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 



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Both R and distributed programming rank highly on my list of “good things”, so imagine my delight when two new packages used for distributed programming in R were released:

ddR ( and

multidplyr (


Distributed programming is normally taken up for a variety of reasons:

  • To speed up a process or piece of code
  • To scale up an interface or application for multiple users

There has been a huge appetite for this in the R community for a long time so my first thought was “Why now? Why not before?”.

From a quick look at CRAN’s High Performance Computing page, we can see the mass of packages that were available for related problems already. None of them have quite the same focus of ddR and multidplyr though. Let me explain. R has many features that make it unique and great. It is high-level, interactive and most importantly, it also has a huge number of packages. It would be a huge shame to not be able to use these packages, or if we were to lose these features when writing R code to be run on a cluster.

Traditionally, distributed programming has contrasted with these principles, with much more focus on low-level infrastructures, such as communications between nodes on a cluster. Popular R packages that dealt with these in the past are the now deprecated packages, snow and multicore (released on CRAN in 2003 and 2009 respectively). However, working with low level functionality of a cluster can detract from analysis work because it requires a slightly different skill set.

In addition, the needs of R users are changing and this is, in part, due to big data. Data scientists now need to be able to run experiments on, and analyse and explore much larger data sets, where running computations on it can be time consuming. Due to the fluid nature of exploratory analysis, this can be a huge hindrance. For the same reason, there is a need to be able to write parallelized code without having to think too hard about low-level considerations, and for it to be fast to write as well as easy to read. My point is that fast parallelized code should not just be for production code. The answer to this is an interactive scripting language that can be run on a cluster.

The package written to replace snow and multicore is the parallel package, which includes modified versions of snow and multicore. It starts to bridge the gap between R and more low-level work by providing a unified interface to cluster management systems. The big advantage to this is that R code will be the same, regardless of what protocol for communicating with the cluster is being used under the covers.

Another huge advantage of the parallel package is the “apply” type functions that are provided through this unified interface. This is an obvious but powerful way to extend R with parallelism, because each any call to an “apply” function with, say, FUN = foo can be split into multiple calls to foo, executed at the same time. The recently released packages ddR and multidplyr extend on the functionality provided by the parallel package. They are similar in many ways. Indeed the most significant way is that they are based on the introduction of new datatypes that are specifically for parallel computing. New functions on these data types are used to “partition” data to describe how work can be split amongst multiple nodes and also a function to collect the work and combine them to produce a final result.

ddR then also reimplements a lot of base functions on the distributed data types, for example rbind and tail. ddR is written by Vertica Analytics group, owned by HP. It is written to work with HP’s distributedR, which provides a platform for distributed computing with R.

Hadley Wickham’s package, multidplyr also works with distributedR, in additional to snow and parallel. Where multidplyr differs to ddR is that it is written to be used with the dplyr package. All methods provided in the dplyr package are overloaded to work with the data-types provided by multidplyr, furthering Hadley’s eco-system of R packages.

After a quick play with the two packages, many more differences emerge between the two packages.

The package multidplyr seems more suited to data-wrangling, much like its single-threaded equivalent, dplyr.

The partition()  function can be given a series of vectors which describe how the data should be partitioned, very much like the group_by() function:

# Extract of code that uses the multidplyr package
planes %>% partition() %>% group_by(type) %>% summarize(n())

However, ddR has a very different “flavour”, with a stronger algorithmic focus, as can be seen by the example packages:  randomForest.ddRkmeans.ddR and glm.ddR, implemented with ddR. As can be seen in the code snippet below, certain algorithms such as random forests can be parallelised very naturally. Unlike multidplyr, the


function does not give the user control over how the data is split. However, provided in the


function is the


argument, which gives the user control over which workers to collect results from. Also, the list returned by


can then be fed into a

to aggregate the results, for example, using

randomForest::combine() .
# Skeleton code for implementing very primitive version of random forests using ddR
multipleRF <- dlapply(1:4, 
 randomForest::randomForest(Ozone ~ Wind + Temp + Month,
 data = airquality,
 na.action = na.omit)

listRF <- collect(multipleRF)
res <-, collect(multipleRF))

