Fantastic data initiatives and where to find them
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It was great to be at Big Data London last week. Beyond the novelty of interacting with complete humans again (and not just their heads via Teams calls) I was also lucky enough to be asked to present on a topic that I’m passionate about: finding fantastic data initiatives that will generate business value and underpin a data-driven transformation.

In particular, I described the use of ‘data domains’ as a great mechanism to align business and data stakeholders and create an environment in which fantastic initiatives can be discovered. If you missed the talk, here is a quick summary of what was discussed.

The data opportunity

Data is the lifeblood of the digital age – delivering strategic value if allowed to course through the arteries of an organisation. It holds the potential to create smarter, leaner organisations able to survive and thrive in an increasingly competitive (and digital) market.

The Big Data LDN showcased the best and brightest technologies available to deliver on this vision, but the reality is that creating lasting data-driven value is not (just) about the tech. Building a data-driven organisation is more about culture than code, more about change than cloud, and more about strategy than software.

In my talk I focused on a topic that is pivotal to finding and prioritising great initiatives that will deliver lasting value for your business. I spoke about why this isn’t a trivial consideration, before describing our use of ‘data domains’ as a mechanism to create a focal point for discussion of specific data initiatives and a way to improve collaboration between business and data functions. But first, a thought about data literacy…

Becoming a data-literate business

Investing in data literacy within your organisation (building to a universal understanding of the language of data and the opportunity it represents) is an important way to connect both your technical and non-technical teams. By uniting people capable of building new data solutions with stakeholders in other departments, as well as enthusiasts who are intellectually curious about the potential of data, project leads and their IT teams, you begin to form cross-departmental working groups that offer more diversity of thought to address different business scenarios. These communities will come up with your ‘long list’ of initiatives – where ‘fantastic’ can be found.

Identifying fantastic initiatives that drive value

Whilst thinking up data initiatives seems easy, finding the exact right data initiatives is not. There are many reasons why this is difficult, but a major factor has to be the knowledge and experience gap that exists between business stakeholders and data practitioners.

Prioritising the right initiatives is essential to building confidence in the business around new ways of working. And it is incredibly easy to choose the wrong initiative, creating a capability that is disconnect from business behaviours, or something that works really well on a laptop but has no hope of being deployed into production or becoming part of BAU.

The role of data domains

As a team, we get to help customers discover the exact right capabilities to create. To achieve this, we use a range of mechanisms to create the right collaborative environment between data professionals and business stakeholders – in my talk I described the use of Data Domains as an important tool to enable fantastic initiatives to be discovered and prioritised.

We can think of data domains as themes that allow us to connect aspiration business objectives with specific data capabilities. Domains can be used to engage leaders and create a focal point for the discussion of specific initiatives, so that the right business outcomes can be identified and delivered.

Here’s 5 key domains that in my experience cater for the majority of customer scenarios and are highly observable in terms of their impact:

Using data domains takes us away from any language around the potential of data or AI, and instead focuses on the conversation on business aspirations. It also helps to prioritise and focus the conversation – of course, we all want to create more informed, engaging, intelligent, efficient, sustainable businesses but what is the highest priority? If we want to create a more intelligent business, then what are the most important decisions we’d love to get right every time? If we want a more efficient business, then which process would we love to automate?

A case in point. 

One of the best examples of the power of using domains I’ve seen was when working with a major insurance company.  This company had previously invested in data through the lens of a series of innovative initiatives focused on solution areas.  They had invested in ’an AI initiative’, ‘a blockchain initiative’, ‘a big data initiative’ etc.  None of these investments had delivered any value, and the leadership were running out of patience with their data programs.

When we collaborated with them to help them move forward, we looked at the use of data domains to create a more business-focused framework against which to guide investment. We determined that the ‘intelligent’ lens (iteratively enabling more effective decision making) was the best domain to start with – and one that would deliver the impact they were looking for.

We refocused the investment on decision improvement. We identified key decision-making processes across a range of business areas (from resource forecasting to customer call handling), turning them into more effective, data-led decisions.

