Data Maturity Survey
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Over two-thirds of firms who have a data strategy, admit that it is not widely understood across the organisation, according to a Mango Solutions study

● 68% of organisations have established data science teams
● 62% of data leaders cite skills gaps or recruitment as core challenges
● 72% of companies say their data quality and integrity requires improvement
● 92% of businesses plan to rely on data to drive predictive decision-making by 2023

14th October 2021 – Mango Solutions, an award winning data science analytic consultancy has announced the results of a study which reveals that while 95% of companies questioned have a data strategy, less than a third of those (29%), claim their data strategy is clear and widely understood.

The data maturity survey, which polled the opinions of 100+ data professionals at the September 2021 Big Data LDN event, shows that the companies questioned are investing heavily in data capabilities, resulting in maturing data functions – for example, 41% have established data science functions in the last two years. 93% of data managers surveyed claim their company has a well-structured data estate and 92% claim to have well established data management processes.

However, the study also revealed that companies are not yet reaping the benefits from their data capabilities. Only 26% of respondents say that the quality and integrity of their data is high and suitable for analysis, and only 43% already rely on data to drive predictive decision-making.

Establishing a data-driven culture is just as important as providing people with data capabilities if companies are to realise the potential value from their data. The Mango survey shows good progress here, since 88% of respondents already have an established internal data community that works across the business and enjoys good stakeholder relationships. However, 56% of those surveyed still feel there is room for improving their data community.

Data science also seems to be delivering on its potential too. Reassuringly, 68% of organisations boast established data science teams that already work effectively with collaborative tools and platforms, with 85% of respondents claiming their business sees value in the function. 29% of respondents say there is room for improvement when it comes to establishing an effective data science function. Effective data governance is an essential part of improving the effectiveness of data analytics and data science functions, and this statistic aligns with Gartner’s prediction that, by 2024, 30% of organisations will invest in data and analytics governance platforms, increasing the stability, scale, trust and impact of insight and analysis.

Predictive decision-making is also a key focus area for data managers, with 92% of businesses claiming they will rely on data to drive predictive decision-making by 2023, more than double the 43% of organisations who claim their business already relies on data to drive predictive decision-making. 59% of companies report that they already successfully derive, share and action insight delivered through dashboards and reports and a further 26% will be following suit over the next 18 months.

Rich Pugh, Chief Data Scientist at Mango solutions, said: “While the majority of organisations surveyed say they have an established data capability, a large proportion admit that they need to improve the way they use it, to help derive data-driven value. These improvements are obviously best done strategically, but whilst 95% have a data strategy, only 29% of those have one that’s clear and widely understood. This is a real concern – creating a clear and understood narrative around the role of data is essential to the success of a data strategy. Without this, data leaders are at risk of not bringing the organisation with them on their journey, and missing out on the potential value of their data opportunity.”

– Ends –


data literacy
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The pandemic has emphasised the importance of a data-led approach and raised the bar for data literacy. Emerging from the crisis, the businesses that succeed will be those that put value at the heart of analytics initiatives.

By Rich Pugh, 24th May 2021, posted in Training Journal

To a large extent, the data that we gather, examine and use to help us make decisions has been kept in the hands of a few data experts with the appropriate skills and understanding to interpret and use to benefit their organisation. This is not surprising; the enormous growth of data has meant that most employees haven’t had the right training or skills to work effectively with data in order to extract the best value from it.

The goal with data democratisation is to enable the development of informed decision-making to a broader set of people. 

This is changing, however, thanks to the advent of technologies that have the capability to support the sharing and interpretation of data for non-data experts. Advancements like these have made it possible to disseminate data throughout the organisation, and that’s a good thing. If implemented the right way, this democratisation of data has the potential to propel businesses to new levels of success.

Dissolving the bottleneck that often characterises the point of entry to a company’s data means that everyone should be able to use it to make informed decisions, faster. Fundamentally, devolving good data visualisation capabilities across an organisation allows decision makers to make more informed decisions, or to identify evolving trends and patterns that may constitute an opportunity or threat.

While data democratisation is certainly the key to transforming how organisations operate, there are challenges to be addressed, specifically around literacy. If you give a lot of data to someone who isn’t skilled at interpreting data, how would you expect that person to discover things?

