dataIQ award winners
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Mango are delighted to have been awarded the 2020 Data IQ Best Data & Analytics Team (Enabler) award as part of the people category.  The virtual awards ceremony took place early yesterday evening with Pete Scott, Mango’s Client Services Director accepting the award on behalf of the team.

As is sadly the case with virtual ceremonies, there wasn’t a cocktail or DJ in sight; nonetheless, the shortlist comprised very strong competition. Pete Scott said, “It really is fantastic to be recognised for such a prestigious award, designed to showcase the best of the data and analytics industry. Mango’s astonishing team of Data Science Consultants focus on solving real challenges through data and are dedicated to delivering customer-centric, data-driven value.  As a team they deliver expertise and innovative solutions in strategic advice, data and analytic project delivery, through to building analytic team capability.”

The consulting team, consisting of 35 data scientists and engineers with more than 200 years’ combined expertise between them, demonstrate exemplary technical excellence, collaborative working practices and processes, best practice frameworks and a commitment to proactive stakeholder engagement. The award entry demonstrated these commitments in abundance and in addition, it was their external engagements, notably their community and outreach activities showcasing Mango as being at the heart of an innovative data science community, which were no doubt recognised by the screening panel.

Mango would like to acknowledge the support of their key stakeholder partnerships, where the benefits of true collaborative relationships are realised. Working through the restrictions impacted by the COVID-19 pandemic, has certainly shown the benefit of Mango’s ‘agile’ project management practices, an approach that has allowed for reactive changes in accordance with rapidly changing conditions.

“This is an amazing achievement for Mango”, concluded Pete, “and reflects not only on the brilliance of the consulting team, but also on the support they receive from all areas of our 70-people consultancy. We all celebrate this win.”

Congratulations to all of the worthy award winners and shortlisters this year, we are very proud to have been amongst such stiff competition!

race to become data-driven
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Many companies are investing in some sort of data project – data analytics, big data, AI, machine learning data science. However, independent data projects do not make you data-driven.

The race to secure a data-driven and robust future is instead an integral part of a strategic journey, where you look to position data to empower and deliver on your business objectives.

As we return to our new normal, the importance of deeper insights has perhaps never been so critical to our decision making. Organisations are under pressure to make the right decisions to enable them to survive and transform their business model in the most appropriate way – this can only be achieved using data and analytic techniques to turn data into value in a repeatable, business-focused manner. Unfortunately, there is no simply plug and play solution to becoming data-driven. Instead, it’s about taking a data-driven approach across the business, putting the information and critical skills you have at the heart of the strategy, supported with the right technology to deliver the fundamental insights you require to not only survive but also thrive in this increasingly competitive landscape.

Here are some top tips that can help businesses succeed with their race to be data-driven:

Embrace a data-driven Future

The race to be data-driven has never been so important with so many businesses emerging from this period faster and hungrier having invested in data and analytics – this produces a new competitive landscape where the more intelligent, efficient and engaged organisations will hold a significant advantage. The need to be data-driven requires leadership alignment and a cultural shift to instigate success, and it needs to happen now. Driving champions that can help instigate data-led actionable change is of paramount importance for the commercial future.

Align Data Investment to Business Outcomes

Data investment has to align with agreed measures of success in business terms. Does the immediate strategy require cost reduction, revenue generation or creating richer experiences to regain customers? Prioritisation at this stage becomes incredibly important. What decisions will drive the most effective results and what is the potential impact of each decision to the organisation? By knowing, defining and sharing a set of goals, it becomes clearer what the company is working towards, and ensures that all stakeholders and teams share a common understanding of what data-driven success looks like.

Upskilling your data and analytic talent

Ensuring you have the right team and skills to scale your analytic initiatives is perhaps one of the most significant challenges you’ll face. What resourcing model is right for the business and how might you best establish a core, centralised best practice team of data professionals? – one community striving in one direction to empower the business and implement data-driven success. A data-driven company is one where the entire organisation leads with data, where data literacy is spread through every tier of the organisation. Defining the skills and competencies against those critical dependencies is essential across every level of your workforce.

