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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

adding data thinking to software solutions
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Whilst the world of Data and AI offers significant opportunity to drive value, knowledge of their potential and mechanics are mostly confined to data practitioners.

As a result, when business users look for solutions to their challenges, they are typically unaware of this potential.  Instead, they may ask technical teams to deliver a software solution to their problem, outlining a capability via a set of features.

However, when making this leap we risk missing out on the opportunity to build more effective systems using data and analytics, creating “part solutions” to our challenge.

Let’s use a real-life example to illustrate this …

Case Study: Customer Engagement

One of our customers is a major financial services firm, which has a number of touch points with their B2B customers.  This can include a variety of interactions including customer support calls, service contract renewals and even customer complaints.  These interactions are driven by their large, globally dispersed customer team.

The goal of the customer team is to increase retention of their high-value customers and, where possible, to upsell them to more expensive service offerings.  As such, they see that every interaction is an opportunity to build better relationships with customers, and to suggest compelling offers for new products.

Let’s imagine the head of this customer team looks for support to better achieve their aims … 

Software-first Projects

A classic approach would be for the customer team to turn to the world of software for support.

Knowing the possibilities that a modern software system can bring which puts all the information about a customer in front of the customer team member during interactions (akin to a “Single Customer View”).  This information could include:

  • General customer details (e.g. sector, size)
  • Purchasing history (e.g. services they current subscribe to, volumes)
  • Usage (e.g. how often they use a particular product or service)
  • Recent interactions (e.g. what happened during the last interaction)
  • Offers (e.g. what did we last offer them and how did they react)

This could create an invaluable asset for the customer team – by having all of the relevant information at hand they can have more informed discussions.

However, the customer team still needs to fill the “gap” between being presented information and achieving their goal of customer retention and product upsell.  They do this using standard scripts, or by interpreting the information presented to consider appropriate discussion points.

So while the software system supports their aims, the human brain is left to do most of the work.  

Data-first Projects

In the above example, the head of the customer team didn’t request a software system – instead, she turned to an internal data professional for advice.  After some conversations, the data professional identified the potential for analytics to support the customer team.

They engaged us with the concept of building a “next best action” engine that could support more intelligent customer conversations.  Working with the customer team and the internal data professional, we developed a system that presented the relevant information (as above), but crucially added:

  • Enriched data outputs (e.g. expected customer lifetime value)
  • Predicted outcomes (e.g. likelihood that the customer will churn in next 3 months)
  • Suggested “next best actions” (e.g. best offer to present to the customer which maximised the chance of conversion, best action to reduce churn risk)

These capabilities spoke more directly to the customer team aims, and demonstrated a significant uplift in retention and upsell.  The system has since been rolled out to the global teams, and is considered to be one of a few “core applications” for the organisation – a real success story.

Software vs Data Projects

It is important to note here the similarities in the delivery of the system between these 2 approaches: fundamentally, the majority of the work involved in both approaches would be considered software development.  After all, developing clever algorithms only gets you so far – to realise value we need to implement software systems to deliver wisdom to end users, and to support resulting actions by integration with internal systems.

However, the key difference in mindset that leads to the approaches described are driven by 2 characteristics:

  • Knowledge of the Data Opportunity – a key factor in the above example was the presence of a data professional who could empathise with the head of the customer team, and identify the potential for analytics. Having this viewpoint available ensured that the broader capabilities of software AND data were available when considering a possible solution to the challenge presented.  Without access to this knowledge, this would likely have turned into a “single customer view” software project.
  • An Openness to Design Thinking – in the world of software design best practices, there are 2 (often conflated) concepts: “design thinking” (empathise and ideate to develop effective solutions) and “user-centred design” (put the user first when designing user experiences). In software-first projects, the focus is often on the delivery of a solution that has been pre-determined, leading to a user-centred design process.  When we consider the world of data, the lack of understanding of the potential solutions in this space can lead more naturally to a “design thinking” process, where we focus more on “how can we solve this challenge” as opposed to “how do I build this software system really well”. 

Adding Data Thinking to “Software-First” Projects

So how do we ensure we consider the broader opportunity, and potential that data and analytics provides, when presented with a software development project?   We can accomplish this with 3 steps:

  1. Enable a Design Thinking Approach

Design thinking allows us to empathise with a challenge and ideate to find solutions, as opposed to focusing on the delivery of a pre-determined solution.  Within this context, we can focus on the broader aspirations, constraints and consequences so that a solution can be considered which connects more closely to the business outcomes.

  1. Include Data Knowledge

During this design thinking activity, it is essential that we have representatives who understand the potential that data and analytics represents.  In this way, the team is able to consider the broader set of capabilities when designing possible solutions.

  1. Design the Data flow

Data is always a consideration in software design.   However, the potential of analytics requires us to think differently around the flow of data through a system with a view to delivering value-add capabilities.  This takes us beyond thinking about how we store and manage data, and towards a situation where we consider new data sources, data access, and the lifecycle of model-driven data outputs (such as predictions or actions).  This is particularly important where the “data” opportunity may be added to a system at a later date, once core “nuts and bolts” functionality has been delivered.

Data + Software + Design Thinking

The approach described here enables us to leverage the opportunity that resides on the bounds of data and software, and fundamentally deliver more value to users by delivering richer capabilities more aligned to business outcomes.

Moreover, we’ve seen that effective application of design thinking, combined with deep knowledge of data, analytic and software, has enabled us to deliver significant value for customers that goes way beyond solutions that may have been originally imagined.

Author: Rich Pugh, Chief Data Scientist

 

ascent acquisition
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Author: Rich Pugh, Chief Data Scientist, Mango Solutions

We are excited to announce today the acquisition of Mango Solutions by Ascent.

We founded Mango back in 2002, where we had a mission to make data an essential part of strategic decision making. Over the years, having grown from strength to strength, we’ve been fortunate enough to work with some leading organisations around the world to unlock the value that data science can deliver.

Ascent, a fast-growing European software services company, specialises in delivering effective digital transformation. Backed by a heritage of significant technical capability in software design and engineering skills, product development, IoT and integration – Ascent adds a new dimension at the intersection between data and software.

This acquisition represents an ambitious growth strategy for us, opening newly accessible markets where demand for insight and intelligence is rising. Enriching our proposition with impressive software talent and technology capability will put us in a greater position, allowing our customers to benefit from an end-to-end data-driven proposition with enhanced end-user capability.

The sector has evolved rapidly over the past 18 years, and as founders of Mango we are immensely proud of our organic growth. We have delivered value that has surpassed expectation, and always delighted our customers. We are also proud of our award-winning data science consultancy team, who will sit at the heart of this engine – delivering insight, modelling scenarios and determining the next best decisions for businesses.

After joining as partners initially, it was clear from the start that Mango and Ascent’s vision and shared values aligned. This next step for us is a natural evolution that adds layers to the existing Mango team. I’m truly excited about the future and growth of Mango, as I continue my role at Ascent as Chief Data Scientist.

Our journey to date has been made possible by the contributions of our customers, partners and of course, our amazing team. We look forward to sharing the next part of our chapter with you.

Read the official press release