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Whilst data science teams struggle to adjust to the new norm of working from home, a competent technology platform enabling remote working combined with the right skills and toolkits are essential to remain productive.

The answer lies in looking beyond the technology and looking instead at the culture within the organisation.  Now, more than ever, it is crucial to ensure there is a data-driven culture within the very fabric of the organisation that filters down to all employees in order to compete…

Data is one of the most available, and yet under-utilised, resources within any organisation and a lack of remote technical infrastructure should not be regarded as a reason not to use this data as a means to remain productive or to significantly jeopardise deadlines.

Since the dawn of time, and accelerated through several industrial revolutions, businesses and individuals have aspired to do more with less resources. However, with labour-productivity growth figures at an all-time low perhaps, for the moment at least, organisations are not maximising the ability of current resources where they could be embracing and harnessing technological change. One of the big “resources” of modern businesses, noted for its value but widely regarded as under-utilised, is data.

Boosting productivity with data is one of the biggest challenges in the modern enterprise, and it all comes down to finding a way to extract and maximise value. In the business world, value is all about achieving the maximum productivity possible. This aligns well with the central purposes of data science, which we at Mango define as “the proactive use of data and advanced analytics to drive better decision making.” That is to say, data scientists take existing data resources, and use these to create “more” using analytical techniques. These can be applied to many use cases, but driving productivity from data often falls into a number of key categories – delivering solutions to business problems, reporting in real-time and using trend data to drive future growth.

The first key of successful data science is that it is using data to solve real business problems. This means increasing integration between data and data science teams by fostering collaboration and creative discussions to help them understand what is needed and what is possible. Clarity of purpose and communication enables better understanding of what is needed, and the resources they need to get there. Meanwhile, the business team will not only be able to explain what they need, how and why, but may also know of department specific or team specific data sources which could aid in the creation of a new solution. This allows all teams to work more productively, with data scientists better informed about what the solution is for, and business teams more confident that the solution will work.

However, data science does not always need to be about creating detailed new solutions out of nowhere. Often, given the wealth of data that businesses hold, data scientists can use historical trend data to drive insights in future activities. This can be external facing (such as optimising strategies for engaging with customers) or internal (such as predicting the impact of different hiring patterns or organisational reshuffles). While historical data cannot offer a perfect answer to how a situation may play out in future, previous patterns can help shape models that at least offer a probable explanation of what might happen next. This helps make more informed decisions quicker and more accurately drive company performance.

Thirdly, data scientists can boost productivity both of their own teams and the organisation as a whole by implementing more real-time analytics solutions. These make use of not only historical data, but also look to proactively generate insights alongside action. While data scientists have traditionally been viewed as part of the strategic pipeline – that is, as providers of insight into weighty, considered decisions – the evolution of real-time analytics and streaming data enables data scientists to provide tools that support in a tactical role at great speed in changing conditions. This can support business productivity “on the fly” – such as when dealing with a customer during a complaints call, or with on-tap insights around successes and challenges with existing data science project implementations.

The benefits of data science for boosting productivity are hard to understate, however against the grain of our “as a service” technology era, data science is more than just a plug-and-play solution.  The productivity of a data science team itself, and the business as a whole, relies on more than just tools or training or the right resources. Instead, it comes down to creating a culture of data science – and this is something championed from the top down. Harnessing data as a resource, and then finding a way to use it effectively within the work environment requires businesses to build a foundation of curiosity and an acceptance of the possibilities around data. Creating a thriving data science culture will be the difference between a vital productivity boom and a state of data overload – something worth considering this World Productivity Day.

If your teams are struggling to operate effectively whilst working remotely, please do contact us to how we might support you maintain productivity.

Read the published article in Gigabit Magazine 

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In part one of this article featured recently in DataIQ, we looked at why building an analytics community is key to data-driven transformation. The challenge faced by many companies lies in adopting and implementing data-led strategies whilst retaining market share in the face of sleeker, disruptive competitors for whom data is already part of their DNA.

Placing data at the core of organisational strategy requires a fundamental culture shift that is difficult to achieve and maintain without an active and engaged community at its core. The shared sense of purpose and the collaboration a community can deliver helps businesses use data to much greater effect than an organisation that operates in disengaged siloes.

But, building a successful and lasting community culture takes time and effort. Here, we look at some useful steps that organisations can take to establish and maintain a thriving collaborative approach to analytics.