I believe that’s where data literacy is becoming really important, and by this, I mean teaching the broader business what data is about, how you interact with it, how you analyse it, how you identify a trend, and what to do if you think you’ve found something.

The impact of the pandemic on data literacy

Interestingly, the pandemic has shone a spotlight on data and inadvertently played a role in elevating the data literacy of the population at large. Faced with the Covid-19 crisis that took hold over a year ago, many of us tuned into daily briefings to understand the spread of the virus through data and statistics. During these briefings, ‘descriptive analytic’ approaches have been used to present points and trends, regularly exposing the public to the world of data visualisation (charts showing Covid-19 numbers), summary statistics (such as an R number), predictions (forward-looking projections) and simulations (understanding the projected impact of approaches to ‘flatten the curve’).

This has certainly raised the bar in terms of data literacy, or at least expectations around the richness of information that could be presented to underpin a topic – the public has viewed the pandemic through the lens of data and statistics, gaining familiarity with the use of common analytic tools (charts, statistics, predictions) to better understand what is a complex topic.

Throughout the past year, we’ve seen the link between data and action, with phrases such as ‘we’ll follow the data’ being used to explain to the public the rationale behind the road map out of the pandemic. Indeed, both Boris Johnson and Nicola Sturgeon used the same phrase to say that they would be ‘led by data, not dates’ when it came to making decisions with regard to easing lockdown rules and re-opening the economy.

This has emphasised to the public the importance of a data-led approach when it comes to making informed decisions that are meaningful and valuable.

What data democratisation means for your business

As already mentioned, it is not merely sufficient to foster data literacy across the organisation for the purposes of interpreting data. The goal with data democratisation is to enable the development of informed decision-making to a broader set of people. In a crowded market place, this is what creates smarter, leaner organisations with a competitive edge.

Take the ‘left or right Twix’ ad campaign as an example. The campaign envisaged a world where two companies create the exact same product, using the same processes while selling to the same consumers. It’s a good metaphor for disruptive influences in markets being able to create companies that are very similar to established players, which then have to find some way to compete.

Find out more about upskilling your data science teams here.

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29th March, 2021, posted in

How to become a data-driven organization and why this is key for future business success.

A global pandemic has played a big part in elevating the data literacy of the ‘everyday man’, essentially demystifying data science for many of us. There now seems to be a much clearer connection between ‘what the data says’ and ‘what action we’ll take’. It’s a good example of data-led decision-making to drive the best possible outcomes.

In the same way, businesses can also drive better outcomes if they understand this connection and use it to transform their business model to one that is driven by data. Building a data-driven future is what will give businesses their competitive edge at a time when ’survival of the fittest’ counts the most.

With this in mind, many organizations are currently investing in data projects of one kind or another. Whether it’s data analytics, big data, AI, machine learning, data science or any other area of focus, the interest and investment in efforts to become ‘data-driven’ has been given significant extra impetus by the experiences of the last 12 months.

Whether the objective is to drive efficiencies, create competitive advantage or improve decision-making processes, it’s important to remember that isolated or independent data projects do not make you data-driven. Instead, the race to create a data-driven business infrastructure should be seen as a strategic journey, where organizations position data so that it empowers and delivers on business objectives. Success depends on transforming business models so that the whole is greater than the sum of its parts.

So, what does it take to become a data-driven organization? Here are a series of core principles that together, can help build a solid foundation, focus and measurable progress for using data as a strategic asset:


The entire process rests on effective leadership, where a top-down perspective aligns the business with data strategy. Without this approach, it can prove impossible to instigate the culture shift required to truly become data-driven and ensure that initiatives are given the right emphasis, support and representation, as well as driving the education required at a leadership level.


Next, it’s important to evaluate the relevant skills – and the gaps – within existing teams. For instance, it’s not unusual for analytics skills to be spread across departments, but in creating the right focus, business leaders need to transition to a core, centralized practice to ensure consistency. This does not necessarily mean that teams have to be changed, but organizations must create best practice processes to focus their efforts. Ultimately, building a community of data professionals who share knowledge and work together can be hugely beneficial, even if they don’t work in the same teams on a daily basis.

Best practice

Looking more closely at best practice, the objective should be to move from sporadic and isolated data driven initiatives siloed in each department to an approach which ensures consistency of approach across the organization. This should always be based on a common understanding of how to deliver value from data effectively.


