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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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2021 has been a year of inspiring and enabling businesses to be more digitally strong and data-driven. Following our acquisition by Ascent in November 2020, we’ve been able to support customers with integrated software and data capabilities, at a time when many European businesses have had to pivot to stay relevant in an increasingly digital marketplace.

Mango’s data science proposition is now fully embedded at the heart of Ascent, enabling our customers to build more intelligent, efficient and engaging organisations and create sustained competitive advantage. We’re focused on helping customers embrace digital transformation, enhance their digital capabilities and leverage opportunities at the intersection of data, software and platform.

Looking ahead to 2022, the new shape of our business will allow us to continue to deliver value to more and more organisations who are balancing purpose and profit.

Impact – Access to new talent communities and a wider geographical reach.

We’re now part of a 400+ strong team that’s growing – fast – in a vibrant market. Ascent’s HQ is in London, with a data science community located in the South West, specialist engineering hubs in Malta, Bulgaria and Spain, and smaller local teams in 14 countries worldwide.

Impact – End to end approach: data, software and platform

We are now able to offer end-to-end solutions to our customers. From the development of software systems, products and applications to data platforms, engineering, consulting and advanced analytic solutions we now deliver a greater breadth and depth of linked capabilities. You can find out more about this approach here.

Impact – A new consulting & strategy team

This year we have built a phenomenal data consultancy team at the heart of Ascent. We’ve recruited some of Europe’s sharpest data minds into the team, using this talent to help our customers transform their organisations. We’ve worked with customers on data literacy programmes and right-sized data strategies that guide investment, drive value and underscore future commercial success. And once we’ve explored the strategic challenge, we can support customers with the execution experience and skills to get it right first time.

2022 in data is set to be an exciting year, as companies continue to understand the value data science offers in making their organisations smarter and more agile – and therefore more resilient in the face of change. The world has shifted, and companies who kept the pedal down on data and analytics and invested in AI and machine learning to support their data-driven business model will emerge as leaner, smarter, more engaging businesses.

data science strategy
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Data science has made huge gains for early adopters and progressive businesses in the last ten years. For organisations in this digital era, spotting new opportunities, improving processes and working with greater insight into the variables that affect business success is vital.

Mango Solutions’ research report Delivering Value Through Data Science, which was conducted by DataIQ, shows the potential revenue gains from data science and some driving factors to build organisational data maturity. The study surveyed data practitioners, ranging from heads of department to chief data officers and global directors.

Companies surveyed with ‘advanced’ data science capabilities reported incremental value equivalent to 18% of business revenues may be generated as a direct result of an advanced data science approach. Those with less mature data science capabilities were more abstemious in their projections but even organisations at the early stages of data maturity claimed a respectable 4.1 percent revenue increase.

So, whether you are still in the planning phase or practicing advanced data science, with data-driven processes embedded in decision-making, data science will boost revenue.

It’s encouraging that data science is picking up in momentum. While the more radical 5.2% of companies kicked off with data science over 10 years ago, the majority, under 40% of businesses surveyed, began developing their data science capabilities in the last 3-5 years, and almost 30% of businesses surveyed started in the last 6 months. Being somewhere on the data-science journey is a must to support wider digital transformation success.

Getting the C-suite to back the data science strategy and incorporate it into the company strategy can unlock serious potential. Among organisations surveyed at an ‘advanced’ level of data maturity, 17 out of 20 (83.3%) data science functions were created on the initiative of the board. Embedding data science in the company vision is key, as those ‘advanced’ level organisations are more than 50% more likely to recognise data science within the company vision (33.3% v 20.7% on average).

It’s key to establish clear metrics to measure success, in order to be able to prove a return on investment and potentially bid for additional funds to grow the function later on. The report shows that 66% of organisations surveyed with ‘advanced’ data maturity can define the relevant impact metrics for each data science initiative.

Continual championing of data science by company chief executives can help build data maturity. ‘Advanced’ level organisations are more than twice as likely than the average to communicate the impact of data science directly through the chief executive (50% v 24.1%).

Investing in data science should be a long-term strategy, aligned to company objectives.

Our research results show an increasing investment in data science, for a variety of motives linked to the bottom line, including building efficiencies, creating competitive advantage and improving decision-making processes. Through keeping data projects as part of a strategic, leadership-backed and data-driven infrastructure, organisations will leverage data science to boost their business revenue.