  1. Identify the analytics skills already in place

Many organisations already have analytics experts spread across their business with robust processes, excellent project work and strong – if sometimes underground – communication networks already in play. Finding these people represents the first step in unifying skills and experiences. What’s more, it can bring early advocates on board and help establish a network where relationships and contacts can quickly build to help spread the word that a community investment is being made.


  1. Connect and align the members of an analytic community

Once those experts have been found, they must be connected to each other and actively encouraged to add their voices to the cause. Leaders can facilitate the process by providing opportunities to share and present great ideas to others, to provide general education or practical comparison meetings. These are all great ways to start and it’s a process that offers the opportunity to get participants aligned from the beginning.


  1. Inspire the community to collaborate and create efficiencies

Those initial meetings play a key role in establishing and building the spirit that will begin to bind people together. Incentivised action, in the form of activities such as hackathons, workshops, webinars, or conferences, demonstrates to participants that their nascent community can add value to their working life, culture and wider skillset. In these early stages, don’t forget to prioritise community projects and reward actionable insight generated from them.


  1. Drive conversations about best practices and standardising processes

Early community advocates can quickly become ‘data champions’. They represent an important, engaged and active sub-group, and as such, they should be able to review the results of projects and practice together. Building process standards and best practice habits (and documentation) won’t just keep quality high, it will provide an easier entry point for new people with new ideas.


  1. Leverage the talent in the community via internal and external messaging

Regular communication between the people and processes within the organisation will naturally begin to identify wider needs, and it’s at this point that the net can be spread wider. In doing so, the community can recruit a greater depth of talent inside the organisation to lend their own experiences to data practice, while recruiting from outside to fill gaps.  Encouraging and supporting employees to attend external tech meetups and report back on practices, for example, means ideas and methodologies can then be adopted internally.


  1. Enable a possible conversation about the centralisation or upskilling of analytic talent

At this point, leaders can begin to visualise people and processes as their own skills ‘academy’. From the experience, results and best practices now achieved, it soon becomes practical to form a fully functioning business centre to drive, execute and hugely expand the broader work of the organisation.


  1. Building an analytics community is not achieved overnight and takes time and patience.

Data is the most important tech trend since 1990, and implementing a data driven strategy has never been more important for companies wishing to retain market share in the face of tech-borne, agile competitors. Building a data analytics community which unifies talent across the business provides key benefits and quick wins towards embedding the right culture and building the required capability.

Author: Liz Matthews, Head of Community and Education, Mango Solutions

Read the full article as published in DataIQ. 

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Liz Matthews, Head of Community and Education at Mango Solutions looks at the importance of building an analytic community to enable data-driven transformation from within.

17 March 2020 Published in DataIQ

The disciplines and practices around data science, big data and advanced analytics have established themselves as vital business tools, with organisations increasingly looking to analytics-led strategies and data-based decision-making for strategic business gain. The underlying business goals can vary dramatically – from client acquisition, reducing churn rate or optimising store locations, for example – but in each case, a data-driven approach can deliver critical competitive advantage.

However, many companies – particularly those with roots in the pre-digital era – struggle with the challenges of adopting and implementing data-led strategies while retaining market share in the face of sleeker, disruptive competitors for whom data is already part of their DNA.

This raises some big questions, particularly around how organisations can carry out their data-driven transformation process or project for maximum deliverable insight. How can they capitalise on what was learned to maximise opportunities for wider organisational effect? Equally important is which skills and team ethics are needed to build newer, bigger projects moving forward?

And here is one of the most important points: becoming data-driven requires more than the purchase of a technical solution or the hiring of data scientists. It requires that data is placed right at the core of an organisation’s strategy. This philosophy will enable the fundamental culture-shift required to realise the potential of the insights that data can generate and to create successful outcomes for the organisation.

That’s easier said than done and, typically, there are three key challenges that companies currently face in becoming data-driven:

1. They don’t understand what analytic skills they already have

Remember, analytics can encompass a wide range of practices, from expertise in Excel to the application of data science and advanced analytics. Potentially, each has a huge role to play in data-driven transformation projects, but understanding where these skills sit can be difficult, especially in large and complex organisations.

2. Their analytics skills are spread across the organisation

Without a genuine sense of community in existence, key analytics processes and approaches can vary considerably across an organisation. The problem is that this can not only create barriers for discussions around best practice, but can also lead to inefficiencies and missed opportunities to improve skills and learnings which could have made a positive impact on the business.

