The Role of Data in Business

Healthcare is in crisis and it’s only getting worse! Hospitals constantly face fluctuating demand: Staff shortages during peak seasons Overcrowded emergency rooms Wasted resources during low demand periods What if you could predict these patterns in advance and prepare for them? Time Series Models analyze historical data to identify trends and patterns over time — like seasonal spikes or daily fluctuations. ✅ Step 1: Collect Historical Data Gather key data points, including: Patient admissions Emergency visits Staff availability Resource consumption (beds, medication, equipment) ✅ Step 2: Identify Seasonal Patterns The model can uncover hidden trends: Higher ER visits during flu season Increased staffing demand on weekends and holidays Decline in outpatient visits during summer months ✅ Step 3: Predict Future Demand Once patterns are identified, the model can forecast: When patient volume will spike How many staff members will be needed What resources should be stocked up ✅ Step 4: Scale Across Departments The model can be applied to: Emergency rooms ICU Outpatient clinics Pharmacy services The more data it processes, the smarter it gets & continuously improving accuracy. Using Time Series Models can significantly improve patient care. 1) Hospitals can reduce wait times, enable faster diagnosis and treatment, and ultimately enhance patient satisfaction. 2) It also helps with workforce management by reducing staff burnout, balancing workloads across shifts, and minimizing last-minute scheduling issues. 3) From a cost perspective, these models drive greater efficiency by lowering operational costs, reducing waste of medical supplies and ensuring smarter use of hospital resources. Time Series Models are helping hospitals anticipate demand, optimize resources and improve care. #healthcare #it #healthtech #hospital

Communicating complex data insights to stakeholders who may not have a technical background is crucial for the success of any data science project. Here are some personal tips that I've learned over the years while working in consulting: 1. Know Your Audience: Understand who your audience is and what they care about. Tailor your presentation to address their specific concerns and interests. Use language and examples that are relevant and easily understandable to them. 2. Simplify the Message: Distill your findings into clear, concise messages. Avoid jargon and technical terms that may confuse your audience. Focus on the key insights and their implications rather than the intricate details of your analysis. 3. Use Visuals Wisely: Leverage charts, graphs, and infographics to convey your data visually. Visuals can help illustrate trends and patterns more effectively than numbers alone. Ensure your visuals are simple, clean, and directly support your key points. 4. Tell a Story: Frame your data within a narrative that guides your audience through the insights. Start with the problem, present your analysis, and conclude with actionable recommendations. Storytelling helps make the data more relatable and memorable. 5. Highlight the Impact: Explain the real-world impact of your findings. How do they affect the business or the problem at hand? Stakeholders are more likely to engage with your presentation if they understand the tangible benefits of your insights. 6. Practice Active Listening: Encourage questions and feedback from your audience. Listen actively and be prepared to explain or reframe your points as needed. This shows respect for their perspective and helps ensure they fully grasp your message. Share your tips or experiences in presenting data science projects in the comments below! Let’s learn from each other. 🌟 #DataScience #PresentationSkills #EffectiveCommunication #TechToNonTech #StakeholderEngagement #DataVisualization