Use data to inform any transformation

Workplaces are changing. To evolve effectively and become more agile decisions need to be driven by data. Whether these decisions involve the application of new technology and automation, further investment around digital collaboration or more innovative processes, any implementation needs to be based on actionable data.

Thriving and surviving with a data-driven data strategy is key for success in today’s competitive market, because it presents the ability to make informed decisions and transform quickly based on real insight.

Join Rich Pugh, Chief Data Scientist at Mango and Simon Adams, Change Consultant at Nine Feet Tall, as they discuss the importance of being data driven in this increasingly competitive commercial landscape and why putting data at the heart of your business transformation is imperative if you want to survive and thrive.

The focus for the webinar will be on:

  • Why are organisations racing to become “data-driven”?
  • What exactly does a data-driven organisation look like?
  • What happens if we don’t get there quickly enough?

Join this webinar

 

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.

 

data engineer
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Here at Mango, we are often asked to come and help companies who are in a mess with their data. They have huge technical debt, they can’t link all their data sources and the number of reports they have has ballooned beyond control. Everyone has their own version of the truth and business units are involved in ‘data wars’ where their data is right and everyone else has the wrong data. How does this happen? Put quite simply, hires are focused for the ‘shiny’, interesting aspects of data science where it is easy for the business to see how they get value from that hire – business intelligence (BI), management information (MI), or Data Scientists. This ignores the more technical and less exciting but essential pillar of delivering business value: the data management and data engineering pillar which is critical to underpin any data-driven business.

The thing is, you may have the best data team who can programme, model, visualise and report with data but without well-managed, curated data, over the longer term your systems and processes will be thrown into chaos and your data will become unmanageable. This isn’t because these analytical professionals aren’t doing their job, it’s because their job is extracting value from insight, not making sure the machine behind it all is ticking over smoothly. In F1, the driver would be useless without a whole range of engineers and mechanics. If your business only has BI and MI analysts or Data Scientists, you are asking the driver to win an F1 race with a Morris Minor – you need a Data Engineer.

 

Turning data into wisdom – the role of the Data Engineer

Why does this happen? Quite simply, organisations often might look at the price of hiring a senior experienced head of data/data engineering or a building a data management function and decide they don’t need one and instead hire a significantly cheaper BI resource instead, expecting this person to do it all. As a role, a head of data/data engineering has changed massively since the advent of advanced analytics and now requires both specialist and strategic knowledge to build the reliable systems to collect, transform, store and provision data for analytics or other complex purposes.  The right technical infrastructure required to turn the data into wisdom in a repeatable manner bridge the gap between strategy and execution.

From assessing a proliferation of data silos to hard to maintaining “legacy” data processing systems are just common challenges and with modern platforms, data warehouses are a more collaborative affair than ever before, many of the same principles still hold. A data engineer understands data modelling techniques to build data warehouses that can be trusted, maintained, and that deliver exactly what analysts need.

It’s a false economy to overlook the critical engineering needs that a data-driven busines has. There is also cost in fiscal terms. With poorly designed systems that don’t perform, we have seen costs of transformation projects moving to the cloud double purely because of poor data management. Add to that the cost of having to constantly upgrade database servers so they can keep up with the ever increasing workload and lifetime costs get even higher. This ignores the harder to quantify opportunity cost of not being able to leverage your data, or the cultural impact of business units arguing because they have a different data-driven view of the business.

Its essential to look at the investment in an appropriate data function holistically in terms of long term gain through increased opportunities to leverage data and make better decisions, a more efficient cost base for your technology over the long term alongside an easier transformation pathway when you need to evolve as a business. Without taking that long-term view of your business, it can be hard to see how a data management function can add value. However, without one, the opportunity for improved insight and the cultural benefit of happier staff who understand how to leverage data in a way that is sustainable and beneficial to all involved will be lost.

 

The Key to Extracting Value from your Data

Organisations need a good data engineering function to access the right data, at the right time, and with sufficient quality to empower analytics. But what is the definition of a data engineer’s role and why is this function so crucial to bridging the gap between strategy and execution when it comes to delivering a data science project?

As data experts, we know what companies need to do to become data-driven. If you are struggling to see how a data function fits in your business or don’t know how to move to the data-driven nirvana, we can help guide you on your whole journey, from first steps through to decisions being made from a ‘data first’ mindset.