3. Their community is disconnected

While analytics is now a strategic priority for many organisations, a lack of community means that talent is disconnected and cannot be exploited as a whole. This is a major problem, as evidenced by a recent Women in Data UK survey, which found that more than half of all analytics professionals had no access to an analytic community in their workplace. This fragmentation of culture, experience and expertise makes it much more difficult to set objectives across the community and discuss ways to achieve them to best effect.

The importance of building an analytics community

So, what are the big advantages of building a strong analytics community within organisations focused on data-driven transformation? Firstly, connecting siloed analytic teams or individuals is paramount if a company is to adopt data-driven strategies. With analysts often using similar tools and techniques or approaching questions and problems in the same ways, it’s a classic case of increased communication helping bring minds together to grow together.

Then there’s the huge advantages communities offer to professional and skills development. The Women in Data survey confirmed that the appetite for professional development and learning new skills is high, and an important benefit of a connected analytics community is how it facilitates the sharing of knowledge and up-skilling of individuals.

The survey also revealed that every single respondent wants to improve their skillset. In particular, machine learning, deep learning and expertise in big data analytic technologies such as Spark, Storm and Flink were highlighted as being of most interest. The survey respondents also helped to uncover the barriers to learning these new skills, with time, money, managerial support and, tellingly, lack of access to an analytics community in their organisation all playing a role.


“Sharing ideas between industry sectors is what makes community interesting”


On the flipside, the growth in popularity of analytics community user group meetings and the number of people attending them is a clear signal that data science professionals want to network with peers and build their knowledge. In practise, the most useful user group meetings will include content such as a free workshop on a data science topic or methodology, followed by presentations from volunteers keen to share their own experiences and expertise.

It’s not uncommon to see a big variety of industries represented at these meetings. Members will often provide feedback that sharing ideas between industry sectors is what makes community interesting and worthwhile.

That’s all part of a picture that can help foster and retain scarce talent. Given the demand for data science expertise is greater than ever and with the average time in role for a data scientist being less than two years, it’s easy to see why employers want to minimise churn rates and retain their highly-skilled and knowledgeable data professionals.

Building an inhouse analytics community is a clear indication that an employer values the skills and contributions of its analytics personnel and is committed to providing them with opportunities for professional development and growth. It’s clear that for a business wishing to embed advanced analytics and adopt data-driven strategies, the creation and support of an internal analytics community can prove enormously beneficial in both the short- and long-term.

In part two of this article published in DataIQ, we will discuss the six key steps required to build and develop an analytics community.

Liz Matthews, head of community and education, Mango Solutions

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Results from Inaugural Data Science Skills Survey reveal Lack of Skills, Support and Time to Upskill as Cause of Unrest in Data Science Sector 

28 November, 2019 – London, UK – Research, released today at the annual Women In Data UK conference from data analytics consultancy, Mango Solutions, Women in Data and Datatech Analytics, demonstrates a sector in crisis moving into 2020. As data becomes the currency of the modern business, the race to become data-driven has seen organisations investing heavily in core analytics skills – however, a lack of support, funding and time available for upskilling are all cited as challenges within the UK data science community. Indications are 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.

Lack of support and the right tools

The research from this first skills survey, conducted amongst data science professionals in the UK, reveals that over half of respondents plan to move roles within the next year. When asked what was the greatest challenge faced in delivering value, over a quarter (29%) of respondents cited lack of support from managers and leaders, along with nearly half (44%) saying that bureaucracy is the greatest challenge they face in their roles.  A lack of access to the right tools was also cited as a frustration, with over a quarter (28%) reporting that this was an issue.

According to the survey results, the average tenure in role across all respondents is two and a half years. However, with 56% of respondents indicating an intention to seek new roles within the next 12 months, it’s likely that this churn rate is on an upward trajectory.

Over 50% of respondents identifying as practitioners reported that they have no internal data science community within which to share an active role. Respondents to the survey who identified as managers stated that operating in siloed teams is the greatest challenge (51%) they have when it comes to delivering value within their organisation.

At the moment no formal accreditation for data science roles in the UK is available. Currently there are many points of entry into the profession, which makes such accreditation difficult. Indeed, when asked, over half of respondents indicated that they do not see the need for accreditation.  However, the lack of a standardised set of criteria to form a framework and description for data science roles, including learning and development for skills advancement has the potential to make recruiting for these roles challenging.  

