How to Simplify Data Collection for Tech Companies

Your $1M 'modern data warehouse' is world's most expensive junkyard. Tracking 2TBs of usless data 'just in case' but still takes 2-weeks to answer simple question. Every company says the same thing: “Let’s capture everything, for down the line” That sentence has quietly destroyed more ROI, Snowflake credits, and data trust than any bad tool ever has. A few years ago, I met a retail data leader that proudly told me, “We’re tracking everything. Every click, scroll, hover, API event.” A billion events a day. Storage bills through the roof. And still, the FP&A didn’t trust a single revenue number. Irony right Analysts couldn’t even tell if someone added or removed an item from a cart because every event stored the entire basket state. So the engineers were drowning in data. The business was starving for insight. And leadership thought the solution was… to collect even more for 'in case'. That’s not data maturity. That’s data garbage. Most mid-market firms believe more data = more answers. But that logic is backwards. When you collect everything: Your warehouse becomes a landfill. Pipelines grow, meaning more debugging. Your team spends 80% of time cleaning, 20% analyzing. Governance dies because no one knows what matters. Every metric becomes a debate, not a decision. “Collect everything” doesn’t make you smart, it makes you slow, expensive, and untrusted. Here's how to fix this 1. Start with the business question, not the data source Before tracking another event, ask: “What question are we trying to answer and who needs it?” If it doesn’t tie to a decision, action that enables the end user to achive their objective Don’t collect it. Data only valuable if used, storing it it's worth $0. 2. Move from data hoarding to event intent. Stop dumping entire states Start tracking facts, atomic events one line that captures who, what, when, and why. They represent a business action or customer intent. For example: - Don't “Full basket snapshot” → 200 useless fields. - Do “Product_Added_to_Cart” → who, what, when, how much. That’s where clarity starts. This is gone build foundation for it as AI needs context, not just more data. 3. Build a tracking plan that acts like a product spec. Define which events are allowed, what’s mandatory, and who owns them. If it’s not in the plan, it doesn’t get into your warehouse. Most warehouse look 'modern' outside but 'legacy' inside. You didn't fix nothing, just made it worse. 4. Kill the “just in case” mindset. You don’t need another terabyte of noise. You need confidence in the 1% of data that drives 99% of value. One org had TBs of data, queries that cost > $5,000 month, that they didn't need at all. If it takes three department, two weeks to answer “What’s our active customer count?” You’re not data-driven. Companies winning with AI and analytics aren’t hoarding petabytes. They’re the ones who know exactly which 1% of their data actually matters.

How I saved a tech company $128,557 in 22 days (without hiring more staff): $128,557. Gone. All because they didn’t trust their CRM to do its job. Here’s the story: - 185 employees - A growing tech company - Held back by CRM inefficiencies Their marketing director was wasting 5.5 hours a day on low-level tasks: Manually entering lead data into three CRMs: - HubSpot - Google Sheets - Salesforce The result? - Typos and bad data - Gut-feeling decisions - Delayed reporting Let’s crunch the numbers: 110 hours a month—lost. 165 working days a year—wasted. A staggering $128,557—down the drain. Here’s how we fixed it: First, discovery. We identified every inefficiency and bottleneck. Then, build. We integrated HubSpot with Salesforce and eliminated Google Sheets. Finally, testing. Everything was run in Salesforce Sandbox, approved, and launched. 22 days later, their system ran like clockwork. The result? 110 hours saved every month $128,557 in yearly costs eliminated And their marketing director? Back to focusing on real high-level work. P.S. What’s one inefficiency that’s holding your business back right now?

Spent hours wrestling with reports that felt more like elaborate puzzles than useful data. For my team of 400 Infrastructure Support and Helpdesk Engineers, managing rosters and tracking performance against KPIs was, frankly, a nightmare. It was a manual grind, dependent on a few key people, and took up way too much valuable time each week just to get basic numbers. Analysis? Forget about it. I started thinking there had to be a smarter way. A way to actually use our data, not just generate it. So, we began mapping skills, understanding our business needs, and figuring out how to link the two logically. Then, we built a system that automatically generated rosters based on those connections. We also integrated our ITSM solution to capture tech and business metrics in real time. The result? We finally automated roster creation and reporting. It freed up about 20 man hours every single week. More importantly, it gave us actionable intelligence much faster, helping us actually *improve* operations instead of just reporting on them. It’s amazing what happens when you shift from manual data collection to automated insight generation. What are some of those time-consuming, manual processes in your field that you're itching to automate? I'd love to hear what you're tackling. #Automation #DataAnalytics #TeamManagement #OperationalExcellence #ITSM

