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As lockdown slowly starts to ease, business leaders must decide whether they will return to their offices, and if so, do they bring back all employees or work on a part-office and part-remote model from now on? It will be a different decision for every business, and so Intelligent CIO spoke to seven technology experts across multiple industries to get their thoughts on what should be the top considerations based on their predictions for the future.

August 2020 posted (pg. 38-40) in  Intelligent CIO 

Rich Pugh, Chief Data Scientist and Co-Founder of Mango Solutions, identifies how data analytics can help organisations increase agility and velocity post COVID.

“According to a recent survey of 300 analytics professionals, conducted by Burtch Works and the International Institute for Analytics, 43% of respondents stated that analytics is at the front of their activities helping their organisations make major decisions in response to the COVID-19 crisis.

“If different departments have embedded analysis teams, supported by off-the-shelf or customised tools, and they can model the outcome of multiple situations at different points on the development and supply chains, organisations will be better equipped to address potential risk and make informed plans to handle all likely outcomes. Better still, insights generated by data analytics teams can be shared across departments and with the company as a whole to ensure everyone knows the warning signs to look for, and the best courses of action to help the company succeed.”

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Bruce Seymour, Account Director for Pharmaceuticals and Life Sciences at Mango, discusses how data science can be used to help deliver better clinical trials.

28th July 2020 Posted in High Tech Digital 

In the wake of Covid-19, the development of a vaccine against the virus has become crucial for the world to begin to overcome the chaos and devastation the virus is continuing to cause.

As part of the drug development process, clinical trials are critical to delivering a new treatment for the coronavirus and are a natural requirement to meet stringent drug-approval regulations.  However, there are two facts that stand out when we begin to talk about clinical trials. Firstly, a single clinical trial can easily cost more than $100 million, and secondly, only 14 percent of drugs in clinical trials go on to secure regulatory approval.  There is an opportunity to reduce the cost, complexity and time involved in completing clinical trials with the right platforms and processes in place, for example with the adoption of less static and more adaptable open source technologies.

Former FDA Commissioner, Dr. Scott Gottlieb, viewed this as a strategic imperative, particularly when it comes to some of the biggest dreams in healthcare today – effective, efficient precision medicine. To get there, Gottlieb recognised something that industries have been working on for a while: the need for better data – better data sharing to combine different data sources, better data integration to bring in insights from electronic health records, and better data integrity to support organisations like the FDA or the MHRA in making informed decisions to deliver better health outcomes. In short, it is data science that could prove transformative in taking today’s healthcare innovations ensuring a successful clinical trial to deliver tomorrow’s treatments – Covid-19 included.

The healthcare research industry is no stranger to statistical analysis, yet there is still much it could learn from a more holistic data science approach. After all, while it seems that some of the “fail fast, fail often” attitude of Silicon Valley is echoed in the current approach to success in clinical trials, there are tools and approaches from the data-driven business model that clinical trials could learn from. There are three phases of the process where this warrants consideration.

Trial planning

Nearly every stage of the clinical trial process is marred by challenges in enrolment timelines – according to a study from 2014, roughly 80% failed to meet their timelines, and 30% of phase III trial terminations were due to enrolment difficulties rather than issues with the drug itself. Making informed decisions based on data can help with every step from trial design and assessment of operating characteristics through to site selection and participant recruitment.  For example, historical site performance characteristics, combined with data on the rate of the illness in a population and competitor intelligence on trial sites can all be combined with the right expertise to find the best sites for efficient drug trials. Meanwhile, both existing patient data files and social media or forum data could all help find and recruit study participants. However, this is only feasible if researchers can adopt the types of natural language processing tools that other businesses use to ingest and analyse this type of unstructured raw data.

Trial execution and monitoring

Trial monitoring and helping trials progress through each stage of the process is one of the most impactful areas for data science for a number of reasons. Firstly, before any trials even begin, researchers can model the quantitative characteristics of the proposed study design to predict the probability of being able to make a decision at the end of a trial, which helps de-risk the initial expenditure.

In phase one trials, researchers need to understand how a drug performs, what effective dosages could be, and any potential side effects. In a standard statistical analysis, researchers may be able to correlate a dose and an effect but may miss vital contextual data that could better inform treatment. In a data science approach, all variables are included – and data such as patient demographics can be cross-correlated with both treatment efficacy and side effects to understand the impact of different variables (age, weight, other conditions, diet and so on) on treatment. This can also provide wider context on the important factors affecting treatment that can shape the future phases of the study, as well as inform a potential precision medicine strategy for the drug.  In phases two and three, data science-driven approaches to overcoming data siloes, a common problem in modern enterprises where departments have historically worked in isolation and embracing diverse data sources can also help to create better models of how the drug will perform outside of the clinical setting and predict real success.  All of this is important for creating both a successful trial, and a successful long-term product.

Trial reporting

Finally, good data practices and a successful trial outcome are both meaningless without being able to prove the integrity and accuracy of data to the regulatory body at the end of the study to gain approval. By capturing model data and producing an audit trail of all decisions made and the reasons behind it, researchers have the best chance of convincing regulators that a successful trial outcome is enough to green light the drug. Think of it like accounting: how many multi billion-dollar corporations are balancing their books by hand? The same should hold true for proving regulatory compliance.