Author: Dean Wood, Principal Data Scientist

Hyperautomation symbol
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 In October, Gartner released a report on the Top 10 Strategic Technology Trends for 2020.  In somewhat prophetic fashion, Gartner identified “Hyperautomation” as the #1 trend for 2020 – as we plan for the post-COVID commercial environment, with leadership looking to create more streamlined organisations, their timing couldn’t have been better.

“Hyperautomation” is found at the intersection of Robotic Process Automation (RPA) and Machine Learning (ML) – it combines RPA’s approach to automating business processes, with ML’s ability to drive insight from data.  It the potential to significantly reduce costs  by automating processes that may include intelligent decisioning, at a time where leaders everywhere need to create more efficient, smarter organisations.

In many ways, there is nothing new here – a key goal of data science investment for the last ~10 years has been to automate decision-making processes based on AI and ML.  As an organisation, we are frequently asked to deliver initiatives that aim to automate decision making processes using a combination of data science and software engineering.

What is new here is the perspective – the “RPA-first” approach that underpins “Hyperautomation” is another tool in the arsenal when we look at automating process, and drives increased collaboration across analytic and IT functions.

Perhaps the most important aspect of the rise of “Hyperautomation” is its impeccable timing.  Not only are we needing to create more streamlined organisations (due to COVID and the impending recession), but it comes at a time when (in some quarters) serious questions are being raised about the value generated from investment in data science.  With talk of an impending AI-Winter, and anecdotal stories of data science teams struggling to deliver realisable business value, talk of “Hyperautomation” provides a great opportunity – a chance to deliver on the potential of analytics to drive measurable cost reduction.

“Hyperautomation” is an opportunity to capture the imagination and focus of the business – to more deeply engage with them in a collaborative fashion to explore possible processes that could be automated.  And when we find high-value process that could be automated, then we have more tools in our arsenal with which to build a solution.

To use a recent example, we were engaged by a client to automate their “price comparison” process, where customers would email details of a quote and ask whether our client could beat it.  Using a mixture of technologies and machine learning, we were able to dynamic read and understand the given quote, generate a comparative quote, and use NLP to dynamically create a response using an appropriate tone of voice.  The initial automation “success” was low, with only 8% of cases being full automated.  However, that already delivered sufficient business value to demonstrate a return on investment in a few months.  Moreover, the data generated by the “manual” process is already being used to dynamically improve the model, leading to an increased success rate and more savings.  All in all, this “humans pretending to be AI pretending to be humans” model really provides a platform for ongoing efficiency gains and cost reductions.

 As businesses emerge post-COVID, we’re all going to be in a difficult financial position, in an ultra-competitive landscape with lots of unknowns. To get through, companies will be looking to drive costs efficiencies wherever they can, making it a great time to talk about the application of “Hyperautomation” as a way to reduce the unnecessary day to day burden of process heavy tasks.

Reducing Costs With AI & Hyperautomation in a Post-COVID World Webinar

Join Rich Pugh as he provides an insight into AI and Hyperautomation – how businesses will be adopting this very technique as they strive to reduce costs.

REGISTER FOR THE WEBINAR HERE

 

an agile approach
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Working at a distance is hard. It requires discipline, planning, trust, and an acceptance that things may not be smooth. It can however, be empowering, productive and highly successful. But how do we enable our data science teams to stay connected to the culture and values any organisation stands for, and one another – both professionally and socially – when we lose the ability for physical interaction? Arguably, COVID-19 has done more for digital transformation than the past 10 years of technological evolution and as a result the workplace has changed forever. Meaning a whole new set of tools and processes combined with effective stakeholder management, in a positive, success-led manner.

Below are some practical hints and simple processes that can be adopted with your teams that we deploy in our own Project Management, to ensure we stay productive in a suboptimal environment.

