help build data maturity. ‘Advanced’ level organisations are more than twice as likely than the average to communicate the impact of data science directly through the chief executive (50% v 24.1%).

Investing in data science should be a long-term strategy, aligned to company objectives.

Our research results show an increasing investment in data science, for a variety of motives linked to the bottom line, including building efficiencies, creating competitive advantage and improving decision-making processes. Through keeping data projects as part of a strategic, leadership-backed and data-driven infrastructure, organisations will leverage data science to boost their business revenue.

This research is being discussed by data leaders at DataIQ Transform https://www.dataiq.co.uk/dataiq-transform-2021 on 4th November.  A copy of the research can be obtained here  

 

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You may remember that we launched a competition last month to celebrate the EARL Conference taking place. The competition was to win a free online training course for a team on either ‘Introduction to R for Analytics’ or ‘Introduction to Python for Analytics’.

We were thrilled to receive lots of brilliant entries and it was difficult to pick a winner. But after much deliberation the chosen winner of the competition is…..

Kirsten McMillan from Dogs Trust! 

Congratulations to Kirsten and her team. The Dogs Trust mission is to bring about the day when all dogs can enjoy a happy life, free from the threat of unnecessary destruction. They are a British animal welfare charity and humane society which specialises in the well-being of dogs. They are the largest dog welfare charity in the United Kingdom, caring for over 15,000 animals each year.

When asked why Kirsten would like to win this training for her team she said:

‘They are all extremely hard working and have proved hugely focused and flexible during the pandemic. They are all dedicated to improving canine welfare, which has become a significant issue during lockdown, due to the increased purchasing of dogs.’

We also asked Kirsten as part of her competition entry what her teams analytical objects are:

‘Our projects are very varied – so our analytical objectives are constantly changing. However, one of our aims is to become the go-to place for all UK dog statistics. Consequently, we are currently focusing on automating the collection, cleaning, and analysis of big data, along with the production of powerful datviz to be made available both internally and externally.’ 

On the team win Kirsten said;

‘We are so pleased and excited to have won this amazing prize! We are very much looking forward to the ‘Introduction to R for Analytics’ training with Mango Solutions, which will undoubtedly help us to help our much-loved dog population!

We are excited to support Dogs Trust move forward with their analytical journey, and perhaps see them present at a future EARL Conference!

EARL is back in 2022 in-person – the conference will be hosted from the 6th to the 8th of September in London. Abstracts will open for talks in January 2022 – sign up here to receive all the latest news.

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Welcome to our Analytics Leadership Series of blogs, where we investigate key challenges for analytics leaders and how they can:

  • Effectively demonstrate a return on investment (ROI) from data and analytics
  • Make a practical move to an open-source analytical environment
  • Master stakeholder relationships for a strong company data culture
  • Implement a Best Practice approach

In this first blog of the Analytics Leadership Series, we look at what makes a great analytics leader and the core challenges they face.

The qualities and skills of a successful data science team leader

A great analytics leader will succeed in four key areas. With some flair, they will be able to:

Match the right technical skills to business requirements

It takes understanding of data science use cases and a change mindset to align the demands of data-driven business improvements to the technical capabilities of the team so that you achieve the required solution for the business.

Proactively identify opportunities for improvement

It’s one thing to respond to immediate business needs – for example addressing a fall in conversion rates. But a great analytics leader will also identify opportunities from experience and through deep domain knowledge to help improve business performance– for example learning hidden patterns in customer behaviour for personalising campaigns.

Create a culture of learning and development

A good analytics leader will manage hiring and team structuring. However, a great one will maintain high levels of job satisfaction amongst its data scientists and analysts by creating a culture of learning and development to constantly upskill their team.

Inspire and encourage innovation

In a role that touches multiple scientific disciplines, a great analytics leader will inspire innovation by encouraging team members to hypothesise and experiment.

Guide businesses through a data journey

It is the responsibility of an analytics leader to tease out the “unknown unknowns” in a business, e.g. understanding the importance of unconstrained demand and the appropriate KPIs that measure the success of demand forecast.

Core challenges facing today’s analytics leaders

As an organisation matures, the remit, challenges and priorities of the analytics leader will change. Over the course of this ever-evolving journey, the analytics leaders will face a series of core challenges.

Understanding WHY the business needs data

Pinpoint the business objectives. Understand and quantify what the organisation wants to improve on to achieve its business goals and work out which of these will benefit from using data.

Knowing WHAT to prioritise 

There are plenty of prioritisation frameworks available to help leaders, such as value / complexity matrix, and RICE (reach, impact, confidence and effort) that will help leaders quickly estimate the value of a project and a path for up-scaling.

Managing WHO to hire/work with to build capability

The analytics leader has several options here. You can either:

Hire directly: importantly in a competitive market, this approach is good for retaining knowledge in the business.

Contract staff: this is a more straightforward way of directly aligning human resources to a business case for well-defined, short-term projects.

Use service firms: this approach usually involves a high initial investment but is great for kick-starting the data journey.

Take a hybrid team route: this involves augmenting teams to balance in-house know-how with outside expertise, which also helps build in-house knowledge in the long term.

The challenge of HOW

From a practical point of view, a key challenge for analytics leaders is how to get things done. For example, how to de-risk data projects, how to measure success, how to tie a project’s outcome to its value to the business, and how to make the analytics process a core part of the business so that it becomes a sustained business as usual (BAU) element.

Fundamentally, a great analytics leader needs to be a partner to the business, with a value-based approach to driving initiatives. This is a leader who will be trusted to make decisions when tackling these tough core challenges.

Are there any aspects of your analytics journey that we can help with?

About the Author, Xinye Li, Head of Data Science

Heading up our Data Science team at Ascent, Xinye’s technical expertise compliments the team’s skills with vast data led solutions. With degrees in physics/astronomy and economics Xinye started his working career in the marketing effectiveness world at a consultancy. From there on he’s worked at both agency and in-house data teams across a myriad of industries. Enjoying the hands-on aspects of project delivery, he also likes getting his hands dirty and solving problems with structure and logic. He joins the Ascent team with innovative experience gained from half of his professional career based in consultancy and client-led senior data roles.

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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.