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CIO’s globally ranked analytics and business intelligence as one of the most critical technologies to achieve the organisation’s business goals, with data and analytics skills topping the list as the most sought-after talent. As we embrace digital transformation, it’s clear that the need to upskill and resource data science teams has become far more pronounced.

Building a successful and lasting data community can often be one of the most significant hurdles to overcome, which takes time and effort. Often establishing and maintaining a thriving collaborative approach to analytics, with the right ecosystem for your community, can be a challenge.

Naturally there’s a growing need to ensure analytic teams have access to the best tools and latest methodologies to perform their analysis and find business wisdom. Alongside identifying the analytics skills already in place, a great place to start is also to identify the best tool for the job.

Nurture and aligning members of the community

Pulling together existing disparate data science resources into a single, connected community of practice, creates a secure foundation to grow analytic talent. Having such a community means the business will have a better understanding of the skill sets that exist within the organisation already, as well as best practice examples for approaching different scenarios and a better awareness of the tools and solutions that can be used.

Defining the right tools for the community

R and Python are still the two most popular and adopted programming languages. Both tools are open source, free to use and cover pretty much everything data science-related.

R was developed specifically for statistical analysis, so naturally is the popular language choice for statisticians. R has a large user community and an actively developed large library of packages which enables effective analytics. However, R can require a steeper learning curve and people who do not have prior programming experience may find it difficult to learn.

Python on the other hand, is considered the easier of the two most popular languages to learn. Its domination in machine learning is well-known. With an increasing community base, Python is commonly taught in Computer Science lessons in Schools and therefore the rated language of choice in academia. However, Python can be considered to have its limitations especially around speed and memory, so best practice use should be applied when considering Python.

It’s not a debate as such on which language to use, but more a conversation around empowering a team to become multilingual and multiskilled, so they can use the best language for the application.

Up-skilling of analytic talent

For an organisations analytics function to thrive, it’s critical to continually attract, develop, and retain key data skills & capabilities. Understanding the mix of skills within a data science team, as well as identifying gaps to unify skills & knowledge, is vital to drive analytic value. Establishing the support of a dedicated Learning and Development partner, who provides live, instructor led, data science training programmes, designed to equip and enthuse a data team with the latest approaches, can help address this challenge & unlock business gold.

Enabling training at all levels of data awareness will be critical, and this should even include training on how to use information, to guide decision-making.

Building a successful community provides a solid basis for working out where the talent pool needs to be extended, unifies talent across the business and enables quick wins towards embedding the right culture and building the required capability.

After 20 years of experience, we are a trusted data science L&D partner to leading brands worldwide. We train thousands of data science and analytical teams every year from a range of industries and backgrounds.


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There continues to be a lot of hype around data science (and it’s evolving world of connected buzzwords such as AI, ML etc).  This is driving unprecedented investment in the field, driving the race for data & digital talent into overdrive. But there is scarce data available on the return on investment in data science.  This can make it difficult to make a good business case for investment, and can hamper good discussion around how to size your data team. 

To resolve this, in November we released a report, Delivering Value Through Data Science, conducted by Data IQ, the only membership business focused on the needs of data and analytics professionals from large and midsized businesses. That’s an important start because if you want expert outcomes, we believe you should ask the experts… 

Before we get into the results, I want to sound a note of caution (well, I’m a statistician after all – no figures without caveats from me!).  Whilst the data we got back is interesting, this is a relatively small sample size and more research is needed in this area.  And of course, surveying companies associated with Data IQ will also introduce bias around the maturity of the organisation and the leadership qualities around the data teams. 

That being said, here’s an eye-popping but very debatable figure: The successful application of data science could lead to a 17.9% uptick in revenue for organisations with a “mature” data science capability. 

Ambitious but not impossible, perhaps? Previous successful generations of major IT change such as ERP or e-commerce could justifiably be said to have significantly inflated revenues at many companies (think of Tesco in online retail, for example). But we need to acknowledge that it’s very hard to pin success down precisely to a specific action; growth comes in many ways. How, for example, do you measure quality of execution, committed leadership, motivated employees? These challenges do not, as yet, have ready answers within the data science community. 

So while impressive, a figure of 17.9% feels unachievable for an organisation starting on their data journey.  Too high to be believed, perhaps (but clearly a figure many of the leading data-enabled organisations will stand behind).But, working in the space and seeing data science in action across organisations, the average figure of 6.7 per cent revenue growth (across all organisations surveyed) seems plausible. So, let’s agree in principle that careful investment in an ability to interrogate data and (crucially) act on these results and good things will happen for your business. 

