R infrastructure enables accurate Covid reporting across health partnership organisations
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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.

Fantastic data initiatives and where to find them
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It was great to be at Big Data London last week. Beyond the novelty of interacting with complete humans again (and not just their heads via Teams calls) I was also lucky enough to be asked to present on a topic that I’m passionate about: finding fantastic data initiatives that will generate business value and underpin a data-driven transformation.

In particular, I described the use of ‘data domains’ as a great mechanism to align business and data stakeholders and create an environment in which fantastic initiatives can be discovered. If you missed the talk, here is a quick summary of what was discussed.

The data opportunity

Data is the lifeblood of the digital age – delivering strategic value if allowed to course through the arteries of an organisation. It holds the potential to create smarter, leaner organisations able to survive and thrive in an increasingly competitive (and digital) market.

The Big Data LDN showcased the best and brightest technologies available to deliver on this vision, but the reality is that creating lasting data-driven value is not (just) about the tech. Building a data-driven organisation is more about culture than code, more about change than cloud, and more about strategy than software.

In my talk I focused on a topic that is pivotal to finding and prioritising great initiatives that will deliver lasting value for your business. I spoke about why this isn’t a trivial consideration, before describing our use of ‘data domains’ as a mechanism to create a focal point for discussion of specific data initiatives and a way to improve collaboration between business and data functions. But first, a thought about data literacy…

Becoming a data-literate business

Investing in data literacy within your organisation (building to a universal understanding of the language of data and the opportunity it represents) is an important way to connect both your technical and non-technical teams. By uniting people capable of building new data solutions with stakeholders in other departments, as well as enthusiasts who are intellectually curious about the potential of data, project leads and their IT teams, you begin to form cross-departmental working groups that offer more diversity of thought to address different business scenarios. These communities will come up with your ‘long list’ of initiatives – where ‘fantastic’ can be found.

Identifying fantastic initiatives that drive value

Whilst thinking up data initiatives seems easy, finding the exact right data initiatives is not. There are many reasons why this is difficult, but a major factor has to be the knowledge and experience gap that exists between business stakeholders and data practitioners.

Prioritising the right initiatives is essential to building confidence in the business around new ways of working. And it is incredibly easy to choose the wrong initiative, creating a capability that is disconnect from business behaviours, or something that works really well on a laptop but has no hope of being deployed into production or becoming part of BAU.

The role of data domains

As a team, we get to help customers discover the exact right capabilities to create. To achieve this, we use a range of mechanisms to create the right collaborative environment between data professionals and business stakeholders – in my talk I described the use of Data Domains as an important tool to enable fantastic initiatives to be discovered and prioritised.

We can think of data domains as themes that allow us to connect aspiration business objectives with specific data capabilities. Domains can be used to engage leaders and create a focal point for the discussion of specific initiatives, so that the right business outcomes can be identified and delivered.

Here’s 5 key domains that in my experience cater for the majority of customer scenarios and are highly observable in terms of their impact:

Using data domains takes us away from any language around the potential of data or AI, and instead focuses on the conversation on business aspirations. It also helps to prioritise and focus the conversation – of course, we all want to create more informed, engaging, intelligent, efficient, sustainable businesses but what is the highest priority? If we want to create a more intelligent business, then what are the most important decisions we’d love to get right every time? If we want a more efficient business, then which process would we love to automate?

A case in point. 

One of the best examples of the power of using domains I’ve seen was when working with a major insurance company.  This company had previously invested in data through the lens of a series of innovative initiatives focused on solution areas.  They had invested in ’an AI initiative’, ‘a blockchain initiative’, ‘a big data initiative’ etc.  None of these investments had delivered any value, and the leadership were running out of patience with their data programs.

When we collaborated with them to help them move forward, we looked at the use of data domains to create a more business-focused framework against which to guide investment. We determined that the ‘intelligent’ lens (iteratively enabling more effective decision making) was the best domain to start with – and one that would deliver the impact they were looking for.

We refocused the investment on decision improvement. We identified key decision-making processes across a range of business areas (from resource forecasting to customer call handling), turning them into more effective, data-led decisions.

Engaging stakeholders: building bridges with data domains

The example above was a great success because it created a consistent language across the business for data-led value generation and enabled business stakeholders to more quickly buy into the change required. Designing successful data initiatives using data domains can be an effective way to create a bridge between functional areas successfully aligning business outcomes and data potential. For example formulating what you are trying to do / become vs what you are trying to build.

Are you ‘ready’?

Identifying a fantastic initiative is the first part of the solution – but we need to consider an organisation’s readiness to undertake the initiative. Some tough questions to ask yourself include:

  • Are we ready to implement the change required to realise the value?
  • Do we have the right data available to succeed? How do we address this if not?
  • Do we have the right platform to support this initiative?
  • Do we understand our stakeholder needs well enough? Are we able to see the situation through the right frame(s) of reference?
  • Can we measure and report on our impact?