1. Talking beatS WRITING

Success comes from communication. We’ve all had weeks, days  and even moments when we’ve wanted to shoot email in the face and bury it six feet beneath the surface, and at times of social isolation, the need to ensure the appropriate use of email is paramount. For many people, email is the standard go-to device for managing workload, but to truly engage people in the business, we need to shift our thinking so that communications are more interactive giving all participants a voice. On day 2 of isolation, we instigated a daily stand-up at team level – just a short 15-minute meeting where everyone got to highlight issues, blockers, concerns, achievements, and questions – both work and personal. Aside from providing a platform to check the health and physical wellbeing with demonstrating empathy for everyone’s personal circumstancces, it gives an opportunity for everyone to stay connected, see each other, and generate a sense of ‘all-in-it-togetherness’ that the written word simply cannot achieve. Do this every day, make it a ritual, and maybe it will stay with you once we’re out the other side.

2. Protocol has changed

Strong and effective leadership is based on understanding the purpose, people and processes related to any given activity. In a remote setting, this is more difficult to achieve. The best decisions are made when they are informed through experience, so take the opportunity of inviting senior stake holders, engage and break down barriers across the organisation. Ensure clarity as to why data is at the heart of every business decision. Get them involved in daily stand-ups, include them in team discussions – not to lead, but to participate – and give them the opportunity to understand the context within which the business is now operating. As a workforce we are all learning this new paradigm together, and only together will we find the best way to deliver.

3. Create space for your staff and trust them to deliver

The working day at home; wake up, stumble to the bathroom, see your laptop enroute, open and get sucked in. Before you know it, family life is happening around you and the ability to distinguish between work ‘you’ and ‘non-work’ you is increasingly diminished. Many successful people live like this all their lives – I once had a CEO tell me he purposefully didn’t distinguish between work and home – but I don’t believe this is the norm, nor do I believe it to healthy for the majority of us. And we can do things to help.

Create space in the working day to step away. Encourage everyone to do the same. Be tolerant if individuals need to focus on other priorities at certain times and trust your colleagues to get the job done. I am sure that working patterns will shift markedly as a result of COVID-19, but we will also be a more productive country as a result. Encourage your staff to connect with their families, give them space to work to a pattern that allows them stay focused. Do this for them, and you’ll be surprised at how big a mountain they’ll move for you.

4. Maximise the adoption of shared platforms

Alongside your daily standups, encourage the daily adoption of data science tools as an outlet for question, advice or even unload after a bad day. Mango heavily relies on instant messaging tools such as Microsoft Teams and Slack, which offer a great way for our team to communicate and share their own tips and tricks. We also conduct a weekly analytics club for showcasing ideas and progress with projects, and encourage conversations throughout the week with games like ‘Whos Desk is this?’ and ‘Two truths and a lie’. Shared collaboration tools such as trello, planner or JIRA offer a great platform for sharing to do lists and help understand generally how projects are progressing. Coding in remote teams only enforces the need for good coding practices, structured review processes, creating readable and reproducible code, and making use of version control software’s such as Git and GitLab. While working remotely, these practices and tools enhance our ability to share code with the team. Afterall Data Science is a Team sport.

5. Be open to challenge and let your staff see their voice

We do so many things whilst ‘at work’ and our experience and activities are often so much more than the job we have. We make friends, we come together around similar interests and passions, and we help each other when needed. None of this happens simply because we have a job; it happens because at the heart of it, we are driven by the need to be active in our community. But people also need to feel that they have a voice. COVID-19 and the related isolation has been an imposition like we have never experienced before, and everyone is working out what works best for them. Leaders are trying to put mechanisms in place to allow workable solutions, but these won’t always be right. Its important to give those experiencing it (and by that we mean EVERYONE!) a chance to feed in. Create space for staff to share challenges, offer solutions, and be prepared to act on them. Giving staff the ability to see change as a direct result of their needs will help them see that they can make a difference. This is a critical component of community building and will and it will bring your teams closer together.

We might just find , the end result is a whole new set of tools and processes, combined with effective stakeholder management, lending itself to in a positive enforced success-led initiative .

Author Pete Scott, Client Services Director at Mango.

 

 

successful strategies to navigate your team
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Having access to the right data, at the right time, and in the right format, in order to inform your business strategy has always been critical in defining commercial success.  But in today’s exceptionally challenging business environment, where market conditions are changing daily (and in some industries, such as oil and gas, hourly), the availability of business-critical data, and the insights derived from it, is the key determinant in ensuring commercial survival.