To summarise, distributed programming in R has been slowly evolving for a long time but now in response to the high demand, many tools are being developed to suit the needs to R users who want to be able to run different types of analysis on a cluster. The prominent themes are as follows:

  • Parallel programming in R should be high-level.
  • Writing parallelised R code should be fast and easy, and not require too much planning.
  • Users should still be able to access the same libraries that they usually use.

Of course, some of the packages mentioned in this post are very young. However, due to the need for such tools, they are rapidly maturing and I look forward to seeing where it goes in the very near future.

Author: Paulin Shek

data team
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As more and more Data Science moves from individuals working alone, with small data sets on their laptops, to more productionised, or analytically mature settings, an increasing number of restrictions are being placed on Data Scientists in the workplace.

Perhaps, your organisation has standardised on a particular version of Python or R, or perhaps you’re using a limited subset of all available big data tools. This sort of standardisation can be incredibly empowering for the business. It ensures all analysts are working with a common set of tools and allows analyses to be run anywhere across the organisation It doesn’t matter if it’s a  laptop, server, or a large-scale cluster, Data Scientists and the wider business, can be safe in the knowledge that the versions of your analytic tools are the same in each environment.

While incredibly useful for the business, this can,  at times, feel very restricting for the individual Data Scientist. Maybe you want to try a new package that isn’t available for your ‘official’ version of R, or you want to try a new tool or technique that hasn’t made it into your officially supported environment yet. In all of these instances a Data Science Lab or Analytic Lab environment can prove invaluable to maintain pace with the fast paced data science world outside of your organisation.

An effective lab environment should be designed from the ground up to support innovation, both with new tools as well as new techniques and approaches. For the most part it’s rare that any two labs would be the same from one organisation to the next, however, the principles behind the implementation and operation are universal. The lab should provide a sandbox of sorts, where Data Scientists can work to improve what they do currently, as well as prepare for the challenges of tomorrow. A well implemented lab can be a source of immense value to it’s users as it can be a space for continual professional development. The benefits to the business however, can be even greater. By giving your Data Scientists the opportunity to be a part of driving requirements for your future analytic solutions, and with those solutions based on solid foundations derived from experiments and testing performed in the lab, the business can achieve and maintain true analytic maturity and meet new analytic challenges head-on.

In order to successfully implement a lab in your business, you must first establish the need. If your Data Scientists are using whatever tools are handy and nobody has a decent grasp on what tools are used, with what additional libraries, and at what versions, then you have bigger fish to fry right now and should come back when that’s sorted out!

If your business analytic landscape is well understood and documented, you must first identify and distil your existing tool set into a set of core tools. As these tools constitute the day-to-day analytic workhorses of your business, they will form the backbone of the lab. In a lot of cases, this may be a particular Hadoop distribution and version, or perhaps a particular version of python with scikit-learn and numpy, or a combination.

The next step, can often be the most challenging, as it often requires moving outside of the Data Science or Advanced Analytics team and working closely with your IT department in order to provision environments upon which the lab will be based. Naturally, if you’re lucky enough to have a suitable Data Engineer or DataOps professional on your team then you may avoid this requirement. A lot of that is going to depend on the agility model of you business and how reliant on strict silos it is.

Ideally any environments provisioned at this stage should be capable of being rapidly re-provisioned and re-purposed as needs arise, so working with a modern infrastructure is a high priority. It’s often wise at this stage to consider some form of image management for containers or VM’s, to speed deployment and ensure environments are properly managed. You need to be able to adapt the environment to the changing needs of the user base with the minimum of effort and fuss.