This research is being discussed by data leaders at DataIQ Transform https://www.dataiq.co.uk/dataiq-transform-2021 on 4th November.  A copy of the research can be obtained here  

 

Fantastic data initiatives and where to find them
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It was great to be at Big Data London last week. Beyond the novelty of interacting with complete humans again (and not just their heads via Teams calls) I was also lucky enough to be asked to present on a topic that I’m passionate about: finding fantastic data initiatives that will generate business value and underpin a data-driven transformation.

In particular, I described the use of ‘data domains’ as a great mechanism to align business and data stakeholders and create an environment in which fantastic initiatives can be discovered. If you missed the talk, here is a quick summary of what was discussed.

The data opportunity

Data is the lifeblood of the digital age – delivering strategic value if allowed to course through the arteries of an organisation. It holds the potential to create smarter, leaner organisations able to survive and thrive in an increasingly competitive (and digital) market.

The Big Data LDN showcased the best and brightest technologies available to deliver on this vision, but the reality is that creating lasting data-driven value is not (just) about the tech. Building a data-driven organisation is more about culture than code, more about change than cloud, and more about strategy than software.

In my talk I focused on a topic that is pivotal to finding and prioritising great initiatives that will deliver lasting value for your business. I spoke about why this isn’t a trivial consideration, before describing our use of ‘data domains’ as a mechanism to create a focal point for discussion of specific data initiatives and a way to improve collaboration between business and data functions. But first, a thought about data literacy…

Becoming a data-literate business

Investing in data literacy within your organisation (building to a universal understanding of the language of data and the opportunity it represents) is an important way to connect both your technical and non-technical teams. By uniting people capable of building new data solutions with stakeholders in other departments, as well as enthusiasts who are intellectually curious about the potential of data, project leads and their IT teams, you begin to form cross-departmental working groups that offer more diversity of thought to address different business scenarios. These communities will come up with your ‘long list’ of initiatives – where ‘fantastic’ can be found.

Identifying fantastic initiatives that drive value

Whilst thinking up data initiatives seems easy, finding the exact right data initiatives is not. There are many reasons why this is difficult, but a major factor has to be the knowledge and experience gap that exists between business stakeholders and data practitioners.

Prioritising the right initiatives is essential to building confidence in the business around new ways of working. And it is incredibly easy to choose the wrong initiative, creating a capability that is disconnect from business behaviours, or something that works really well on a laptop but has no hope of being deployed into production or becoming part of BAU.

The role of data domains

As a team, we get to help customers discover the exact right capabilities to create. To achieve this, we use a range of mechanisms to create the right collaborative environment between data professionals and business stakeholders – in my talk I described the use of Data Domains as an important tool to enable fantastic initiatives to be discovered and prioritised.

We can think of data domains as themes that allow us to connect aspiration business objectives with specific data capabilities. Domains can be used to engage leaders and create a focal point for the discussion of specific initiatives, so that the right business outcomes can be identified and delivered.

Here’s 5 key domains that in my experience cater for the majority of customer scenarios and are highly observable in terms of their impact:

Using data domains takes us away from any language around the potential of data or AI, and instead focuses on the conversation on business aspirations. It also helps to prioritise and focus the conversation – of course, we all want to create more informed, engaging, intelligent, efficient, sustainable businesses but what is the highest priority? If we want to create a more intelligent business, then what are the most important decisions we’d love to get right every time? If we want a more efficient business, then which process would we love to automate?

A case in point. 

One of the best examples of the power of using domains I’ve seen was when working with a major insurance company.  This company had previously invested in data through the lens of a series of innovative initiatives focused on solution areas.  They had invested in ’an AI initiative’, ‘a blockchain initiative’, ‘a big data initiative’ etc.  None of these investments had delivered any value, and the leadership were running out of patience with their data programs.

When we collaborated with them to help them move forward, we looked at the use of data domains to create a more business-focused framework against which to guide investment. We determined that the ‘intelligent’ lens (iteratively enabling more effective decision making) was the best domain to start with – and one that would deliver the impact they were looking for.

We refocused the investment on decision improvement. We identified key decision-making processes across a range of business areas (from resource forecasting to customer call handling), turning them into more effective, data-led decisions.

Engaging stakeholders: building bridges with data domains

The example above was a great success because it created a consistent language across the business for data-led value generation and enabled business stakeholders to more quickly buy into the change required. Designing successful data initiatives using data domains can be an effective way to create a bridge between functional areas successfully aligning business outcomes and data potential. For example formulating what you are trying to do / become vs what you are trying to build.

Are you ‘ready’?

