Philip Stubbs

Analytics in the UK... There has been a lot of talk here on LI recently about data professionals needing to be more business savvy and vice versa... Data leaders needing to influence business leaders, data being seen as a business asset, data needing to add value blah blah blah... Many of us have been having this same conversation for what seems like an eternity, and yet we are not really any further forward... Why? What are we doing wrong (or not doing)? Why isn't The Business listening? Well, maybe, just maybe, it's time to change the record... Data will never be "given" a seat at the top table, Data must demand one! The Business will not adopt a data culture, Data must create one! Data should not have to apologise for asking questions or driving change, explain why it's not just "tech" and how it is a value creator... Data leaders should be loud and proud... Data should be celebrated and embraced, otherwise there really will be "no future" for us! So, Never mind The Business, here's The Data... Now sing (shout) after me: We're so pretty, oh so pretty, we're Data! We're so pretty...

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📣 Calling all data analysts! Do you feel the service you provide is recognised within the wider organisation? Sometimes analysts can be perceived as ‘number crunchers’ rather than revered consultants but that perception can be altered by a change in mindset and practices. I have written a short article on this subject (link below) which I hope you find useful - please feel free to share it with your colleagues… https://lnkd.in/e4Resagn These are the kind of issues we address in our workshops. Get in touch if you'd like to know more. #data #training #bigdata

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There was one important data lesson I took away from the recent TV debate for the UK General Election. I’ll start by saying I have no political persuasion. I am somewhat indifferent to all parties running in this year's Election. As the philosopher Ice-T once so eloquently stated; I don’t hate the player, I hate the game. So it was more for curiosity and sport that I tuned in to the TV debate last week between the leaders of the two main parties. Like any good old fashioned boxing match, it helped that one was in the red corner and one in the blue. While I didn’t enjoy the proceedings very much, I have been interested in how one moment has sparked a data debate. Now #data is something I do very much enjoy, and a teachable moment has presented itself. So let’s get to it! The matter in hand was concerned with how Rishi Sunak, current Prime Minister and leader of the Conservative party (fighting in the blue corner), stated that Keir Starmer, leader of the Labour party (fighting out of the red corner), was going to raise everyone’s taxes by £2,000 if Labour are voted in. A claim Sir Starmer rebutted. Repeatedly rebutted actually, as Mr Sunak relentlessly kept throwing the same punch. Discussions since have touched upon the source of this claim, and it’s fair to say that, yes an ‘independent’ group conducted the analysis that led to the £2,000, but even they acknowledged, the analysis was served with a fair few assumptions baked in. Now there’s nothing necessarily wrong with that. Any methodology and analysis will have limitations and often assumptions that need to be acknowledged and understood. I somewhat understand why Mr Sunak would leave out the details when throwing a figure round in a forum such as this. I am someone who knows all too well, when presenting data the more assumptions you explain, the more attention you drain. But I am pretty sure ol’ Rishi, with much of his career in finance, banked on the majority of people not lifting the hood on that stat to see the underlying mechanics. Regardless of the debate and scrutiny of this figure since, the damage was done. And I find it sad that many won’t even peel back the first layer on any information provided to them. We need to develop curiosity and the critical thinking skills, which will enable people to evaluate data sources and defend themselves against the barrage of information they’re hit with on a daily basis.

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Can you spot the issues with this chart? Hint: There are 4. I'm now teaching Data Literacy - the ability to read, understand and communciate Data in an organisation. #DataLiteracy

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One of the most popular phrases in the Data Profession is "a single version of the truth" and it is something that, over the years, has begun to grate on me and many others. Aside from the fact that experience tells us that perspective and objectives play a massive role in what people consider to be 'the truth', the pursuit of a single version often leads to a expensive centralisation of data to achieve a goal that seldom yields the outcomes we seek. To use Nick Vivion’s article, by proclaiming that we can have a ‘Single Version of the Truth’, we essentially confirm that there are multiple versions to begin with, and as such this cannot be truth but opinion. As technology and its capabilities have rapidly evolved, making the accessing and using data easier and simpler, the need for the mythical 'single version' becomes less important. Instead, the significance of well defined, modelled and governed sources where key attributes are defined, licensed, managed and owned, is far more important to the consistency of data products and the insights they produce. It may be time to change the message? #DataFabric #RedefineSource #Data https://lnkd.in/etaaf7vP

