Rosie Day

We have a legacy BI tool that users are still on - they posted a question today about seeing data about a transaction in store . The other users chipped in saying what universe you might need and then pointed out you have to ask IT team to get the data , a colecitve sigh as that’s going to table weeks and budget . I replied in the thread , give me 5 minutes . I used custom sql in #tableau using a parameter to surface the result using JLPs dashboard template . The users are very happy as they have what they need ( I also added images of the products in the transactions ! ) they now all want it and I shared it with them . The parameter is the transaction number so its nice a simple . It’s not the tools we use but the way we implement and govern them that really make the difference . Our tableau instance could be run in the same way as our legacy bi tool , it would have been easier to start with , but going self service and creating a data driven culture has meant we can ask a question and get a answer in minutes not weeks and the business can get on with taking action . Also I feel I have helped which is why I go to work .

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When working with data how reactive are you? Do you respond to every request? Or do you identify what is valuable and take the time to deliver deeper analysis? Which do you think delivers better results for the business? Working with data you have the ability to show the business what works and what doesn't. You can analyse trends. And spot patterns that people have missed. But you can only do that with time. If you act like a data service desk you won't change the business. Delivering meaningful analysis takes time. Slow down with data and deliver better results. Discover more of my posts by following hashtag #datatranslator #data #analytics #dataservicedesk

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So work’s been a bit full on recently and despite the fact that I have a week off to unwind, I’ve been struggling to fully switch off. Power BI is such a huge passion of mine that I couldn’t keep still for long. I’ve been reflecting and it’s such a shame that I’ve not started a personal project in such a long time so today I got stuck in! Stockport County are another passion of mine, and seeing as we have just won the League (Champions again ole ole) this seemed like the perfect theme. When I’m not watching the team, I thoroughly enjoy a game called Football Manager where I can put myself in the manager’s very difficult shoes of looking after the team in a simulated world. Football is very data driven nowadays and Sports Interactive have made it extremely easy to export a snapshot of data. With this in mind, I present to you: My Demo Dashboard! Taaa daaaah. I exported, transformed and imported the data into PBI in a matter of hours this afternoon (With a lovely Kentish Rosé too I might add) and I’m pretty chuffed with how it came out. I previously started this project a couple of years ago, but put it down due to complexities with cleaning and transforming the data. I’m happy to report that I have learn a hell of a lot these last 2 years and so this activity was now a doddle. I like to think that I’ve improved on my presentation skills too. The dataset is rather basic but when interrogated can be quite powerful. Less is more. (Caveat with this dataset is that it's the 22-23 squad) The user (gamer) can see a financial snapshot on the first slide, a drill down into attributes at player level on the second slide and an overall team average on the final page. This allows the user to see top and bottom performers, the costs involved, as well as the ability to zoom out and find weakness areas in the team as a whole. The action then is to scout out and find players that can cover those weaknesses and get them in the team! The best bit? Even if the squad changes this can be updated (Yes this can work for a completely different team too!!!) If Mr Stott were to offer me a role at the County I’m not sure if I could refuse!!!! 🤣 I think I need to get out more….. Anyhoo. Have a lovely week 😊 Stockport County FC #PowerBI #StockportCounty #PersonalDevelopment

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When communicating data, context is 👑 check out my pixel helping me put tomorrows weather into context by telling me how much colder it will be compared with today. 👏 #datacommunication #datastorytelling #dataviz #dataanalysis

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How many can you just... requests do you receive? Stakeholders often only see the output of your work. So may not realise how much work occurs before the output. These requests are common. So how do you reduce them? 1) Set clear expectations. Be upfront with the challenges and set realistic timescales. Explain the reasons why. 2) Explain what data is and isn't available Be clear on data quality Provide detail on your data sources Explain how getting the data in the right format is the bulk of the work. 3) Promote data literacy across the organisation If colleagues understand the challenges, They are less likely to expect instant results. They may also be able to do simple requests themselves. 4) Ask why the request is so urgent? Understand why the request wasn't planned Change the ways of working Promote clear communication and thinking in advance Are there any other things you would try to reduce these requests? Discover more of my posts by following hashtag #datatranslator #data #analytics #canyoujust

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If you’re wanting to move into data leadership positions, it’s often less about your technical skills.👀 I speak to loads of data professionals who have incredible technical skillsets. 📊❄️🧱 They’re the best in class, no question about it. But when they’re given the opportunity to move into leadership roles, they crumble. 🙈 They struggle to: 💥Lead a team 💥Develop a data strategy 💥Work with other non-technical leaders 💥Navigate a more complex & ‘political’ environment 💥Inspire a vision and get the best out of their technical team And it’s almost always because they walk into those roles unprepared for the steep learning curve of managing people instead of data. My best bit of advice – find a mentor who’s made the transition successfully and learn as much as possible from them. 🔥👏 Some food for thought – some of the best CDOs don’t know SQL. So what makes them so successful? #DataRecruitment #DataStrategy #DataLeadership #CDO

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Does anyone use Collections in Tableau Server? I'd like to better understand when to use them vs Favourites. I have some ideas - but each has apparent limitations. 1. Create a collection to share with different user groups. For example, these dashboards are most useful for Sales people. This collection contains what is most important to Finance. Etc. However, it doesn't seem possible to share a collection with a Tableau Group. And sharing with each user one by one is painful. 2. Create dashboard packs. Yes, even in 2024 it's not unusual to print before a meeting. There are often times when someone needs a dashboard from workbook A, a couple of dashboards from workbook B, etc. It would be great if it was possible to download a collection in one hit, creating one document. However, it doesn't seem possible to download a collection to produce one document. Do I have anything incorrect here? Am I missing something else about Collections? #tableau