Truly, we are in uncharted territory for the world economy and there are no precedents to refer to for those businesses trying to navigate a path to survival.  This means that the ability to understand trends to predict outcomes and to measure progress – through data management and analysis – is essential for senior executives to make the right decisions about the direction of their businesses.

In short, never has data science been more important – and having immediate, short and long term data strategies in place will help create the agility required for long term commercial success.

Here are five key data initiatives to focus on during this period:

1. Management Information (MI)

i) If your MI is based on retrospective data that was extrapolated manually through Excel wrangling and presented in a way that is not relevant to the situation because of a time lag, or worse possible errors and therefore counter intuitive, then it will be ignored versus

ii) having the right (MI) and metrics.  You can’t make good decisions without good data, so get your data & analytic teams focused on rethinking your MI suite, injecting forward-looking insight, and ensure it is automated

2. Business scenario simulation

The only thing that is certain right now is that nothing is certain. And if nothing is certain, then scenario planning can help and asking the right questions will be key to success:  what would happen if this situation lasts 3 months?  Or 6 months?  Or 9 months?  What happens if the ‘new normal’ is more, or less suited to your business?  Analytic teams can help you simulate and understand the impact of different scenarios on your business so you can better plan for the future.

3. Prepare for success

How do you ideally plan for your escape velocity to ensure you are in the best position to succeed post Covid-19?  For example, how do you best behave to regain and retain your clients if you’ve recently undergone significant churn?  Data science can help you plan the best approach and strategy to optimise the outcome commercially.

4. Data-driven transformation

A positive side effect of Covid-19 in the business world is that the situation will serve as the catalyst needed to accelerate digital transformation for many organisations, and thus reducing the time to becoming data-driven.  Are you prepared for this change?  If not, you may find the competitive landscape in the ‘new normal’ has shifted, with technology enabling other companies to make better, data-driven decisions and reduce costs. Could you compete with that?  Now is the time to focus on your transformation strategy to prepare you for life post-Covid.

5. Data & Analytic Literacy

Part of becoming data-driven is changing the culture within your organisation to make data-driven decision-making part of the DNA, rather than something that comes ‘from the top’ and trickles down, possibly getting lost on the way. Now is the time to teach your workforce the language of data & analytics, so you can devolve more decision making and give people the skills to thrive in the new data-driven future.  Upskilling, buddying and mentoring schemes can all help with this.

Now is perhaps the right time to make decisions using your data. The Covid-19 situation has become a numbers story on all levels, and it’s the data behind those numbers that is driving many Government, business and personal decisions.  We know from watching the news how regularly those numbers change, and the same will apply to your business.  Remember, agility is the new currency for business and putting effective data strategies into place now will help you emerge on the other side of this as healthy as you were when you went in.

Author Rich Pugh, Chief Data Scientist

Related content

Blog: Future Proofing your Data Science Team

 

Rich Pugh makes DataIQ Top 100 list
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Earlier today, DataIQ unveiled its list of the 100 most influential people in data-driven business, the DataIQ 100, and I’m delighted to report that I was included on that list. It was humbling to be counted among talented individuals, such as Harry Powell, Orlando Machado and Tom Smith, all of whom are blazing the trail in using data to make a real difference in the way they drive their businesses.  The competition was stiff, with a record breaking 1,000+ entries for the coveted 100 places, so we must have done something right along the way!

But as well as being a moment of personal pride, it was actually refreshing to receive good news this week after what feels like a growing sense of doom in the world with the current Covid-19 outbreak.  A beacon of light in dark times, if you like. It’s an interesting time for data scientists: the world is witnessing the bravery of medical staff on the frontline dealing with those affected by the illness, but #flattenthecurve is trending and tales of retail boom or bust (depending on what sector you’re in) are just two examples of data-driven stories that highlight how our profession is trying to make some sense out of this unfathomable situation.

Initiatives such as the DataIQ 100 help showcase the value and positive impact that data can have on business and situational outcomes.  We at Mango are firm believers in the power of data and (advanced) analytics to drive better decisions, not just in the world of business, but to help the most vulnerable in our society and help combat some of the biggest threats facing our future.

