Data Analytics Consultation

Everyone says they want a “data strategy.” Most just want prettier charts. But real data strategy isn’t decoration — it’s plumbing, therapy, and diplomacy rolled into one. It starts with the stuff no one wants to do — the boring, unskippable, actually-matters work: 1 — Define goals What business outcomes are we chasing? If no one can answer that, stop buying tools. 2 — Map sources Find out where the data lives, who owns it, and what’s missing. Translation: prepare for awkward conversations. 3 — Structure & store Connect, clean, and standardize. Build enough structure to move — not to impress. 4 — Transform & measure Turn business logic into data logic. Agree on what “revenue,” “active user,” and “conversion” actually mean. 5 — Visualize & act Make the insights usable. Because a strategy that never leaves the spreadsheet isn’t a strategy — it’s decoration. And through all of it: continuous collaboration. Same goals, same questions, same caffeine supply. Because a data strategy isn’t a toolstack — it’s a process. And the moment you treat it like one, decisions start making sense. ----- I’m Nick — founder of a data consulting team that builds clarity, not chaos. We make data work the way business thinks. DM me to discuss your project!

I've made mistakes that cost me a lot of time and money. Here’s the hard truth I’m learning about data consulting: It's very different from being a data employee. I'm no longer graded on how clean my code is or how many dashboards I churn out. Everything is about the business impact. You can write "perfect" SQL and ship "efficient" models but still miss the mark. Everything now hinges on asking the right questions before writing a single line of code: ▪️Who’s the ultimate stakeholder? ▪️What decision is the stakeholder trying to make? ▪️Which KPI or business event moves the needle? ▪️How will this analysis translate into dollars saved or revenue gained? Skip that step, and your “efficient” model becomes a costly detour. In this week’s Rooftop Insights, I’ll break down: → Why pausing to understand context is non-negotiable → The exact discovery questions that prevent rework → How to transform from order-taker to trusted advisor, delivering real business impact Want it? The link is in the comments.

Data analytics (DA) remains wildly popular, but without a clear understanding of the goals and the risks involved it can result in significant inefficiencies and misalignment with client priorities. Here are some recommendations: ✅ Engage stakeholders to understand their needs and expectations ✅ Define clear objectives for the DA procedures ✅ Determine what specific questions the auditor is trying to answer ✅ Verify the quality of the data, privacy concerns, and potential biases ✅ Creatively use appropriate tools and techniques based on the nature of the data and goals pursued ✅ Communicate results effectively by presenting the findings clearly and concisely. Highlight key results and their implications for the client so they can make informed decisions Without this being addressed, auditors risk “fishing” for interesting findings without a clear direction. This is a waste of time and resources. By understanding the objectives and risks before engaging in data analytics, auditors can ensure their efforts are focused, efficient, and aligned with client goals. This approach enhances the value of the project and strengthens the trust and confidence of clients in the audit function. #dataanalytics #internalauditing #bestpractices #data #analytics #innovation #creativity

5 𝗗𝗮𝘁𝗮 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗦𝘁𝗮𝗿𝘁𝘂𝗽𝘀 𝗮𝗻𝗱 𝗦𝗠𝗕𝘀 𝗙𝗮𝗰𝗲, 𝗮𝗻𝗱 𝗛𝗼𝘄 𝘁𝗼 𝗦𝗼𝗹𝘃𝗲 𝗧𝗵𝗲𝗺 Navigating data and technology challenges can be daunting for startups and SMBs. As someone who has spent decades in the trenches of technology and data management, I've seen firsthand the challenges these organizations face. Here are five data challenges and how to tackle them effectively. 1. 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 - Poor data quality can lead to misguided business decisions. Implementing a robust data management and governance framework will ensure accuracy and consistency. Conducting regular audits and cleansing processes are essential to maintain data integrity. 2. 𝗗𝗮𝘁𝗮 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 - Trust and confidence are critical to winning and retaining customers. With increasing cyber threats, safeguarding your data is paramount. Implement strong encryption methods and ensure data is encrypted at rest and in transit. 3. 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 - Many startups and SMBs struggle with disparate data sources, formats, volume, and frequency of data updates. Developing a sound integration platforms or middleware solutions can streamline data flow and improve accessibility across systems. 4. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 - Implement a scalable, cloud-based data platform like AWS, Google Cloud, or Microsoft Azure. These cloud platforms offer elastic compute and storage resources that adapt to growing data demands without requiring significant upfront investment in hardware. 5. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 - Many organizations have lots of data but lack analytical skills to derive insights. Leveraging experienced data consultants with advanced analytics expertise can transform raw data into actionable insights, driving informed business decisions. Addressing these challenges head-on can significantly enhance your organization's data strategy. If you are facing similar data and technology challenges, connect with me for a free 30 minute consultation. Please share your thoughts or questions in the comments below. #DataManagement #TechLeadership #DataSecurity #DataGovernance