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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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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.

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

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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.”