Matthew Kelliher-Gibson, MBA’s Post

🥵 Data professionals are burnt out. Often, the burnout stems from the tension created by a one-size-fits-all approach to data work. When one function tries to balance long-term strategic initiatives with urgent data support, stress is inevitable. In this piece, we look to the past for answers. We examine how IT and software development teams created clear edges that enable them to do their best work. Then we provide a roadmap to help you apply this principle to data work but separating two high-level workflows: 🔹Data support 🔹Data development Go deeper 👇 https://bit.ly/3P0JnU9

Before you obtain your first batch of data, it's really important to get your data team sorted out. Otherwise, you'll be investing significant finances into something you won't be able to leverage immediately. At first glance, this step may seem pretty straightforward. But trust me, if you want to make the most out of your data, it's worth taking a bit more time to think it through. Simply assuming, "Oh, one data engineer should suffice," won't cut it. ❌ Figuring out the right mix for your data team—whether it's data engineers, data scientists, analytics engineers, or business analysts—depends a lot on the size of your product/research and what you're aiming for. There's no one-size-fits-all answer. But there are some key questions you should consider that can help you shape your dream team ⤵️ → What are your business goals when it comes to data? → How large is your company, and what volume of data does it acquire and process? → What is the budget allocated for building and maintaining the data team? → What type of data do you handle (raw data, clean data, etc.)? → What skills are necessary to effectively manage and analyze this data? → What tools and technologies are essential for data analysis and management? → How do you want to structure your data team (centralized, decentralized, etc.)? Will they need to work closely with other departments like IT, marketing, or finance? → How will the data team communicate its findings and insights to stakeholders across the company? → How are you going to handle things like data privacy and security? → What metrics will help you evaluate if your current data team meets your business needs? There's no one right answer to these questions—it all depends on your unique situation and goals. Is there anything else you'd add to the list? Feel free to drop a comment or reach out if you'd like to delve deeper into this topic! 📥 #datateam #datastrategy #datamanagement #webdata

Do your employees possess the skills to effectively interpret and use data? 🤔 Data literacy is crucial for businesses to make informed decisions and achieve success. This article explores the concept of data literacy and its significance in today's data-driven world: https://ow.ly/pFWB50QKBMV #dataliteracy #business #skills

As Business Analyst and as someone who is transitioning into data engineering, I have often found myself at the intersection of complex data and impactful decision-making. In an era where data-driven insights are pivotal, the tools we use can make or break our efficiency and peace of mind as well. 😓 One common challenge many of us face is the limitations of traditional tools like Google Sheets. While the platforms offer convenience, they can become bottlenecks when dealing with large datasets or complex dashboards. From slow performance to frequent timeouts, these issues can impede our ability to deliver timely and accurate insights. Why does this matter ? 1. Efficiency Suboptimal tools can lead to increased time spent on manual tasks, reducing the time available for strategic analysis. 2. Accuracy Performance issues can result in errors or outdated information, affecting the reliability of our reports. 3. Scalability As data grows, the need for more robust solutions becomes critical to maintain performance and usability. The Solution Investing in optimized resources and advanced analytics platforms can transform our workflow. By leveraging tools designed for scalability and efficiency As we navigate these challenges, it’s essential to advocate for and embrace tools that align with our growing needs. Let’s drive the change towards more efficient and scalable solutions, ensuring we can continue to deliver impactful insights with confidence. The shift to better optimised tools is critical for any business to scale sustainably. #BusinessAnalysis #DataManagement #Efficiency #DataTools #Analytics #Optimization

🚗 As a child, I always liked to take toys apart to see how they worked and find out what was inside of them. I find the exact similarity in decision management and that is what we call decomposing a decision. 🔎 When we decompose decisions into smaller and smaller decisions, then it becomes apparent which specific data we need. I love data, especially when running. It helps me track my performance and make judgments or decisions about my next run: when, where, and how far.  These analogies represent what happens across many organizations when they use data to make operational decisions. ☝ So how do we Navigate Data to Rule-Drive Business Decisions? Many professionals (SMEs) and business operation leaders look at data daily to understand priorities, bottlenecks, highlights, etc. Then, they determine a set of activities for the organization using specific criteria that are important for the business. Oftentimes, these individuals are not programmers; they are domain experts or part of the business operation teams. That means they have strong domain knowledge and little to no technical or coding skills. Operationalizing company guidelines and exceptions requires tailoring generic rules to specific, day-to-day cases by analyzing data to establish concrete criteria for targeting and classification. From a different perspective, the process could be seen as working with Excel or other data tools. I invite you to please read the full article on Navigate Data to Rule-Drive Business Decisions for the full Indepth explanation. #data #ruledriven #operations #businessdecision #rulemanagement #it #analyst #businesstech #decisionintelligence #decisionmanagement #businessleadership https://lnkd.in/g5ihFPMi