As skills and best practice processes become integrated into a data driven culture, it becomes more important to ensure governance increases. Indeed, establishing best-in-class governance and frameworks is essential to ongoing data-driven transformation, because it enables leadership to track progress against goals. In practical terms, leaders need to work with data practitioners to ensure that initiatives meet business objectives, that there is consistency in delivery and prioritization, as well as in the platforms and technologies used. At the same time, every organization must meet their data compliance obligations, especially relating to sensitive or personal information.


Increasing the impact of a data-driven strategy is not just a matter of bringing the specialists together. Educating the business at large about the possibilities of analytics is an important part of the process so the whole business can share a common language around analytics and dispel preconceptions of what analytics can and can’t achieve.


As the impact of education efforts take effect, and business interest and knowledge of the potential of data driven decisions grows, many organizations find they are presented with a wide range of potential initiatives. Clearly, prioritization then becomes important, and key questions about each idea and option should include: will an initiative add significant, measurable value? Is the organization ready to implement data driven initiatives that may deliver meaningful results? Is the right data and platform available to make it work, and is the organization in a position to adopt the new practices each initiative will require?


With priorities determined and actively being implemented, the process requires a structure to measure success in a consistent way so that all stakeholders can see the data driven program at work, rather than isolated instances of innovation. This is often pivotal for organizations in their efforts to move away from a series of data science projects to being a truly data-driven company.

There’s no doubt that investing time and resources in developing a data-driven culture can radically improve insight and decision making. In today’s rapidly changing business environment, spotting new opportunities and challenges, improving processes and working with greater insight into the variables that affect business success is vital. By adopting a rounded process that addresses these critical areas, businesses have the best chance of succeeding in their mission not just to become data-driven, but in their wider digital transformation strategy.

Author: Rich Pugh, Chief Data Scientist, Mango Solutions (an Ascent company)

role of data science in fighting financial crime
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3rd November, 2020, posted in Global Banking & Finance Review

PwC’s Global Economic Crime and Fraud Survey 2020, which surveyed over 5,000 respondents over a 24-month period, found that an average of six frauds per company was reported, costing a massive $42 billion. It also found that just 56% of companies undertook an investigation into their worst fraud incident. The message is clear – fraud is a very real threat, it’s on the rise, and to echo the view expressed in the PwC survey report: “It’s a risk you ignore or underestimate at your peril.”

This kind of financial crime is a worrying trend, and is a key reason why fraud prevention services are looking to data science for help. Working with fraud prevention services, data science can help build and enhance existing intelligence capabilities, as well as further deepen understanding of the crime networks involved in fraud.

When it comes to fighting financial crime, data science has a role to play in two main areas: the technology and tools used to identify fraud, and the education and training necessary for the teams to use these tools to maximum advantage.

  1. The (Data Science) Tools of the Trade

Data science uses advanced analysis to deliver value from data by enabling informed decision-making. To illustrate this in a financial context, a data science initiative may include reducing the number of false positives to improve matching when searching the National Fraud database, improving fraud prevention services’ members’ understanding of the data, and providing further intelligence based on data – such as emerging crime patterns.

By using data science capabilities and expertise in this way, fraud prevention services will be able to utilise this intelligence more effectively and efficiently to help identify current and emerging fraud threats. This data will then be used to inform members and the wider fraud prevention community.

Another way data science can be used to combat fraud in insurance, for example, is through identifying fraudulent claims. With regard to these, data science can help detect claims that look “unusual”. Artificial Intelligence (AI) techniques are used to examine all elements of the claim in real time and match it up with the history of similar types of claims that have been made before. If there is an anomaly, the claim will get pushed to one side for further investigation. This means that insurance companies can ensure they are still able to pay claims quickly, whilst checking thoroughly for fraudulent claims.

  1. (Data Science) Knowledge is Power

From a data science perspective, having the right skills are essential for companies tackling financial crime in order to identify the increasingly sophisticated digital crime frauds and scams being perpetuated across the industry. Financial services fraud teams have been striving to detect fraud for a long time, but not necessarily using the latest, data-driven techniques – many are still immature in terms of their data capability.

In a time of huge advances in technology, new capabilities around the interpretation of data and an enhanced acceleration towards digital transformation partly as a result of a global pandemic, data science offers the strongest solution for companies fighting financial crime. Traditional approaches are no longer enough, and it is essential that financial crime teams build data science skills through education and training if they are to keep up with the growing tide of cyber-crime.