All of these create a compelling case for how better data-driven decision making could lead to more efficient, effective clinical trials for a coronavirus vaccine. However, it’s not as easy as flying in a team of data scientists or a black-box AI solution to try and magically generate the right answer. Subject-matter expertise is critical for interpreting and understanding the data and insights that comes out of these types of datasets – while in-depth data science skill sets may not yet be prevalent among the researcher community. In this likely scenario, it’s about building a cross-departmental collaboration of technologists and scientists who can work together to deliver the best outcome.

The benefit of this over some advanced plug-and-play solution is one of trust. Only by creating a common understanding of the goals of the project can the data scientists ensure their models and solutions meet the needs of the researchers, and can the researchers feel confident in making decisions based on the findings of the data scientists over more traditional regression and t-test type statistics. But with a shared understanding and common goals, adding effective data science initiatives into the clinical trials process will be critical not just to help drug companies improve the efficiency of their go-to-market and approvals process, but in delivering real differences in clinical outcomes for patients who are currently, or may be, affected by Covid-19.

Author: Bruce Seymour, Account Director, Life Sciences at Mango Solutions

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5 tech initiATIVES
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Rich Pugh, Co-founder and Chief Data Scientist, Mango Solutions, shares five key technology initiatives that will enable companies to not just survive in the Covid-19 era, but emerge with a strong competitive intelligence.

28 July 2020 Posted in ComputerWeekly.com


The things we do not know about Covid-19 seem almost endless. There are no clear answers yet to some of the most fundamental questions for any disease – including the best treatment methods, whether people who have had it gain immunity and how long such an immunity might last, and when we can expect an effective vaccine.

Without answers to these questions, and with rapidly extending timescales for a return to normality, there has possibly never been a greater need for clarity than there is today – not to mention the pressure that the race for a vaccine is placing on the pharmaceutical industry.

This drive for more insight, sooner, highlights an important truth that many in the business services world have come to live by – that data-driven digital transformation is critical to success now, during Covid-19, longer term when we return to our (new) normality and further down the line still when we return to business as (new) usual.

Breaking down the buzzword phrase, “data-driven digital transformation” is simply the use of new digital technology to find better, quicker ways to solve problems with data, whether that’s finding an effective vaccine in a time-critical situation, or making strategic decisions about which drug research projects are the most promising to pursue. However, as any CIO can tell you, embracing a data-driven digital transformation journey is not simple and there are no plug-and-play shortcuts to real success. Instead, it’s about taking a data-driven approach, putting the information and skills you have at the heart of the project, and building outwards with the right technology to deliver insights at speed.

With time of the essence, there are five key technology initiatives that will help organisations succeed with data-driven digital transformation in these uncertain days:

  1. Embracing a data-driven future

An uncomfortable truth is that the impact of Covid-19 is likely with us for the long run – not just in its social ramifications, but also in how it will force all businesses to think strategically and do more with less. Your competitors, the ones that make it through the next few months, will likely be faster and hungrier than ever before. This means that you as a business also have to be prepared to become data-driven, and fast. To do this, the culture of your business will need to change, which is no easy feat. Finding early champions of the data-driven mentality – those who are enthusiastic about the potential of data to bring about real and actionable change – and then building momentum using these advocates will be critical. At the same time, it is important to plan opportunities for these champions of the data-driven future to share their vision and drive with the wider company at internal events, hackathons or company offsites to showcase the best practices of a data-driven company.

  1. Planning for success

Every successful initiative – be it data-driven digital transformation or otherwise – begins with a plan and a definition of success. Achievable goals, clearly defined and easy to measure, are critical. As a business, what is it that you need to be successful during the Covid-19 environment and then returning from it fighting fit in the ‘new normal?’ It’s important to understand the metrics you want to drive in order to align data-driven activities to enable them. These can include cost reduction, revenue generation, or creating richer experiences to gain and retain customers. 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 success looks like.

  1. Upskilling for data and analytic literacy

While technology has been a whirlwind of development, successful data-driven digital transformation has been a slow-moving force over the last 10 years. With the Covid-induced changes foisted on industry, the rate of transformation can be expected to quicken. During this period, we’ve all become increasingly used to seeing graphs and metrics reported so people have become more used to interrogating and understanding data. This is an ideal basis for dealing with data and making informed decisions. Changes in workforce competences and essential skills that might have played out over years are now playing out over days and weeks, which means that it’s more critical now than ever to upskill your workforce; globally, 74% of employees report feeling overwhelmed or unhappy when working with data, yet data proficiency is critical to digital transformation and data-driven success.

A data-driven company where only the C-Suite and a few select employees lead with data will always lose out to one that has data literacy spread through every tier of the organisation. Now is the time to consider the skills and competencies you’ll need in the coming months and years, and ensure you’re enabling those critical dependencies at every level of your workforce. While that may mean additional CAPEX at a time when liquid funds are harder to come by, the benefits of a workforce that feels comfortable with data – and understands how to use it effectively for the success of the business – will pay dividends for years to come. Internal skill-sharing and mentorship programmes can also help, utilising your data champions to drive wider enthusiasm and to share best practices in a peer-to-peer environment.