It's practically the data team's mantra: People, processes, tools. So why does it feel like nobody really does them all? I think the answer is that most data teams don't know how. Especially the people and processes part.  We're a group that loves tech and automation. so we naturally gravitate towards the tools. But I think many data types find the other two less interesting - and they don't have many chances to learn while grinding SQL. But I think we can all appreciate the difference between A+ and D- data functions is how they communicate, empower the org, and drive meaningful change. It takes all three pillars.  So let me break down the 80/20 of each for you in 3 minutes: 1) Tools → Reliable: Trust is a non-renewable resource in data. Build tools that won't erode trust. → Usable: It has to be ridiculously easy to get the data you need. Like, a few clicks. Your end users don't love the tools as much as you do. → Traceable: Data without context is useless. Have you noticed how often people ask 'where does this data come from'? Use tools and metadata that make it easy to understand the data lineage. 2) People → Training: Do group training at least quarterly. It's probably the single biggest thing you can do make your team more data-driven. And the single biggest distinction I've seen between A+ and D- data teams.  → Culture-building: It's the data team's job to build a data-driven culture in the team. Your marketing managers trust the fb CAC table like it’s the bible. Sounds like a small switch, but building a culture of trusting and relying on data is paramount. → Politics: Gross, right? But if you feel like the data team doesn't have influence, it's probably because you don't have support from the right senior people. Which means you're probably not securing support correctly. 3) Processes → Consistency: Set up a weekly business review to check key metrics. This’ll give you an intuition for what’s working and what needs to be done very quickly. → Feedback: Have multiple redundant ways to collect feedback on the data. These can be as simple as a Slack channel or as formal as an internal NPS. Regardless, you need to understand how your data’s being used across the org. → Proactivity: When things go wrong with data systems (and they always will), it's important you catch them first. Essential for building that trust. Alright, that was a lot. There's unfortunately a lot more, but these principles alone can get you a great head start in actually living the 'big 3.'

Simplifying Data Analytics for Non-IT Professionals In today's data-driven world, the ability to analyze and interpret data is no longer confined to IT professionals. Non-IT staff across various industries are increasingly expected to harness the power of data analytics to inform decisions and drive business success. This article demystifies the process of data analytics, providing a step-by-step guide that non-IT personnel can easily follow to become proficient in understanding and utilizing data in their roles. Read more

Today’s session with my future data analysts was nothing short of amazing! We rolled up our sleeves and dived deep into the art of data cleaning, exploring various techniques from the basics to the grand master itself: Power Query. Here’s what we covered: Flash Fill & Text to Columns: We started with these lifesavers in Excel. Simple yet effective, these tools quickly cleaned up messy data formats like separating names, addresses, and much more! It’s always fascinating to watch those transformations happen in a flash, right? But hey, if you’ve used Flash Fill, what’s the most mind-blowing transformation you’ve achieved? Using Excel Formulas: From cleaning up spaces, standardizing text, to filtering out duplicates, Excel formulas remain a go-to method. We explored how to use formulas like LEFT, RIGHT, IF, TRIM, and FIND to fix datasets that could otherwise cause havoc in our analysis. But here's where it got REALLY exciting… Power Query – The Grand Master of Data Cleaning: For those who know, Power Query is a game changer. It can transform datasets from nightmares to structured gold! We explored how Power Query: - Cleans large data sets with a few clicks. - Automates repetitive cleaning tasks, saving HOURS of manual work. - Handles everything from merging, appending, and filtering, to splitting columns and removing duplicates.    So why do we often overlook Power Query? Many people stick with manual methods out of habit or fear of learning something new. But let me tell you – once you get comfortable with Power Query, you won’t go back! The power of automation, the seamless transformations, and the way it organizes your workflow is next-level data cleaning. Why MUST we clean data? Dirty data can ruin insights, skew analysis, and cost businesses millions in poor decision-making. Data cleaning ensures: - Accuracy: Getting rid of duplicates, errors, and inconsistencies. - Efficiency: Making data ready for analysis without endless manual adjustments. - Reliability: Your insights are only as good as the data you’re working with! Your turn! I’d love to hear from you:  - What’s your go-to method for cleaning data? - Have you tried Power Query yet? If not, what’s holding you back? Let’s talk data cleaning strategies, tips, and those aha moments when you fixed a dataset that seemed impossible. Drop your thoughts below and let’s keep the conversation going!

As the scope of data teams has increased, so has the number of people working in them. That’s mostly great — data teams now drive business-critical data products and go beyond answering ad hoc questions. But it also warrants questions such as “Do we have the right balance between investing in our foundations and insights” and “Are we operating as efficiently as our peers given the outcomes we achieve”. This post looks at the distribution of data roles within 40 top data teams in the US and Europe to help you answer these questions.