Identifying a fantastic initiative is the first part of the solution – but we need to consider an organisation’s readiness to undertake the initiative. Some tough questions to ask yourself include:

  • Are we ready to implement the change required to realise the value?
  • Do we have the right data available to succeed? How do we address this if not?
  • Do we have the right platform to support this initiative?
  • Do we understand our stakeholder needs well enough? Are we able to see the situation through the right frame(s) of reference?
  • Can we measure and report on our impact?

In summary: 4 step action plan

  • Build data literacy – Invest in building understanding of the data opportunity with non- technical audiences. Spark curiosity and enthusiasm. Connect and unite your technical and non-technical teams. Celebrate ideation.
  • Use data domains to define ‘fantastic’ – Zero in on the impact you are looking to create. What initiatives will deliver this impact? Where will you ‘see’ value – and in what timeframe?
  • Engage stakeholders and build bridges across the knowledge gap – use data domains to engage leaders and create the focal point for discussion of specific initiatives, allowing an idea to become structured and permitting objective evaluation and prioritisation of projects.
  • Develop organisational readiness – Think through critical success factors and objectively assess your readiness to take on the level of change you are proposing. Implement any required corrective action – or resize your ambition accordingly.

Data domains provide a great framework for discussing and evaluating high-impact initiatives with business stakeholders, guiding you towards the most ‘investable’ initiatives that deliver the most value to your business.

Rich Pugh is Mango’s co-founder and Chief Data Scientist. If you would like further discussion around identifying and prioritising the right data initiatives in your organisation, contact us and we’ll be happy to help.

 

BigDataLDN
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Big Data LDN (London), Mango Solutions’ big event takeaways: The ideal forum for sharing data expertise  

What better glimpse of the 2020’s data and analytics community than at the UK’s largest data event, Big Data LDN – and how great to be back! With 130 leading technology vendors and over 180 expert speakers, the London show on 23/24 September generated thousands of data and technology-led conversations around effective data initiatives, with real world use cases and panel debates. Everything from leadership, data culture and communities to the ethics of AI, data integration, data science, adoption of the data cloud, hyper automation, governance & sovereignty, and much more…

With a captive group of decision-makers, Mango Solutions conducted a Data Maturity floor survey of Big Data LDN participants. Respondents were asked to identify where their organisations are on their data journey, including the opportunities for data science strategy articulation, data communities, and what their biggest challenge is; with even the most data-advanced organisations modestly commenting in some areas that they had ‘room for improvement’. The fascinating results of this survey will be revealed shortly.

Conversations were approached with eagerness, around ‘harnessing data’, ‘getting more of it’, ‘data platforms’, ‘monetising data’, ‘kicking-off some AI’ – Big Data LDN being the first in-person analytics event since Covid-19 up-scaled our worlds of work and data consumption. From bright computer science graduates, to startlingly young data analysts, through to a surprising amount of MD and C-suite representation at the other end of the scale. Companies from Harrods to UBS, University of St Andrews – the best of the best of brands came out to share insight and learn – buzzwords aside – the most investable routes from potential to advantage.

A low-key event, but highly intelligent, Big Data LDN gave us a feel for the new ambitious, hybrid workers. Back to face-to-face networking, sizeable audience sessions, hot-desking in the cafe; there was a positive hungry-to-learn feel right through the event. Everyone, whoever they were, seemed on a mission to share intelligence and opinion on data and advanced analytics. No one job title or company outclassed the other – just a mutually supportive community of technology experts; less ego and more mutual respect.

There was the ability to attend a suitable meet-up with practical hands-on use case examples. Mango hosted a data science meet-up with guest speakers from The Gym Group and The Bank of England as well as our own Mango Consultant sharing best practice, which was well attended.

Chief Data Scientist at Mango Solutions, Rich Pugh’s talk on finding fantastic data initiatives was well-received by the audience. This focused on prioritising high impact data initiatives with buy-in from across the business. In particular how the role of data domains, supported by data and literacy, can create bridges between business ambition and data investment. In our experience, too often pitfalls exist on selecting the right data initiatives or even trying to answer the wrong question with data; so keeping a focus on achieving business goals and objectives is critical.

But as well as finding out about the latest advanced data services and technologies, this event was a great opportunity to step back from the day-to-day and be reminded of what needs to come first in any data strategy – achieving company goals, and measuring progress, in the form of clear KPIs which we can impact with data and analytics, before jumping in to make hasty data decisions. In this way, with a clear link between business goals and data, we can measure the return from data investments.

