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

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

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

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

Secrets of Success

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

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

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


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

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

Lessons learned

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

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

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

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

Looking ahead

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



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


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

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

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

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

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


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

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

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


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

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


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

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

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


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

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

In summary…

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

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

Value at the Intersection of Data and Software
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For the last 18 years, Mango have been helping customers deliver on the potential of data and analytics.

When we started Mango back in 2002, the wider world of data and analytics was mostly reactive, with workflows conducted by individuals who produced reports as ‘one time’ outputs. As such, while data professionals wrote code, it could largely be considered a by-product of what they did. The advent of data science, together with the increasing need for just-in-time intelligence, has driven more proactive analytic workflows underpinned by open-source technologies such as Python and R.

Working at the forefront of data science, Mango understands the vital role of technology; to allow data to be transformed into wisdom in a repeatable way and deployed to business users at the right time, to support informed decision making.

There is a clear learning here for modern technology initiatives:

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

To realise business value, it is vital that we balance both data and software elements of technical projects around a common and clear purpose.

Every data project is a software project.

Back in 2012, Josh Wills described a data scientist as someone who is “better at statistics than any software engineer and better at software engineering than any statistician”. While modern data science incorporates a broader range of analytic approaches than statistical modelling alone, Josh’s description of data science at the intersection of analytics and software engineering still holds today.

The changing role of data and analytics from a reactive practice to a strategic approach has driven the need for advanced analytics to be combined effectively with software engineering. If analytics is now an always-on capability, we need to codify the intelligence in systems that can be properly deployed and scaled within a business.

A ‘local’ alternative is just not practical – you can’t become a true data-driven business if analytics is run by experts on their laptops. We can’t stop making intelligent decisions if a data scientist is on leave. If a consumer purchases a product on Amazon, they will not wait hours or days until a statistician crunches the data to come up with other recommended products.

To positively impact a business with data, an end-to-end analytic workflow needs to be implemented using software engineering approaches. This encompasses everything from the creation of data pipelines, the deployment of models, and 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.

Every software project is a data project.

Increasingly digitalisation and regulation have driven more focus on requirements regarding the role of data in software systems. We can consider 3 types of requirement regarding the treatment of data:

  • User – requirements relating to users and preferences to provide a more personalised experience
  • Governance – requirements relating to the way in which data is managed in a secure fashion to confirm with data regulations and protect confidential data
  • Provenance – requirements relating to historical system actions to provide an audit trail, or to enable rollout back to, or understanding of, previous actions
  • Beyond this, the most important consideration in the design of modern systems is the ability to leverage advances in data and analytics to create richer, more useful experiences and applications. A growing understanding of the possibilities offered by analytics allows us to strive to ask better questions – to build software tools that are truly aligned to a users’ objectives.

For example, imagine we are building a software application to be used by call centre staff when speaking with customers. Traditionally, we may have built a system that combined data from various sources to give the user a single view of the customer. Perhaps this included data on previous orders, previous interactions, demographic data etc.

With data science, we could extend the functionality for the user – perhaps to include an understanding of likely customer churn linked to suggested retention actions, or a suggested ‘next best offer’ for the customer, or suggestions around the ways in which to talk to the user. Perhaps when the customer calls the call centre they can be allocated to exactly the right person to talk to, as opposed to being randomly allocated to the next available agent.

The use of data and analytics in software can have a transformative effect on the quality and usefulness of our software systems.

In summary…

Helping customers build capabilities at the intersection of data and software is the most effective way to unlock value in an increasingly digital economy. Technology businesses like ours who want to be part of that customer journey need to be ambidextrous in their approach to data and software, agile in their execution and above all empathetic to each customer’s unique context.

We’re excited to apply our passion for data science to a wider market as we join forces with Ascent – increasing our combined ability to design and deliver ‘the big picture’ for customers that helps them compete and flourish.

Author: Rich Pugh, Chief Data Scientist

ascent acquisition
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Author: Rich Pugh, Chief Data Scientist, Mango Solutions

We are excited to announce today the acquisition of Mango Solutions by Ascent.

We founded Mango back in 2002, where we had a mission to make data an essential part of strategic decision making. Over the years, having grown from strength to strength, we’ve been fortunate enough to work with some leading organisations around the world to unlock the value that data science can deliver.

Ascent, a fast-growing European software services company, specialises in delivering effective digital transformation. Backed by a heritage of significant technical capability in software design and engineering skills, product development, IoT and integration – Ascent adds a new dimension at the intersection between data and software.

This acquisition represents an ambitious growth strategy for us, opening newly accessible markets where demand for insight and intelligence is rising. Enriching our proposition with impressive software talent and technology capability will put us in a greater position, allowing our customers to benefit from an end-to-end data-driven proposition with enhanced end-user capability.

The sector has evolved rapidly over the past 18 years, and as founders of Mango we are immensely proud of our organic growth. We have delivered value that has surpassed expectation, and always delighted our customers. We are also proud of our award-winning data science consultancy team, who will sit at the heart of this engine – delivering insight, modelling scenarios and determining the next best decisions for businesses.

After joining as partners initially, it was clear from the start that Mango and Ascent’s vision and shared values aligned. This next step for us is a natural evolution that adds layers to the existing Mango team. I’m truly excited about the future and growth of Mango, as I continue my role at Ascent as Chief Data Scientist.

Our journey to date has been made possible by the contributions of our customers, partners and of course, our amazing team. We look forward to sharing the next part of our chapter with you.

Read the official press release