Machine learning – top area for upskilling in 2020

Almost half of data scientists who identified as fulfilling a leadership role said that skills shortages are posing the greatest challenge to delivering value within their organisation, with four out of five (86%) of managers reporting that it is difficult to hire talent in the sector.

When asked how they were planning to plug this skills gap, upskilling was the number one strategy being deployed, with over two thirds (69%) of the managers revealing that this is how they plan to address the shortage within their organisations. However, when data scientists were asked what prevents them from learning a new skill, time was cited as the key barrier by 70% of respondents. Additionally, not knowing where to start (32%), and funding (25%), were cited as issues preventing upskilling in the next year.

 Of those data scientists who plan to upskill in 2020, the three most popular topics for future development are:

  1. Machine Learning (57%)
  2. Big Data analytical technologies (e.g. Spark, Storm, Flink) (49%)
  3. Big Data technologies (e.g. Hadoop, Mongo DB, others) (44%)

From a managerial perspective, the data science skills most scarce across their organisations are:

  1. Visualising (43%)
  2. Programming (35%)
  3. Technology (35%)
  4. Communication (34%)

Rich Pugh, Chief Data Scientist and co-founder at Mango said: “Due to the dynamic and growing nature of data science, creating a data science team with the optimum blend of analytic and “soft” business skills is costly and complex. There is a scarcity of resources and a lack of common understanding around existing analytic skillsets and job descriptions.

“As more organisations embrace data-driven transformation, there has never been a more urgent need to upskill and resource data science teams across a wide range of sectors and departments. Data science should be considered as a team sport, with the combined skills of each member contributing to success. If organisations can’t hire people with all the skills required, I would urge them to look at what skills are in existence internally and create a team of people with complementary skillsets. That way, as a collective team, firms can create a solid foundation for driving data-driven transformation.”

Roisin McCarthy, co-founder of WiD, said: “We are asking our members, and the wider business community, to help us to demystify perceptions around Data Science as a way to address the skills gap and appeal to a wider ranging section of professionals. Data-driven organisations have a massive opportunity to attract and recruit the right talent, growing a data science community that is thriving, challenging and lucrative.”


About the Data Science Skills Survey 2019

In partnership with Women in Data and DataTech Analytics, Mango Solutions surveyed 907 UK data science professionals via an online survey in October 2019.  The objective of the survey was to understand their current position, career perspectives, challenges and plans.

About Mango Solutions:

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

Mango’s Data Science Radar’ is one such software tool that allows organisations to build world class data science capabilities. It provides individuals, teams and managers with insight into the skills and abilities across six core data science attributes. The attributes have been devised by Mango Solutions as the ultimate core capabilities and skill sets that data scientists need in the organisations of today and tomorrow.


About Women in Data (WiD):

Women in Data (WiD) UK is a professional network and a force for change in data science and analytics.  It provides a platform for female and gender diverse data professionals to share technical knowledge and experiences, and to encourage more diverse representation in the industry. The past five years have witnessed measurable change and awareness in the industry, and WiD is proud to have built a strong community of over 25,000 members.


For further press information:

Please contact: E:

T: 01252 717040

data-driven digital transformation
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Rich Pugh, chief data scientist and co-founder at Mango Solutions, shares his advice on how leaders can drive effective digital transformation by becoming data-driven.

Posted 22nd January 2019 in Information Age


Digital transformation is, almost to the exclusion of all others, the buzzword of the last few years in technology. To become “digitally transformed”, almost all companies are investing in some sort of data-driven project – data analytics, big data, AI or data science. A survey of senior executives at large corporations in the US by NewVantage Partners suggested that this could be the case for 97% of organisations.

However, these projects are not ends in themselves. Success on the journey leads to a data-driven company that understands data as a strategic asset that can inform better decision-making. However, becoming data-driven requires careful planning and the right acumen. Organisations are on this journey because they want to achieve value out of their data, but what they are attempting isn’t really about digital first and foremost, it’s actually about the necessity of transforming their business model.

With that in mind, here are seven tips that can help businesses succeed with their big data and data science endeavours:

1. First, an organisation has to want to become data driven from a business perspective. That means that the process towards this has to be a top-down one. Without leadership alignment, it will be nearly impossible to instigate the culture shift required to truly become data-driven. This means that the first vital step is to ensure representation for data driven initiatives, as well as broader education at the leadership level.