  1. Business Simulation

To not see Covid-19 coming is excusable; to fail to plan for Covid’s outcome is not. There is no clear outcome at this stage, which means that your organisation needs to plan for the “new normal” to last three months, six months, or multiple years – simultaneously. Planning for uncertain times raises the need to simulate all the different likely outcomes, and then work out what is required for success. If different departments have embedded analysis teams and therefore access to the right analytic tools and skills, they can model the outcome of multiple situations at different points on the development and supply chains, you will be better equipped to address potential risk and make informed plans to handle all likely outcomes. Better still – if you have completed the above key initiatives, insights generated by these analysis teams can be shared across departments and with the company as a whole to ensure everyone knows what warning signs to look for, and the best courses of action to help the company succeed.

  1. Machine Learning/Artificial intelligence

Once you know where your company is, what resources you have, and where you’re going, then – and indeed, only then – you can start looking at advanced analytics and technology. One of the most important analytic approaches is machine learning, which can help find connections and trends in the data that human data analysts may not even know to look for. This is particularly important in a situation like now, where there are simply more unknowns than knowns. However, you can’t make good decisions without good data. If your computer intelligence system is based on backwards-looking data that has been wrangled out of Excel, and produces insights that only the analysts can understand, it is all but useless. Instead, consider opportunities to enable machine learning to solutions and focus on forward-looking insight, ensuring these are able to automatically convert current data into real, actionable insight – which can be easily understood across the business functions.

By taking these five initiatives, companies will be best placed not just to survive in the Covid-19 era, but emerge with a strong competitive intelligence. With the right digital infrastructure in place – supported by a culture that is data-driven and continually innovating, and the right skills to ensure the entire company is able to share in the future vision – companies stand to thrive when we exit this period of uncertainty.

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Rich Pugh, Co-founder and Chief Data Scientist, Mango Solutions, explains how employers can prevent a high turnover rate of data scientists

8 June 2020 Posted in Employer News UK


In a recent study, over 56% of the UK data science professionals polled suggested they would be looking for new jobs in the next 12 months. This is at the same time that around two thirds of companies reported looking to expand their data science initiatives, with three times more postings for jobs than searches for those same roles. In particular, the shortage of applicants is centred around more senior data scientists – those with hands-on data science experience, who can apply advanced data-driven initiatives in a business context.

With this in mind, one thing is abundantly clear: finding ways to motivate and retain your existing data scientists will be critical for success in creating and maintaining a data-driven business. In order to do this, it’s important to understand the primary reasons contributing to the current risk of churn for data scientists. In broad terms, these break down into three categories: difficulties delivering (and likely proving) value, siloed working conditions and access to the right training.

1) Proving the Value

Let’s start with the most serious issues first. From a business perspective, over a quarter of data scientists referenced a lack of support from managers and leaders, almost half also expressing issues with bureaucracy as the biggest challenge they face in their day-to-day work lives. This is indicative of a business that is hiring data scientists without a fully-formed and well-planned data science initiative in place. Likely they are relying on the technology, and not the process, to deliver results against a largely open-ended brief of “delivering insights”, without specific direction.

To address this, businesses need to think critically about what they are trying to achieve with their data scientists. Any data-driven business needs to start with a common understanding of the goals of the project, all of which should be specific, measurable and have clear timelines. Importantly the initiative needs to be pioneered from the top downward rather than vice versa. By doing this, data scientists will have a clearer idea of the brief they are working to, and be better able to prove value, by demonstrating success against the initial project goals. If done properly, this should alleviate the underlying cause of needless bureaucracy and lack of managerial support – a lack of common understanding, common language and trust between managers and practitioners.

2) Working in Siloes

Secondly comes addressing siloed working conditions. Current situation aside, humans are naturally social creatures, but that is not the only advantage of collaborative work practices. Bringing together teams of complementary skills – programmers, communicators, data visualisers and so on – ensures that all the different aspects of a data science project, from brief interpretation to execution, are executed by one team who can work together to ensure cohesion and maintain the focus of the original brief. Connecting those with data science skills and interests via a common language can facilitate knowledge and best-practice sharing, which can help to nurture and grow expertise within the company. This can be achieved through a new community of practice, knowledge-sharing sessions, hackathons or other similar initiatives to challenge individuals and promote integration.

3) Training

Finally, there comes the issue of providing the right training. Over two-thirds of managers (69%) plan to upskill existing professionals to address the skills gap. However, time remains overwhelmingly the constraint for professionals seeking to learn a new skill. Similarly, not knowing where to start and a lack of funding also feature as a concern. All of these reasons filter back to a single root cause – learning and development programmes aligned to address an organisation’s skill gaps. Businesses need to find a way to assess and track the current experience of their teams, and then use this to inform decisions on future training resource (time and money), based on a prioritised list of the skills the team needs and individuals want to develop.

Software tools are available that can be used to do exactly that. By investing in the right resources to develop your existing team, both individuals and businesses stand to benefit: businesses ensure they have the right people with the right skills to deliver industry-leading results, while practitioners feel supported in their learning objectives and feel the business is investing in their progress which can aid in employee retention.

