Importance of Accurate Data Formats

Potential tariffs on imported gold plus other macroeconomic factors have caused Gold prices to top $3000 per Troy ounce! As a happy investor, I was fascinated by a recent FT article on physical gold standards.   Did you know the UK and US have completely different gold bar standards? 🤔 The UK follows the LBMA standard (400 troy ounce bars = 12.5 Kgs) while the US uses the COMEX standard (32.15 troy ounce bars = 1 Kg). As a result, when gold moves between these countries, it first goes to Swiss refineries to be melted down and recast—creating unnecessary costs, delays, and inefficiencies in the physical gold market. Crazy right!   This reminds me of what I see in data analytics every day. Different data formats, privacy regulations (GDPR vs. CCPA), model formats and explainability standards, create the same type of friction in our digital world. It leads to #interoperability issues, increased costs due to additional processing and compliance requirements, as well as slower knowledge sharing and decision making.   Just like the precious metal traveling to Switzerland, our data often requires extensive #transformation when moving between systems or departments. At a minimum, organizations need internal standardization across the below listed areas to improve internal efficiency and avoid unnecessary costs.   • KPI definitions that mean the same thing across all business units • Unified technology stacks for development and deployment • Common data dictionary and metadata management • Consistent data quality metrics and thresholds • Aligned model development frameworks and documentation • Standardized API formats for internal applications • Consistent documentation practices for analytics assets   AI has only amplified this need. When training data, model architectures, evaluation approaches, and governance frameworks vary wildly, your AI investments are bound to yield sub-optimal ROI.   I've seen companies reduce development time for an AI/ML project by ~30% simply by implementing consistent standards across their data and analytics functions. The ROI isn't just in efficiency—it's in enabling true innovation.    #DataAnalytics #BusinessEfficiency #AIStandardization #AILeadership 📈💰

Can you trust your dealership’s reports? Many dealer groups use tools like Power BI and Tableau to visualize data from their DMS, CRM, or other systems. These tools are incredibly powerful for reporting but here’s the uncomfortable truth I have discovered through countless calls. They only work if your data is accurate, consistent, and clean. Here’s the challenge I keep seeing in calls. Normalization gaps: Without a layer to standardize data (e.g., inconsistent op codes or naming conventions), insights across stores or brands can be misleading. Data hygiene issues: Duplicate records, stale customer info, and incomplete data lead to inaccurate calculations and blind spots. Fragmentation: Data flowing in from multiple systems (DMS, CRM, marketing tools) often doesn’t align, leaving leadership teams struggling to connect the dots. Take this example: If store A calls a “brake pad replacement” one thing and store B calls it something else, and this data is fed into your reporting without standardization, your service KPIs will never tell a true story. Or worse, imagine running a marketing campaign based on customer records that are 20% duplicates. These gaps aren’t just technical they’re business-critical. Inaccurate data leads to misinformed decisions, missed opportunities, and wasted resources. To truly trust your reports, you need: 1️⃣ Data normalization: Align fields and formats across systems to ensure consistency. 2️⃣ Hygiene processes: Remove duplicates, fix stale records, and validate data in real-time. 3️⃣ Centralized data: Integrate all your systems into a single source of truth to avoid fragmented insights. When these elements are in place, tools like Power BI and Tableau become exponentially more valuable. Instead of visualizing bad data, you’re unlocking reliable, actionable insights for every department—from sales to service to inventory. The question for dealer groups is this: Are you investing as much in your data quality as you are in your reporting tools? For the groups we’re working with at QoreAI, it’s transformative: ✅ Reports they can trust. ✅ Smarter decisions powered by accurate insights. ✅ Confidence in their data—and their strategies. If you’re not 100% confident in the accuracy of your reports, maybe the problem isn’t the tools but the data itself. What’s your biggest challenge when it comes to reporting? Let’s discuss below. #QoreAI #AutomotiveRetail #DataQuality #AIinAutomotive #DealerGroups #DataInsights

Did you know that poor data quality costs companies an average of $12.9 million annually? By removing errors, eliminating duplicates, and standardizing data formats, organizations can avoid the pitfalls of poor-quality data that can lead to misguided strategies, incorrect forecasts, and wasted resources. Tip 1 – Remove Duplicates Why? - Duplicate records can inflate metrics, skew analysis, and lead to costly errors. Example: - The Veterans Health Administration (VHA) paid over $204 million for the duplicate claims. SQL: - SELECT DISTINCT claim_id FROM claim; Tip 2 – Standardize Data Formats Why? - Inconsistent formats (e.g., dates, currencies) lead to confusion and errors in reporting. Example: - In 2014, Tesco overstated its profits by $315 million due to improper handling of financial data. SQL: Direct Update: - UPDATE transactions SET amount = amount * 1.39 WHERE currency_code = 'GBP'; Dynamic Update: - UPDATE transactions t JOIN exchange_rates e ON t.currency_code = e.currency_code SET t.amount = t.amount * e.exchange_rate WHERE t.currency_code = 'GBP'; Tip 3 – Handle Missing Values Why? - Missing values can distort your analysis and lead to incorrect conclusions. Example: - During the 2008 financial crisis, Fannie Mae and Freddie Mac struggled with incomplete loan data, making it difficult to assess risks accurately. This contributed to their $187 billion government bailout. SQL: - SELECT customer_id, COALESCE(email, 'No email provided') AS email FROM customers; Which tip resonates most with you? 🤔 or share your own!💭 #datacleaning #dataanalyst #dataanalytics #sql #datascience #opentowork

Ever tried reading through a spreadsheet that looks more like a work of abstract art? 😅 When data is entered inconsistently across teams, it becomes a time-consuming maze to make sense of even the simplest details. If we’re serious about digital transformation, we have to get serious about data standardization—because our data can only be as good as the consistency that fuels it. Why does this matter? Without standardized practices, mismatched formats and typos are more than just minor annoyances—they’re productivity killers and accuracy hazards. According to Data Ladder LLC, *‘Mistyping or misspellings are one of the most common sources of data quality error,’* and these seemingly small errors can have ripple effects across any business that relies on accurate reporting and forecasting. If you're investing in a SaaS product, you want to ensure that what you’re feeding into it is high-quality data. Inaccurate data leads to poor insights, missed opportunities, and a lot of wasted time reconciling errors. Standardization isn’t just about creating order; it's about creating a reliable foundation for growth. Let’s take the time now to align our data entry practices and set ourselves up for success. Because digital transformation? It’s only as easy as the data we put into it! Just my $0.02. #proptech #realestatetech #communitydevelopment #placemaking #homebuilding #digitaltransformation

Inconsistent Data Formats - Logistics Use Case Example: One supplier sends shipment data in a spreadsheet, another in a PDF, and a third via an EDI message. Your internal team must manually convert these into a consistent format for their system(s). Impact: This manual process is time-consuming, error-prone, and labor intensive, resulting in incorrect data entries, delayed processing, and resource constraints. Solution: Standardize data formats. Ensure that data from different providers follows a standardized format. This reduces the need for manual scrubbing and facilitates easier integration into internal systems.