My main ‘don’t forget’ take-aways from the event:

  • It’s about aligning data initiatives with business aims. At Big Data LDN, there was consistent messaging around an ‘outcome-first mindset’. To make sure you start with business outcomes and objectives before considering the data, software and platform. It’s a valuable mantra to operate by, to make sure you always deliver the right data science, BI or software.
  • Focus on dis-benefits as well as benefits. As much as we focus on what things we should do to get value from data, we mustn’t lose focus on stopping doing the things we shouldn’t keep doing, or considering the opportunity and benefits lost when we overlook or don’t implement a particular data capability.
  • A robust data strategy must show the benefits for each stakeholder. Different people need different things from a data strategy. To gain buy-in and approval from all stakeholders, you must clearly show each of them the value they will get. In return, you will get not only their sign-off, but also, more valuably, their advocacy.

A parting word

We all learned many things from Big Data LDN 2021 – For me, I’ve learned our industry collaboration and community of data experts is unquestionable. We know remote working works, but you can’t beat the energy of this in-person event to confirm why you love doing what you do. For me, sharing our data science insight, and learning more about our peers’ technologies and services was truly rewarding.

Big Data Challenge – Our survey respondents were asked their no.1 post-Covid data priority, and we received a real mixture of enlightening and some more predictable responses. We will share these challenges with you and explain how we can support in our analytic leadership series.

As a relatively new member of the Consulting Team, working alongside Rich Pugh, Jon will be working with our clients and supporting them through their data journeys. He brings significant expertise to the team from his experience in data maturity, data & analytics, data visualisations and implementing strategies that inform successful business change and improvement, from his previous roles at Oracle, EY, HP and IBM.

If you would like to speak to a member of our consulting team about your data-driven journey, please email us.

 

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Mango’s ‘Meet-Up’ at Big Data London on 22nd September features guest speaker Adam Hughes, Data Scientist for The Bank of England, whose remit involves working with incredibly rich datasets, feeding into strategic decision-making on monetary policy. You can read about Adam’s incredibly interesting data remit and his team’s journey through Covid-19, in this short Q&A.

Can you tell us about your interest in data and your role at Bank of England?

Working at the Bank it’s hard not to be interested in data! So much of what we do as an organisation is data driven, with access to some incredibly rich datasets enabling interesting analysis. In Advanced Analytics, we leverage a variety of data science skills to support policy-making and facilitate the effective use of big, complex and granular data sets. As a data scientist, I get involved in all of this, working across the data science workflow.

What’s the inspiration for your talk  – effectively data science at speed?

As with so much recently – Covid. With how fast things have been moving and changing, traditional data sources that policymakers were relying on weren’t being updated fast enough to reflect the situation.

Can you tell us about your data team’s journey through covid-19 and the impact it has had?

In a recent survey, the Bank of England sought to understand how Covid has affected the adoption and use of ML and DS across UK Banks. Half of the banks surveyed reported an increase in the importance of ML and DS as a result of the pandemic. Covid created a lot of demand for DS skills and expertise within the Bank of England too. Initially this led to some long hours, but it was motivating and generally rewarding to work on something so clearly important. Working remotely 100% of the time was a challenge at first, but generally the transition away from the office has been remarkably smooth in terms of day-to-day working (though there are still disadvantages due to the lack of face-to-face contact). As outputs have subsequently been developed and shared widely in the organisation, they have been an excellent advert for data science, showing the value it can add. In particular, it’s been great to see the business areas we worked with building up their local data science skills as a consequence.

What’s the talk about and what are the key takeaways?

The talk will cover some of the techniques we used to get, process and use new data sources under time pressure, including what we’ve learnt from the process. The key takeaways are:

  • Non-traditional datasets contain some really useful information – and can form part of the toolkit even in normal times;
  • Building partnerships is key;
  • A suite of useful building blocks, such as helper packages or code adapted from cleverer people helps speed things up;
  • Working fast doesn’t mean worse outcomes.

We look forward to seeing you at Mango’s Big Data London, Meet Up, 22nd September 6-8pm, Olympia ML Ops Theatre. You can sign up here.

Guest speaker, Adam Hughes is one of The Bank of England’s Data Scientists, https://www.linkedin.com/in/adam-james-hughes/

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Mango’s ‘Meet-Up’ at Big Data London on 22nd September features guest speaker Premal Desai, Head of the Data & AI function for The Gym Group.

For the Gym Group, advancing customer retention and pricing strategies have been critical drivers to success. Their focus over the last few years has been to deliver value from their analytic investments and to develop a stronger analytical culture. You can read about their data journey in this short interview with Premal – the key successes and challenges they have overcome.