In summary: 4 step action plan

  • Build data literacy – Invest in building understanding of the data opportunity with non- technical audiences. Spark curiosity and enthusiasm. Connect and unite your technical and non-technical teams. Celebrate ideation.
  • Use data domains to define ‘fantastic’ – Zero in on the impact you are looking to create. What initiatives will deliver this impact? Where will you ‘see’ value – and in what timeframe?
  • Engage stakeholders and build bridges across the knowledge gap – use data domains to engage leaders and create the focal point for discussion of specific initiatives, allowing an idea to become structured and permitting objective evaluation and prioritisation of projects.
  • Develop organisational readiness – Think through critical success factors and objectively assess your readiness to take on the level of change you are proposing. Implement any required corrective action – or resize your ambition accordingly.

Data domains provide a great framework for discussing and evaluating high-impact initiatives with business stakeholders, guiding you towards the most ‘investable’ initiatives that deliver the most value to your business.

Rich Pugh is Mango’s co-founder and Chief Data Scientist. If you would like further discussion around identifying and prioritising the right data initiatives in your organisation, contact us and we’ll be happy to help.

 

BigDataLDN
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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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Sara Hamilton Shortlisted for Women in Tech Excellence Awards 

We’re delighted that Sara’s efforts have been recognised in her recent shortlist nomination for this award, in the Transformation Leader:Tech category. 

Despite a record number of entries this year, Deputy Director, Product & Managed Service’s Sara’s technically accomplished team mentor, coach and passionate approach for user experience, alongside her desire to streamline processes, improve efficiency and develop products from conception – made her worthy of reaching the shortlist. 

As an active member of Ascent’s Women in Tech user group, Sara actively aims to contribute to change towards workplace diversity, with her belief that it can bring huge benefits to tech companies in terms of creativity, improved problem solving and improved products. As an active social campaigner, Sara believes how employers should emphasise how there is no heroism in martyrdom or being seen to be working and the real money is in working effectively and sustainably – something she actively encourages as part of her team.  

Experienced in agile transformation, scrum implementation, product and programme ownership, internal audits and quality management, Sara has been responsible for introducing agile processes into product development at Mango. With colleagues, describing Sara as a ‘fantastic team lead, who has instigated change at a team and process level – testing technology delivery and bringing a genuine culture of ownership to the team’, it’s not surprising Sara’s talent’s have been recognised.  

We look forward to the attending the awards dinner on 24th November and congratulating Sara on her achievements at Mango, alongside colleague Layla Marshall, Ascent’s Director of Product & Marketing who has also been shortlisted for the ‘Outstanding Returner Award’.     

 

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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, https://www.linkedin.com/in/adam-james-hughes/

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.

 

Dockerisation
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Best practice in data science can lead to long-lived business results. A structure that encourages repeatable processes for generating value from data, leads to a fully productive team working, allowing reproducible results, time and time again. When this process is ingrained across a company’s culture and the business and data teams are working together in harmony with the business goals, then the value of data can be realised into an overall centre of excellence and a shared language for best practice.

A shared language of best practice

Layers of operational best practice allow a standard practice to be adopted – ensuring the best possible outcome of your data science investment. For a data science team, best practices could relate to developing models or structuring analysis, quality standards or how a project is delivered. Alternatively, they could even align to the selection of your data and analysis tools, as these can easily impact the success of your project.

With data science teams coming from a diverse range of backgrounds and experiences, what may be obvious to one can be a novelty to another. A shared language of best practice allows collaborators to focus on the all-important value generated. A workflow that adheres to a best practice ensures quality, whether that be business value of insights to the accuracy of models. Best practices take the guess work out, minimise mistakes and create a platform for future success.

4 best practices every data delivery teams should focus on:

  • Reproducibility – Whatever the task is. If your results can’t be repeated, then is it really done?
  • Robustness – Results and quality of analysis can have a huge impact, ensuring your best practices that has checks and balances will lead to better quality
  • Collaboration – What use are your results if they are difficult to share. Having standards for collaboration means business value can be attained
  • Automation – It is very easy to do work with no automation, frameworks for automation can help accelerate teams

Best practice in Dockerisation

My talk at the Big Data London Meet Up ‘ How Docker can help you become more reproducible’, takes one element of best practice in data science, focusing on Dockerisation which is proving to be a powerful tool – one that is already turning established best practices in teams on its head. The tools allow teams to collaborate much easier, to be much more reproducible and automate workflows, in an impressive way. Yet, it has not had as much adoption within data science as it has within software engineering. My talk will explore just how Docker can super charge workflow and your valuable use cases.