There are three constructive steps businesses can take to prevent data scientist churn, and deliver better results for their business: ensure data science projects are endorsed from the top down with clear timelines and objectives; arrange opportunities for collaboration and knowledge-sharing; and invest in the right training at the right time to support employees. By doing this, businesses will not only address the central frustrations of existing data scientists that currently drive them to seek alternative positions, but they will create better business processes that lead to better results from data science practices – and all of this has the added benefit of shared costs and reduced frustrations that occur from constantly trying to hire for a position it would take a unicorn to fill.

About the author

Richard Pugh is Mango Solutions’ Chief Data Scientist and has over 20 years’ experience of working with data. As co-founder of Mango, Richard has led a wide range of ground- breaking data science projects for some of the most forward-thinking companies in the world.

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Simon Asplen-Taylor, CEO of DataTick, and Rich Pugh, chief data scientist at Mango Solutions, discuss data science’s role in Covid-19 recovery

26 June 2020 Posted in Information Age

If it isn’t difficult enough emerging from the current crisis and recovering your business to pre-pandemic market trading levels, there is a new threat to your business. Some of the smartest companies, as well as the emergence of some new start-ups, are using data science to help them reduce cost, automate processes, make more intelligent decisions and therefore emerge from this situation stronger and fitter than before. If you don’t accelerate your data science capabilities, there’s a good chance you’ll be left behind post Covid-19. Here are 7 steps to ensuring your data project helps you compete:

1. Define your leadership hypothesis

The first thing you should arrive at is a Leadership Hypothesis — how do the leadership believe the use of data science helps you to steer your business through these troubled waters? The focus for most companies should be on the Four C’s: cash generation, customer retention and cost management, and care of employees.

Whilst not every organisation is in the same situation, these are the areas likely to be most critical to support a business to recover and grow quickly. Will it all be about reducing cost through automation? Or more intelligent pricing decisions? Or increased acquisitions through more impactful marketing? Charging ahead with data science and AI without a consideration of how this might impact the business and support you will likely not succeed.

2. Ensure your organisational culture is setting you up for success

Next, you need to start with the basics, and by this we mean start by changing your organisational culture to be more data savvy, whilst understanding the Leadership Hypothesis. To get the most value out of your data in order to help you compete, your organisation needs a clear leadership structure, defined and led by the person who leads your data initiatives — such as your chief data officer (CDO) — working directly with the board and senior executives.

Boards and execs need to start demanding more data from the business in order to make better decisions, and not just at C level, but throughout the organisation. This is where driving more value from data begins. Let’s face it, if the whole world can switch to working from home in less than a week, then instilling a data culture should be possible within a relatively short timeframe! The CDO should evangelise the opportunities for data science, explaining some of the best examples, and ways that data science can help. Coming out of this should be some knowledgeable business sponsors armed with their challenges.

3. Define your data science process

The next step is to enable business sponsors to know how to engage with data science. The business domain experts from across the business should come armed with their challenges. But to get the most out of data science capabilities, it’s important to have a clearly defined data science process so that the business can understand what help they can expect, where it will come from, and when.

It’s important to find a process that aligns to the culture and aspirations of the company and make sure it’s repeatable, scalable, and builds a common language across analytic and business functions. A good industry-wide process exists — it’s called the CRISP-DM life cycle (Chapman, Clinton, Kerber, et al, 1999). It was first set up for data mining, one aspect of data science, but can be applied to all. In this way, everyone knows the stages of the lifecycle and timescales, and will know how and when to engage in the process. This process should be owned by the head of data science or data science lead.

4. Challenge your challenge

Once a challenge has been identified, it should then be explored from a number of perspectives to ensure it is valuable, solvable and realisable. Many a data science project has failed before a line of code has been written by not asking the right question, whether the data is available, or whether the business is ready for the change an analytic approach will require. Once agreed, data science teams need to work collaboratively with business stakeholders to iteratively develop appropriate solutions rapidly and clearly. This will involve some experimentation against an agreed baseline so we can measure value and progress, and there will need to be rigorous testing and validation by the business.

5. Automate for quick access to data

Key to the success of the data science team is easy access to the data sets they need to perform science on. These need to be built, automated and deployed to an environment where this is possible. The vast majority of companies’ data sources that are valuable for generating value are within their existing structured systems, and the use of automated data warehousing tools will enable new data sets to be created quickly.

6. Get the model outputs into production

Your organisation will then need to address one of the biggest challenges in data science, and the one that will inhibit your organisation getting value from data. That is getting the output from the models into production. Most organisations have failed to deal with this, and now is the time to deal with it head on. IT departments have many years’ experience getting systems through testing and into production. Rarely do you see IT and data science working together well, but this is an area where the CIO should take the lead, albeit that the IT teams will need to work in an agile way to ensure rapid deployment.

7. Privacy and security are key

Finally, we’ll be developing new models rapidly, and as we develop these new data capabilities, we should not forget the vulnerabilities and the unintended consequences of so doing. Protecting our data is not an option, it’s a necessity. Ensuring your data is secure, and that your models are transparent and ethical, matters now more than ever. Everyone, including the chief data officer, head of data science and the business sponsors will need to work closely with the data privacy officer (DPO), who will represent the interests of the customer.

These are challenging times for any organisation, but follow these steps and allow data science to unlock the key to recovery from Covid-19!

Written by Simon Asplen-Taylor, CEO of DataTick, and Rich Pugh, chief data scientist at Mango Solutions

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“There is no ‘i’ in team,” as the well-worn saying goes. History has proven that it does indeed take a team, rather than an individual, to achieve incredible results.