2. The next step is to assess the skills within existing teams. Within an organisation, analytics skill can be spread through departments, and as part of a data-driven journey, business leaders need to transition to a core, centralised practice to ensure consistency. This does not necessarily mean re-distributing teams, but instead uniting these individuals to create a series of best practices. In addition, internal events and hackathons can help to bring together your data professionals into one community striving in one direction to empower the business.

3. Once there’s a community in place, organisations can look to actively shape and define best practices, as well as how different roles impact the analytic function. The goal here is to move from sporadic projects conducted under the direction of each department to instead guarantee consistency of approach across the organisation, with a common understanding of how to deliver value from data effectively.

4. As the role of analytics becomes more strategically important to the business, it becomes necessary to ensure governance increases. As part of this, business leaders need to be asking their data practitioners to ensure that initiatives meet business objectives, that there is consistency in delivery and prioritisation, as well as in the platforms and technologies used. In addition, it’s important the business leaders observe and adhere to data ethics, especially regarding sensitive or personal information.

5. With leadership bought in and a core data driven practice through the organisation, the task now becomes educating the business at large about the possibilities of analytics. Business leaders need to work with their data practitioners to teach the whole business a common language around analytics and dispel preconceptions of what analytics can and can’t. This will open up more fertile ground for working out what business questions data can and cannot help solve.

6. As business interest and knowledge of the potential of data driven decisions grows, so does the lists of potential initiatives. Here, prioritisation becomes incredibly important. Business leaders need to focus on whether each initiative meets the following four criteria:

a. Will it add significant, measurable value?
b. Is the organisation ready to implement this programme? Do we have the right data and platform to make it work?
c. Is there actually a solution possible or is the technology still not available?
d. Is the business ready to adopt the new practices this initiative will require?

7. Now, with initiatives actively being implemented, the business needs to look to structure and measure success in a consistent way so that employees at all levels can see the data driven programme at work, rather than isolated instances of innovation. This is key for moving away from a series of data science projects to being a truly data-driven company. At the same time, businesses should look at how they track and progress data science maturity in different departments to create an on-going plan for success.

Thriving with data science is key for success in today’s market, because it presents the ability to transform quickly and efficiently based on real insight. By following these seven tips, businesses have the best chance of succeeding in their mission to become data-driven – and therefore in their wider digital transformation strategy as a whole. This will result not just in a successful adoption of data science tactics, but in wider effectiveness as a smarter, more agile business that delivers better solutions to customers ­– a critical differentiator in 2019.

Author: Rich Pugh, Chief Data Scientist at Mango Solutions

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National Coding Week is a recognised volunteer-led activity that aims to help build people’s confidence and skills when it comes to coding. Essentially, it recognises the importance of coding and developers in the modern business world. It’s a role that is becoming increasingly critical for organisations that strive to remain competitive in today’s landscape. With this in mind, seven IT experts have shared their thoughts and advice to other businesses when it comes to the importance of coding, and why it should be a more recognised skill.

Rich Pugh, Chief Data Scientist, Co-Founder at Mango Solutions:

“In order for businesses to remain competitive, it’s important they put data science and advanced analytics at the heart of their operations and invest in the necessary skills, training and support to derive value from them. Data is being created faster than ever before, however most organisations today have so much data, it’s falling between the cracks – taking valuable insights with it. The aim of National Coding Week is to improve digital literacy among the adult population in order to fill the growing skills gap and ultimately help businesses and organisations get the most value out of data. Programming and analytics are the must have skill sets for future generations at work, and it’s vital this is embedded into our schools – I believe it’s just as important as maths or English.

“Businesses that build their data science capabilities will have a far greater chance of success in the information age when it comes to increasing efficiencies, optimising revenues, and providing richer experiences for clients. It’s important to deal with the culture and consistency across the organisation, which can be hard work, but is the most effective solution in comparison to having silos of analytics and data. People who build their digital skills will be rewarded both financially and in terms of personal fulfilment.”

Josh Flinn, Director of Product Strategy & Innovation at Cybera:

“Change and progress in the technology industry is constant. The challenge is there is a huge talent shortage – there simply aren’t enough individuals with the right digital skills. In fact, in the UK alone there are an estimated 600,000 technology vacancies, with 52% business claiming it is hard to find the talent they need to fill the roles. This is why volunteer-led initiatives like National Coding Week are essential, providing accessible resources and opportunities to encourage more people to develop their existing skillsets. Together with the support of business these initiatives will help close the skill-shortage gap.”