This talk will be of interest to any data scientist who has had trouble with, deploying or working with engineering teams, reproducing colleagues’ analysis. It will also be of interest to anyone wanting to know how docker can scale a team, making it less intimidating and perfectly arming practitioners with the tools to give it a go.

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

Kapil Patel is one of Mango’s Data Science Consultants.

 

Beth Ashlee Senior Data Scientist
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A worthy nomination for Beth Ashlee as we receive news of her nomination for the Data IQ New Talent/ Data Apprentice Award.

As an instrumental member of our Mango’s consulting team, we’re keen to share her journey from Summer Intern to Senior Data Scientist within an impressive 5 year time frame.  We all aware that the field of data is a fast moving, but Beth has achieved a phenomenal amount in her time with us.  With each experience, she has shown a remarkable desire to learn from it, evolve and nurture both her consultancy and technical competency and so we believe this shortlist is worthy of recognition.

Beth’s accomplished engagement style

As a master communicator, Beth excels in her role as a consultant at Mango using her empathetic communication style – enabling her to establish meaningful relationships alongside the ability to easily translate business value across an organisation.

She has demonstrated the ability to easily adapt to an ‘insourced’ team leader role or team lead as part of an ‘outsourced’ project and as a naturally competent people person, she makes everyone around her feel valued and motivated.

Beth is one of Mango’s standout Senior Data Scientists, and after a succession of promotions now leads and mentors the Graduate team in both a technical and personal development capacity. Demonstrating a ‘role model’ leadership style, it’s easy to see why all of our graduate cohorts across the past 2 years have developed into capable and credible data professionals that are proactively supporting the growth of our business today.

Congratulations Beth on your achievements, we certainly look forward to celebrating with you at the Data IQ awards night on 30th September.

fraud in finance
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Attending the recent Transform Finance ‘Virtual Fraud in Financial Services’ online event was a great opportunity to share experiences and knowledge with experts from across a highly innovative industry.

The event took in a range of critical themes, including our session with Cifas focusing on ‘Using Advanced Analytics To Detect, Prevent And Tackle Fraud’. Reflecting on the experience overall, there were a number of key takeaways that illustrate both the challenges and opportunities facing an industry increasingly reliant on technology to deliver effective services while tackling some major obstacles to success:

  1. The Changing Fraud Landscape

Many organisations are investing heavily in their use of data and technology to positively prevent and detect crime and fraud. This is essential as the emerging trends are worrying – The 2021 Identity Fraud Study revealed the true scale of identity fraud scams to consumers and businesses alike. While total combined fraud losses climbed to $56 billion in 2020, identity fraud scams accounted for $43 billion of that cost with ‘traditional’ identity fraud losses totalling $13 billion.

With fraudsters becoming more sophisticated, it is essential that organisations across the diverse finance industry constantly adapt to change with technology and application of data.

  1. Innovation And Cooperation

In a changing world, it’s clear that financial institutions understand the need for greater collaboration and data sharing across the industry. There is growing recognition that a more joined up approach to issues such as fraud detection and prevention is key to delivering effective outcomes. As an example, cooperation between ‘core’ public and private finance organisations with third party organisations, including regulators, mobile providers and social media companies is essential best practice when it comes to reducing the incidences and impact of fraud.

This extends to the wider implementation of technologies such as digital signatures and document sharing through digital channels and the development of digital currency for financial inclusion. In all these emerging areas, working together is key, while the application and proactive use of data, good management, metrics and governance can help ensure success and make sure that industry-wide goals in relation to fraud are clear.

  1. Tackling Major Fraud Trends

As part of our contribution towards the Transform Finance event, our Chief Data Scientist, Rich Pugh and Sandra Peaston, Director of Research & Development at Mango customer, Cifas, joined a panel session to discuss the use of data and intelligence to support fraud prevention.

Among the areas discussed, the panel agreed that it is essential that the industry operates from a level playing field and by sharing intelligence via organisations such as Cifas. In doing so, finance businesses across the sector are much better placed to adapt to new trends. For instance, an approach called ‘transfer learning’ can enable small organisations to benefit from the insight and data generated by larger businesses to react more quickly and effectively. There should also be ongoing efforts to improve customer education and awareness, reinforcing the idea that individuals always need to remain vigilant.

Looking to the future, data and technology will play an increasing role in combating and detecting crime. By maximising collaboration between corporates, banks, regulators and other key stakeholders, the industry will move towards a scenario where fraud detection happens in real time to minimise risk and loss.

To achieve these goals, organisations must focus on improving their capabilities, modernising fraud operations and maximising the technical competency of their teams. Those that do will be ideally placed to play a full and effective role in tackling fraud while gearing for growth.

For more information on how Mango is supporting working and detecting financial crime, read our Cifas case study.