Assembling the right team for a particular task is all about the perfect blend of skills and capabilities. From five-time FIFA World Cup champion team Brazil, to Black Widow, Iron Man and The Hulk saving the universe, it was initially down to Brazil’s national team managers or Nick Fury to sort the right talents into the right combinations.

8 June 2020 Posted in DataIQ

That’s easier said than done and success depends on tackling a number of challenges. Typically, siloed teams in modern businesses mean managers are often unaware of what their existing teams are capable of, or what new skills are needed to plug the gaps. When it comes to data and analytics, building the winning team is all about aligning business objectives, the ability to demonstrate value and ROI back to the organisation, high performance at the task, and then retaining that team talent to fight another day, on another project.

Like football or cricket, data science is a team sport and depends on an understanding of the skills and capabilities of the whole team, plus a common language to bring a variety of skills and expertise together to understand and talk about what needs to be done. It’s this thread that helps to build a commonality of purpose and means that what is presented to the outside world is more powerful.

You are not going to recruit three magical people with all the expertise.

But how do you go about assessing what skills you already have internally and what skills you need to recruit/upskill for?

What’s important is that you determine what you need at your current phase of business and then build a team with the right data and analytic skill to deliver on your objectives. For example, if your business wants to save costs through automating some processes, that would require a specific set of programming skills, as well as statistics and business knowledge.

This does not mean you’re going to recruit three magical people with expertise in all those areas, but rather three different people, each of whom have a key strength in each of these things. There are software tools available that can help to identify who in the team may, for example, have the best technical skills to deliver projects and add business value, or the best communication skills to communicate complex technical details for non-technical audiences.

The key priority when taking this approach to building data science project teams is constantly to attract, develop and retain key skills and capabilities. Most of us have a strong desire continually to grow ourselves, keen to engage in professional development wherever opportunity presents. In some cases, this happens as a natural consequence of engaging in a wide variety of project work.

Six core capabilities define data science competencies.

But in others, it happens due to development planning aligned with our career framework. In these instances, using software solutions designed to align development needs with key data science traits can support teams in growing capabilities in the areas that are of most interest to them. Here are six core capabilities that we believe can help define the existing data science competencies, align strategic approaches to learning and development and resource projects to maximise value:

1. Communicators: Data doesn’t sell itself – it needs a communicator to guide the way. Because of this, many great data scientists are master communicators, able to lead key business decision-makers into an ongoing conversation about the right questions to answer with data and communicate analytic results in a meaningful way.

2. Data-wranglers: All great analysis starts with data, or rather starts with data in multiple locations, in different formats, languages and time zones. Data-wranglers understand that defining the question and the approach to creating insight stems from getting the data into a useable format.

3. Programmers: These cool, rational individuals are masters of multiple technical languages, excel at combining constructed analysis workflow, but also enjoy building applications from scratch. Scripting and programming are two very different, activities!

4. Technologists: Never satisfied with good enough, they find the best tool to aid with every challenge, constantly exploring how evolving tools and techniques can add value to the data science workflow. They use their technical knowledge to understand how best to deploy data science on scalable infrastructure.

5. Modellers: By creating quantitative descriptions of data, they create insight that is a key deliverable for the data science project team. Modellers are the ultimate investigator – when they’re on the team, if there is information that can be gleaned from a system, they will find it.

6. Visualisers: They are experts at converting information into a landscape that can be explored with the eyes to create an information map. This skillset is absolutely indispensable for organisations that are lost in information.

Having a thorough understanding of capabilities and skill level mapped against traits like these for the team can help guide and shape the data science project team best suited to the task. The result is a significantly more engaged workforce with a set of skills that the business understands and needs to deliver data-driven value.

Written by Rich Pugh 

Data in retail
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Rich Pugh, Chief Data Scientist at Mango Solutions looks at utilising data science in retail to help plan ahead and get through these challenging times.

11 June 2020 Posted in  A1 Retail

Retailers who have been shuttered since March are having to consider making significant changes on a number of levels when many re-open. Even for businesses that, on the surface of it, seem to have thrived (think grocery and e-commerce businesses) actual profits are likely to have remained flatter than many might think, due to increased costs in the supply chain, logistics, resourcing, and investment in additional worker protection and sick pay. Simultaneously, profiles of consumers themselves may be changing. All of this creates a backdrop of uncertainty, where retailers have to adapt to a “new normal” that we cannot yet fully understand.

Data Science in Retail: Planning for “what if?”

Right now there are several open questions that matter to retailers – including, but not limited to – “will working from home become the new norm – and will this change how and when people shop?”, “when can stores fully reopen?”, “will customers still shop the way they did before?”, “what will the new competitive landscape look like”. With a recession looming on the horizon, retailers need to make effective decisions to deliver budget and resources to the right initiatives. The question is, which initiatives are the right ones?

The truth is, there is no absolute right answer here; there simply isn’t enough clarity on what the future holds. Retailers need to use the data they have on current events, historic trends and consumer behaviour (mixed with knowledge and insight about the current environment) to build models to explore “what if” scenarios. Building these kinds of models, particularly at short notice and for smaller outlets, takes time and considerable data engineering skill to bring together the different data streams into a cohesive solution that can visualise the best outcomes for different situations.