Liam Butler, AVP at SumTotal:

“The IT department can be a rich source of differentiation, innovation, and competitive advantage for an organisation, but businesses are faced with a growing shortage of skilled IT Professionals. Mobile, Big Data and cloud-based architectures are creating significant challenges for the entire IT ecosystem and with scarce resources, many IT professionals may find themselves pushed into an area they are not completely confident operating in. In the face of this critical skills shortage, organisations need to be mindful of what they are asking their IT teams to do.

Comprehensive training and certification can help IT professionals stay ahead of the changing technology landscape, while at the same time validating their skills and knowledge. Effective training will not only help to avoid the time, costs, and headaches of replacing scarce resources, it also helps maintain the subtleties and nuances of the IT operations within a specific organisation – providing both continuity and consistency – while ensuring no IT Professional ever hits their point of professional failure.”

Anu Yamunan, VP, Products at Exabeam:

“In the past several years, we have seen the emergence of a new standard of employee for the technology industry. The modern-day tech worker has to be technical, but also creative, innovative and an incredibly talented problem solver. Coders tick every single one of these boxes. As a developer, similar to a painter, they are taking a blank canvas and constructing something extraordinary out of nothing—and have to navigate any issue that comes their way. When something goes wrong with their code or the original code does not work, it is up to them to fix it as fast as possible. Employees with coding skills are now essential personnel in the modern enterprise. The demand for coding skills is already high, but as we continue to see the evolution of AI and machine learning, it will only become greater. These technologies are transforming the way we process and analyse data, which offers incredible insight to inform sales and marketing, network security teams and more. National Coding Week serves as a great platform to highlight how we need more people with these skills to manage evolving technologies.”

Bob Davis, CMO at Plutora:

“In today’s software-driven world, organisations must be able to deliver high-quality software in order to succeed and grow. Organisations can only gain this competitive advantage through quick, efficient, and quality software releases, orchestrated by development teams with strong and fluent coders. Coders are the bricklayers of the software world, and they ensure every project starts with a sturdy foundation upon which developers can build. It’s essential for every organisation – whether it’s a startup or an experienced enterprise – to retain talented coders on staff who are able to meet the demands of fluctuating software needs quickly and at scale. Coding is becoming the language of business, and every organisation needs to be able to communicate.”

Jen Locklear, Chief Talent Officer at ConnectWise:

“This National Coding Week, businesses need to highlight the importance of women in technology by educating young women. More companies are moving to support and educate females at a younger age about their prospects within the technology industry. By supporting organisations and non-profits like Girls Who Code, you equip young women with the necessary tools and opportunities to succeed in the competitive tech industry. As technology continues to infiltrate projects and daily assignments as early as elementary school, young women learn how to deploy the skills necessary to build confidence and authority within the space. Remember this when hiring recent grads who have most likely grown up around technology. To educate women already in the workforce, invest in seminars, training, and conferences that will build upon existing knowledge while also forging connections and empower them to break glass ceilings.”

Neil Barton, CTO at WhereScape:

“Automation is enabling businesses to get things done faster and with greater efficiency. In the case of machine learning processing, data infrastructure automation is the key to ensuring organisations are leveraging trusted data, by generating repeatable code and metadata that provides strong data governance and transparent lineage. By utilising automation in this and other ways, organisations are also able to lift the mundane and repetitive coding off of the plates of its developers and instead provide opportunities to contribute within other aspects of development that will greatly impact the bottom line and be personally rewarding.”

With all of the different benefits that digital skills create for a business, it’s important that organisations of all shapes and sizes continue to encourage staff when it comes to learning new skills. Join the conversation this #NationalCodingWeek.

To find out about how your business can become data-driven email

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When we think of empathy in a career, we perhaps think of a nurse with a good bedside manner, or perhaps a particularly astute manager or HR professional. Data science is probably one of the last disciplines where empathy would seem to be important. However, this misconception is one that frequently leads to the failure of data science projects – a solution that technically works but doesn’t consider the problem from the business’ point of view. 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.

To examine the role of empathy in data science, let’s take a step back and think about the goal of data science in general. At its core, data science in the enterprise context is aimed at empowering the business to make better, evidence-based decisions. Success with a data science project isn’t just about finding a solution that works, it’s about finding one that meets the following criteria:

  • The project is completed on time, on budget, and with the features it originally set out to create
  • The project meets business goals in an implementable and measurable way
  • The project is used frequently by its intended audience, with the right support and information available

None of these are outcomes that can be achieved by a technical solution in isolation; instead, they require data scientists to approach the problem empathetically. Why? Because successful data science outcomes rely on actually understanding the business problem being solved, and having strong collaboration between the technical and business team to ensure everyone is on the same page – all of which is essential, and a key resource for getting senior stakeholder buy-in.

