fraud in finance
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Attending the recent Transform Finance ‘Virtual Fraud in Financial Services’ online event was a great opportunity to share experiences and knowledge with experts from across a highly innovative industry.

The event took in a range of critical themes, including our session with Cifas focusing on ‘Using Advanced Analytics To Detect, Prevent And Tackle Fraud’. Reflecting on the experience overall, there were a number of key takeaways that illustrate both the challenges and opportunities facing an industry increasingly reliant on technology to deliver effective services while tackling some major obstacles to success:

  1. The Changing Fraud Landscape

Many organisations are investing heavily in their use of data and technology to positively prevent and detect crime and fraud. This is essential as the emerging trends are worrying – The 2021 Identity Fraud Study revealed the true scale of identity fraud scams to consumers and businesses alike. While total combined fraud losses climbed to $56 billion in 2020, identity fraud scams accounted for $43 billion of that cost with ‘traditional’ identity fraud losses totalling $13 billion.

With fraudsters becoming more sophisticated, it is essential that organisations across the diverse finance industry constantly adapt to change with technology and application of data.

  1. Innovation And Cooperation

In a changing world, it’s clear that financial institutions understand the need for greater collaboration and data sharing across the industry. There is growing recognition that a more joined up approach to issues such as fraud detection and prevention is key to delivering effective outcomes. As an example, cooperation between ‘core’ public and private finance organisations with third party organisations, including regulators, mobile providers and social media companies is essential best practice when it comes to reducing the incidences and impact of fraud.

This extends to the wider implementation of technologies such as digital signatures and document sharing through digital channels and the development of digital currency for financial inclusion. In all these emerging areas, working together is key, while the application and proactive use of data, good management, metrics and governance can help ensure success and make sure that industry-wide goals in relation to fraud are clear.

  1. Tackling Major Fraud Trends

As part of our contribution towards the Transform Finance event, our Chief Data Scientist, Rich Pugh and Sandra Peaston, Director of Research & Development at Mango customer, Cifas, joined a panel session to discuss the use of data and intelligence to support fraud prevention.

Among the areas discussed, the panel agreed that it is essential that the industry operates from a level playing field and by sharing intelligence via organisations such as Cifas. In doing so, finance businesses across the sector are much better placed to adapt to new trends. For instance, an approach called ‘transfer learning’ can enable small organisations to benefit from the insight and data generated by larger businesses to react more quickly and effectively. There should also be ongoing efforts to improve customer education and awareness, reinforcing the idea that individuals always need to remain vigilant.

Looking to the future, data and technology will play an increasing role in combating and detecting crime. By maximising collaboration between corporates, banks, regulators and other key stakeholders, the industry will move towards a scenario where fraud detection happens in real time to minimise risk and loss.

To achieve these goals, organisations must focus on improving their capabilities, modernising fraud operations and maximising the technical competency of their teams. Those that do will be ideally placed to play a full and effective role in tackling fraud while gearing for growth.

For more information on how Mango is supporting working and detecting financial crime, read our Cifas case study.

 

financial fraud
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The recent Transform Finance ‘Virtual Fraud in Financial Services’ event offered some fascinating insight into the risks facing the sector and how organisations are investing in advanced technologies to detect, prevent and tackle fraud.

An important and recurring theme was the role of data analytics in meeting the challenges presented by rising levels of fraud and the increasing sophistication of fraudsters. Bringing insight and experience to life for attendees, Mango Chief Data Scientist, Rich Pugh, was joined by Sandra Peaston, Director of Research & Development at Cifas to discuss their use of data and intelligence to support fraud prevention.

Cifas is the UK’s leading fraud prevention service, managing the largest database of instances of fraudulent conduct in the country. Its members are organisations from all sectors, sharing their data across to reduce instances of fraud and financial crime.

As a data-centric organisation, Cifas wanted to develop deeper insight into emerging fraud trends, understand which were the most significant and then quickly share that information with its members for further action.

Getting ahead of the game was key, and as Sandra Peaston described, “We wanted to use our data to speed up the early-stage intelligence process so our members didn’t need to report trends to us. Unlocking the power of the data we already hold was the challenge that took us to Mango.”