When it comes to fighting financial crime, data science is the key to tackling an evolving financial landscape, governed by complex compliance requirements, to prevent increasingly sophisticated criminal attacks.

By Tim Oldfield, Financial Services Client Director at Mango Solutions

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28th January 2021, Posted in AI Business.

When news that an infectious disease caused by a newly discovered coronavirus first began to circulate, it was clear that data and analytic techniques from the world of epidemiology was going to be a vital tool in the fight against the pandemic.

However, I hadn’t anticipated the role the pandemic would play in elevating the data literacy of the population at large, further elevating “data science” into the public lexicon.

Understanding the pandemic

As the urgency of the COVID-19 crisis took hold, many of us tuned in to the UK Government’s daily briefings to understand the spread of the virus through data and statistics. Across the media and in the daily briefings, “descriptive analytic” approaches have been used to present points and trends, including daily infection rates, areas of high risk and factors accelerating infection.

As such, the public has been regularly exposed to the world of data visualization (charts showing COVID-19 numbers), summary statistics (such as an R number), predictions (forward-looking projections) and simulations (understanding the projected impact of approaches to “flatten the curve”).

We’ve also seen the need for accurate and timely data – the more we know, the better and faster both government and public can act to drive down infections and the overall risks. We’ve even seen open discussion around data issues, and how this has impacted our ability to see the true picture of the spread of the virus.

I feel this has raised the bar in terms of data literacy, or at least expectations around the richness of information that could be presented to underpin a topic – the public has viewed the pandemic through the lens of data and statistics, gaining familiarity with the use of common analytic “tools” (charts, statistics, predictions) to better understand what is a complex topic.

Indeed, anyone interested enough in key trends such as local and national infection rates would find it relatively straightforward to follow various data sources updated and analyzed on a daily basis. The daily data and analysis provided by the Office for National Statistics (ONS), for example, is shared, recycled and augmented by professional and amateur data scientists alike.

Driving decisions

Perhaps more importantly, the public has been frequently exposed to the links between “data”, “analysis” and “action”.

This clear connection between “what the data says” and “what action we’ll take” may seem obvious to data professionals, but I can’t remember another example when “data-led decision making” was so openly discussed. In fact, this concept underpins every aspect of our pandemic response, driving informed decisioning around measures put in place – the phrase “we will follow the science” could just as easily be interpreted as “we will follow the data”.

Indeed, the vital role of data science and analytics was highlighted again most recently with the identification of a new, more infectious variant of COVID-19 in the south east of England, which prompted the government to announce a new national lockdown to try and combat the surge in cases.

As highlighted by the World Economic Forum (WEF) in July, the pandemic created an urgent need for rapid decision making, informed and supported by constantly changing data sets, backed by effective visualization. It points towards the importance of “agile data science methods that address the speed, urgency, and uncertainty of decision making” as one of the key learnings about the impact of COVID-19 on data science and other applicable disciplines such as artificial intelligence (AI). Around the world, this has been key to influencing behavior and helping to build and maintain public support for tough restrictive measures.

The role of data science

From providing the insight that has enabled governments and healthcare systems to act quickly, to keeping the public informed and improving the efficacy of protection measures, the profile and appreciation of the world of data and analytics has skyrocketed.

Over the last year, the role of data science (or at least the concept of data-led decision making) has shaped how we live and work, whilst also facilitating significant drug discoveries in the global pharmaceutical sector’s quest to develop a COVID-19 vaccine. This has highlighted the breadth of challenges to which a “data” mindset can be applied – helping us tackle challenges in society as well as being a vital business tool.

As we look ahead, I see an ongoing need for businesses to embed data science to become more intelligent, efficient and relevant in an increasingly-competitive global market. For many businesses, it feels like 2021 will be a decisive year in the quest to create digital, data-driven organizations.
Beyond that, I hope the increasing understanding in the public of the potential for data science can support initiatives to create a healthier, greener, kinder world.

Rich Pugh is Chief Data Scientist at Mango Solutions – an Ascent company

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Rich Pugh at Mango Solutions describes how the synergy between software, data and analytics is the gateway to realising business value.

