Data Science Competency for a post-COVID Future
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Written by Rich Pugh, Chief Data Scientist, Mango Solutions (an Ascent company)

The COVID-19 pandemic disrupted supply chains and markets and brought economies around the globe to a standstill.

Overnight, governments, public sector agencies, healthcare providers and businesses needed access to timely and accurate data like never before. As a result, demand for data analytics skyrocketed as organisations strived to navigate an uncertain future.

We recently surveyed data scientists working in a variety of UK industry sectors, asking them about:

  • how their organisation’s reliance on data changed during the pandemic
  • how their teams are having to re-align their skills sets to deliver the intelligence that’s needed; and
  • top trends on the horizon as organisations pursue a data-driven post-COVID recovery.

What they told us offers some interesting insights into the fast-evolving world of data science.

Decision intelligence gets real

Our findings highlight how the sudden disruption of the COVID-19 pandemic brought the importance of data analytics sharply into focus for business leaders and decision makers across the enterprise.

Almost two-thirds (65%) of those surveyed said that demand for data analytics rose across their organisation. The top request areas for problem-solving and enabling informed strategic decisions included:

  • Immediate crisis response (51%) – risk modelling, digital scaling and strategy as organisations looked to make near-term decisions to address key operational challenges.
  • Informing financial/cost-efficiency decisions (33%).
  • Logistics/supply chain (26%).

As reliance on data became mission-critical, data scientists in some industry sectors were at the nerve centre of COVID-19 response efforts as organisations looked to solve real-life problems fast.

Data scientists are adapting their skills sets quickly

As organisations beef up their data strategy to better prepare for future disruptive events and thrive and survive in the new normal, data scientists are having to adjust to new ways of working and adapt their skills sets fast. Indeed, 49% of data scientists say their organisation is now investing in building their internal capabilities through learning and development programmes, with 38% actively recruiting to fill gaps.

Now part and parcel of the enterprise decision-making team, data scientists confirm they are having to hone their business and communication skills to ensure they are able to support business leaders across the organisation better. Indeed, an impressive 34% identified working more effectively with business stakeholders was now a top priority. With data now being used more broadly across the organisation, one-third (33%) of the data scientists confirmed that they plan to boost their own communication and business skills so they can interact more cohesively with business leaders – and collectively identify the right problems to solve for their organisation.

Top data trends for 2021

As organisations continue to push ahead with operationalising their data and analytics infrastructures to handle complex business realities, data scientists are scaling up their deployment of machine learning algorithms to automate their analytical models.

According to our poll, upskilling their machine learning (ML) skills was identified as the #1 priority for 45% of data scientists as they look to accelerate their AI and ML computations and workloads and better align decisions throughout the organisation.

Similarly, big data analytical technologies (such as Spark, Storm and Fink) was the top priority for 39% of UK data science teams, as was getting to grips with deep learning (39%) as analytics teams look to jointly leverage data and analytics ecosystems to deliver coherent stacks that facilitate the rapid contextualisation decision-makers need.

Finally, with more people across the organisation becoming increasingly dependent on data-driven decision making, data scientists are having to find new ways to present data in ways that business teams will understand.

In a bid to democratise data and support faster decision making on the front line, they’re working on increasing their skills in areas like data visualisation (27%) and modelling (23%) so they can tease out trends, opportunities and risks in an easily digestible way that makes it easy for decision-makers to consume and engage.

New opportunities on the horizon

In a post-COVID world, organisations are looking to tap into an increasing number of data sources for the critical insights they’ll need to tackle emerging challenges. In response, data scientists are having to extract and analyse data quickly – even in real-time – and in the right way. Integrating data-driven insights into the decision-making process.

In response, data scientists are having to upgrade their technical and business skills as organisations look for efficient and innovative ways to use the big data at their disposal.

In summary, the research highlights both how important it is to align central data communities in order to boost and demonstrate value across the business, while ensuring that investment in L&D programmes is fully aligned with developing trends and business objectives.

 

 

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

 

going pro blog
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Becoming a professional athlete isn’t just about pure talent and hoping that will be enough to excel. Going pro means setting out a clear plan and following through with sustained training, the right nutrition, coaching and support. Not to mention an incredible amount of discipline and determination to get the most from your talent!

In a similar way for businesses, becoming data-driven can’t depend solely on investing in a data project and hoping it will succeed.  Typically, organisations have similar challenges to amateur athletes in that they are successfully trying aspects of analytics with notable successes, but just cannot withstand the test of time to be repeatable, scalable and consistent. Or, they simply don’t know where to start with analytics to achieve maximum deliverable insight. This tends to have a knock-on effect, causing concerns over stakeholder buy-in, with the result that the analytics team continues as a siloed entity with sporadic projects and no guarantee of consistency of approach across the organisation. They fail to make the transition from talented amateur to pro athlete and so great talent is wasted as funding and enthusiasm runs dry.

As the role of analytics becomes more strategically important to the business, it becomes necessary to follow a standardised process for delivery. As part of this, business leaders need to ensure that initiatives meet business objectives and that there is consistency in delivery and prioritisation, as well as in the platforms and technologies used.  To move forward, you have to evaluate where you are, what needs to be put in place to succeed, and enable the transition to implementation and data-driven value. In other words, you need to go pro in analytics.

It sounds easy enough. But as most pro athletes know very well, taking the leap from amateur to pro warrants a whole new game plan, and then sticking to it – a rather daunting prospect for most of us. The good news is that Mango can help! As experts in data science and analytics, we’ve honed in on the key pillars of a data-driven transformation and drawn up a 5-step game plan aimed at helping you to scan and audit what your business has in place, identifying what’s needed, and where to focus next. Here’s a snapshot of how it works.

Join our webinar

If you’d like to find out more, why not join our webinar Going Pro in Analytics: Lessons in Data Science Operational Excellence where Deputy Director at Mango, Dave Gardner and Mango Account Director Ian Cassley discuss what organisations need to do to ‘go pro’ with their analytical platforms, capabilities, and processes once the limitations of sticking plaster solutions and ‘quick and dirty’ approaches start to bite:

Register Now