Engaging stakeholders: building bridges with data domains

The example above was a great success because it created a consistent language across the business for data-led value generation and enabled business stakeholders to more quickly buy into the change required. Designing successful data initiatives using data domains can be an effective way to create a bridge between functional areas successfully aligning business outcomes and data potential. For example formulating what you are trying to do / become vs what you are trying to build.

Are you ‘ready’?

Identifying a fantastic initiative is the first part of the solution – but we need to consider an organisation’s readiness to undertake the initiative. Some tough questions to ask yourself include:

  • Are we ready to implement the change required to realise the value?
  • Do we have the right data available to succeed? How do we address this if not?
  • Do we have the right platform to support this initiative?
  • Do we understand our stakeholder needs well enough? Are we able to see the situation through the right frame(s) of reference?
  • Can we measure and report on our impact?

In summary: 4 step action plan

  • Build data literacy – Invest in building understanding of the data opportunity with non- technical audiences. Spark curiosity and enthusiasm. Connect and unite your technical and non-technical teams. Celebrate ideation.
  • Use data domains to define ‘fantastic’ – Zero in on the impact you are looking to create. What initiatives will deliver this impact? Where will you ‘see’ value – and in what timeframe?
  • Engage stakeholders and build bridges across the knowledge gap – use data domains to engage leaders and create the focal point for discussion of specific initiatives, allowing an idea to become structured and permitting objective evaluation and prioritisation of projects.
  • Develop organisational readiness – Think through critical success factors and objectively assess your readiness to take on the level of change you are proposing. Implement any required corrective action – or resize your ambition accordingly.

Data domains provide a great framework for discussing and evaluating high-impact initiatives with business stakeholders, guiding you towards the most ‘investable’ initiatives that deliver the most value to your business.

Rich Pugh is Mango’s co-founder and Chief Data Scientist. If you would like further discussion around identifying and prioritising the right data initiatives in your organisation, contact us and we’ll be happy to help.


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.



global data science
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2020 has been a year for the history books – unfortunately though for reasons we’d all prefer to forget! Many businesses have experienced a really tough year, but rather than seeing it as one long crisis, I prefer to view it as many different waves of mini-market changes.

To explain, factors like lockdown rules and related government guidance that have and continue to change in accordance with the severity of the Covid-19 pandemic, has led to many different market environments. It’s a constantly evolving reality and those companies who are best able to predict and adapt have been able to steal a march against more flat-footed rivals –  online retailers that now have the definite edge over bricks and mortar retailers are a perfect example. 

Looking inward

As a business owner, the crisis has been a time of doubt, and has prompted an in-depth analysis of costs and revenues. Many organisations have been forced to explore ways of creating more value with existing or fewer resources. Trimming waste from budgets, and ensuring ROI, has been essential for us and many others, and there is a strong desire to embed a data-driven approach to areas that previously might not have been considered. At Mango, conference budgets for instance have been transferred and utilised elsewhere in the business, allowing our data science teams to widen their approaches and deliver more value. 

Implementing a data-driven approach allows organisations to optimise their business effectively, which has helped them to make quicker, effective decisions. In fact, a recent report by Sisense found that 49% of respondents surveyed said analytics were more or much more important than before COVID-19. The changed circumstances has led to a requirement for more agile approaches backed up by predictive analytics. 

Whilst many organisations were in crisis mode in the early parts of the year, the new circumstances have allowed time for consideration and change. Business as usual was never going to be as effective in a swiftly transforming world, and it has created an opportunity for companies to try different things, bring forward innovation and change approaches to markets. We’ve seen technology providers embrace the opportunity by speeding up release cycles and driving their engagement with totally different markets. The ease with which my mum started using Teams for video calls was a fascinating compliment to the developers of that product and I’ve no doubt that the digital revolution for marginalised groups such as the elderly has been enhanced massively.

Looking outward

We work within a range of styles with our customers – some prefer to completely outsource, while others look to us to develop and enhance an existing team. When lockdown bit early, many companies immediately put a halt to recruitment processes, which meant that in order to execute workloads, we were able to help create data science teams for customers to deploy and maintain momentum around data-led initiatives.