In short, empathy factors in throughout every stage of the process, helping create an idea of what success looks like and the business context behind that. Without this, a data scientist will not be able to understand the data in context, including some of the technical aspects such as what defines an outlier and subsequent treatment in data cleaning. The business process, even with less technical understanding, will have far better insight into why data may look “wrong” than a data scientist alone could ever guess at. Finally, empathy helps build trust – critically in getting the support of stakeholders early in the process, but then also in the deployment and evaluation stage.

Given the benefits, empathy is key in data science. To develop this skill, there are some simple techniques to drive more empathetic communication and successful outcomes. The three key questions that data scientists should be looking to answer are: “What do we want to achieve?” “How are we going to achieve it?” and “How can we make sure we deliver?”

What do we want to achieve?

For the first point, one approach is to apply agile development methodology to the different users of a potential solution and iterate to find the core problem – or problems – we want to solve. For each stakeholder, the data science function needs to consider what type of user they represent, what their goals are and why they want this – all in order to ensure they understand the context in which the solution needs to work. By ensuring that a solution addresses each of these users’ “stories”, data scientists are empathetically working to recognise the business context in their approach.

How are we going to achieve it?

Then it’s a case of how to go about achieving a successful outcome.  One helpful way to think about it is to imagine that we are writing a function in our code: given our desired output, what are the necessary inputs? What operation does our function need to perform in order to turn one into the other? Yes, the “function” approach does not only apply to data, but also to the process of creating a solution. Data scientists should be looking at an input of “the things I need for a successful solution” a function for “how to do it” and then an output of the desired goal. For example, if the goal is to build a successful churn model, we need to consider high level inputs such as sign-off from relevant stakeholders, available resources and even budget agreements that might contain the project. Then, in the function stage, it may be time to discuss the budget and scope with senior figures, work out if additional resources need to be hired and any other items needed to drive the right output at the end. This can then be broken down into more detailed individual input-function-output processes to get desired outcomes.  For example, working out if additional resources need to be hired can become a function output that will now have a new set of relevant inputs and actions driving the solution.

How can we make sure we deliver?

Finally, there are questions that need to be asked in every data science project, no matter what the scope or objective. In order to ensure that none of them are omitted, stakeholders should form a checklist, a strategy that has been successfully used in aviation or medical surgery to reduce failure.  For example, preparing to build a solution that suits the target environment shouldn’t be a final consideration, but instead a foundational part of the planning of any data science project. Thus, a good checklist that data scientists could consider in the planning stage could include:

  • Why is this solution important?
  • How would you use it?
  • What does your current solution look like?
  • What other solutions have you tried?
  • Who are the end-users?
  • Who else would benefit from this solution?

Only with this input can data scientists build a deployable model or data tool that will actually work in context, designed for its eventual users rather than for use purely in a theoretical context.

Empathy may seem an unusual skill for a data scientist, however embracing this value fits into a wider need for a culture of data science within organisations, linking business and data science teams rather than keeping them in siloes. By encouraging dialogue and ensuring all data science projects are undertaken with the stakeholders in mind, data scientists have the best chance of building the most effective solutions for their businesses.

Read the article at Computer

Citizen Data Science Suffers from Inflated Expectations
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After attending Mango’s EARL conference in London, Martin Veitch has written for IDG Connect:’ Citizen Data Science Suffers from Inflated Expectations‘ where he shares his views about how companies that best exploit data science will be well positioned to make smarter decisions –  but it’s more than just a quick fix.

Additionally, Veitch references some of the great speakers at EARL to explain how data science, and in particular the R language, can be used for good, and talks to Mango’s Chief Data Scientist and Co-founder, Rich Pugh, about how the term ‘data science’ is becoming misused.

“The idea that data science is for everybody isn’t just wrong, it’s dangerous. If you misuse data and don’t understand data relationships you can make bad decisions that can have serious results.” said Rich.

Read the full article here:

Author Martin Veitch in IDG Connect publication.

How culture can enable firms to be successful in adopting a data-driven approach
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Adopting a data-driven approach can drive successful business performance which is something enterprises are constantly striving to achieve. Rich Pugh, Co-founder and Chief Data Scientist, Mango Solutions, discusses the importance of thriving with data science and how it can bring about success for enterprises in today’s tech industry.