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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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Tris J Burns 🔥says 'data is a business discipline with technical components', so how do you develop the skills to excel at being a data professional? Talk to experienced data professionals and a lot of them will tell you they 'fell into data'. Many started out in business disciplines but were the person who gravitated most to the technical parts of their roles. They became known as the Excel guru or the person who could best work with the ERP system. In this sense they developed business skills first, and technical skills second. Today there are focused courses to enable you to get a data role. This is great for learning technical skills - but often not so good at developing business skills. So how do you develop them? 1) Talk to people across the business Find out how what they do makes the business money. Ask them about their problems. 2) Develop a basic understanding of economics and the industry you are in. Does a change of government affect your sector? What about a change in interest rates? How does people's behaviour affect your business? 3) Study different businesses Waiting for a train, how do those companies make money? Buying your groceries, how does the supermarket make money? Do they buy your data with a loyalty scheme? While technical elements can be really interesting, understanding how data can really change a business is much more rewarding. Discover more of my posts by following hashtag #datatranslator #data #analytics #business

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Data teams can rarely affect a business on their own They need to work with stakeholders to figure out: How the business makes money What areas the business can improve How to scale things that the business does well Sitting in isolation and taking orders doesn't do anyone any favours. Spotting things the business can improve and not sharing them doesn't change a business. Building new data products and then surprising your colleagues with them isn't going to drive adoption. If you work in data, get out there and talk to your colleagues. It will be amazing what you find out and solve together. Discover more of my posts by following hashtag #datatranslator #data #analytics #stakeholders

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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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In the first of a series of posts about planning a successful data migration project, I want to talk about the importance of a data analysis audit.    At Binary10, we like to say the devil is in the detail, because with data migration projects it pays dividends to be across the minutiae as early as possible.   Understanding the data quality, structure and gaps from the beginning is the foundation of a successful project.    And this allows the audit to be the source of truth for the data mapping exercise and producing the blueprint for the whole project.    It’s a critical step in the success of a project. It’s never overlooked, but in our experience, it’s often undertaken by generalists rather than specialists and this can cause problems further down the line.

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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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tell me you're a data professional without telling me you're a data professional... when I build brio train tacks, I build them only using track pieces. Once it's up and running then I will swap out pieces of track for the fancy bits i.e a windmill. Foundations over fancy features for release 1.0. #dataleaders #dataprofessionals #dataleadership #cdo #dataanalytics

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You decide to invest significant sums into a data science team in the hope of uncovering value. Yet few of your staff know how to interpret and work with data. Using data effectively should increase productivity, increase efficiency, and enable better decision making. But this only happens if everyone can understand why it matters and how to apply it. Before investing in data science, consider upskilling your organisation so they are ready to act on any insights. Until then Dave in accounts is busy copying and pasting between 20 different spreadsheets. Discover more of my posts by following hashtag #datatranslator #data #analytics #dataeducation

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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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#IronViz Update #1 Story ✅ Initial Layout ✅ Wireframing ✅ Data Sources 75% created, sourcing the rest Data cleaned ❌ Data Validated ❌ Viz Extension Tutorials ✅ Viz Extensions Working ❌ Colour Scheme ✅ Mind already blown with what I’ve taken on for myself ✅ Excited ✅ Scared ✅ I love #IronViz #Tableau #DataFam

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Relex, Recharge, Refocus, time almost up in Corfu, been a great week a way to unwind and rediscover the book, a must-read for anyone in the data analytics world #development #refocus #datastorytelling

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Working in data at small organisations comes with a few challenges: You often have fewer people to learn from internally. You may struggle to keep up with changes in the industry. You see large companies with experts for the 6 roles you have to cover. So how do you keep up with industry changes and make sure you know enough not to be left behind? Getting to meetups is a great way to do this. Yesterday evening I attended the Harvey Nash Data & BI Leaders Event brilliantly organised by Josh Wilson. 4 fantastic talks and an interesting Q&A which focused on the changing workforce, skills development and where AI can help and not hinder our work. Ryan Parish spoke about how we will soon have 5 generations in the work force, with varying degrees of growing up with and without technology. Equally very different ideas on how work should benefit them. The potentially for massive change is there, but I think it will happen far more slowly than most did in the room. Carl Smith presented an AI use case which he effectively scaled, being able to take product descriptions from rubbish, to adequate. This is where I think AI comes in a lot, while not perfect, it can elevate most things to a level of mediocre far more efficiently than getting people to do it. Faye Murray (EMBA) tackled the common challenge we face of getting leadership buy in and funding for data projects. As an industry we definitely need to get better at demonstrating value and delivering clear returns for businesses. (Don't forget risk reducation delivers value too) Christopher Knight demonstrated how we need to think about AI as another tool in our armoury, but how we can look to keep it front of mind when delivering projects. He also spoke well about the need for people who haven't just studied the tech in data. That diversity of though helping come up with different ideas and challenging technological ideals. It was awesome to meet Danielle Brocklehurst in person, and to meet some new faces in Ian Wallis and Christopher Stephens among others. If you work in smaller data teams these events are invaluable for helping develop your ideas and learning from other experiences. Discover more of my posts by following hashtag #datatranslator #data #analytics #networking

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