Several months on and as we face renewed restrictions, I believe that this time around a lot of organisations will regard it as an opportunity to roll out new methods and move further towards harnessing the power of data science. Covid-19 has provided a stimulus to boards to be creative and flexible since all businesses have been affected. It’s an ideal opportunity for organisations to step up and adapt their business model to take advantage of areas such as innovation and data science, which might well have been on the agenda, but were probably tucked away a bit further down ‘for review’ in a few years’ time. Taking action and investing now is vital and a relative “free hit” for leadership teams.

Virtual is going to be totally dominant from now on. Those companies who have embraced it wholeheartedly will have a massive advantage, and I think we’ll see an acceleration in the adoption of online only business in pretty much every aspect of our lives. We see this as beneficial for Mango in that a transition to a digital approach to business necessitates a primarily analytic led strategy.

Looking forward

With most organisations moving towards a less office-based environment, there are opportunities to change styles of working and this will include how analytic code is held and distributed. This may well involve outsourcing of analytic development, where virtual teams can become extremely effective. In the future we are likely to see more confederacy in teams enabling organisations to extend teams and create focused high delivery groups from different resources. I think we’ll see much more team augmentation with increasingly effective outcomes. It’s exactly why now is the time to invest in data science initiatives.

Author: Matt Aldridge, CEO at Mango Solutions

race to become data-driven
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Many companies are investing in some sort of data project – data analytics, big data, AI, machine learning data science. However, independent data projects do not make you data-driven.

The race to secure a data-driven and robust future is instead an integral part of a strategic journey, where you look to position data to empower and deliver on your business objectives.

As we return to our new normal, the importance of deeper insights has perhaps never been so critical to our decision making. Organisations are under pressure to make the right decisions to enable them to survive and transform their business model in the most appropriate way – this can only be achieved using data and analytic techniques to turn data into value in a repeatable, business-focused manner. Unfortunately, there is no simply plug and play solution to becoming data-driven. Instead, it’s about taking a data-driven approach across the business, putting the information and critical skills you have at the heart of the strategy, supported with the right technology to deliver the fundamental insights you require to not only survive but also thrive in this increasingly competitive landscape.

Here are some top tips that can help businesses succeed with their race to be data-driven:

Embrace a data-driven Future

The race to be data-driven has never been so important with so many businesses emerging from this period faster and hungrier having invested in data and analytics – this produces a new competitive landscape where the more intelligent, efficient and engaged organisations will hold a significant advantage. The need to be data-driven requires leadership alignment and a cultural shift to instigate success, and it needs to happen now. Driving champions that can help instigate data-led actionable change is of paramount importance for the commercial future.

Align Data Investment to Business Outcomes

Data investment has to align with agreed measures of success in business terms. Does the immediate strategy require cost reduction, revenue generation or creating richer experiences to regain customers? Prioritisation at this stage becomes incredibly important. What decisions will drive the most effective results and what is the potential impact of each decision to the organisation? 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 data-driven success looks like.

Upskilling your data and analytic talent

Ensuring you have the right team and skills to scale your analytic initiatives is perhaps one of the most significant challenges you’ll face. What resourcing model is right for the business and how might you best establish a core, centralised best practice team of data professionals? – one community striving in one direction to empower the business and implement data-driven success. A data-driven company is one where the entire organisation leads with data, where data literacy is spread through every tier of the organisation. Defining the skills and competencies against those critical dependencies is essential across every level of your workforce.

Use data to inform any transformation

Workplaces are changing. To evolve effectively and become more agile decisions need to be driven by data. Whether these decisions involve the application of new technology and automation, further investment around digital collaboration or more innovative processes, any implementation needs to be based on actionable data.

Thriving and surviving with a data-driven data strategy is key for success in today’s competitive market, because it presents the ability to make informed decisions and transform quickly based on real insight.

Join Rich Pugh, Chief Data Scientist at Mango and Simon Adams, Change Consultant at Nine Feet Tall, as they discuss the importance of being data driven in this increasingly competitive commercial landscape and why putting data at the heart of your business transformation is imperative if you want to survive and thrive.

The focus for the webinar will be on:

  • Why are organisations racing to become “data-driven”?
  • What exactly does a data-driven organisation look like?
  • What happens if we don’t get there quickly enough?

