Where Should You Fix Data Quality Issues
Data quality impacts everything, especially when building a single source of truth. But where do you fix quality issues? Often, it’s a choice between fixing data in the System of Record (where data is first captured) or in the Decision Support System (DSS), where data supports business insights and decisions. Typically, data flows from the System of Record to the DSS. Which makes it easy to identify where the issues is. But identifying the issue is only half the job—the real challenge is…
Data Warehousing Projects: 3 Indicators of Success
Data warehousing projects are highly technical, and typically involve a lot of different moving parts. So how do non-technical business leaders know if the project is going well? While you should listen to your data team there are a couple of leading indicators for project health. Leading Indicators: Documentation – While no project ever has “perfect” documentation, there should be something in place—and it should improve as the project progresses. Data Quality Checks – A healthy project…
The Silent Killer of Data Projects
Data quality is one of the silent killers of data-driven solutions. If the data is wrong, even a fraction of the time, no one will trust it. So, it’s important to tackle quality as early as possible. Here’s a couple of ways to prevent your project from being killed off: Build a Strong Data Model – I talk about data modeling a lot and for a good reason. Without a well-structured data model, it’s nearly impossible to ensure your business logic is consistently applied. A solid model helps catch…
Start Using Your Data to Drive Business Results—Here’s How
How do you start using your data to drive business results? It all begins with taking that first step in data management. Unlike software development, you don’t need to have every step mapped out from the start. Don’t get me wrong, it’s important to have a goal. But you’ll often end up changing direction as you go. Because as you dig into your data, you may encounter challenges like inaccessible data, poor quality, or data that isn’t as valuable as expected. Rather than following a rigid…
3 Strategies to Make Data Integration Easier in Your Data Warehouse
The hardest part about building a data warehouse isn’t getting data into it… It’s integrating and modeling that data to create a single source of truth. Anyone can pull data from multiple sources, but the real challenge is fitting it in to a data model. When you have different ways data can be generated and historical data from legacy systems that need to fit into a unified source of truth, not everything aligns seamlessly. However, there are a few strategies you can use to make integration…
Still Using Excel for Data Integration? There’s a Better Way
Data integration is one of the hardest things to do in data. It’s the thing that everyone dreads and the thing everyone has to do. And if you’re doing it within Excel by using XLOOKUPS (or VLOOKUPS or INDEX(MATCHES)), it quickly spirals into a chaotic mess. Soon enough, it’s like that one messy closet in your house that you never open and try your hardest to forget. That’s why integrating data in a data warehouse environment is a game changer. It provides structure, scalability, and the…
The Secret to Building a Strong Data Strategy: 3 Pillars You Can’t Ignore
While there are several critical areas within a comprehensive data strategy, there are three foundational pillars that your business needs to get right: 1. Data Integration This is how you create a “single source of truth” around a thing your business care about (orders, customers, product etc…). Creating a single source of truth is vital for efficiency and decision-making. Data integration ensures that your key business metrics—whether customer profiles, order histories, or product…
AI Can’t Work Without the Right Data—Is Your Strategy Ready?
If you’re following developments in AI, you’ve likely come across the term RAG (Retrieval-Augmented Generation). But what does it mean, and why is it important for your business? RAG refers to a process where a Large Language Model (LLM)—such as ChatGPT—retrieves specific, relevant information from a connected data source to enhance the quality of its generated responses. Instead of relying purely on its pre-trained knowledge, the LLM pulls real-time, contextual data to answer questions more…
Pipeline Errors Happen: Are You Prepared?
How do you or your analytics team handle errors in a pipeline? By pipeline, I mean any type of low-code, no-code, all-code solution that runs on a schedule and performs some sort of activity. Because here’s the truth. Pipelines fail. It doesn’t matter if it’s a ETL, ELT, or application integration pipeline. It will fail. It doesn’t matter if you have a Dev, Test, Prod environment. It will fail. It doesn’t matter if you have 1 person or 10 people supporting it. It will fail. Eventually. So…
Speed Up Onboarding with This Visual
Picture this: You’re onboarding a new employee fresh out of college, and they have no idea what the difference is between a Broker and a 3PL. Traditionally, it takes about six months, on average, to bring them fully up to speed. But what if there was a way to accelerate this learning process by visually showing how everything in your industry is interconnected? That’s where conceptual models come in—they provide a fast, straightforward way to demonstrate relationships and roles within your…