In many field surveys, results suffer when teams rely on paper forms, manual data entry, and delayed supervision. Digital collection can reduce errors, speed up reporting, and make fieldwork easier to manage in low-connectivity settings. This module introduces mobile data collection and contrasts the paper-based workflow with a mobile-based process that combines field capture, transfer, and analysis. It presents common data collection tools, then explains how KoBoToolbox and the Open Data Kit Collect application work together from account setup and project creation to form building in the online builder or through Excel, deployment to a server, and submission from Android devices, including offline completion and later syncing. For field teams and monitoring staff, the material is valuable because it clarifies the full digital workflow needed to improve data quality through skip patterns and built-in checks, reduce time and cost by limiting manual entry, and strengthen supervision through remote monitoring and faster adjustments while collection is still ongoing.

From Complex Databases to Click-Friendly Dashboards: How I Helped a Fitness Company Simplify Data Access A UK-based fitness company approached me with a challenge: 👉 Their critical company data was stored in RDS (behind a bastion host) and user exercise recordings in S3 buckets. 👉 Their developers were excellent at coding but struggled with managing and querying databases. 👉 They needed a secure, easy-to-use UI to search, filter, and visualize data — without exposing the database directly. 🔧 My Solution I designed and implemented a cost-effective, secure, and scalable solution using AWS native tools: • AWS Lambda + Python → Queried the database, processed results, and reduced query handling time by 60%. • API Gateway → Acted as a secure bridge between the UI and backend, eliminating direct DB exposure. • Custom Dashboard → Integrated token-based authentication, advanced filters (date/device), and direct S3 downloads — reducing manual effort by 70%. ⚡ Results Delivered ✅ Developers can now search and filter data without writing SQL queries (saving 10+ hours per week) ✅ Secure access ensured with zero risk of exposing the database ✅ Exercise recordings can be downloaded directly from S3 in 1 click ✅ The entire project was delivered in just one month, improving developer productivity by 50% 💡 What started as a database headache turned into a streamlined, developer-friendly dashboard that saves time, boosts productivity, and keeps data safe. ⸻ 📖 I shared the full breakdown here: https://lnkd.in/dprXudBr #work #devops #data #remotework #freelance #aws

I've spent enough time making that mistake, using all sorts of tools (Clay, Bardeen, Relay, etc) and come to the same conclusion every time. It's awful and can't replace human judgment. It's also an expensive process to get right, with lots of "context engineering" needed. I've changed my approach. Instead of letting AI do the judgment, I let AI lookup the signals that make a company part of our market. Small yet key distinction. AI focuses on gathering reliable data. You decide what makes a company qualified with simple filters. No more "is this company qualified yes/no?" - giving AI freedom to hallucinate and provide lazy responses. Here's how: I've done this for 2 different tech companies in the last 6 months. It has allowed me to segment funnels of over 500 thousand companies to find the few thousand relevant ones. You get this right and you won't need to search for leads in a long time. You'll have your complete market segmented. Sounds simple - very hard to get right. Step 1: Find your input database Clay, #Apollo, #LinkedInSalesNav... wherever your potential leads might exist. Step 2: Define signals tied to your value prop This is where most people fail. Your qualification criteria should connect directly to where you create value: You make company retreats → lookup company's past retreats You're an ad agency → lookup company's active advertising spend You sell CRM software → lookup company's current CRM stack You're a recruiting agency → lookup company's hiring metrics and open roles This is where #GTMengineer expertise matters. Capturing the right signal is the highest value in this process. Clay is the tool to architect all of this. For experiments, tools like Extruct AI can help build vertical signals by crawling websites, news outlets, etc. Step 3: Let AI extract this data per company, but don't let it decide ❌ Instead of: "Score this company 1-10 for qualification" ❌ Instead of: "gpt, is this company a good fit?" ✅ Do: "Find evidence of this company's retreats and internal events from 2021- 2024 into structured data" ✅ Do: Filter the data to segment your relevant market: TAM filter→ "part of our total market" SAM filter→ "we can serve them" SOM/ICP filter→ "ideal prospect to target now" The output will be your entire market segmented in actionable data, one company at a time. Not cheap, I know - but worth it. The difference? → AI focuses on what it's good at: data extraction → You're in control of lead segmentation → Auditable and fixable results → Less false positives → One time big expense, infinite upside if done right Most get it backwards. They want AI to be the decision maker when it should be the researcher. Your qualification logic is your competitive advantage. Don't outsource it. What's your experience with AI qualification?