I pitched a LOT of internal data infrastructure projects during my time leading data teams, and I was (almost) never turned down. Here is my playbook for getting executive buy-in for complex technology initiatives: 1. Research top-level initiatives: Find something an executive cares about that is impacted by the project you have in mind. Example: We need to increase sales by 20% from Q2-Q4 2. Identify the problem to be overcome: What are the roadblocks that can be torn down through better infrastructure? Example: We do not respond fast enough to shifting customer demand, causing us to miss out on significant selling opportunities. 3. Find examples of the problem: Show leadership this is not theoretical. Provide use cases where the problem has manifested, how it impacted teams, and quotes from ICs on how the solution would have greatly improved business outcomes. Example: In Q1 of 2023 multiple stores ran out of stock for Jebb Baker’s BBQ sauce. We knew the demand for the sauce spiked at the beginning of the week, and upon retroactive review could have backfilled enough of the sauce. We lost an expected $3M in opportunities. (The more of these you can provide the better) 4. Explain the problem: Demonstrate how a failure of infrastructure and data caused the issue. Clearly illustrate how existing gaps led to the use case in question. Example: We currently process n terabytes of data per day in batches from 50 different data sources. At these volumes, it is challenging to manually identify ‘needle in the haystack’ opportunities, such as one product line running low on inventory. 5. Illustrate a better world: What could the future world look like? How would this new world have prevented the problem? Example: In the ideal world, the data science team is alerted in real-time when inventory is unexpectedly low. This would allow them to rapidly scope the problem and respond to change. 6. Create requirements: Define what would need to be true both technologically and workflow-wise to solve the problem. Validate with other engineers that your solution is feasible. 7. Frame broadly and write the proposal: Condense steps 1-5 into a summarized 2-page document. While it is essential to focus on a few use cases, be sure not to downplay the magnitude of the impact when rolled out more broadly. 8. Get sign-off: Socialize your ideal world with potential evangelists (ideally the negatively impacted parties). Refine, refine, refine until everyone is satisfied and the outcomes are realistic and achievable in the desired period. 9. Build a roadmap: Lay out the timeline of your project, from initial required discovery sessions to a POC/MVP, to an initial use case, to a broader rollout. Ensure you add the target resourcing! 10. Present to leadership alongside stakeholders: Make sure your biggest supporters are in the room with you. Be a team player, not a hero. Good luck! #dataengineering

🟠 Most data scientists (and test managers) think explaining A/B test results is about throwing p-values and confidence intervals at stakeholders... I've sat through countless meetings where the room goes silent the moment a technical slide appears. Including mine. You know the moment when "statistical significance" and "confidence intervals" flash on screen, and you can practically hear crickets 🦗 It's not that stakeholders aren't smart. We are just speaking different languages. Impactful data people uses completely opposite approach. --- Start with the business question --- ❌ "Our test showed a statistically significant 2.3% lift..." ✅ "You asked if we should roll out the new recommendation model..." This creates anticipation and you may see the stakeholder lean forward. --- Size the real impact --- ❌ "p-value is 0.001 with 95% confidence..." ✅ "This change would bring in ~$2.4M annually, based on current traffic..." Numbers without context are just math. They can be in appendix or footnotes. Numbers tied to business outcomes are insights. These should be front and center. --- Every complex idea has a simple analogy --- ❌ "Our sample suffers from selection bias..." ✅ "It's like judging an e-commerce feature by only looking at users who completed a purchase..." --- Paint the full picture. Every business decision has tradeoffs --- ❌ "The test won", then end presentation ✅ Show the complete story - what we gained, what we lost, what we're still unsure about, what to watch post-launch, etc. --- This one is most important --- ✅ Start with the decision they need to make. Then only present the data that helps make **that** decision. Everything else is noise. The core principle at work? Think like a business leader who happens to know data science. Not a data scientist who happens to work in business. This shift in mindset changes everything. Are you leading experimentation at your company? Or wrestling with translating complex analyses into clear recommendations? I've been there. For 16 long years. In the trenches. Now I'm helping fellow data practitioners unlearn the jargon and master the art of influence through data. Because let's be honest - the hardest part of our job isn't running the analysis. It's getting others to actually use it.