Join this webinar


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

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

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

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

Join our webinar

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

Register Now

graduate data scientist placement
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Pure Planet Placement

Climate change and the rise of machine learning are two dominating paradigm shifts in today’s business environment. Pure Planet sits at the intersection of the two – it is a data-driven, app-based, renewable energy supplier. They provide clean renewable energy in a new, technology focused way.

Pure Planet are further developing their data science capability, with a hoard of data from their automated chat bot ‘WattBot’, among other sources, they are positioning themselves to gain real value from plumbing this data into business decisions to better support their customers. Mango have been working with Pure Planet and their data science team to build up this capability and have developed the infrastructure (and knowledge) to get this data into the hands of those that need it – be it the marketing department, finance, or the operations teams – they all have access the insights produced.

Thanks to this great relationship, Mango and Pure Planet were able to organise a Graduate Placement and I was able to spend a month integrated into their data science team in Bath.

Consumer Energy is Very Price Sensitive

To a lot of consumers, energy is the same whoever supplies it (provided it is green…) and so price becomes the one of the dominating factors in whether a customer switches to or from, Pure Planet.

With the rise of price comparison websites, evaluating the market and subsequently switching is becoming easier than ever for consumers, and consequently the rate customers are switch is increasing. Ofgem, the UK energy regulator, states: ‘The total number of switches in 2019 for both gas and electricity was the highest recorded since 2003.’ –

Pure Planet knows this, and regularly reviews its price position with respect to the market, but the current process is too manual, not customer specific, and hard to integrate into the data warehouse. Ideally, competitor tariff data could be digested and easily pushed to various parts of the business, such as in finance to assess Pure Planet’s market position for our specific customer base, or to operations as an input in a predictive churn model to assess each customer’s risk of switching.

It is clear just how valuable this data is to making good strategic decisions – it is just a matter of getting it to where it needs to be.

Can We Extract Competitor Quotes?

Market data on prices from all the energy providers in the UK is available to Pure Planet from a third party supplier, making it possible to get data on the whole market. Currently, it is possible to manually get discrete High/Medium/Low usage quotes only. These are average usages defined by Ofgem.

An alternative was found by accessing the underlying data itself and re-building quotes. This would allow us to reconstruct quotes for the whole market for any given customer usage – far more useful when looking at our real position in the market for our customers.

The data exists in two tables: tariff data and discount data. From this it should be possible to reconstruct any quote from any supplier.

An Introduction to the Tariff Data

The two data files consist of the tariff data and the discount data.

The tariff data gives the standing charge and per-kilowatt hour cost of a given fuel, for a given region, for each tariff plan. This is further filtered by meter type (Standard, or Economy 7), single/dual fuel (if both gas and electricity are supplier, or just one), and payment method (monthly direct debit, on receipt of bill, etc.). Tariff data is further complicated by the Inclusion of Economy 7 night rates, and multi-tiered tariffs.

The discount data describes the value of a given discount, and on what tariffs the discount applies. This is typically broken down into a unique ID containing the company and tariff plan, along with the same filters as above.

Most quotes rely on discounts to both entice customers in, and to offer competitive rates. As a result, they are key to generating competitor quotes. However, joining the discount and tariff data correctly, to align a discount with the correct tariff it applies to, presented a significant challenge during this project.

The way the discounts had been encoded meant that it was impossible for a machine to join them to the tariffs without some help. To solve this problem a function had to be developed that captured all the possible scenarios and transformed the discounts into a more standard data structure.

The Two Deliverables

After an initial investigation phase, two key deliverables were determined. The first was a python package to help the users easily process the discounts data into a form that could easily and accurately join onto the tariff data. The second was a robust understanding of how quotes can be generated from the data. The idea being the package would be used in the ETL stage to process the data before storing it in the data base, and the knowledge would be mapped from python to SQL and applied when fetching a quote in other processes.

Although most tariffs and discounts were straight forward, for the few remaining there were several complications. As ever in life, it was these tricky ones that were the most interesting from a commercial perspective – hence the need to get this right!

The Methodology

Investigation and package development were undertaken in Jupyter notebooks, written in python, primarily using the `pandas` package. Here, functions were developed to process the discounts data into the preferred form. During development, tests were written with the `pytest` framework to check the function was doing the logic as intended. Each test tested a specific piece of logic as it was added to the function. This was a true blessing, as on more than one occasion the whole function needed re-writing as new edge cases were found, proving initial assumptions wrong. The new function was simply run through all the previous tests to check it still worked, saving vast amounts of time, and ensuring robustness for future development and deployment.