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Data Navigator is the industry benchmark solution for #Independent #Schools in the UK, developed in collaboration with Independent Schools'​ Bursars Association #ISBA and free to its members. The dashboard is specifically designed to equip #bursars, #governors, and senior leadership teams with immediate access to reliable data, presented in a meaningful context with relevant benchmarks to support informed decision-making and strategic planning. All information is presented in a sophisticated interactive digital format, greatly enhancing your ability to share and communicate relevant insights. Examples of information provided include: - Comparative benchmarks on #pension contribution rates relevant for any school looking at their options for pensions and wider benefits package. - Benchmarks on #marketing effectiveness and conversion rates in the admissions pipeline, relevant for any school concerned about their ability to continue to attract sufficient families after VAT changes. - Financial #cost benchmarks relevant for any school considering shared services models or other options to improve operational effectiveness. This dashboard is invaluable for any school to model the impact of changes and use the output to support decision making and business cases. Contact me if you want to know more and follow me for insights relevant to independent schools.

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Data needs to mind its own business! 🙉🙈🙊 Seriously data peeps... You need to look after your business stakeholders & colleagues. 😘 What else did you think I meant? 😂 But, why do I say this? Well, because too often: 💡The Business wants... Answers ⁉️ The Data provides... Questions 🛟 The Business wants... New solutions 🚫 The Data offers... New problems 🫰The Business wants... To save money 💰 The Data asks... For more money ⏰ The Business wants... To save time 🐌 The Data takes... Forever and a day 🏠 The Business wants... To keep it simple 🕌 The Data loves... To overcomplicate things And yet we still wonder why so many data initiatives (and teams) fail... 🤦 The moral of this data story is very simple - give the Business what it wants! 🤯 Not sure what the Business wants? We'll try actually talking (& listening) to them! 🗣️👂 The Business is not sure what it wants!? Well then your job is to help guide them.🧑‍✈️ So tell me data peeps... How do you mind your business? Let me know in the comments. Geek out 😎

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Have to recommend this one for anyone working in data. This course quite literally changed the way I talk to people about data and has made me immeasurably more confident while doing so. The global network of data professionals, and very active WhatsApp group, are as impactful to me now as they were three years ago. Caroline Carruthers Peter Jackson Carruthers and Jackson #data #analytics #datascience #dataLeadership

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You may have heard about “data observability” and are not sure what it is compared to “data quality”, or simply are just curious how you can further improve your services. The value delivered by experiences you create means thinking about data end to end, not just about improving reporting, or data quality in a bubble, its about thinking about the user experiences, the products in a system and the service design of these. If you want to further help and elevate your data outcomes and teams have a read of the below. Not only is the Gartner hype cycle a useful cross check on “what’s hot or not” :) but where you understand or think your strategy, teams, and partners are in this to help you have even better conversations with them on your roadmaps ahead. This link also authored by Barr Moses https://lnkd.in/emJmeNgv will give any reader new to the topic a good grounding. Data is complex, and fast moving so its ok not to know it all. Colleagues like your data teams want to help you as leaders not fall into shiny AI or analytic tooling traps and will love you asking their views on the (perhaps!) unshiny stuff such as observability! We’ve been improving outcomes in services for years by focussing on end to end experiences and working with even better methods to deliver through software engineering, why would we not have the same end to end strategic user centered focus on data? #Data #Digital #Technology #UserExperiences #AI #Transformation Thank you Barr!