We have definitely seen plenty of anecdotal evidence through our 19 years delivering data science projects, that back up at least this 6.7% figure: 

  • We’ve seen successful numerous data-led customer retention project reduce churn by at least 8% in our customers;  
  • Our higher ROI single data science project (a next best action engine in finance) was directly responsible for an 8 figure uptick in revenue;  
  • Deploying analytic solutions in areas where subjectivity has previously reined will routinely yield incremental gains of over 30% in target metrics. 

In our experience, the scale of the return is typically driver as much by change and culture as it is by algorithms and code.  Blocking the road to change are the familiar foes of inertia, cost justification, change management and skills. Ultimately, leaders must lead and drive their organisations forward to become data-enabled and data-driven because, as ever, business alignment is key.  

The DataIQ survey found a 4x increase in value where strategy is aligned with investments. Again, this matches well with our experience – doing data for the sake of doing data is very unlikely to yield results, and walking around your business with an AI-shaped-hammer looking for something to hit is not the way forward.  Data and analytics needs to be focused on, and deliver on, your business ambitions. 

Those that don’t lead from the front and pursue long-term data science programs will see rivals make better, more auditable decisions faster and will become cannon fodder for internet giants edging into their markets and for startups with no legacy. 

Today, pundits queue up to tell us becoming a data business is essential, and of course they’re right. We can’t all be Google but we can learn about data, harness it and apply it. Goldman Sachs hired more software engineers than Facebook, Gousto applies data to “little touches” that delight customers, Black and Decker reengineered production and dispensed with what one executive called “lazy, rusty asset syndrome”. These are all companies applying data to understand customer behaviour, improve service and drive competitive differentiation. This is not all about Tesla, Netflix and Amazon: data science needs to be pervasive and ubiquitous across all sorts of organisations. Start small to convince with data science quick wins by all means, but leaders also need to be persistent and not expect overnight gains. As with other game-changing capabilities such as AI, they need to be in it for the long term and learn as they evolve. 

“Data that is loved tends to survive,” said Kurt Bollacker and companies need to learn to love their data. The opportunities (and the risks) are too high to act it any other way. “Culture eats strategy for breakfast,” said Peter Drucker, so build a culture of continuous business improvement supported by data (and data science) now that will lead to data maturity and better, more consistent results over time. 

Start now. Download the research paper, compare and contrast against your own experiences and start thinking about your next steps. You may not see a 17 per cent revenue hike anytime soon, but you’ll be on the right path. 

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Virtual Classrooms – bringing data science teams together

Throughout lockdown, we’ve all been encouraged to tap into virtual training opportunities – either through free online learning or professional online training courses. What isn’t being talked about is the reduction in group training – and the positive effects virtual training can have on individuals when they are able to share the experience and interact with each other as a group.

Joint training sessions have very quickly been pushed to the wayside as focus is switched to the needs of individual stuck at home.  But is this the right way to go? Surely, training as a team is an integral part of enabling individuals to feel part of a team again, bringing likeminded colleagues together to interact and socially engage for a common goal – to complete the training.

It’s all very well us undertaking personal training while at home, but it’s the exchange of knowledge and sharing of frustrations that naturally come as part of the training process that is currently being missed – if we’re stuck on a subject or training module, then we want the opportunity to voice this with our peers rather than struggle on.

Virtual training courses and workshops that are delivered live to a group by a trainer are the most impactful. Chrissy Halliday, Mango’s Customer Success Manager said:

I’m frequently engaged with our customers about education and training programs and I’ve found a common theme emerging during this period of lockdown; keeping remote data science teams connected is vital whilst striving to provide a sense of “business as usual” during these challenging times. Everyone has quickly adapted to the new virtual world we find ourselves in, and training is a wonderful way to empower teams with the latest tools & methodologies to continue to deliver value.”

Virtual team training enables individuals to talk and discuss amongst the group, allowing the trainer to advise and demonstrate in real time to their audience, ultimately providing the whole team with a platform to question, absorb and understand the subject matter.

At Mango, we believe that face-to-face training is key to delivering content flexibly to our clients. Our ‘virtual classrooms’ provide attendees with valuable access to an experienced senior Data Scientist, who is on hand to answer any queries that inevitably crop up throughout the training session – just as they would do in ‘normal times’ when delivering face-to-face training on premise to our customers.

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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.