Once you have rapidly deployable environments at your disposal, you’re ready to start work. What form that work takes should be left largely up to your Data Science team, but broadly speaking they should be free to use and evaluate new tools or approaches. Remember, the lab is not a place where production work is done with ad hoc tools, it’s a safe space for experimentation and innovation, just like a real laboratory environment. Using the knowledge gained from running tests or trials in the lab however, can and should inform the evolution of your production tools and techniques.

A final word of warning for the business: A successful lab environment can’t be achieved through lip-service. The business must set aside time for Analysts or Data Scientists to develop the future analytic solutions that are increasingly becoming central to the success of the modern business.

For more information, or to get help building out an Analytics Lab of your own, or even if you’re just starting your journey on the path to analytic maturity, contact

Author:  Mark Sellors, Mango Solutions

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Since we first demoed it at our really successful trip to Strata London last year, a few people have asked us how we made the awesome looking Data Science Radar app that we were running on the tablets we had with us. In this post we’ll take a look at how we did it, and hopefully show you how easy it is to do yourself.

Mango is primarily known for its work with the R language, so it should come as no surprise that this is the secret sauce used in the creation of the app. More specifically, we used a Shiny app written by one of our Senior Data Scientists, Aimee Gott. The app uses the radarchart package which you can find on github.

I think the fact that it was written with Shiny has actually surprised a few people, largely because of how it looks and the way that we run it.

The tablets in question are cheap Windows 10 devices, nothing special, but we had to come up with a way of running the application that would be simple enough for the non-R-using members of the team. This meant that anything from the everyday world of R had to be abstracted away or made as simple to use as possible. In turn this means, not starting RStudio, or having to type anything in to start the app.

R and the required packages are installed on the tablets, ready to start the configuration that would allow the whole Mango team to use them in the high pressure, high visibility setting of a stand at an extremely busy conference.

We wrote a simple batch file that would start the app. This only got us part of the way though, because the default browser on Windows 10, Microsoft’s Edge, doesn’t have a full screen mode, which makes the app look less slick. We therefore changed the default browser to Microsoft’s IE, and put it in full screen mode (with F11) when it first opened. The good news here is that IE, remembers that it was in full screen mode when you close and re-open it, so that’s another problem solved. The app now opens automatically and covers the full screen.

The code for the batch file is a simple one-liner and looks like this:

"C:\Program Files\R\R-3.3.0\bin\Rscript.exe" -e "shiny::runApp('/Users/training2/Documents/dsRadar/app.R', launch.browser = TRUE)"

Next, it was necessary to set the rotation lock on the tablets, to avoid the display flipping round to portrait mode while in use on the stand. This is more cosmetic than anything else, and we did find that the Win10 rotation lock is slightly buggy in that it doesn’t always seem to remember which way round the lock is, so that it occasionally needs to be reset between uses. Remember, our app was written specifically for this device, so the layout is optimised correctly for the resolution and landscape orientation, you may want to approach that differently if you try this yourself.

We also found that the on-screen keyboard wasn’t enabled by default with our devices (which have a detachable keyboard), so we had to turn that on in the settings as well.

Having the application start via a Windows batch file, isn’t the prettiest way of starting an app as it starts the windows command prompt before launching the application itself. This is hidden behind the application when it’s fully started, but it just doesn’t look good enough. This problem can be avoided with a small amount of VBScript, which runs the contents of the batch file without displaying the command prompt. Unfortunately the VBScript icon you end up with is pretty horrid. The easiest way to change it is to create a shortcut to the VBScript file and then change the icon of the shortcut, which is much easier.

Here’s the VBScript:

Set objShell = WScript.CreateObject("WScript.Shell")

objShell.Run("C:\Users\training2\Desktop\dsRadar.bat"), 0, True

Check out the video below to see it in action, we hope you agree that it looks really good and we hope you find this simple method of turning a shiny application into a tablet or desktop app as useful as we do!