LLMs are shepherding in a new era of AI, no doubt about it. And while the volume and velocity of innovation is astounding, I feel that we are forgetting the importance of the quality of the data that powers this. There is definitely a lot of talk on what data is used to train the massive LLMs such as OpenAI, and there is a lot of talk on leveraging your own data through finetuning and RAG. I also see an increased attention on ops, whether it is LLMOps, MLOps or DataOps, all of which is great to keeping your system and data running. What I seeing far less attention to is managing your data, ensuring it is of high quality and that it is available when and where you need it. We all know about garbage in garbage out -- if you do not give your system good data, you will not get good results. I believe that this new era of AI means that data engineering and data infrastructure will become key. There are numerous challenges to get your system into production from a data perspective. Here are some key areas that I have seen causing challenges: 1. Data: The data used in development is often not representative of what is seen in production. This means the data cleaning and transforms may miss important aspects of production data. This in turn degrades the model performance as they were not trained and tested appropriately. Often new data sources are introduced in development that may not be available in production and they need to be identified early. 2. Pipelines: Moving our data/ETL pipelines from development to staging to production environments. Either the environment (libraries, versions, tools) have incompatibilities or the functions written in development were not tested in the other environments. This means broken pipelines or functions that need rewriting. 3. Scaling: Although your pipelines and systems worked fine in development, even with some stress testing, once you get to the production environment and do integration testing, you realize that the system is not scaling the way you expected and are not meeting the SLAs. This is true even for offline pipelines. Having the right infrastructure, platforms and teams in place to facilitate rapid innovation with seamless lifting to production is key to stay competitive. This is the one thing I see again and again being a large risk factor for many companies. What do you all think? Are there other key areas you believe are crucial to pay attention to in order to achieve efficient ways to get LLM and ML innovations into production?

The third Wyckoff law states that the changes in an asset's price are a result of an effort, which is represented by the trading volume. If the price is in harmony with the volume, there is a good chance the trend will continue. Usual cases (Proportional): -Large Volume, Large Range -Small Volume, Small Range Unusual cases (Divergent): -Large Volume, Small Range -Small Volume, Large Range Volume Spread Analysis (VSA) is a trading methodology that analyzes the relationship between volume and price movements in financial markets. Volume Analysis: VSA focuses on analyzing the trading volume accompanying price movements. Quantitative traders can incorporate volume data into their trading models to gain insights into market dynamics. By studying changes in volume, they can identify periods of accumulation (buying) or distribution (selling) and gauge the strength of market trends. Price-Volume Patterns: VSA identifies specific price and volume patterns that suggest potential market movements. Quantitative traders can develop algorithms that scan historical price and volume data to detect these patterns automatically. For example, they might look for price bar formations with high volume, indicating strong buying or selling pressure. Confirmation and Filters: VSA can act as a confirmation tool for quantitative trading strategies. Traders can use VSA analysis to validate other technical indicators or signals generated by their models. This helps reduce false signals and increases the robustness of the trading strategy. Market State Analysis: VSA can provide insights into the overall market state, such as the presence of institutional buying or selling, accumulation or distribution phases, or the presence of market manipulation. Quantitative traders can use this information to adjust their trading strategies accordingly. Risk Management: VSA can assist in risk management by providing additional information about market sentiment and potential reversals. By incorporating VSA analysis into their risk management models, quantitative traders can dynamically adjust their position sizes, stop-loss levels, or exit strategies based on volume and price movements. It's important to note that the effectiveness of VSA in quantitative trading depends on the quality and accuracy of the volume data being used. Reliable and accurate volume data is crucial for proper analysis and interpretation of VSA signals.

🤔 What good is BigQuery without historical #GA4 data? One of the core functions of digital analytics is understanding changes over time. You can't see how website traffic, user behaviour, or conversions have changed without historical data. This makes it difficult to identify trends, measure the impact of marketing campaigns, or track progress towards goals. Historical data provides context for understanding current data points. For example, A sudden spike in traffic might be cause for concern, but if you see it happened at the same time last year, it might be a seasonal trend and less alarming. If you have only recently connected GA4 with BigQuery, you may not have all the historical data in your BigQuery project. This is because, by default, the GA4 data is imported to BigQuery only from the date you first connected your GA4 property to your BigQuery project. If you want historical GA4 data in your BigQuery project, you need to backfill GA4 data in BigQuery. For more details, check my comment. 👇

Post-deployment data science begins with understanding how production data differs from training data. This is where theory meets reality and can make or break your model’s performance. [1] In training, ground truth labels are available, making performance evaluation straightforward. But in production, especially for scenarios like predictive maintenance, true labels might not be accessible. How can you evaluate a model without them? (You can with performance estimation algorithms) [2] Then there’s data drift. Training data distributions are stable and predictable. But once in production, data can shift over time, and your model has to keep up to stay relevant. [3] Data quality also changes, i.e., training data is cleaned and standardized, but production data often arrives with quality issues that can drag down model performance if not addressed. [4] Lastly, concept drift. In training, your model is built on patterns that are familiar and consistent. But in production, new patterns and behaviors can emerge as the world evolves. This means your model may need to be retrained every time there is concept drift. Is your data science team ready for production data and all the challenges that come with it?