Some time ago, the DataIQ 100 committee asked me, and other members of the 2020 DataIQ 100 list, for our views on the industry’s future, and one of the key themes to emerge from this was skills.  The feedback was unanimous, that demand will continue to outstrip supply.

At the end of 2019, Mango, alongside Women in Data, conducted its own research into this topic and discovered that over half of data scientists planned on moving roles within the next year.  A lack of support, funding and time available for upskilling were all cited as challenges within the UK data science community – all indications that vital steps need to be taken to assess skills gaps and plan to unite individuals to create effective, skilled teams that can rise to the growing data challenge for businesses.

I hope that the important work data scientists are doing in the background of this current crisis – from work in the pharmaceutical sector to expedite the release of a vaccine, to work in the retail sector to ensure firms can weather this storm or that food supply chains run smoothly – can shine a light on the difference that data can make and encourage others to join the profession in the future.

In the meantime, I am proud to be included among such industry luminaries and hope that, together, we will be able to inspire others to join our crusade.

Here’s my #DataIQ100 profile 

 

building project teams
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Project managers guide to playing to a team’s capability with Data Science Radar

Data science consulting is a rapidly evolving beast that is often difficult to tame. The many moving parts require a broad set of capabilities that, in many cases, simply don’t exist in any single person. Engaging our customers in what is a relatively new discipline, and one which is rife with exaggeration, misconceptions and misaligned expectation requires a team effort. To be successful, finding the right fit of team is a critical component of working productively at both technical and strategic level. For us, Data Science Radar (DSR) enables an efficient and effective route to building project teams aligned to both the culture and needs of any client. And it can do the same for you.

Building the winning formula for your team

Beyond the shape and relative strengths that one can evidence by looking at the output of DSR, I’d suggest it’s the inputs that are most important. The language of advanced analytics can be ambiguous at best, so having a tool that makes clear how analytics can be categorised around central themes provides a foundation that has brought our organisation together.

Mango isn’t unique in the way it might conduct its’ business, but I’ve yet to see another consultancy that has a common language set that enables everyone – practitioners, managers, leaders and support staff – to understand relative skills and capabilities in such a diverse and complex environment.

Why is this important?

Knowing the Radar’s of your team gives a greater understanding of the skills, capabilities of the whole team or common language to manage resourcing. My belief and observations are that having a common language enables our whole organisation to understand and talk about what we do. This brings us together, helps us build a commonality of purpose, and means that what we present to the outside world is more powerful. Additionally, the route to our messaging is more efficient and on point. Having an organisation that can sing from the same hymn sheet (my Welsh roots coming through here!!) sounds like an obvious requirement to building a data-led culture.

Strategically and culturally DSR has provided a platform for us to get stronger. But its more than a strategic platform. Operationally, DSR is used everyday to identify who in our team may have the best technical skills to deliver projects and bring value to our clients (Fig 1). Not just individually, but as a project team. Getting the right mix of skills, all executing aligned to respective strengths, means we can be confident to deliver on time and to the exacting standards we set ourselves. Being the right cultural and strategic fit only takes us so far – backing this up with the right technical mix ensures success across the full project lifecycle.

Develop and retain key skills

As a growing company in a fast-moving industry its critical to continually attract, develop and retain key skills and capabilities. Many of our staff have a strong desire to continually grow themselves and are keen to engage in professional development wherever opportunity presents. In some cases, this happens as a natural consequence of engaging in a wide variety of project work. But in others, it happens due to development planning aligned with our career framework. Before DSR, this meant looking at generic descriptions of technical capabilities – eg using, teaching, mentoring, coaching in R – which whilst valuable didn’t offer a level of specificity many of our team were after. Post DSR, we’re able to explicitly align development needs with DSR traits and allow our team to grow capabilities in the areas that are of most interest to them. We can also guide and shape this outcome based on business need, meaning we have a significantly more engaged workforce with a set of skills that the business needs. It’s a win-win for everyone, including our clients.

Software as a service

Data Science Radar is a piece of software, powering analytic capability. It isn’t a panacea that provides users with a utopian state of analytic health, nor is it the silver bullet that ensures everyone in your organisation understands the language of analytics. But it can be the differentiator that helps secure competitive advantage.