Author: Mark Sellors


Beth Ashlee Senior Data Scientist
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Spotlight on Beth Ashlee – Senior Data Scientist


Name: Beth Ashlee

Job title: Data Science Consultant

Qualification(s): BSc Biomedical Science

Time in current role:  4 years

Beth Ashlee joined Mango initially as an intern whilst studying Biomedical Science. 4 years on and she’s recently been promoted to a position of Senior Data Scientist. During this time, she has experienced many diverse opportunities and pathways that have accelerated her analytical competency.

In addition to having been exposed to a myriad of technical-based scenarios through her delivery of client training in R and Python, Beth spends much of her time collaborating on a variety of projects such as Shiny app development, data exploration or productionising models. One of Beth’s passions is her team lead responsibility for Mango’s graduate recruitment programme where she actively trains and mentors her team on both professional and personal development.

Beth is a master communicator which is reflected in the shape of her Data Science Radar – a tool used to assess core Data Science competencies. Soft skills in data science are essential to establishing meaningful relationships alongside the ability to translate business value across an organisation, an area where Beth certainly excels. Outside of work, Beth enjoys travelling to new places and attending music festivals.


Beth’s Top 3 traits: 

  • Programmer 
  • Communicator 
  • Data Wrangler

Beth scores high in both Visualisation and Programming which ties in with the types of projects she has been working on most recently. 

As would be expected given her role as a Consultant and Trainer, Beth scores strongly as a Communicator. During a recent Government project, which required significant stakeholder engagement, these skills proved essential for helping to mobilise teams around the possibilities of advanced analytics.

Beth has identified that modelling is something she needs to work on to become a more well-rounded data scientist. To support this development, she has recently been doing more self-learning and is now working on a client facing modelling project.

Having a thorough understanding of capabilities and skill levels mapped against core competencies like these for the team, can help guide and shape the data science project team best suited to the task. The result is a significantly more engaged workforce with a set of skills that the business understands and needs, to deliver data-driven value. For more information on Data Science Radar, check out our Building a Winning Data Science Team page.

Would you like to join our award-winning 2020 Data IQ Best Data and Analytics Team? Mango are currently recruiting.


Related blogs:

Spotlight on a Data Consultant: Karina Marks

Spotlight on a Junior Data Scientist: Joe Russell

global data science
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2020 has been a year for the history books – unfortunately though for reasons we’d all prefer to forget! Many businesses have experienced a really tough year, but rather than seeing it as one long crisis, I prefer to view it as many different waves of mini-market changes.

To explain, factors like lockdown rules and related government guidance that have and continue to change in accordance with the severity of the Covid-19 pandemic, has led to many different market environments. It’s a constantly evolving reality and those companies who are best able to predict and adapt have been able to steal a march against more flat-footed rivals –  online retailers that now have the definite edge over bricks and mortar retailers are a perfect example. 

Looking inward

As a business owner, the crisis has been a time of doubt, and has prompted an in-depth analysis of costs and revenues. Many organisations have been forced to explore ways of creating more value with existing or fewer resources. Trimming waste from budgets, and ensuring ROI, has been essential for us and many others, and there is a strong desire to embed a data-driven approach to areas that previously might not have been considered. At Mango, conference budgets for instance have been transferred and utilised elsewhere in the business, allowing our data science teams to widen their approaches and deliver more value. 

Implementing a data-driven approach allows organisations to optimise their business effectively, which has helped them to make quicker, effective decisions. In fact, a recent report by Sisense found that 49% of respondents surveyed said analytics were more or much more important than before COVID-19. The changed circumstances has led to a requirement for more agile approaches backed up by predictive analytics. 

Whilst many organisations were in crisis mode in the early parts of the year, the new circumstances have allowed time for consideration and change. Business as usual was never going to be as effective in a swiftly transforming world, and it has created an opportunity for companies to try different things, bring forward innovation and change approaches to markets. We’ve seen technology providers embrace the opportunity by speeding up release cycles and driving their engagement with totally different markets. The ease with which my mum started using Teams for video calls was a fascinating compliment to the developers of that product and I’ve no doubt that the digital revolution for marginalised groups such as the elderly has been enhanced massively.

