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Recently, I had the pleasure of co-facilitating a discussion around the obstacles faced by data professionals when it comes to collaborating with other teams in a data context at the Chief Data, Analytics Officers & Influencers (CDAOI) event in London, a popular industry event attended by over 200 senior decision makers from across Europe.

This particular topic is one that’s close to my heart, because at Mango we’ve made it our mission to empower organisations to make informed decisions using data science and advanced analytics to drive bigger gains, lower costs, and maximise performance. And absolutely key to this is enabling the fundamental culture-shift needed to realise the potential of the insights that data can generate.

What made the discussion even more interesting is the impact that data-driven digital transformation is having on the role of the Chief Data Officer (CDO) and Chief Analytics Officer (CAO). Historically the roles have always focused on data collection, storage and management. But, as businesses increasingly embrace a data-centric culture, the C-Suite is making space for CDOs and CAOs who have the leadership and creative skills to foster collaboration across skills and teams, helping to embed a data culture into the heart of a business.

Feeding into the CDAOI event’s mission to champion practical strategies for aligning analytics projects with corporate objectives and embed a business-wide data culture, as well as discussing barriers experienced by data professionals, we also debated solutions to overcome these in the quest to find common ground and promote better understanding and collaboration.

Here are a few of the highlights from our discussion:

  • There is a fair amount of box-ticking going on. i.e. everyone has a big data project or tool, so I will get one too. Culturally this is dangerous, as it has the potential to isolate the very people you want to include in the digital transformation process.
  • We’re training our team to not refer to ‘data’ per se, but rather talk about ‘business problems’. Framing the conversation in a more colloquial way that is more relatable creates more opportunity to introduce data as a way to collectively help address business challenges.
  • Many organisations don’t seem to consider the need to allocate budget to drive a culture change. The desire for change is there, but they seem reluctant to invest in the kind of education and awareness that’s needed to shift the organisation’s cultural needle towards data-driven digital transformation.
  • Change must not be too scary. Step-by-step, making the change fit for purpose, constantly championed by the C-suite, will result in success.”

According to a recent survey undertaken by EY and Nimbus Ninety, 81% of senior executives interviewed agreed that data should be at the heart of all decision-making, but just 31% had actually taken the step to restructure their organization to achieve this. That leaves a huge majority of organisations who recognize the potential of data but have yet to find a way to embed a data driven culture within their business.

If you would like to find out more about how to embed a data-driven culture in your business, chat to us. We’ll work with you through each aspect of achieving your data science capability.











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The first thing that hit me about this year’s Google Next was the scale. Thousands of people wandering around Moscone Centre in San Fran with their name badges proudly displayed really brought home that this is a platform of which its users are proud. I was told by several people that the size of the conference has doubled in size year on year, which, given there were 35,000 people at this year’s event, may well prove a logistical nightmare next year.

I was really struck by the massive enthusiasm of the Google team, the way in which the company’s leadership is aware of Google’s market position and how they seem to be keen to take pragmatic and strategic decisions based on where they are, versus where they might like to be. The opening up to other cloud platforms via the Anthos announcement seems a good way for Google to position itself as the honest broker in the field – they have identified legacy apps and codebases as difficult to turnover, something which I think many organisations will feel comfortable.

There were rafts of customer testimonials and whilst many of them did not seem to contain much in the way of ROI details, the mere fact that Google could call these C-level Fortune 500 companies onto the stage speaks towards a clarity of intent and purpose.

The nature of many of these types of events is one that is fairly broad, and considering that Mango is a relatively niche player, it is sometimes difficult to find the areas and talks that may resonate with our posture. That was true of these sessions., but not entirely surprising.  The widescale abuse of terms like AI and Machine Learning carries on apace, and we at Mango need to find ways to gently persuade people that when they think of AI, they’re actually meaning Machine Learning, and when they talk about Machine Learning they might well be talking about, well, models. The current push in the industry is to be able to add these complex components at a click of a button, by an untrained analyst who can then roll it into production without proper validation or testing. These are dangerous situations and reminded me of the importance of doing some of the hard yards first i.e creating an analytic culture and community to ensure that the “right” analytics are used with the “right” data. It’s clear however that the opening up of cloud platforms is creating an arena in which advanced analytics will play a crucial role, and presents massive opportunities for Mango in working with Google and our customers.

We’ve loved being back in San Francisco and its always lovely to be around passionate and energetic advocates. Hopefully London Next later in the year will be equally energetic and fun.

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KISS (Keep It Simple Stupid) in Data Science

Helen Tanner, from Data3, gave an interesting presentation on the value of making simplicity the priority when choosing metrics and models. Data3’s mission statement is to help businesses get more value from their data. Helen recalled two examples where Data3 opted for metrics that were easy to understand metrics rather than the complex alternatives proposed by academic papers. These simple metrics were quickly adopted by business leaders.