Having been approached by Cifas, Mango quickly deployed a team of data scientists to establish the right technical environment. As Rich Pugh explained, “The Cifas team has amassed some incredible data assets, but with many areas of potential focus the key question was: where could we deliver quick impacts against their priorities?”

The Mango project team focused on two core areas. The first was a ‘Match’ project, built to reduce false positive rates and improve the Cifas rules engine. This was supported via the development of a probabilistic matching engine prototype, designed to improve the existing matching and reduce member friction.

The second part of the solution was an ‘Intelligence’ project. This focused on the development of a fuzzy search capability and a signal detection tool to automate the previous manual fraud detection processes to uncover hidden and emerging fraud patterns. This insight would then be used to enrich intelligence and feedback to members.

As Sandra explained to event attendees, “We needed an intelligent way of dynamically identifying an emerging fraud trend, and key to this was the speed at which this happens. By working with Mango to uncover the huge power that sits within our data to a level of granularity that we couldn’t manage before, we can help members to prioritise and make them more efficient.”

Together, Cifas and Mango have deployed a best-practice framework using intelligence tools that demonstrably reveal hidden patterns that human beings would struggle to detect. Looking ahead, the teams will continue to innovate and use data science to unlock insight relating to fraud and e-crime, refining algorithms over time to become even more effective in countering criminal activity and finding ways to stay ahead of malicious actors.

RStudio Managed Service
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Author: Rich Adams, RStudio Partner Manager

Free webinar: How to successfully manage your R environment – the RStudio managed service platform (22nd July @ 4PM BST)

In a free session on Thursday 22nd July, we’ll be discussing how data science teams can confidently and securely collaborate with large data sets in R, supported  with the right expertise where capacity or skills may otherwise be lacking internally.

With guest speakers Lou Bajuk, Director of Product Marketing, RStudio and Will Yuill, Principal Public Health Analyst, Hertfordshire County Council, we’ll explore how data science teams can develop a best practice managed service production environment and achieve maximum return on investment from their data science cloud platform. Register here

What’s the webinar about?

 As a language, R can come with restrictions when it comes to the implementation and necessary technical know-how of installing, configuring, and supporting a centralised platform for maximum adoption.

Many teams lack the required support from IT or the necessary knowledge that makes an environment suitable for future scalability. This can impact a team in their ability to manage large data sets, collaborate with ease and often mean a duplication of effort.

This webinar focuses on how to develop a best practice production environment, ensuring technical excellence and maximum return on investment from your data science platform.

Also under discussion is:

  • How to effectively reduce barriers to scaling your R environment through a ‘RStudio Managed service’
  • How Hertfordshire County Council overcame their barriers through the extra pressure of Covid-19 through a managed Services platform

Why is it important?

As we have seen from this year, scaling of data science teams and investment in data-driven strategies is even more crucial than ever.

If like Hertfordshire County Council your team has seen a rapid development, yet you lack the internal expertise and resources to support an RStudio environment – a managed services platform maybe the secure, compliant and effective cloud environment that can be up and running effectively almost immediately.  This expert Managed Service removes the need for specialist in-house IT expertise and guarantees a service level agreement to meet your requirements in terms of configuration, maintenance, and system updates.

Can you join us on 22th July, 4pm to learn more?

The Public Health Evidence & Intelligence Team at Hertfordshire Country Council will discuss why this is already providing an effective solution for them.

Register for the webinar here 

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Stewart Smythe at Ascent asks whether greater mutual understanding is needed to underpin the technology industry

In a commercial context, empathy is a theme that relates to a whole range of interactions from leadership to customer service and experience design, but in the technology services industry, it’s something of a left-field concept that is often overwhelmed by a focus on the solution. Tech companies everywhere talk about ‘understanding’ their customers, but how many invest more deeply than that?

Arguably, and particularly in the shadow of universal adversity that Covid cast over the last year, humanising the relentless digital challenge has become even more relevant – and critical.

Making empathy the cornerstone of a technology proposition allows organisations to create space for a different kind of customer relationship – grown up, resilient, flexible, commercially agile, constantly mindful of its fundamental aims. But how have some tech organisations arrived at this point when others seem to view the last 12 months as little more than a rollercoaster ride that will soon return them to a safe and familiar starting point?