17th December 2020, Posted in Business Reporter

For years IT teams have sought to store and protect data effectively. As a result, massive IT estates were developed and deployed, all the while providing both guarding gateways and authorities to end users.

Today’s changed conditions require IT and technology to adapt and fast. It’s vital for organisations to utilise all assets effectively across the business, and a key component of this is understanding and delivering value from data. The rebalance from reactive to proactive process requires massive shifts in attitudes and skills and demands a flexible forward-thinking IT strategy that allows data to be transformed into decision making wisdom in a repeatable, sustainable way, available to users at the right time.

As a result, today’s data-centric IT strategies are increasingly seen as crucial to business but are still elusive across organisations. In order to thrive, digital transformation programmes must address not only data but analytics, software, skill sets and mindsets. Without addressing each of these areas, transformation can be short lived, misunderstood and not developed.

Developing high level synergies with a consistent approach towards analytics and data, and the software used, is vital in order to create a digital transformation platform. This concept of ‘synergy’ can be summed up simply by viewing every data project as a software project, and every software project as a data project. In doing so, it’s useful to look at these key components more closely:

The role of data and analytics

Modern data science takes in a broader range of analytic approaches than ever. For example, the role of data and analytics has evolved from a reactive exercise to a strategic discipline. This, in turn, has driven the need for advanced analytics to be combined effectively with software engineering, because if analytics is now an ‘always-on’ capability, businesses need a systemic, intelligent approach that enables solutions to be properly deployed, scaled, supported and extended.

Think of it this way: an ‘old school’, manual or ad hoc process risks missing the point of advanced analytics entirely. For instance, no business can truly describe itself as a ‘data-driven’ business if analytics is run by experts – no matter how capable – armed only with their minds and laptops. What’s more, intelligent decision-making can’t be a process that goes on holiday with the data scientist.

Similarly, any organisation that wants to operate in real time can’t wait for a statistician to analyse the latest data sets to identify new opportunities, risks or react to customer behaviour. Instead, to positively impact a business with data, an end-to-end analytic workflow must employ software engineering principles. This includes everything from the creation of data pipelines and deployment of models, to the creation of user interfaces and applications that can convey insight in the right way, linked directly to operational systems to action and process outcomes.

The role of software

Software plays a key role in providing standardised approaches for using data and analytics across an organisation. As a way of rolling out best practice it can be unrivalled and is an effective way of addressing regulatory and compliance issues around data and its application.

Using software as a vehicle to bring analytics, data and end users together results in an increased ability to leverage advances in data and analytics to create richer, more powerful and more useful experiences and applications. There’s no doubt that any organisation that can broaden its understanding of the possibilities offered by analytics can not only ask better questions of itself and its stakeholders, but then use that insight to build software tools that are truly aligned to a users’ objectives.

Data projects and software projects

Every data project is a software project, and every software project is a data project.

In real world terms, imagine designing a software application for call centre staff speaking with customers. Traditionally, this may have been built on a system that combined data from disparate sources – such as previous orders, interactions and demographic data – to give the call centre professional a single view of the customer.

But by applying data science, functionality can be broadened to include analysis of likely customer churn, which is then linked to suggested retention actions, such as new offers or scripts to help encourage customer loyalty. Similarly, software that works in synergy with data and analytics can route new customer calls to exactly the right service expert, instead of being randomly allocated to the next available agent.

Put in these terms, the contrast between traditional and modern approaches is very apparent. The bottom line is that the use of data and analytics in software can have a transformative effect on the quality and usefulness of our software systems creating win-win situations for customers and organisations.

Ultimately, the application of technology is about achieving business strategy goals, and increasingly, this requires flexible, integrated software and data capabilities to deliver effective digital transformation. Companies that can achieve this will do better than their competitors, grow more quickly and see exponential commercial gain.

Creating synergy between software, data and analytics is no longer a ‘nice to have’ – it is increasingly a minimum requirement in order to deliver business value.

Rich Pugh is Chief Data Scientist at Mango Solutions – an Ascent company

the validation of R
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December 15th 2020 – London, UK – Mango Solutions has launched an extension to the ValidR brand, a quality-controlled version of the popular, open-source analytic language R. This addition comprises a carefully curated validated collection of the 150 most popular industry leading packages, such as those within the “tidyverse”. This is welcome news, particularly among highly regulated sectors, including fintech, government and utilities, which will benefit from a compliant version of R that provides practitioners and business stakeholders with a secure solution that instils confidence in the safe usage of R across their organisation.

