maths & statistics awareness month
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April marks Mathematics and Statistics awareness month #MathStatMonth in the USA, with the aim of increasing the level of interest in these subjects. Working in an increasingly data-driven world, the ability to harness meaningful insights from data is an essential business and requires specialised data science expertise.

Data science is the proactive use of data and advanced analytics to drive better decision making. This ‘proactive’ use of data is what distinguishes data science from traditional ‘statistical analysis’ and needs to be an active part of an organisation in search of insight, better decision making or improvement.

Data science as a career choice for maths, statistics, and science graduates

Many graduates from maths, statistics and science backgrounds are increasingly attracted by a career in data science. Our current graduate placement Student, Elizabeth tells us more about her early interest in data science and why it presents a natural career path for those interested in mathematics and statistics. “Data science combines the skills and applications of mathematics and statistics with the use of big data and innovating technology to solve a variety of problems. I’m particularly interested in providing solutions to real-world problems and communicating these results at a high level within a business”, says Elizabeth.

“Throughout my placement I have seen the application of using mathematics and statistics within data science projects in performing exploratory data analysis to creating statistical models. My personal interest is in different types of statistical models, and I am due to study Time Series and Bayesian statistics in the final year of my degree”.

Elizabeth has benefited from seeing how mathematics and statistics have been used to model complex situations and improve business decisions from the optimum timing of routine maintenance, saving unnecessary reactivity and costs to creating descriptive, diagnostic and predictive insights which delivered great value and significant return on investment during her time at Mango.

Growing demand for data science

With the demand for Data Scientists still on the rise into 2021, the pandemic has created an even more urgent need for rapid decision making, informed and supported by constantly changing data sets, backed by effective visualization (highlighted by the World Economic Forum (WEF) in July).

Rich Pugh, Mango’s Chief Data Scientist summarises, “Leaders increasingly understand the potential of using data to create smarter, leaner, more engaging organisations. As such, we are still seeing growing demand for “data scientists” who are able to turn that data into acumen in a repeatable and scalable way. As a multi-disciplinary practice, “data science” relies on the combination of “advanced analytics” and “computer science” skill – this, combined with an ability to creatively explore challenges that can be solved, is at the core of realising the value promised by data science”.

“At it’s core, data science relies on mathematics and statistical rigour to provide robust algorithms that can be relied upon to solve often-complex challenges. As interest in data science continues to grow, the work at the Royal Statistical Society becomes increasingly important – to drive the discussion around statistical governance, and the correct and ethical application of statistical routines”, Rich concludes.

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It’s mostly preaching to the converted to say that ‘open-source is changing enterprises’. The 2020 Open Source Security and Risk Analysis (OSSRA) Report found that 99 per cent of enterprise codebases audited in 2019 contained open-source components, and that 70 per cent of all audited codebases were entirely open-source.

Hearts and minds have most certainly been won, then, but there are still a surprising number of enterprise outliers when it comes to adopting open-source tools and methods. It’s no surprise that regulated industries are one such open-source averse group.

It’s still difficult to shake off the reputation open-source resources can have for being badly-built, experimental, or put together by communities with less recognisable credentials than big players in software. When your industry exists on trust in your methods – be it protecting client finances in banking, or the health of your patients in pharma – it’s often easier just to make do, and plan something more adventurous ‘tomorrow’.

This approach made a certain amount of sense in years past, when embracing open-source was more a question of saving capex with ‘free’ software, and taking the risk.

Then, along comes something like Covid-19, and the CEO of Pfizer – who are now among those leading the way in a usable vaccine – singing the praises of open-source approaches back in March 2020. Months down the line, AstraZeneca and Oxford University’s 70 percent-efficacy Covid-19 vaccine emerged. AstraZeneca is having a public conversation around how it’s “embracing data science and AI across [the] organisation” while it continues to “push the boundaries of science to deliver life-changing medicines”.

Maybe tomorrow has finally arrived.