Undoubtedly, the pandemic has hastened the pace of change across the technology landscape, but in much broader and more nuanced ways than the headlines about remote working and rapid product development would have us believe.

Some technology businesses have taken the opportunity to rethink their approach to customer relationships and have learnt that working closely together in exceptional circumstances brings out the best in everyone. In contrast, others have had to be more tactical to protect revenues and sustain momentum, employing short term promotions and heavy discounting to keep spending going.

In my business, we allowed empathy to guide the investments and changes we made in our business model, and we made different and longer-range choices based on what Covid took from customer strategies: certainty and confidence.
We did three fundamental things as part of this approach.
We wrote new contracts that put a proportion of our revenues at risk against our customer commitments to make it easier for customers to make technology investments during uncertain times.

We recut our delivery phasing to get value into the hands of customers even earlier, helping them build confidence in the relationship and see a return more quickly.

We adjusted our resourcing approach and made some new hires to ensure we were in a position to guarantee resource continuity with customers across longer programmes, sustaining existing team dynamics and maintaining consistency and predictability.

Building empathy into the business model is an approach that gives technology partners the opportunity to fully live up to their core values, taking on the weight of customer responsibility and really understanding their pressures and drivers. Any good business builds meaningful relationships with customers – but going a step further and working with customers to define success, and failure, on their own terms is an opportunity to contribute more value.

In short, mutual understanding, or empathy, is not just a way for technology partners to behave in an emergency, it’s an exceptionally effective business model for the long term. Those who have seen the best from adaptable technology partners are sharing their experiences as the model for future relationships and, for them, there’s no going back.

Published in: Business Reporter

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Towards the start of my placement, I was introduced to Shiny apps: {shiny} is an R package which allows users to create applications directly within R. I knew that this was something I wanted to learn more about. Additionally, I was introduced to Git, a version control system which is a best practice within data science and software development. As a result, I started a personal project with the aim of creating a Shiny app as a tool for learning Git, its main target audience being new Graduates and Placement Students.

The project had two main goals: the first to create an app for learning how to use Git locally; the second to expand on this to include remote Git.

Goal 1: Local Git

To create the app as an R package, I decided to use the framework provided by the R package {golem}. Using this framework had many advantages including keeping track of dependencies and easy app modularisation. I used several other packages when creating the app, including {shinydashboard} to allow for a dashboard layout. The code for each page of the dashboard is contained within its own module, which means that the code is well organised and easy to read.

One challenge I faced was the development of a few UI features. Most of these were solved by using the package {shinyjs} which allows users to improve Shiny apps using JavaScript. I used this to hide and disable relevant action buttons and when creating a bottom navigation bar. This navigation bar is used to move between the pages of the dashboard. This proved difficult, but with the help of the open-source community I was able to resolve the issue, creating a key feature of the app’s UI.

After developing the first stage of the app, I gave a demonstration to data science colleagues who gave positive feedback, with ideas for the future development of the app. Once the first version of the app was complete, it was time to test it. I used the package {shinytest} to automate the testing via a snapshot-based testing strategy. Once the tests passed, I finally deployed the app using RStudio Connect, which allows users to access it via a URL. I also deployed it via shinyapps.io.

Following the completion of the first version of the app, I gave a presentation at the BarcelonaR conference, demonstrating the app and the code behind it. The code for this version can be found on GitHub along with smaller example apps.

Goal 2: Remote Git

The next, and major release, of the app continues the materials for learning Git both locally and remotely. It also includes a project designed to introduce the user to the concept of an Agile framework, as well as a practical scenario for using Git.

Moreover, I created a help page and took the opportunity to learn how to send emails from within a Shiny app. This was a successful learning exercise, however the main challenge that followed this was its maintenance. Eventually I removed this feature and instead created a help page that contains a number of Git references.

Towards the end of the development, I gave another demonstration to data science colleagues, receiving positive feedback. The app also received direct user testing from a new Graduate. Once some changes were made based on this feedback, I tested the app again using {shinytest}. Finally, I deployed this via RStudio Connect, ready to be used.

Results from the Project

I learnt a lot from this project, such as how to create a production grade Shiny app, best practices for using Git, and R package building. Moreover, the app will help users gain knowledge and experience of using Git for version control, specifically Graduates and Placement Students.

