Wallabi’s Post

Since leaving the military, I've had the world's best crash course in data, analytics, and AI. From a small natural language generation startup headquartered in Chicago to a global business intelligence tool at the top of the food chain to working with some of the most talented co-founders I could ask for. I've been incredibly lucky. Through that journey, I learned one big thing about analytics & business intelligence: 🧐 We need some fresh perspectives. The world is changing rapidly, and our tools aren't keeping up. Now is the time to demand more of your business applications. AI is here, it's not going away, and it can do so much more than build yet another dashboard for you. Here's a blog post I wrote on the topic. If you're leading a growing company or team, you probably know the pain. We'd love to hear about it!

How data analysts help small businesses in monitoring and decision-making?

"[...] there is one other, foundational, aspect to data that I did not fully appreciate. That aspect is this: there are two distinct environments in which data analysis occurs. 1. Some data are the result of an experiment or special study. You make a change, and then compare the results of that change with some control group. Imagine doing an A/B test. For the sake of convenience, let’s call this approach ‘𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝗹 𝗦𝘁𝘂𝗱𝗶𝗲𝘀’.     2. Other data occur as a by-product of some operation. As a business process runs, it throws off some data that you observe. Imagine monitoring web traffic, inventory turns, or working capital at your company. We’ll call this approach ‘𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝘁𝘂𝗱𝗶𝗲𝘀’. The two environments lead to two different attitudes to data analysis.[...] Good read by 📚 Cedric Chin https://lnkd.in/eiiNQRsq #productmanagement #productanalytics #BCnoBSprodmgmt  

Let's cut through the noise: What's up with data fidelity and why should it matter to your business? Get all the deets right here! #dataenrichment #datafidelity #handcuration #Webresearcher #datacleansing #marketresearcher #marketanalyst #lessburn #yougrowwegrow

You're a data analyst or data leader or executive, and you're in a meeting, and two people show up with different values for the same number. The number in question is an important metric for "knowing how the business is doing". Chaos ensues. Some examples might include calculations for: ⟫ Sales forecast amount ⟫ The company's monthly recurring revenue (MRR) ⟫ A churn rate ⟫ A product conversion stat ⟫ The effectiveness of a marketing campaign But it could be anything your business wants to measure to drive operational efficiency. 🔥🔥 This confusion can lead to a lack of trust in the data team. Despite all the effort to build a great semantic layer and a great high-quality data pipeline, the impression from leadership is that the company still lacks the ability to make good decisions with data. 🔥🔥 We wrote about the negative impact of ❗️redefined metrics❗️ -- why they happen, how to detect them, and how to prevent and resolve the problem. You may be surprised to discover that your single source of truth isn't as "true" as you thought. https://lnkd.in/gUXMh4bU

🥵 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

Business Intelligence was a great idea, make better decisions by taking all of the data you already have into account. The result was a flood of information that made finding insight more difficult and slowed the decision-making process. There has to be a better way. Look at what this company is doing. Full disclosure: I have known some of these people for a while now and I am very impressed by them. Am I projecting what I know about their past successes on this new venture? Check them out and let me know what you think.

You often hear entrepreneurs say, "We don't know what we don't know," when talking about deficiencies in data gathering. But when you have data in silos, it's more a case of "We don't know what we DO know." #payrollvaultstt #data #business

This started as a normal blog post but by the end was more personal. This post outlines the basic strategies I used to go from first time Data Science Manager to Sr Director in 3.5 years. ⭐️ You need to split out ad hoc support work from data development work 📵 Funnel all requests to centralized channels and away from individuals 📊 Capture, prioritize, and track all requests 📑 Create formal or informal SLA for requests and deliveries Once you start to get your hands around the whirlwind 🌪️ go and find data use cases. They are not going to come to use in the beginning. It is hard work but rewarding to get off the treadmill and start getting work with long term bottom line value done.

Data Dictionary vs. Business Glossary: Benefits, Challenges, and Solutions In your organization, are you ever confused by different definitions of business terms? Do you ever wonder why the number of customers differs between two reports?