Looking outward

We work within a range of styles with our customers – some prefer to completely outsource, while others look to us to develop and enhance an existing team. When lockdown bit early, many companies immediately put a halt to recruitment processes, which meant that in order to execute workloads, we were able to help create data science teams for customers to deploy and maintain momentum around data-led initiatives.

Several months on and as we face renewed restrictions, I believe that this time around a lot of organisations will regard it as an opportunity to roll out new methods and move further towards harnessing the power of data science. Covid-19 has provided a stimulus to boards to be creative and flexible since all businesses have been affected. It’s an ideal opportunity for organisations to step up and adapt their business model to take advantage of areas such as innovation and data science, which might well have been on the agenda, but were probably tucked away a bit further down ‘for review’ in a few years’ time. Taking action and investing now is vital and a relative “free hit” for leadership teams.

Virtual is going to be totally dominant from now on. Those companies who have embraced it wholeheartedly will have a massive advantage, and I think we’ll see an acceleration in the adoption of online only business in pretty much every aspect of our lives. We see this as beneficial for Mango in that a transition to a digital approach to business necessitates a primarily analytic led strategy.

Looking forward

With most organisations moving towards a less office-based environment, there are opportunities to change styles of working and this will include how analytic code is held and distributed. This may well involve outsourcing of analytic development, where virtual teams can become extremely effective. In the future we are likely to see more confederacy in teams enabling organisations to extend teams and create focused high delivery groups from different resources. I think we’ll see much more team augmentation with increasingly effective outcomes. It’s exactly why now is the time to invest in data science initiatives.

Author: Matt Aldridge, CEO at Mango Solutions

Ask the Expert: Accurate vs fair data science A-level playing field?
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Algorithms have never been more publicly debated than in the last couple of weeks. As data science experts, it didn’t come as a surprise to us that an algorithm was used by the government to aid in the grading of students’ A-level grades in this extraordinary year. The use of data science to set (or help set) grades is, in principle, a sound idea. What did surprise us was the way in which the algorithm was developed and communicated to students and the public. This was likely a key factor in why so many people across the country were dissatisfied with the results it generated.

It all comes back to a question of ethics and empathy. Data science is most likely one of the last subjects where empathy would seem important. However, it’s this misinterpretation that often leads to the breakdown of data science projects – the model works, but does it consider the problem from the right perspective? Is it answering the right question? After all, empathy isn’t just about compassion or sympathy, it’s the ability to see a situation from someone else’s frame of reference.

So, in this instance, the A-level algorithm may have worked technically (without access to the detailed workings or results of the model we can’t say), insofar that it produced results accurate at the overall population level in comparison to previous years’ results. However, was the question of empathy taken into account, and therefore was accuracy the right thing to be aiming for?

We asked our Deputy Director of the data science consulting team, Dave Gardner, for his thoughts on the use of data science in assessing school performance:

“Without being privy to either the detailed results the algorithm generated, or the process by which is was built, it is impossible to say exactly what went wrong. We can however examine some of the decisions that we do know were made from the point of view of empathic and ethical data science.

“If part of your objective is to produce a set of results as similar as possible to that of previous years then the idea of incorporating past school performance to your algorithm does have some merit. Speaking from a purely technical perspective it’s a reasonable path to take – some schools may be optimistic, pessimistic, or interpret guidelines in different ways.

“The core issue however wasn’t with the algorithm’s accuracy at the population level, but with its (perceived) fairness at an individual level. Whether it is fair to give students a set of grades calculated by an algorithm to be the most likely outcome of a normal exam process, based partly on historic performance at the school level, isn’t a question data science can answer. However, it absolutely should have been a question it asked.

“Similarly the decision not to incorporate teacher’s predicted grades was clearly contentious but the thinking behind this decision was not well explained, and appeared to have been made without consultation with the right stakeholders.