 Introducing Premal Desai, Head of the Data & AI

Premal currently leads the Data & AI function for The Gym Group where he oversees a high performance team covering Engineering, Analytics and Data Science. Prior to this, Premal spent almost a decade with Thomson Reuters, leading Strategic Marketing & Analytics functions globally. He has previous experience across Europe working for Orange, and spent several years with PA Consulting in their Strategy & Marketing Practice. Premal is a 2021 Board Advisor for the Business of Data, a Seed Investor for a number of start-ups and outside of work, Premal is a passionate Liverpool FC fan + renowned for his desire of fast German automobiles!

Can you tell us a bit about your role at The Gym Group?

I’ve been fortunate to have a varied and interesting career over the past 20 years – spanning both client side and consultancy roles. I’ve worked across different sized organisations, covering Telecoms, Financial Services and most recently Health & Leisure. The two common passions for me are getting to grips with understanding numbers and customers! I currently look after the Data & AI team at the Gym Group which allows me to combine these passions – I have a small, but very capable team of Engineers, Analysts and Data Scientists who support the business to uncover insights and drive growth.

Can you tell us a bit about your journey with data at The Gym Group?

The Gym Group are generally a data-oriented business, which is fortunate given my role! Over the past 3 years, we’ve been on a mission to grow our capability across a number of areas – the depth and breadth of data used across the organisation, to improve the level of granularity and sophistication of data & analytics and to turn data into more of a strategic asset. As part of this, we’ve also expanded the range and skills of our people, how we consume data and grown the range of insight we’re able to provide.

How important has being data driven been as part of your recovery post covid?

During COVID, data was absolutely central to give the business confidence across a number of areas during a unprecedented period of time. For example, we wanted our customers to feel safe using our facilities once they opened – and so the company developed a capability showing live Gym Busyness via mobile – and we used various models to help us determine appropriate levels of Gym occupancy. As a business, it’s actually helped us to see data use cases more strategically and we’re delighted to have seen really positive business recovery since Gyms re-opened in April 2021.

Can you tell us about your talk ?  what should we expect and what are the key takeaways?

The talk will be focused on a couple of really important commercial drivers for our business – Customer Retention and Pricing – and the data / data science journey we’ve been on in these areas over the past couple of years. For balance, we’ll talk about both successes and some practical challenges. In addition, we’ll also speak to the importance of culture and ROI, and some factors that I think have been important on our journey. In terms of key takeaways – I encourage you to attend the talk!

Keen to join us? Premal’s talk at the Data Science Meet-Up is featured as part of the Big Data LDN line up at 6-8pm, 22nd September , AI & MLOPs Theatre. Register here.

We hope to see you there.

 

Dockerisation
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Best practice in data science can lead to long-lived business results. A structure that encourages repeatable processes for generating value from data, leads to a fully productive team working, allowing reproducible results, time and time again. When this process is ingrained across a company’s culture and the business and data teams are working together in harmony with the business goals, then the value of data can be realised into an overall centre of excellence and a shared language for best practice.

A shared language of best practice

Layers of operational best practice allow a standard practice to be adopted – ensuring the best possible outcome of your data science investment. For a data science team, best practices could relate to developing models or structuring analysis, quality standards or how a project is delivered. Alternatively, they could even align to the selection of your data and analysis tools, as these can easily impact the success of your project.

With data science teams coming from a diverse range of backgrounds and experiences, what may be obvious to one can be a novelty to another. A shared language of best practice allows collaborators to focus on the all-important value generated. A workflow that adheres to a best practice ensures quality, whether that be business value of insights to the accuracy of models. Best practices take the guess work out, minimise mistakes and create a platform for future success.

4 best practices every data delivery teams should focus on:

  • Reproducibility – Whatever the task is. If your results can’t be repeated, then is it really done?
  • Robustness – Results and quality of analysis can have a huge impact, ensuring your best practices that has checks and balances will lead to better quality
  • Collaboration – What use are your results if they are difficult to share. Having standards for collaboration means business value can be attained
  • Automation – It is very easy to do work with no automation, frameworks for automation can help accelerate teams

Best practice in Dockerisation

My talk at the Big Data London Meet Up ‘ How Docker can help you become more reproducible’, takes one element of best practice in data science, focusing on Dockerisation which is proving to be a powerful tool – one that is already turning established best practices in teams on its head. The tools allow teams to collaborate much easier, to be much more reproducible and automate workflows, in an impressive way. Yet, it has not had as much adoption within data science as it has within software engineering. My talk will explore just how Docker can super charge workflow and your valuable use cases.