🔍 Why Past Production Records Matter in Mining Operations ⛏️ Understanding historical production performance is critical for evaluating the health and potential of a mining asset. Over a 16-year period (2009–2024), this operation processed 352.13 million tonnes of ore at an average 0.57% Cu grade — yielding over 1.84 million tonnes of copper with 91.84% average recovery. 📊 What Past Production Tells Us: ✅ Orebody Performance Trends – Head grade evolution reveals ore depletion or variability. – Recovery efficiency reflects metallurgical adaptability. ✅ Operational Optimization – Milling throughput patterns help benchmark plant performance. – Annual production shifts inform asset utilization and downtime analysis. ✅ Forward Planning & Life-of-Mine (LOM) Forecasts – Historical data supports resource-to-reserve conversion assumptions. – Aids in calibrating cut-off grades, mining schedules, and expansion viability. ✅ Risk & Investment Assessment – Year-over-year trends in grade, tonnes, and recovery inform economic robustness. – Identifies inflection points where intervention improved outcomes. 📉 Example Insights from the Dataset: ·       Peak production achieved in Year 2 with >146 kt Cu at 0.86% grade. ·       Metallurgical recovery peaked at ~94.7% in Year 8 — showing process optimization. ·       Recent years show grade softening to ~0.49–0.53%, but recovery remains resilient. 🔧 Takeaway: Past production records are more than just historical numbers — they are strategic tools for validating feasibility studies, guiding process improvements, and building confidence in future project development. #Mining #Geology #Copper #MinePlanning #Metallurgy #ResourceEstimation #FeasibilityStudies #ProductionData #MineOptimization #GeologicalModelling

M&A deals don’t fail in the boardroom They fail in due diligence. 70% of acquisitions destroy value. AI slashes due diligence time by 30-50% (McKinsey). I leveraged 8 AI tools to turn M&A From a bottleneck into a competitive edge. The result?  45% faster deal closures and fewer post-merger surprises. Here's how AI transformed our approach: 1️⃣ Contract Analysis (Kira Systems):  ↳ Extracted key terms in hours, not weeks.  ↳ Turned contract insights into negotiation leverage. 2️⃣ Data Centralization (DiligenceVault):  ↳ Eliminated version control chaos.  ↳ Aligned stakeholders instantly, speed wins bids in tight windows. 3️⃣ Data Automation (Datasite):  ↳ Automated data room setup and tracking.  ↳ Freed my team's bandwidth for actual deal strategy. 4️⃣ Legal Risk Detection (Luminance):  ↳ AI caught anomalies human reviewers missed.  ↳ Mitigated post-deal surprises by 76%. 5️⃣ IP Portfolio Analysis (PatentSight):  ↳ Quantified IP's true worth in days.  ↳ Prevented us from "paying for air" in three recent acquisitions. 6️⃣ Financial Analysis (MindBridge):  ↳ Flagged revenue irregularities before they became problems.  ↳ Financial transparency is non-negotiable. 7️⃣ Compliance Checks (ComplyAdvantage):  ↳ Automated regulatory compliance at scale.  ↳ Shielded deals from expensive regulatory traps. 8️⃣ HR Analytics (PeopleInsight):  ↳ Spotted talent flight risks pre-acquisition.  ↳ Aligned human capital to deal goals from day one. 💡 Key Takeaway: AI isn't replacing dealmakers, it's supercharging them. How are you using AI to speed up complex processes in your field? Let’s discuss 👇 ♻️ Repost this to help your network.  🔔 Follow me, Nadir Ali, for more insights on Strategy, Leadership and productivity.