R is rapidly gaining traction across a wide range of industries for the flexibility, capability and commercial advantage it provides. A recent poll by Mango Solutions highlighted IT restrictions (37%) – mainly data security and technical support processes – and compliance (18%) as amongst the key concerns of commercial organisations, around the use of open-source R.

Driven by a global community of innovative coders and members of the growing R community, the power of R arises due to the volume and limitless scope of additional packages made available via several public repositories. As with most open-source software, the inherent risks prevent adoption and usage, as indicated in the poll.

In the absence of a rich analytics language like R, up to 29% of respondents in the poll admitted that MS Excel was still their organisations’ primary data and modelling tool. Spreadsheets can be difficult to analyse when working with extensive data sets, with data often becoming distorted or lost. The recent Covid-19 Track and Trace System “Excel-gate” disaster being just one example of where using Excel for large, complicated data processes has led to costly and critical errors. ValidR ensures organisations can have confidence in the use and application of R open-source software and provides an accessible solution for data teams.

Chief Data Scientist and Mango Solutions co-founder Rich Pugh said: “The concerns identified in the poll are certainly valid for highly regulated sectors where support, governance and traceability are absolutely vital. Open-source software has traditionally been uncontrolled and open to technical, operational, regulatory, security and legal risk, potentially threatening brand reputation. Our customers have been asking for a simpler business model that is equally able to satisfy the needs of IT and compliance teams. The validation of R packages presents ease of reproducibility, standardisation, and assurance for business”.

Pugh explained: “Mango’s long-standing expertise in R software and validation means we are uniquely placed to assess the quality of R packages in accordance with ISO 9001 quality requirements, The R Foundation for Statistical Computing guidance on regulatory compliance and industry validation requirements”.

ValidR can be deployed with RStudio Package Manager (RSPM), the leading repository management server giving organisations central and consistent accessibility to a validated set of packages across the organisation. Matt Sawkins, Product Manager at Mango, commented: “ValidR meets the compliance requirements of our customers in sectors that do not require the stringent additional, compliant environment standards as provided by ValidR Enterprise.

Presenting long-term analytical advantage, this solution seeks to mitigate any uncertainty and reduce the cost of maintaining open source software, with guaranteed reproducibility for any data science team”.

Validating R, already has proven application in the highly regulated pharmaceutical sector. It involves an extensively validated, tested, peer reviewed seven-step framework in adherence to industry best practice. This rigorous process has opened up markets in other sectors allowing a wider uptake of R, allowing teams to fully adopt this rich analytic language with greater confidence, assurance and certainty.


ValidR, available from 15th December, reduces industry risk and IT costs by validation of:

  • 150 of the most essential and requested R packages on the Comprehensive R Archive Network (CRAN)
  •  Industry ‘must-haves’ and core packages across all industry sectors
    Commonly used and de facto collections, including the tidyverse
  • Mango’s unique expertise and analysis of CRAN and client validation requests
  • For more information, please see ValidR
Acent press release for Mango acquisition
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London UK,  18th November 2020

Ascent, a UK software strategy and development firm, has acquired award-winning* Mango Solutions, one of the UK’s largest data science consulting firms, as it vies for a bigger slice of the fast-growing market for data science, advanced analytics, data engineering, and IoT (Internet of Things) solutions.

Although financial terms of the deal were not disclosed, the acquisition is expected to add 65 employees and approximately £5M in annual revenues to Ascent.  Prior to the acquisition both companies were close business partners. The combined business will employ more than 340 people.

Ascent is backed by private equity house Horizon Capital and led by CEO Stewart Smythe.  Ascent had previously invested £30M less than 12 months ago, which was used for earlier acquisitions.  Smythe confirmed he has access to significant additional funding as required to deliver the Ascent plan.

Commenting on the deal Smythe said: “We’ve enjoyed a proven working relationship with Mango Solutions for some time now, and are so confident in the quality of their work and their people that we decided to make the partnership permanent.