This talk will be of interest to any data scientist who has had trouble with, deploying or working with engineering teams, reproducing colleagues’ analysis. It will also be of interest to anyone wanting to know how docker can scale a team, making it less intimidating and perfectly arming practitioners with the tools to give it a go.

I look forward to seeing you at Mango’s Big Data London, Meet Up, 22nd September 6-8pm, Olympia AI & MLOPS Theatre. You can sign up here

Kapil Patel is one of Mango’s Data Science Consultants.

 

Beth Ashlee Senior Data Scientist
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A worthy nomination for Beth Ashlee as we receive news of her nomination for the Data IQ New Talent/ Data Apprentice Award.

As an instrumental member of our Mango’s consulting team, we’re keen to share her journey from Summer Intern to Senior Data Scientist within an impressive 5 year time frame.  We all aware that the field of data is a fast moving, but Beth has achieved a phenomenal amount in her time with us.  With each experience, she has shown a remarkable desire to learn from it, evolve and nurture both her consultancy and technical competency and so we believe this shortlist is worthy of recognition.

Beth’s accomplished engagement style

As a master communicator, Beth excels in her role as a consultant at Mango using her empathetic communication style – enabling her to establish meaningful relationships alongside the ability to easily translate business value across an organisation.

She has demonstrated the ability to easily adapt to an ‘insourced’ team leader role or team lead as part of an ‘outsourced’ project and as a naturally competent people person, she makes everyone around her feel valued and motivated.

Beth is one of Mango’s standout Senior Data Scientists, and after a succession of promotions now leads and mentors the Graduate team in both a technical and personal development capacity. Demonstrating a ‘role model’ leadership style, it’s easy to see why all of our graduate cohorts across the past 2 years have developed into capable and credible data professionals that are proactively supporting the growth of our business today.

Congratulations Beth on your achievements, we certainly look forward to celebrating with you at the Data IQ awards night on 30th September.

financial fraud
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The recent Transform Finance ‘Virtual Fraud in Financial Services’ event offered some fascinating insight into the risks facing the sector and how organisations are investing in advanced technologies to detect, prevent and tackle fraud.

An important and recurring theme was the role of data analytics in meeting the challenges presented by rising levels of fraud and the increasing sophistication of fraudsters. Bringing insight and experience to life for attendees, Mango Chief Data Scientist, Rich Pugh, was joined by Sandra Peaston, Director of Research & Development at Cifas to discuss their use of data and intelligence to support fraud prevention.

Cifas is the UK’s leading fraud prevention service, managing the largest database of instances of fraudulent conduct in the country. Its members are organisations from all sectors, sharing their data across to reduce instances of fraud and financial crime.

As a data-centric organisation, Cifas wanted to develop deeper insight into emerging fraud trends, understand which were the most significant and then quickly share that information with its members for further action.

Getting ahead of the game was key, and as Sandra Peaston described, “We wanted to use our data to speed up the early-stage intelligence process so our members didn’t need to report trends to us. Unlocking the power of the data we already hold was the challenge that took us to Mango.”

Having been approached by Cifas, Mango quickly deployed a team of data scientists to establish the right technical environment. As Rich Pugh explained, “The Cifas team has amassed some incredible data assets, but with many areas of potential focus the key question was: where could we deliver quick impacts against their priorities?”

The Mango project team focused on two core areas. The first was a ‘Match’ project, built to reduce false positive rates and improve the Cifas rules engine. This was supported via the development of a probabilistic matching engine prototype, designed to improve the existing matching and reduce member friction.

The second part of the solution was an ‘Intelligence’ project. This focused on the development of a fuzzy search capability and a signal detection tool to automate the previous manual fraud detection processes to uncover hidden and emerging fraud patterns. This insight would then be used to enrich intelligence and feedback to members.

As Sandra explained to event attendees, “We needed an intelligent way of dynamically identifying an emerging fraud trend, and key to this was the speed at which this happens. By working with Mango to uncover the huge power that sits within our data to a level of granularity that we couldn’t manage before, we can help members to prioritise and make them more efficient.”

Together, Cifas and Mango have deployed a best-practice framework using intelligence tools that demonstrably reveal hidden patterns that human beings would struggle to detect. Looking ahead, the teams will continue to innovate and use data science to unlock insight relating to fraud and e-crime, refining algorithms over time to become even more effective in countering criminal activity and finding ways to stay ahead of malicious actors.