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Stop collecting so much data. Data is everywhere, but is it all useful? Often organisations collect every possible bit of data just because of the potential they believe they can get from it. But collecting all that data comes at the cost of collecting, storing & cleansing without any guaranteed return. The key is knowing a clear use for your data, I want to collect x to be able to solve or understand y. Without that clear purpose, the data can be unused, misunderstood, or used to solve a 'problem' that doesn't exist. Three questions to ask before collecting data: 1) What is the problem you are trying to solve? 2) Who are you solving the problem for, and do they actually want it? 3) What is the overall outcome you want to achieve, what does success look like? Is there anything else you consider when collecting data? Discover more of my posts by following hashtag #datatranslator #data #analytics #datacollection

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Single person data teams seem to be more common than I realised. And it's possibly one of the toughest positions to have in the data space... I've spoken to quite a few people recently who are the only data person within their organisation. Some of these orgs are small (20-30 ppl), while others are larger (200+). I don't know what the right ratio should be for data roles to non-data roles across all industries, but I can say that being the only data person in the org comes with a long list of unique challenges. You might assume in many cases that they were hired as the first data person with the goal of undertaking a needs assessment and scaling the data team from there. Sadly, this doesn't seem to be the case in most instances I've come across. Their job often requires them to provide the full range of data needs wholly on their own. When I ask if management plans to scale the team, the answer is usually 'no'. The other huge shock was that these roles are typically NOT strategic in anyway, and are in place to provide hands-on data support for the business. This alone is a clear indicator of a LOW data maturity organisation. Effectively, this person is the data leader for the org, but with no way of delegating work and a large number of stakeholders to support, their ability to be influential and drive forward a data culture/strategy is severely constrained. Here's a short list of *some* of they challenges they face: 🔸 Organisational Dynamics 🔸 Workload Management 🔸 Resource Constraints 🔸 Technical Challenges 🔸 Lack of Collaboration 🔸 Skill Set Limitations 🔸 Scalability Issues 🔸 Burnout Risk Like I said, being a one person data team is seriously tough. If any of this resonates with you, you might benefit from working with me as a coach/advisor. I help data leaders build strategies to become more influential in their organisations and to create a more data-centric culture. Companies will often pay for this sort of thing too out of their consultancy or L&D budgets. So why not grab 30 mins with me for an informal chat via the button atop this post or on my profile? Let's chat.

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💚 💛 ICYMI :: NFU Mutual have chosen Data Literacy Academy to partner on their data academy 🥳 Not only is it exciting to be partnering with a fantastic organisation and really brilliant Data Literacy Manager in Farran Florne… But it also answers a key question we’re often asked… ‘Where do we start?’ Well, in this case we’ve aligned the programme kickoff directly to a strategic transformation of the Underwriting department. 130+ underwriting professionals creating a data cultured underwriting team. To have business-side leaders like Jason Eatock and Jack Seale driving the initiative makes a huge difference to value delivered. A-Team! 🔥 #data #dataliteracy #datafluency #dataculture

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Don't we just love it when every challenge is labelled as Data! We have to accept that there is "bad data", but by over simplifying everything as a "Data" problem, we never address the real issues, pointing resources in the wrong direction. Data is mostly the output from an activity or a process, and yes that could mean that the Data output is of poor, but we should not be attenpting to "fix" it after the fact. Equally the way we use consume Data is only as effective as understanding the questions we need to answer well enough to put our Data to the best use and avoid incorrect assumptions. The practicalities of connecting, organising, modelling the data into products and making it available to consume is made easier today through Fabric Architectures and Mesh Operating Models, but if we do not address the cultral and process aspects, the best data platform in the world won't make our data better. We know Data is a team game, as such, we must encourage and support everyone to take personal accountability for the data we create and the processes we follow to create it. If we do that well then Data quality is within everyone's gift to improve and not the accountability of a single team. #DataCulture #DataFabric #DataProcess #DataQuality

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It's tomorrow! 📅 If you're working with Power BI or Fabric, this is your chance to see Tabular Editor at the London Fabric User Group. We're all set for this exciting session, thanks to London Data School. See you tomorrow! #PowerBI #MicrosoftFabric #TabularEditor #LondonDataSchool #DataAnalytics

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Having had the pleasure of helping resolve a variety of #PowerBI and #DataVis queries this week, it really got me thinking about the benefits of such powerful software. Whilst this could have been a long post, I'll keep it relatively short and just detail my Top 3. Which is a whole lot easier than naming my Top 3 movies! 1. Simplification of complex data. Converting (often) large amounts of data into user-friendly visuals and discovering insights into the previously unknown. 2. Facilitation of story telling. Conveying a clear and compelling narrative. Helping the data become more engaging and open to all consumers. 3. Optimisation of decision making. Identifying key trends and metrics to help make/drive informed and timely decisions. There is so much to consider when visualising data, but the benefits are truly endless, priceless! And remember. It's not work if you #lovewhatyoudo