“Achieving business strategy goals in mid and upper-mid market customers now requires ambidextrous, integrated software and data capabilities to deliver effective digital transformation.  Companies that can achieve this will do better than their competitors, grow more quickly and see exponential commercial gain. Delivering long-term value to these businesses demands extensive expertise in advanced analytics and data science techniques like statistical modelling and machine learning; exactly the sort of deep capability that Mango Solutions brings to the table.  We expect the combined businesses to deliver organic growth in the region of 20 percent annually.”

Smythe estimates that more than 80 per cent of new businesses coming to Ascent for help in 2020 had requirements that escalated rapidly from software into data analytics and modelling. The acquisition of Mango Solutions means that Ascent is now well placed to design and deliver integrated ‘big picture’ digital strategies for businesses looking to do something new, do something better, or both.

Smythe says that Covid-19 has also been another factor driving demand in recent months.  Many European businesses have rapidly pivoted to prioritise digital experience, which requires deep insight into customer needs, habits and behaviours to successfully extend their relationship online, or convert them from other channels. This requires extensive blending of front end, customer-facing online capabilities with commercially-driven data analytics, modelling and insights.

Rich Pugh, Chief Data Scientist at Mango Solutions, will continue in this role at Ascent.  Regarded as one the UK’s leading data experts, Pugh will help champion Ascent’s newly-acquired ability to deliver advanced analytics and data science to a much broader customer base. Rich will continue to be a leading authority in the data science community across Europe.  Pugh also believes that both businesses share a passion for equipping customers with new capabilities to build digital muscle and make better decisions.

Pugh said: “The most exciting thing about joining Ascent is that we will see our vision and expertise become accessible to so many more businesses so much sooner.   What was once the domain of only the largest enterprises is now accessible to the mid and upper-mid market – at a time when demand for insight and intelligence is surging.  Ascent is the accelerant to take Mango’s capability and leadership in data science to this broader market.”

Smythe added: “The vast majority of boards now believe that there is huge value in understanding and exploiting the data that they have in their businesses, and that extracting this value is an immediate priority for them.  It is no longer seen as the preserve of large multinational or niche sub sector businesses.”

Simon Hitchcock, Managing Partner at Horizon Capital, concluded: “Mango is the third acquisition we have made with Ascent since our investment in the business 12 months ago.  These acquisitions have enhanced the impressive organic growth of the business under Stewart Smythe’s leadership and are creating a market leader in the digital transformation market.  We’re looking forward to welcoming the Mango Solutions team to Ascent and see their data capabilities as a core part of the business going forward.  We look forward to supporting Ascent in further acquisitions of founder-led, technically brilliant businesses with strong cultures like Mango.”

About Ascent

Founded in 2005, Ascent is a European software strategy and development company headquartered in the UK with technology hubs in Malta and Bulgaria. Ascent employs over 275 experts across 14 countries, specialising in software product development, business intelligence, advanced analytics, data science and IoT (Internet of Things) solutions for customers.

Ascent helps more than 100 mid-market and enterprise businesses in the UK and Europe connect data, software and purpose to deliver commercial success. Ascent’s key customers include brand leaders in smart home devices, automotive, manufacturing, financial services, ecology, logistics, eGov, health, and food and drink.

About Horizon Capital LLP

Horizon Capital is a private equity investor specialising in technology and business services. The firm was established by senior investment professionals who identified a significant market opportunity to invest in businesses in these sectors valued up to £50m.  The partnership prides itself on its approach to helping business owners and managers realise their ambitions. Buy and build is at the heart of every Horizon Capital investment and the firm is a market leader in supporting companies pursuing this strategy. Horizon Capital has a proven track record in generating premium returns on investments. The unprecedented growth it delivers in its portfolio companies has been underpinned by deep and long-term investor relationships that span across two decades.

For more information please visit


open source
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Survey reveals IT restrictions and compliance as main obstacles to using open-source R, specifically among highly regulated sectors including finance, government and pharmaceutical.


16 October, 2020 – London, UK – Research released today from data analytics consultancy Mango Solutions, found that just over half of data professionals polled say their companies have concerns related to the use of R – an open-source analytics language rapidly gaining traction for its flexibility, capability and commercial advantage across a range of industries.  The research highlighted that IT restrictions (37%) – mainly data security and technical support processes – and compliance (18%), were the key concerns organisations currently have around the use of open-source R.