Successful models that can demonstrate what success would look like in different scenarios can be an invaluable tool, providing retailers with a way to prioritise investment across common trends. In addition, such models can help with resource allocations. The right models, used correctly, can help inform strategy on where shops should start reopening, at what scale, with what conditions in place, and with what staff and skill sets. All of this will be essential for effectively re-starting retail for the “new normal.”

Understanding your “new” customer

The second consideration is that customer behaviour and concerns have likely shifted – and there’s no way to guarantee it will revert back to normal once the lockdowns are eased. Rising unemployment and talk of a recession may have customers on edge about making impulsive purchases, for example, which means changing the way you sell, and market, to address this. But step one is understanding who your new customers are, what their pain-points are and what their likely behaviour will be.

While the process of mapping personas is not a new one, the current situation throws a spanner into the works and relying on single data streams is now likely providing an incomplete view of the situation. For example, existing customer persona mappings might have worked on making product suggestions based on a user’s purchase history, but now their willingness to try out new products may have decreased. This means trying to bring in everything from market research data to transaction history to social media trends to map sentiments and trends and understand how to interact better with consumers in the brave new world they find themselves in. Investing in data science approaches to bring together these disparate data sources, and the right analytics support to understand them, will be critical to selling to this new wave of mid- and post-Covid shoppers.

Customer acquisition

With an understanding of who your new customers are, what concerns your existing customers might have, and the right models to build strategies for different scenarios, you are now in a position to do what successful retailers do best – convince customers to buy their products.

The question now becomes how you do this most efficiently. Modelling the best approach to customer (re)acquisition can be challenging given the lack of historical data on the current situation. However, combining this trend information with ongoing testing can help retailers know how, when and where (online or in store, for example) to engage with customers to convince customers to buy, increase order value and promote repeat business and customer re-engagement. By A/B testing different messages and strategies, as well as bringing in historic data and external behavioural analysis to elucidate any trends appearing in the data, retailers stand to benefit from more, happier, customers. This can also reinforce and inform future modelling as to what business strategies are and are not working in the current climate.

The COVID-19 crisis is undoubtedly devastating for many – both on personal and professional levels – but it also presents the retail sector with an opportunity, an imperative even, for innovation. This task may seem daunting but there are resources that can help. Working with the right provider can help retailers bring data science to the heart of their business faster, not only navigating the challenges of today, but preparing for a data-driven future with the right skills and resources throughout the organisation to plan for any “what if” scenario.

Written by Rich Pugh co-founder and chief data scientist at Mango Solutions

top tips for driving data-driven change
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Rich Pugh, chief data scientist at Mango Solutions, and Simon Adams, managing consultant at Nine Feet Tall, give their tips for driving change within data-driven transformation.

26 May 2020 Posted in  Information Age

Data is crucial in delivering transformation within a business.

At a time when many companies across a broad spectrum of industries are examining underneath the bonnet to see how healthy the engine looks, one of the considerations must be how the organisation can run with increased agility and efficiency in order to emerge from this situation fitter and stronger. ‘Digital transformation’ is a term that may make eyes roll upwards, but ‘data-driven’ transformation has to be the way forward for many organisations to ensure that all business decisions are just that, driven by data.

So when teams within organisations are created with the shiny new title of ‘digital transformation team’, do we really understand where their focus lies? Is it on the infrastructure – digitising legacy processes – on improving client experience, or are they looking at how to get data flowing better through the entire organisation?

These are important processes, as it’s the latter that underpins the whole business of transformation, because, unless data is captured, utilised and shared, any technology upgrade will be in vain. And done correctly, data-driven transformation will fundamentally change how an organisation operates and delivers value to its customers, by putting data – and data analytics – at the heart of decision making across all areas of its business.

The huge elephant in the room for businesses is that according to IDC, 40% of all technology spending will go toward digital transformation, with enterprises spending in excess of $2 trillion through 2019. And yet despite this huge level of investment, 70% of all digital transformations fail to deliver. This huge rate of failure is happening because technology is over-prioritised, whilst everything that makes technology actually work – people, processes, culture and mindset – is undervalued.

In short, too many organisations fail to understand that successful data-driven transformation is about instigating a cultural change that requires organisations to truly redefine how their people operate internally and what their processes need to be to support transformation. So, if change is essential for success, where does an organisation start? Here are our six top tips for driving change in data-driven digital transformation:

1. Make a case for change

As a rule, humans and organisations don’t like change. Instead, they find reasons not to make changes happen. This is why, whatever the reason why change may be deemed necessary (perhaps it’s in reaction to a threat or a movement towards an opportunity, either internally or externally), the reason for the change must be articulated in a way that helps others understand why change is necessary, and crucially, why they should be motivated to support data-driven transformation. Furthermore, as obvious as it may seem to the CEO, COO or IT director as to why the change needs to happen, it must be translated so your people (that is, those individuals who are usually most removed from the decision-making process BUT most impacted by the change), can relate to it. This links to the next tip.