At Mango, our primary interest is in data science and analytics, but we also have a great interest in the open-source programming language R when we’re thinking about statistical programming. We’re not attached to R for any other reason than we find it hugely effective in overcoming the obstacles the pharmaceutical industry recognises implicitly – accessing better capabilities, and faster.

With a growing number of pharmaceutical companies starting to move towards R for clinical submissions, we thought it would be useful to find out why. Asking experts from Janssen, Roche, Bayer and more, we collected first-hand use cases, experiences and stories of challenges overcome, as well as finding out how these companies are breaking the deadlock of open-source’s reputation versus its huge potential for good in a world where everything needs to move faster, while performing exceptionally. Watch the full round table recording here.

If you’d like to find out more, please get in touch and we’d be happy to continue the conversation.

Author: Rich Pugh, Chief Data Scientist at Mango

Value at the Intersection of Data and Software
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For the last 18 years, Mango have been helping customers deliver on the potential of data and analytics.

When we started Mango back in 2002, the wider world of data and analytics was mostly reactive, with workflows conducted by individuals who produced reports as ‘one time’ outputs. As such, while data professionals wrote code, it could largely be considered a by-product of what they did. The advent of data science, together with the increasing need for just-in-time intelligence, has driven more proactive analytic workflows underpinned by open-source technologies such as Python and R.

Working at the forefront of data science, Mango understands the vital role of technology; to allow data to be transformed into wisdom in a repeatable way and deployed to business users at the right time, to support informed decision making.

There is a clear learning here for modern technology initiatives:

Every data project is a software project, and every software project is a data project.

To realise business value, it is vital that we balance both data and software elements of technical projects around a common and clear purpose.

Every data project is a software project.

Back in 2012, Josh Wills described a data scientist as someone who is “better at statistics than any software engineer and better at software engineering than any statistician”. While modern data science incorporates a broader range of analytic approaches than statistical modelling alone, Josh’s description of data science at the intersection of analytics and software engineering still holds today.

The changing role of data and analytics from a reactive practice to a strategic approach has driven the need for advanced analytics to be combined effectively with software engineering. If analytics is now an always-on capability, we need to codify the intelligence in systems that can be properly deployed and scaled within a business.

A ‘local’ alternative is just not practical – you can’t become a true data-driven business if analytics is run by experts on their laptops. We can’t stop making intelligent decisions if a data scientist is on leave. If a consumer purchases a product on Amazon, they will not wait hours or days until a statistician crunches the data to come up with other recommended products.

To positively impact a business with data, an end-to-end analytic workflow needs to be implemented using software engineering approaches. This encompasses everything from the creation of data pipelines, the deployment of models, and the creation of user interfaces and applications that can convey insight in the right way, linked directly to operational systems to action and process outcomes.

Every software project is a data project.

Increasingly digitalisation and regulation have driven more focus on requirements regarding the role of data in software systems. We can consider 3 types of requirement regarding the treatment of data:

  • User – requirements relating to users and preferences to provide a more personalised experience
  • Governance – requirements relating to the way in which data is managed in a secure fashion to confirm with data regulations and protect confidential data
  • Provenance – requirements relating to historical system actions to provide an audit trail, or to enable rollout back to, or understanding of, previous actions
  • Beyond this, the most important consideration in the design of modern systems is the ability to leverage advances in data and analytics to create richer, more useful experiences and applications. A growing understanding of the possibilities offered by analytics allows us to strive to ask better questions – to build software tools that are truly aligned to a users’ objectives.

For example, imagine we are building a software application to be used by call centre staff when speaking with customers. Traditionally, we may have built a system that combined data from various sources to give the user a single view of the customer. Perhaps this included data on previous orders, previous interactions, demographic data etc.

With data science, we could extend the functionality for the user – perhaps to include an understanding of likely customer churn linked to suggested retention actions, or a suggested ‘next best offer’ for the customer, or suggestions around the ways in which to talk to the user. Perhaps when the customer calls the call centre they can be allocated to exactly the right person to talk to, as opposed to being randomly allocated to the next available agent.