“Had the explicit focus been on generating the fairest possible set of results for each individual (for the right definition of fairest) then I suspect we would have ended up with a very different, and much better received, algorithm.”

It’s no secret that there has been some amazing work done through the power of data science and analytics – but the lesson to be learnt here is the importance of understanding that there is a difference between accurate and fair data science, and at the end of the day, empathy is key.

Author: Dave Gardner, Deputy Director, Mango Solutions

going pro blog
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Becoming a professional athlete isn’t just about pure talent and hoping that will be enough to excel. Going pro means setting out a clear plan and following through with sustained training, the right nutrition, coaching and support. Not to mention an incredible amount of discipline and determination to get the most from your talent!

In a similar way for businesses, becoming data-driven can’t depend solely on investing in a data project and hoping it will succeed.  Typically, organisations have similar challenges to amateur athletes in that they are successfully trying aspects of analytics with notable successes, but just cannot withstand the test of time to be repeatable, scalable and consistent. Or, they simply don’t know where to start with analytics to achieve maximum deliverable insight. This tends to have a knock-on effect, causing concerns over stakeholder buy-in, with the result that the analytics team continues as a siloed entity with sporadic projects and no guarantee of consistency of approach across the organisation. They fail to make the transition from talented amateur to pro athlete and so great talent is wasted as funding and enthusiasm runs dry.

As the role of analytics becomes more strategically important to the business, it becomes necessary to follow a standardised process for delivery. As part of this, business leaders need to ensure that initiatives meet business objectives and that there is consistency in delivery and prioritisation, as well as in the platforms and technologies used.  To move forward, you have to evaluate where you are, what needs to be put in place to succeed, and enable the transition to implementation and data-driven value. In other words, you need to go pro in analytics.

It sounds easy enough. But as most pro athletes know very well, taking the leap from amateur to pro warrants a whole new game plan, and then sticking to it – a rather daunting prospect for most of us. The good news is that Mango can help! As experts in data science and analytics, we’ve honed in on the key pillars of a data-driven transformation and drawn up a 5-step game plan aimed at helping you to scan and audit what your business has in place, identifying what’s needed, and where to focus next. Here’s a snapshot of how it works.

Join our webinar

If you’d like to find out more, why not join our webinar Going Pro in Analytics: Lessons in Data Science Operational Excellence where Deputy Director at Mango, Dave Gardner and Mango Account Director Ian Cassley discuss what organisations need to do to ‘go pro’ with their analytical platforms, capabilities, and processes once the limitations of sticking plaster solutions and ‘quick and dirty’ approaches start to bite:

Register Now

dataiq awards best data & analytics team
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Earlier this month we were delighted to discover that Mango Solutions had been shortlisted for the 2020 Data IQ  ‘Best Data & Analytics Team (Enabler)‘ award. We’ve always said the secret of Mango’s success is our people and this acknowledgment represents much valued recognition of our team of data professionals and their talent, service excellence, business value and innovation generated from data.

As a data science consultancy, we’ve been helping businesses deliver value from data for 18 years. Home to 35 data scientists who have more than 200 years commercial data science experience expertise between them, there is no doubt that we’ve managed to hone the perfect Analytics team, based on an ethos of true collaboration, expertise and delivering instrumental value for our customers.

The team is continually dedicated to delivering the highest possible customer experience, supported by our best practice framework and agile project management, which has certainly helped continue our exemplary levels of service, without impact, over the recent se challenging few months.

Extra special congratulations also go to Rich Pugh who has been shortlisted for the ‘Data & Analytics Leader of the Year (Enabler)‘ award. This year has seen some challenging business conditions, but Rich Pugh’s natural leadership qualities have continued to inspire his peers, colleagues, customers and the broader community alike – reinforcing his integrity, passion and authenticity as a natural leader.

Congratulations once again to both Mango’s Data Science team and Rich Pugh for reaching their respective Data IQ shortlists; we very much look forward to attending the online awards ceremony on 30th September.