Since finishing the project, I have continued to expand my knowledge of the Shiny ecosystem by exploring code profiling, load balancing and load testing. {shiny} is an excellent package, allowing for flexibility and creativity.

 

world environment day
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First held back in 1974, World Environment Day has become a powerful way for the United Nations to engage with “governments, businesses and citizens in an effort to address pressing environmental issues.” This year, a key theme is #GenerationRestoration and also sees the launch of the UN Decade on Ecosystem Restoration: a global rallying cry for everyone to do their part in healing the planet.

Back in the 70s, the prospect of a looming global environmental crisis was, for most people, simply not among the major issues of the day. Experts and activists aside, few could have imagined the pace at which events would develop and how urgent the need for action would become.

In a similar way, half a century ago, the idea that technology could have a positive impact on protecting the planet was much closer to science fiction than reality. Today, however, the world looks to digital innovation as one of the main strategies to combat climate change, with data science among the industries now playing a vital role on both a macro and micro scale.

Globally, data scientists blend human expertise with technology to assess data and review the impact of problems causing climate change. This insight informs government policy, which then filters across the economy and society to deliver meaningful impact. In the UK, for example, it is now national policy to cut carbon emissions by 78% by 2035, and data science will play a key ongoing role in the further development of policies in the years ahead.

On a day-to-day basis, businesses everywhere will need to make a major contribution if this target is to be reached. The sustainability of every organisation depends on addressing the impact of its operations across the supply chain. Everything from water consumption, pollution and plastic reduction, to carbon emissions, waste and recycling, is part of the equation – and data science modelling is increasingly being used by businesses to assess the likely impact of their actions and the quality of decision-making.

In recent years, the Mango team has worked on sustainability projects to monitor and measure world poverty, reduce water waste and to understand the proportion of electricity generated using low-carbon sources. We remain committed to broadening the availability of data science expertise and technology to make a difference to ensure data science empowers #GenerationRestoration.

Delivery by Mango

Protecting and preserving biodiversity

Mango built and mentored several data science production projects across the ONS including the United Nations funded Sustainability Development Goals, a system to monitor and measure world poverty based on 19 sustainable goals, from ending hunger and poverty to achieving sustainable energy and gender equality to protecting and preserving biodiversity. Data science is a powerful tool which can be used to inform businesses and improve their water consumption as well as having world-wide applications in reaching the UN targets of providing clean, accessible water to all.

Reducing water waste

Mango helped i20 realise their data capabilities but develop a solution that greatly improved the performance of their smart network solutions, leading them into the world of AI and data analytics. This has enabled water companies to shift to conditional based maintenance and reduce the number of water leaks. One client reduced leakage by 15% in the north of their city within 2 weeks of using the solution.

Enabling the adoption of renewable energy

Britain’s first all-digital, renewable energy supplier Pure Planet choose to work with Mango to harness their data to drive data-driven pricing solutions and help to drive service efficiency. Mango added value in terms of broadening the scope and skills of the data science team and in helping them to establish common frameworks and processes to make data science easier with repeatable and scalable models.

Assessing the proportion of low carbon sources of energy

Mango were involved in a project that assessed the proportion of electricity generated using low-carbon sources including solar, wind, hydro or nuclear. All the data and deployment workflows were developed to schedule daily updates of carbon intensity data and re-deploying the app when the data was pushed to main branch.

Data science is used widely in business as an integral part of a businesses to positively impact change in a number of ways https://earth.org/data_visualization/ai-can-it-help-achieve-environmental-sustainable/

Data scientists, looking to add their support to the wider effort to protect the environment, can get more information from the Open Sustainable Technology website which provides a list of all sustainable, open, and actively maintained technology projects worldwide and details of how to get involved.

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.

 

 

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

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

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

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

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

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

Growing demand for data science

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

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

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

demand forecasting this easter
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Using data to accurately forecast demand for chocolate this Easter

Easter is a significant holiday for many businesses especially within the UK. The holiday period brings an increase to food and drink sales, with Easter being the second most popular period of the year for chocolate consumption. Many Britons book holiday trips in this period as well, making it an important time for the leisure industry.