Data is like the new oil. This analogy was first drawn by The Economist and in some respects, it is true. Successful businesses today run on data and, like oil, data is near-useless unless it is refined and treated in the correct way. But refining is a difficult process and with many business executives overwhelmed by the ‘hugeness’ of modern data, it’s easy to regard plug-and-play business intelligence, AI or Machine Learning solutions as a one-stop data-to-value machine.

The problem is that all too often, these tools aren’t able to deliver the value expected of them; even if the technology finds an important and relevant correlation, businesses are unsure how to act on the information effectively and understand the full context of the finding. Insight becomes an attention-grabbing statistic in a slide presentation, or potentially a one-off decision made based on a piece of information and then nothing further. It’s hard to quantify what the long-term value of this was, because the full context is missing.


What does it take to be truly data-driven?

A data-driven organisation values its data as a primary asset and constantly strives to turn data into operational acumen to drive better decision making. This is where data science comes in – or more specifically, a company-wide culture of data science. Rather than just a tool to turn data into insight, data science is a way of blending together technology, data and business awareness to extract value, not just information, from data. While 81% of senior executives interviewed for a recent EY and Nimbus Ninety report agreed that data should be at the heart of all decision making, just 31% had actually taken the step to restructure their organisation to achieve this. That leaves a huge majority of organisations who recognise the potential of data, but have yet to find a way to embed a data-driven culture within their business.


Start at the top

So where do you start? Firstly, an organisation has to want to become data-driven from a business perspective. That means that the process towards this has to be taken from the top down. Without leadership alignment, it will be nearly impossible to instigate the culture shift required to truly become data-driven. This means that the first vital step is to ensure representation for data-driven initiatives, as well as broader education at the leadership level.

The next step is to assess the skills within existing teams. Within an organisation, analytics skills can be spread through departments and as part of a data-driven journey, business leaders need to transition to a core, centralised practice to ensure consistency. This does not necessarily mean re-distributing teams, but instead uniting these individuals to create a series of best practices. In addition, internal events and hackathons for example can help to bring together your data professionals into one community, striving in one direction to empower the business.

Once there’s a community in place, it’s then a case of getting these people to work towards what ‘best practice’ looks like, as well as how different roles impact the analytic function. The goal here is to move from sporadic projects conducted under the direction of each department to instead guarantee consistency of approach across the organisation, with a common understanding of how to deliver value from data effectively. It’s not about applying a one-size-fits-all approach, but instead fostering cohesion and solidity to ensure the team can agree about what needs to happen, when.

Engage and educate

Once you have your team of data science experts, it’s time to engage with the business as a whole. Educating the business requires the whole data science team to be confident with what analytics can achieve for the business and even more importantly, what it cannot achieve that the business might be expecting. This will then need to be communicated in a clear way – using language that the business teams will understand will help break down any preconceptions. This can be daunting and often, data science teams will find themselves faced with a huge variety of interest levels. Many who hear about the potential of data science will feel it has little bearing on their work – and discussions about its potential may go in one ear and out the other. However, there will also be people who are inspired by what data can do for them and want to get more involved. These people can be future champions for driving a data-driven culture beyond the core team.


Put it into practice

As business interest in and knowledge of the potential of data-driven decisions grows, so does the list of potential initiatives. In this regard, prioritisation becomes incredibly important. Business leaders need to focus on whether each initiative meets the following four criteria:

  • Will it add significant, measurable value?
  • Is the organisation ready to implement this programme? Do we have the right data and platform to make it work?
  • Is there actually a solution possible or is the technology still not available?
  • Is the business ready to adopt the new practices this initiative will require?

Finally, it’s about finding a way to quantify the value that the data science community now brings to the business and ensure that the success thereof becomes a repeatable part of the business process. With initiatives actively being implemented, the business needs to look to structure and measure success in a consistent way so that employees at all levels can see the data-driven programme at work, rather than isolated instances of innovation. This is key for moving away from a series of data science projects to being a truly data-driven company.

Thriving with data science is key for success in today’s market, because it presents the ability to transform quickly and efficiently based on real insight. By building data science solutions around real business problems, in conjunction with the whole business team, organisations are more likely to see the value thanks to an ongoing culture of problem solving with data science. This will result not just in a successful adoption of data science tactics, but in wider effectiveness as a smarter, more agile organisation that delivers better solutions to customers.

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