The use of data and analytics in software can have a transformative effect on the quality and usefulness of our software systems.

In summary…

Helping customers build capabilities at the intersection of data and software is the most effective way to unlock value in an increasingly digital economy. Technology businesses like ours who want to be part of that customer journey need to be ambidextrous in their approach to data and software, agile in their execution and above all empathetic to each customer’s unique context.

We’re excited to apply our passion for data science to a wider market as we join forces with Ascent – increasing our combined ability to design and deliver ‘the big picture’ for customers that helps them compete and flourish.

Author: Rich Pugh, Chief Data Scientist

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

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

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

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

Embrace a data-driven Future

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

Align Data Investment to Business Outcomes

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

Upskilling your data and analytic talent

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

Use data to inform any transformation

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

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

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

The focus for the webinar will be on:

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

Join this webinar

 

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Finding the Use Cases

So you’ve gathered the data, maybe hired some data scientists, and you’re looking to make a big impact.

The next step is to look for some business problems that can be solved with analytics – after all, without solving some real business challenges you’re not going to add much value to your organisation!

As you start to look for analytics use cases to work on, you may soon find yourself inundated with a range of possible projects. But which ones should you work on? How do you prioritise?

Mango have spent a lot of time over the last few years helping organisations to identify, evaluate and prioritise Analytic Use Cases. Picking the right projects-—particularly early on in your data-driven adventure-—will have a significant impact on the success of your analytic initiative. This article is based on some of the ways in which we coach companies around the building of Analytic Portfolios and what to look for in projects.

Evaluating Analytic Use Cases

The prioritisation of analytic use cases will be largely driven by the reason your data initiative was created and what ‘success’ for your team really looks like.

However, for this post, I’m going to assume the aim of your initiative is ultimately to add value to the organisation, where success is measured in financial terms (either saving money or adding revenue).

Generally, you’ll probably want a mixture of tactical and strategic initiatives – get some quick wins under your belt while you’re working on those bigger, longer-term challenges. However, when you’re looking at projects to work on you should consider a number of aspects:

  1. The Problem is Worth Solving

This might sound obvious, but a big factor in assessing an analytic use case is the potential value it could add. Delivering a multi-million pound project that decides what colour to paint the boardroom isn’t going to win many fans.

Ensure you understand:

  • How delivering this project would add value to your organisation
  • Exactly how that value will be measured
  1. The Building Blocks are in place

Understanding the ‘readiness’ (or otherwise) of a project to be delivered is a major factor in determining whether to prioritise it. Key aspects to consider include:

  • Data – is there enough data of sufficient data to solve this challenge?
  • Platform – is the technical platform in place to enable insight to be derived?
  • Skills – do you have the skills required to implement the solution?
  • Deliver – is there a mechanism in place to deliver any insight to decision makers?
  1. The Analytic Use Case is Solvable

The world of analytics is awash with marketing right now, promising silver-bullet solutions based on Machine Learning, AI or Cognitive Computing. However, the simplicity or otherwise of a potential solution should be considered when prioritising a use case. You don’t want to end up with a portfolio of projects whose solutions are at the periphery of what’s currently possible.

  1. The Business is Ready to Change

This is–without doubt–the primary factor in the success (or otherwise) of an analytic project. You could have the best data, write the best code and implement the best algorithm – but if the business users don’t behave differently once the solution is implemented, the value you’re seeking won’t be realised.

Before you build, make sure the business is willing to change their behaviour.

Evaluating possible projects in this way can help you to build a portfolio of Analytic Use Cases that will add significant, measurably value to your organisation. Moreover, making the right decisions early can help you build momentum around data-driven change, leading to a more-engaged business community ready for change.

Mango Solutions can help you navigate this process successfully. Based on insight and experience gained over 15 years working with the world’s leading companies, we have developed 3 workshops to help overcome some of the common challenges and roadblocks at different stages of your journey.

Find out which of the three workshops would be valuable to your organisation here.