Data science enables businesses to effectively plan for this busy period. Analysis of historical sales data can be used to predict future demand, allowing businesses to accurately plan stock levels; real-time analysis provides visibility of the state of a product, enabling businesses to quickly resolve issues regarding the manufacturing of products like chocolate bunnies.

Accurate forecasting to match demand

Demand forecasting is a commonly used approach which allows businesses to effectively predict future sales, plan and schedule production, improve budget planning, and develop efficient pricing strategies. Predictive analysis is used to understand and forecast demand over time, helping businesses make well-informed decisions.

Adapting in line with the coronavirus pandemic

The COVID-19 pandemic has brought great challenges within businesses. Easter brings challenges itself with the date of the holiday moving each year, however in 2020, businesses were simply not prepared for the impact of the pandemic. Easter egg sales fell by £36 million with many retailers having to sell lots of eggs at discounted prices. Some retailers were also unprepared for the boom in online sales and were not able to meet demand. On the upside, many businesses are better prepared for this year’s Easter period, with many focussing on online operations. Through demand forecasting techniques and last year’s data, businesses have been able to better prepare for this year’s demand. Many, for example Hotel Chocolat, are offering a limited range of Easter eggs this year.

As well as benefitting the retail and leisure industries, demand forecasting is used by other organisations over Easter. The NHS use forecasting techniques to predict demand and capacity for their services. This has been particularly important during the pandemic. In the January peak, NHS hospitals were caring for over 34,000 COVID-19 patients in England, approximately 80% higher than the first peak in 2020. Demand forecasting and mathematical models are being used to predict hospital bed demands frequently, tailored to specific hospitals, to help the NHS and government plan for future holiday periods such as Easter.

Demand forecasting is an effective approach that is being used by many businesses to plan for this year’s Easter holiday. Data-driven businesses can make well-informed decisions for the future, and as a result many will be better prepared this Easter.

Can we help with any aspect of your demand forecasting? Read our case study to find out how we have helped other companies with this.

world water day
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As we celebrate World Water Day, we consider the lack of access to safe and affordable water – a dangerous reality for billions across the world. It has a damaging effect on not only the health of billions of people but also on many aspects of their lives. With climate change comes droughts, floods and scarcity of water, bringing with it social and economic devastation. For example, clean water and sanitation is an important factor in reaching equity amongst gender.

By 2030, the UN aims to achieve universal and equitable access to safe and affordable drinking water and adequate sanitation and hygiene for all. To reach these targets, it is vital that businesses create and achieve their own targets for water consumption to help make a positive change. Unfortunately, many of these targets are not being met.

Using the power of data to make a positive change

In data-driven world, data science allows us to harness the power of data to make positive change. It can be used in several ways to improve water quality and water consumption around the world:

  • The innovation of machine learning can be used to improve the way in which water is collected and transported to reduce CO2 emissions. Moreover, it can be used to improve the treatment and utilization of water.
  • Real-time monitoring gives communities the power to ensure water is safe to drink while saving on resources.
  • Data analysis allows predictions to be made about the quality of water given a number of factors like weather and pollution. This allows for better planning when it comes to supplying clean drinking water to those in need.
  • Identification of water supply issues can be significant in preventing the spread of diseases through water supplies.

Mango realises the importance of using data to help other businesses bring positive change to the world. Data science can be used to inform businesses on their water targets and make steps towards reaching them. As well as harnessing data, it is equally important to involve stakeholders in decision making to identify, understand and overcome water challenges.

Reducing water waste

Water waste is a problem that many businesses face, with more than 25% of water wasted being due to leaks. This can be significantly reduced through the use of data science and in turn help businesses reduce their water consumption.

One company, i20, provides smart network solutions designed to help water companies reduce their leaks and bursts, energy use and CO2 emissions. The company recognised that it was collecting a large amount of data but were not harnessing it. Mango were able to not only help i20 realise their data capabilities but develop a solution that greatly improved the performance of their smart network solutions, leading them into the world of AI and data analytics. This has enabled water companies to shift to conditional based maintenance and reduce the number of water leaks. One client reduced leakage by 15% in the north of their city within 2 weeks of using the solution.

Data science is a powerful tool which can be used to inform businesses and improve their water consumption as well as having world-wide applications in reaching the UN targets of providing clean, accessible water to all.

Find out more about how Mango has helped i2o harness their data.