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In my latest article I’m sharing my framework for maximising productivity as a Data Science Leader. Includes: * The types of tasks a Data Science leader at every level should be spending their time on. * How to quickly and efficiently prioritise many competing demands to maximise the value you can deliver as a leader. * How to take control of your time. * A framework for categorising and ordering tasks for maximum productivity. #datascience #leadership

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Data visualisation doesn't have to be limited to charts and graphs. One of the most effective methods I've used is a poster (see below, client and personal information redacted). Rather than presenting to a circle of passive listeners around a board table, I had stakeholders get up out of their chairs and gather around a big print. Then we walked through the findings together. ✓ Stakeholders were enticed by the different communication approach (and a break from sitting down!). ✓ The physical act of standing and congregating increased engagement. ✓ It was a conversation not a presentation. ✓ Discussion happened naturally; questions and ideas flowed without prompting. ✓ I was part of the team not an expert outside it. ✓ The poster can stayed up afterwards, for continued contemplation and for others in the organisation to see. The result was fantastic. Stakeholders walked away with an accurate understanding and were energised to take the next steps. They also weren't drowsy. 😄 This approach isn't going to work with all stakeholders – like everything, context matters. Some things to consider before using it: ➤ Do the stakeholders know who you are? ➤ Do the stakeholders know what you do? ➤ How senior are the stakeholders? ➤ How formal is the workplace? ➤ Is it a place where high-level findings are sufficient or are stakeholders looking for hard numbers? ➤ Would it be enough to provide further detail separate from the poster? It also wouldn't work with all data types. In this instance, the data came from user research. Have you ever used this approach to visualise and communicate? What do you think about the idea? Would very much love to hear your comments.

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How much does data accuracy matter? In this case - quite a lot! While data folk bang on about data quality and accuracy, not all of it matters that much. In some cases, you just need an indication that something is working or isn't. In other cases, it is vital that your data is 100% accurate. Particularly when it deals with people's lives. In the energy industry, I have seen data that would lead to debt collectors being sent to a person's house. A person who had never been supplied by that company. Now that matters. The government knowing you are alive. That also matters. Discover more of my posts by following hashtag #datatranslator #data #analytics #dataquality

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What a great evening discussing the future of project risk management at the August Project Data Analytics meetup. Rather than a standard presentation, we had a very interactive conversation. Expertly chaired by James Garner and Donnie MacNicol. I really enjoyed it. I also took along some copies of my book on next gen risk management. First edition, for the discerning risk professional 😉 We discussed how the traditional process elements of a risk managers role (documenting risks, updates, chasing risk actions, risk dashboards etc) can be automated through workflows today. We then explored what the remainder of the role is. There was general agreement that we need a human to help tease out the potential challenges, assist with the analysis, interpret, make decisions. But is that a risk manager, risk analyst or someone else? Process vs insights. We then discussed what we actually mean by risk. Does the very definition of risk create silos and focus on process rather than improving things? If we change it to minimising the downside and maximising the upside, we consider all potential sources of variance. We bring in change control, logistics, technical queries and much more. These become features in a machine learning model that help us to develop insights based on context and conditionality. By pooling our data, we leverage the wisdom of the crowd. Then, we can overlay some of the methods that Donnie summarised to explore how human behaviours can influence project outturns and inject variance. A new role is then created. A variance analyst. Potentially the most important role on a project, anticipating where the variance is likely to emerge from and actioning it accordingly. Doesn't that sound like a more fulfilling role than a risk manager? I really liked how Donnie teased out the separation between process compliance and the human element of surfacing potential variance events. I was particularly engaged by the challenge around optioneering. The balance between looking at design options (and risk) with teams of people vs optioneering for every decision; using generative methods.1000s of options created in real time, optimised to your specific circumstances. Is the priority for your project to minimise cost or maximise investment certainty? I remain 100% convinced that a new profession is emerging. Are you part of it or are you staying old school? Jump in the comments and let's debate it. P.S. There were women in the audience, just not in this photo! Stay tuned for news on how we plan to improve diversity in our events and community.

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