Approximately 300 respondents were surveyed, working primarily in the government, finance and pharmaceutical/life science sectors – highly regulated industries in which support, governance and traceability are vital. Chief Data Scientist and Mango Solutions co-founder Rich Pugh said: “We can understand the concerns, considering that open-source software has traditionally been uncontrolled, with usage risks extending to technical and operational, regulatory, security, legal and brand reputation.”

“The fact is open-source enables developers, boosts innovation and advances competitiveness and it’s no surprise that technology innovators are using 60 to 80% of open source code. In the data science space, R is one of the most popular analytic languages in the world. Driven by a global community of innovative coders and members of the R community, the power of R arises due to the volume and limitless scope of additional packages made available via several public repositories.”

But as with most open-source software, the inherent risks remain, leading to a reluctance of trust in use of R because data practitioners are unable to satisfy their IT and compliance team’s requirements, as demonstrated by the survey findings.

Highly worrying is that in the absence of an analytics language like R, up to 29% of respondents admitted that MS Excel was their company’s primary data and modelling tool. In the light of recent news where 16,000 Covid-19 cases were unreported due to an Excel error the fact that nearly a third of companies are relying on Excel as their primary data tool is concerning.

With the majority of respondents being data practitioners, it seems likely that they cannot satisfy their IT teams that R offers a reproducible and safe software process. Were usage not limited or restricted there certainly would be an appetite to use open source R – 55% of respondents confirmed that their R usage would increase if company permissions allowed.

About Mango Solutions:

Mango Solutions has been empowering organisations to make informed decisions using data science and advanced analytics since 2002. In addition to delivering data science projects for some of the world’s best-known companies, Mango also offers a comprehensive range of training and upskilling programmes for all user levels to help organisations build a successful data science capability internally.


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d-wise and Mango Solutions form partnership to accelerate Open Source Software (OSS) adoption for clinical trials

The newly formed partnership between d-wise and Mango Solutions is set to be the catalyst for OSS adoption within clinical trial analysis. OSS enables flexible and deeper analysis of data sets that promise to bring data science to life science product development.

According to a 2020 biostatistics study, validation concerns were listed as the key barrier preventing full adoption of open source for product development. This partnership equips enterprise life science companies with a solution that provides hundreds of pre-validated R packages along with the strategy and system to fully leverage open source software across the enterprise.

Life science sponsors now have the confidence to innovate their clinical analytic environments with pre-validated compliant and validated systems and tools, which are being embraced ahead of costly and restrictive legacy programming tools, exemplified in this case study at a leading enterprise pharmaceutical sponsor.

Before pre-validation of OSS, d-wise offered strategic guidance and services to assist IT teams in navigating the intricate steps to select and utilize OSS. Now using these pre-validated OSS packages from Mango Solutions, the clinical analysis environments designed and built by d-wise seamlessly generates data science analysis with incredible visual and analysis capabilities.

“Through the adoption of Mango Solution’s ValidR Enterprise solution, pharmaceutical sponsors have accessibility to 400+ validated, production-ready R packages that pass through a rigorous 7-step validation process. Our partnership with d-wise is the first step to wide adoption of OSS for product development presenting significant opportunities of a technology agonistic, open source environment. ValidR satisfies FDA guideline 21 CRF Part 11, mitigating risk associated with drug design, development and clinical trials. Through the partnership, life science sponsors will be supported to build a lasting capability around modern, analytic application development”. – Mango Solutions

“d-wise has extensive experience in building, configuring and integrating computing solutions in the regulated clinical trials environment. Our partnership with Mango extends our capabilities in this area through products like ValidR Enterprise and additional consulting experience in data science. Our combined balance of validated platforms and industry experience serves both companies well and will provide an exceptional range of services and products for the industry.”. – d-wise

About d-wise

Thought leaders in clinical analytic environments, the world’s largest life science companies trust d-wise to lead strategy and modernization of validated systems and technologies. CROs & sponsors trust d-wise clinical analysis expertise and to navigate technology change in product development data science.

About Mango Solutions

Mango Solutions has been empowering organisation’s to make informed decisions using data science and advanced analytics since 2002. In addition to delivering data science projects for some of the world’s best-known companies, Mango also offers a comprehensive range of training and upskilling programmes for all user levels to help organisations build a successful data science capability internally.

Mango’s ValidR provides highly regulated industries a secure and compliant access to a production ready version of R. Visit or follow @MangoTheCat.