Gartner indicates that 80% of analytic projects fail. Reasons vary from lack of executive buy-in, misaligned expectations, disconnect between technical teams and users, and ill-defined business problems. DSR doesn’t eradicate these problems, but it does provide a platform through which many of these issues can be surfaced, discussed, understood and acted upon. Once better understood, it can then be used to ensure the right people are tasked with the right actions to execute effectively. DSR removes square pegs commonly put in round holes. It can bring an organisation together; help empower growth and provide clarity in terms of existing capabilities. Once you know these things, building project teams for the future becomes a natural next step, and keeps you always looking at where the gaps are, and how to close them.

We never built DSR to close strategic or cultural gaps, but that’s what it has done. And its enabled a more empowered and more engaged technical workforce too.

Pete Scott is Client Services Director here at Mango. For more infomation about a demo of Data Science Radar please click here.

R language
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Happy 20th BiRthday, R!

If you’re kind enough to follow my blogposts, watch my YouTube videos or attend any of my presentations, you’ll be very familiar with my belief that data-driven success in any organisation is two-thirds due to people and processes, and only a third due to technology. Am I being unduly hard on the technology?

A poor worker, it is said, always blames their tools, and if that’s accurate, then we must surely also credit the tools when the workers get it right. Today seems like an appropriate day to shine the spotlight on one of those tools: the R language, which this year celebrates its 20th anniversary!

I guess any milestone anniversary forces you to take a moment to reflect on a journey that has taken place, but this anniversary is particularly close to our hearts as it’s the language that Mango was really built upon back in 2002.   With less than 10 employees three years later, we offered our first R consultancy project and we did – and still do – play an active role in the R community, hosting meetup groups and events around the world to nurture talent and promote the latest innovations.  Indeed, Mango was a founding member of The R Consortium, a group which aims to offer support to key organisations and groups developing, maintaining, distributing and using R software.

One of the events we organise is the annual EARL Conference in London, an event dedicated to the commercial use of the R language.  Every year it’s awe-inspiring to see the breadth and calibre of presentations go up and up, along with the attendee numbers.  I believe this year we are expecting 400+ attendees and 64 presentations from all sectors, as some of the world’s leading practitioners share their projects, ideas and solutions with the audience. My favourite part of this conference is learning how data scientists are using R to make other people’s lives better, whether that be in medical research, social care, improving transportation or even aiding the peace process in Colombia.  Heady stuff.

Originally conceived as an idea in 1992 by Ross Ihaka and Robert Gentleman in Auckland, New Zealand, its name, R, comes from the first names of the authors, as well as being a nod towards S, the language that preceded R.

It’s easy to see how the language has grown in popularity to supporting over 2 million users today:

  • It’s free to use; a compelling proposition when you consider costly alternative proprietary software licenses
  • Advanced Analytics: Because of R’s architecture and licensing the very latest algorithms from research are readily available. This makes R an ever-evolving language which encapsulates the most modern statistical techniques and practices.
  • High-quality reporting: The ease at which high-quality graphics and interactive web applications can be created and written to a multitude of devices has seen R set the standard for graphical analytics. Tools such as R Markdown allow users to weave together narrative text and code to produce elegantly formatted reports.
  • Easy to integrate and extend: Many business intelligence systems and statistical reporting platforms now offer R connectivity as part of their extended offering; it links easily to various data sources and other programming languages enabling users to make use of additional algorithms and power up their statistical capabilities.

But perhaps one of the key drivers in the success of the R Language is that it came along at exactly the right time.  In the last 20 years organisations have been looking to use data and advanced analytics to generate value and change, and R’s openness together with its incredible community has really been around at the right time to enable that.  It has enabled the data science movement to gather momentum, breaking new ground at the same time by driving things like modern data visualisation thinking and integrating with evolving big data technology.

Perhaps R hasn’t yet come of age, but as I look back over the last twenty years, I feel proud to have been at least a small part of its growth.  I can’t wait to hear what more has been achieved with the language at this year’s EARL event and, even as a proud Welshman, if it steers England to win the Six Nations tournament, I’ll certainly give the tool the credit it deserves!

Happy 20th Birthday, R!

 

Author: Rich Pugh, Chief Data Scientist