2. Shared vision

The leadership team must act as the evangelists for change; connecting people with the ‘change’ story and the vision and purpose for the transformation. A designated ‘chief storyteller’ must describe the future in a way that creates a positive picture of what the change will look like; people need to understand what they will be doing differently when the change is realised in order to truly get behind the process. This means creating meaning to what they will do and how they will do it so they can find their compelling reason to feel inspired, energised, and motivated to deliver their best work. The creation of a firm destination is particularly important at a time when hyped terms, such as AI or ML, can distract from the vision. The result of this approach? Alignment within the business which will drive people to rally around delivering change.

3. Be prepared to be flexible

We all love agile working, and the process of change management should be no different. Agile, loosely defined, is the “ability to move and think quickly and easily.” So, recognise, accept, and even embrace that you can’t plan for everything, and also understand that, as you learn, your plan will evolve. Although in business we’re all very destination-focused, on a data-drive journey, sometimes even your end goal could change, and that’s OK as long as you’ve created a culture of agility. Try to focus on eliminating wasted activity, amplify learnings as you go, be prepared to make decisions late in cycles, and then always strive for fast delivery. You’ll crack this by encouraging a culture of flexibility, iteration and ownership in work across all levels of the change process.

4. Change transition

Driving successful change requires not only understanding where one starts from and where one wants to get to, but just as importantly, recognising what the transitional checkpoints in-between are. Let me use an analogy to explain. You’re on the London underground at South Ken (your current state reality), and you want to get to Kings Cross (your desired end state). You have several routes and options to get you to Kings Cross, so your first decision is, which is the best route for me? Factors at play here could include time and speed of arrival, convenience (number of changes), preferred route, and opinions of others. You decide to take the Piccadilly line, a direct line with eight stops to Kings Cross. While on the tube, you continually check each stop as you come into the station and listen to the driver as they announce the next station. These check-ins reassure you that you are on the right train to Kings Cross, and happily, with no surprises, you arrive at your destination.

Now, the process of transformation is fundamentally more challenging than getting the tube across London, but the point is the same: know your beginning, know your end, know and map out your route, and create your checks and measures to check your progress along the way, all the time managing emotions and expectations as you drive the process of change.

5. Build momentum & provide support

It’s not enough when embarking on the change required to drive transformation to create a cheerleader moment at the start, and then assume everybody is then on board for the whole process. Instead, the leaders in the business have to reinforce the business case continually, staying committed to the compelling shared vision, and communicating the why, the what and the how on an incredibly frequent basis. Make the process repeatable, not just an occasional spark of genius. Turn the wheel and add value. When people are back at their desks doing their day job, how do they utilise what they have learned to add value? They need continued support from the business to achieve the value required.

And don’t just focus on the transformation project alone; instead also think about the bigger picture: how do you want your people to act after the transformation has occurred and we are back into the humdrum world of Business As Usual (BAU)?

6. Re-enforce the vision

Here’s the thing though; in today’s world, there is actually no such thing as BAU. To remain competitive and relevant, it isn’t enough for businesses to implement change, and then believe their work is done. Instead, the challenge is to keep driving forward, to shift old mindsets and behaviours in favour of new, to replace old ways of working with new and improved ones, and to keep a vision (and motivation) for (constant) change alive. Everyone is a decision maker to some degree, and data is the enabler for everyone to do business in an optimal manner, so a shared vision of what a data-driven business looks like is key.

If we accept that data-driven transformation is a given rather than a ‘nice to have’, then ensuring they’re in the 30% of successful digital transformation projects rather than the unsuccessful 70% must be the priority for any organisation’s leadership team. Experience tells us that if you can manage the change management required and have everyone rally round the vision, you’ll be on the path to success.

Written by Rich Pugh, co-founder and chief data scientist at Mango Solutions, and Simon Adams, managing consultant at Nine Feet Tall.

boosting productivity
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Whilst data science teams struggle to adjust to the new norm of working from home, a competent technology platform enabling remote working combined with the right skills and toolkits are essential to remain productive.

The answer lies in looking beyond the technology and looking instead at the culture within the organisation.  Now, more than ever, it is crucial to ensure there is a data-driven culture within the very fabric of the organisation that filters down to all employees in order to compete…

Data is one of the most available, and yet under-utilised, resources within any organisation and a lack of remote technical infrastructure should not be regarded as a reason not to use this data as a means to remain productive or to significantly jeopardise deadlines.

Since the dawn of time, and accelerated through several industrial revolutions, businesses and individuals have aspired to do more with less resources. However, with labour-productivity growth figures at an all-time low perhaps, for the moment at least, organisations are not maximising the ability of current resources where they could be embracing and harnessing technological change. One of the big “resources” of modern businesses, noted for its value but widely regarded as under-utilised, is data.

Boosting productivity with data is one of the biggest challenges in the modern enterprise, and it all comes down to finding a way to extract and maximise value. In the business world, value is all about achieving the maximum productivity possible. This aligns well with the central purposes of data science, which we at Mango define as “the proactive use of data and advanced analytics to drive better decision making.” That is to say, data scientists take existing data resources, and use these to create “more” using analytical techniques. These can be applied to many use cases, but driving productivity from data often falls into a number of key categories – delivering solutions to business problems, reporting in real-time and using trend data to drive future growth.

