data science team
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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. 

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  

 

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.

 

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