It's all too common to see teams drowning in data prep (and hating it). The inefficiency of data cleanup doesn't just waste time; it hinders decision-making, delays insights, and lowers team morale. So, what's the solution? The answer lies at the source. 📈 Automate Data Collection: Use tools that automatically capture and organize data from various sources. This reduces manual entry and ensures consistency. 📏 Make Data Governance Easy: Establish clear rules and guidelines for data entry. This includes validation rules and required fields to ensure data accuracy from the start. 🗂️ Standardize Data Formats: Implement standardized formats and naming conventions across your data sources. This makes it easier to clean and integrate data without constant reformatting. 🔍 Regular Data Audits: Conduct periodic audits of your data to identify and rectify inconsistencies. This proactive approach helps maintain data integrity over time. Then to take it to the next level, focus on transforming your data and ensuring those transfromations are the single source of truth. 🏢 Centralize Data Storage: Store all data in a centralized location accessible to all relevant teams (ideally a Data Warehouse). This eliminates the need for multiple versions of the same data and reduces errors. 🔄 Leverage ETL (or Reverse ETL) Tools: Use ETL tools to automate the process of cleaning and preparing data for analysis & push it from this source to your point solutions. This makes the data prep process faster and more efficient while ensuring everyone operates off of the same information. Benefits? You get to: Spend more time on insights, not prep

Startups don’t need a Fortune 500 data stack. They need one that works. But too often, early teams either: 1️⃣ Ignore data and make gut decisions. 2️⃣ Over-engineer with tools they don’t need yet. Here’s how to keep it simple and effective: 1. Start with Questions, Not Tools What are some key metrics that actually matter for your stage? Can you answer them today without building a full stack? If not, only add the tools that solve real gaps. 2. Keep Data Where People Already Work Fancy dashboards are useless if no one looks at them. Push key metrics to Slack, Notion, or email so they stay visible. Don’t create more friction—integrate with existing workflows. 3. Track Events, but Keep It Simple Define a minimum set of product events (signup, activation, retention). Standardize naming early (or regret it later). Avoid tracking everything—signal > noise. 4. Own Your Definitions from Day 1 What counts as an "active user"? What’s the exact definition of churn? Align early so your team isn’t debating numbers six months later. 5. Build for Flexibility, Not Complexity Start with spreadsheets or simple dashboards. Don’t rush into a data warehouse before you need it. Choose tools that can scale when the time is right. At early stages, speed matters more than perfection. Track what’s essential, automate where possible, and evolve as you grow. What’s been your biggest headache when setting up a data stack?

Most sales teams struggle with messy data and manual prospecting. Here's our proven 6-step system that changed everything: 1️⃣ Build precision-targeted lists - Pull rich data from LinkedIn + Crunchbase + BuiltWith - Filter by ICP markers: company size, tech stack, funding - Schedule real-time updates to keep data fresh - Automatically enrich and update contacts, and companies in your CRM- push qualified leads straight to HubSpot /Salesforce 2️⃣ Auto-score every lead - Company size = +10 points - VP/Director level = +15 points - Recent funding/hiring = +20 point - Sync scores with Bombora +6sense intent data or use Clay 3️⃣ Data enrichment - Fill gaps using Hunter.io for emails - Track tech stack changes - Monitor hiring signals - Pull mutual LinkedIn connections - Export to Apollo.io for outreach 4️⃣ Personalization at scale - Dynamic fields: {Tech Stack}, {Funding News} - Company-specific insights in every message - Sync with lemlist + Smartlead /Instantly.ai - A/B test messaging performance 5️⃣ Instant research briefs - Auto-generate company summaries - Pull stakeholder insights - Track industry news + trends - Share via Notion /Slack with team 6️⃣ Role-specific scripts - Custom templates by position - Industry-specific pain points - Data-backed objection handling - Dynamic fields for relevance The best part? Once set up, this runs on autopilot. We went from 2 hours of research per prospect to 10 minutes.