The first key of successful data science is that it is using data to solve real business problems. This means increasing integration between data and data science teams by fostering collaboration and creative discussions to help them understand what is needed and what is possible. Clarity of purpose and communication enables better understanding of what is needed, and the resources they need to get there. Meanwhile, the business team will not only be able to explain what they need, how and why, but may also know of department specific or team specific data sources which could aid in the creation of a new solution. This allows all teams to work more productively, with data scientists better informed about what the solution is for, and business teams more confident that the solution will work.

However, data science does not always need to be about creating detailed new solutions out of nowhere. Often, given the wealth of data that businesses hold, data scientists can use historical trend data to drive insights in future activities. This can be external facing (such as optimising strategies for engaging with customers) or internal (such as predicting the impact of different hiring patterns or organisational reshuffles). While historical data cannot offer a perfect answer to how a situation may play out in future, previous patterns can help shape models that at least offer a probable explanation of what might happen next. This helps make more informed decisions quicker and more accurately drive company performance.

Thirdly, data scientists can boost productivity both of their own teams and the organisation as a whole by implementing more real-time analytics solutions. These make use of not only historical data, but also look to proactively generate insights alongside action. While data scientists have traditionally been viewed as part of the strategic pipeline – that is, as providers of insight into weighty, considered decisions – the evolution of real-time analytics and streaming data enables data scientists to provide tools that support in a tactical role at great speed in changing conditions. This can support business productivity “on the fly” – such as when dealing with a customer during a complaints call, or with on-tap insights around successes and challenges with existing data science project implementations.

The benefits of data science for boosting productivity are hard to understate, however against the grain of our “as a service” technology era, data science is more than just a plug-and-play solution.  The productivity of a data science team itself, and the business as a whole, relies on more than just tools or training or the right resources. Instead, it comes down to creating a culture of data science – and this is something championed from the top down. Harnessing data as a resource, and then finding a way to use it effectively within the work environment requires businesses to build a foundation of curiosity and an acceptance of the possibilities around data. Creating a thriving data science culture will be the difference between a vital productivity boom and a state of data overload – something worth considering this World Productivity Day.

If your teams are struggling to operate effectively whilst working remotely, please do contact us to how we might support you maintain productivity.

Read the published article in Gigabit Magazine 

seven steps
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In part one of this article featured recently in DataIQ, we looked at why building an analytics community is key to data-driven transformation. The challenge faced by many companies lies in adopting and implementing data-led strategies whilst retaining market share in the face of sleeker, disruptive competitors for whom data is already part of their DNA.

Placing data at the core of organisational strategy requires a fundamental culture shift that is difficult to achieve and maintain without an active and engaged community at its core. The shared sense of purpose and the collaboration a community can deliver helps businesses use data to much greater effect than an organisation that operates in disengaged siloes.

But, building a successful and lasting community culture takes time and effort. Here, we look at some useful steps that organisations can take to establish and maintain a thriving collaborative approach to analytics.


  1. Identify the analytics skills already in place

Many organisations already have analytics experts spread across their business with robust processes, excellent project work and strong – if sometimes underground – communication networks already in play. Finding these people represents the first step in unifying skills and experiences. What’s more, it can bring early advocates on board and help establish a network where relationships and contacts can quickly build to help spread the word that a community investment is being made.


  1. Connect and align the members of an analytic community

Once those experts have been found, they must be connected to each other and actively encouraged to add their voices to the cause. Leaders can facilitate the process by providing opportunities to share and present great ideas to others, to provide general education or practical comparison meetings. These are all great ways to start and it’s a process that offers the opportunity to get participants aligned from the beginning.


  1. Inspire the community to collaborate and create efficiencies

Those initial meetings play a key role in establishing and building the spirit that will begin to bind people together. Incentivised action, in the form of activities such as hackathons, workshops, webinars, or conferences, demonstrates to participants that their nascent community can add value to their working life, culture and wider skillset. In these early stages, don’t forget to prioritise community projects and reward actionable insight generated from them.


  1. Drive conversations about best practices and standardising processes

Early community advocates can quickly become ‘data champions’. They represent an important, engaged and active sub-group, and as such, they should be able to review the results of projects and practice together. Building process standards and best practice habits (and documentation) won’t just keep quality high, it will provide an easier entry point for new people with new ideas.


  1. Leverage the talent in the community via internal and external messaging

Regular communication between the people and processes within the organisation will naturally begin to identify wider needs, and it’s at this point that the net can be spread wider. In doing so, the community can recruit a greater depth of talent inside the organisation to lend their own experiences to data practice, while recruiting from outside to fill gaps.  Encouraging and supporting employees to attend external tech meetups and report back on practices, for example, means ideas and methodologies can then be adopted internally.


  1. Enable a possible conversation about the centralisation or upskilling of analytic talent

At this point, leaders can begin to visualise people and processes as their own skills ‘academy’. From the experience, results and best practices now achieved, it soon becomes practical to form a fully functioning business centre to drive, execute and hugely expand the broader work of the organisation.


  1. Building an analytics community is not achieved overnight and takes time and patience.

Data is the most important tech trend since 1990, and implementing a data driven strategy has never been more important for companies wishing to retain market share in the face of tech-borne, agile competitors. Building a data analytics community which unifies talent across the business provides key benefits and quick wins towards embedding the right culture and building the required capability.

Author: Liz Matthews, Head of Community and Education, Mango Solutions

Read the full article as published in DataIQ.