Helen also described the problems of explaining unsupervised models to key business stakeholders. Being complex and unsupervised, these models seem like “black boxes” to businesses.  Helen then revealed two cases where Data3 opted to use a Decision Tree supervised learning model over better performing unsupervised learning models. Data 3 chose to use decision tree models as their mechanics rely on thresholds, which can readily be translated into tree-diagrams and bar-charts to help business stakeholders understand how the model works.

Voice Search – The Stats behind the Hype

Kevin Mason, Strategy Director at Proctor + Stevenson, presented his analysis on the value of investing in voice search. Kevin listed many examples of the hype around voice search as the next big trend in consumer technology. The focus of Kevin’s analysis was Google’s claim that “20% of all searches on mobile devices now use voice search”.  Kevin outlined his reasons for being sceptical of this claim.

Building on the work done by Will Critchlow (CEO of Distilled), Kevin broke Google’s “voice search” into four categories – control actions (“call mum”) , informal repeated queries (“will it rain today?”) , personal searches (“play my wedding video”) and real search (“where is the best Pizza in my area?”).

Real searches only account for only 19% of all of Google’s claimed voice searches, leaving actual voice searches on mobile devices at 4%. With 96% of search coming through text, Kevin now advises most businesses that voice search optimization is not a good return on investment. However, Kevin also revealed the bias in voice search towards local businesses and how they could benefit from investing in search optimization.

Approaches to address matching 

Nigel Legg, of Knowtext, gave us a fascinating insight into the problems faced by the UK Government when trying to match addresses to people. Nigel showed us an example of the same address presented in five different ways.

Nigel then presented three different approaches that his team had tried to match addresses with more accuracy. The outcome of his team’s analysis was that a Complex SQL model, produced by Dr Hufton at the Department for Community and Local Government, performed the best compared to a Conditional Random Field Machine Learning model and the commonly used Levenshtein distance algorithm. His team are now testing the SQL model’s performance at scale and resilience to new types of addresses.

It was a great evening followed by networking and free drinks – if you’d like to join us at the next Bristol DS meet up, visit our site for more information and to also view the slides from the meetup.


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We are thrilled to announce that Julia Silge from Stack Overflow will join us at EARL London (10-12 September) as a keynote speaker. After wowing us all at EARL Seattle last year we knew we had to get Julia over to London!

About Julia Silge

Julia Silge is a data scientist at Stack Overflow, with a PhD in astrophysics and an abiding love for Jane Austen. She is both an international keynote speaker and a real-world practitioner focusing on data analysis and machine learning practice. She is the author of Text Mining with R, with her coauthor David Robinson. She loves making beautiful charts and communicating about technical topics with diverse audiences.

We will be shortly interviewing Julia to find out her views on all things R and what she is looking forward to at this year’s EARL.

If you’d like to join Julia as a speaker you have until 8 April to submit your abstract!

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Amid stronger business competition than ever before, companies need to do more than simply embrace buzzwords or trends. It’s something we see all the time when out in the field talking to customers, or speaking at events. When it comes to the role of data, the emphasis should instead be on instilling transformation into the very DNA of an organisation.

Quick fixes are not the order of the day and, while the utilisation of tools such as Artificial Intelligence (AI) and Machine Learning (ML) may reap initial rewards, focus needs to switch to a longer term, more all-encompassing cultural shift surrounding data analytics.

This is, and has been, Mango’s view over the past 16 years, and is one that’s expanded on in detail by Rich Pugh, Mango’s chief data scientist and co-founder, and CEO Matt Aldridge, in the Future of Data Report, recently published in The Times. According to Rich, the notion that ideas like AI or ML can just be plugged in and the company then watches as money pours out of their servers is dangerous. But at least it’s opened the door to having the conversation about how companies can become data driven. “Our organisation is focused on facilitating these conversations that we believe should have been occurring 16 years ago, so we can help companies avoid quick buzzword-led reactions and instead strive for a cultural transformation based on data. The question for all reverts to ‘where are you on your data-driven journey and what’s the best way forward for your company?”

Download the Future of Data Report, as seen in the Raconteur in The Times, to read Rich and Matt’s full article. Other data-focused topics covered in this comprehensive 16-page report include the ‘data versus humans conflict’, the new discipline of ‘infonomics’, the use of AI for creating value from unstructured data, the future Data Scientist, and an infographic that tracks the volume of data generated in a single day. We’ll be sharing Mango’s views on some of these very topical themes, so watch this space.

In the meantime, get in touch with us if you’d like to find out how to transform your business model using the power of data.