Once developed, the functions (along with their tests) were structured into a python package. Clear documentation was written to describe both the function logic, but also higher-level how-to guides to enable use of the package. All development was under version control using git and pushed to bit bucket for sharing with the Pure Planet data team.

Pure Planet uses Amazon Web Services for their cloud infrastructure, and as a result I became much more aware of this technology and what it can do. For example, using the Amazon Web Services Client to access data stored in shared S3 buckets. It was great to see how their data pipeline was set up, and just how effective it was.

To prove the understanding of how quotes were built up, a notebook was written to validate generated quotes by comparing these to the quote data fetched manually. This incorporated the newly developed package to processes the discount data and join this to the tariff data, followed by implementing the quote logic in pandas to generate quotes. It was then possible to compare the generated quotes to the manual quotes to prove the success of the project.

And Finally…

Big thanks to Doug Ashton and the fellow Data Science team at Pure Planet for making my time there so enjoyable. I really felt part of the team from day one. I would also like to extend my thanks to those at Mango and Pure Planet who made this graduate placement opportunity possible.

Author: Duncan Leng, Graduate Data Scientist at Mango

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Aligning effective stakeholder engagement as a driving force to delivering business value from data science.


Opening up the opportunities with data

If you’re trying to help your organisation become more data-driven you’ll almost certainly have come across pockets of indifference, or active resistance, to integrating data & analytics into the way their team works.

While it can be tempting to leave those teams behind in the hope that they will jump on the bandwagon when success stories from the more enthusiastic areas of the business start to be generated, in reality their inertia tends to slow the pace of organisational change down even indirectly. Instead you have to tread a middle ground – one where you don’t expend too much energy trying to enthuse a hard to please audience but do enough to keep them on board in an efficient way.

To achieve this it’s important to understand and address the reasons colleagues may not see the value in data, and the motivations of those who actively push back on it.


Effective stakeholder engagement: Making sure advanced analytics is properly understood

It can be frustrating trying to engage colleagues who don’t understand the value data can bring them. To someone passionate about the possibilities of analytics it seems so obvious! In my experience the best way to solve this is to focus the conversation on trying to understand their role and the issues they are facing so that you can make that connection between business value and data yourself, rather than trying to explain analytics and asking them to make that leap between abstract concept and practical outcome. If they won’t engage then sharing case studies from the relevant areas of other organisations is a great way to warm them up to the conversation.

Data detractors are even harder to work with – why are they resisting positive change when all we’re trying to do is help? The answer to this tends to be one of two things – either they have been burned by failed attempts at data-driven change in the past, or they feel threatened by the idea of data playing a larger role in decision making processes.


Early adopters

The only way to bring round the first group is to prove that value can be delivered successfully, words won’t matter to those with entrenched views whereas actions just might. Start small and make sure you do something that adds value to them early in the journey, you’ll either gain their buy-in or, at worst, build up a track record of success that reduces their negative influence on your ability to progress.

The second group most often feel threatened because they perceive the consequence of increased organisational reliance on data over intuition as reducing their role and power. A senior leader will often see the value they bring as being their ability to make good decisions based on the experience and knowledge they have built up over their career, telling them that data can make better decisions can therefore be seen as an almost existential threat to their position! The key here is to be sensitive to this – try to engage them 1-1 where they are less exposed to being shown up in front of colleagues and show them how data can augment, rather than replace, their experience in decision making.


Aligning analytic opportunities and business strategy

Often the confusion around the jargon of data science causes a lack of understanding and a propensity to create barriers from within the business.  Through a deeper understanding, business leaders can understand the value and potential that advanced analytics can bring. Mango works with businesses to help inspire and align effective stakeholder engagement to the possibilities of becoming a data-driven organisation.

Keen to know more?  Our Never Mind the Buzzwords webinar provides an insight into the benefits of close alignment between analytic opportunities and business strategy and could help address any specific barriers to change.

Author : Dave Gardner, Deputy Director