Data Wrangling
Use programmatic data cleaning with libraries like Pandas, not manual cleaning in Excel.
An analyst spent two days manually correcting date formats and removing duplicates from a 50,000-row spreadsheet in Excel. The next month, she received a new version of the same file and had to do it all over again. Her colleague, faced with the same task, wrote a Python script using the Pandas library. Her script cleaned the data in 30 seconds. When the new file arrived the next month, she simply re-ran the script, saving herself two days of tedious, error-prone work and ensuring the cleaning process was perfectly consistent every time.
Stop doing one-off data cleaning scripts. Do create reusable data cleaning pipelines instead.
A data scientist wrote a script to clean a specific dataset for a project. A few months later, a different team needed to use similar data and wrote their own, slightly different cleaning script. This resulted in multiple versions of the “truth.” A smarter approach is to create a centralized, reusable data cleaning pipeline. This pipeline, built as a series of modular functions, can be imported and used by anyone in the company, ensuring that everyone starts their analysis from the same clean, consistent, and trusted data source.
The #1 hack for dealing with missing data that will improve your model’s performance.
The most common approach to missing data is to just drop any rows that have missing values. A data scientist did this and lost 30% of his dataset, resulting in a poorly performing machine learning model. The hack is to use intelligent imputation instead. Rather than deleting the data, he used a simple technique to fill in the missing numerical values with the mean of that column. This preserved his valuable data, and his model’s predictive accuracy increased significantly. For some problems, more advanced imputation can yield even better results.
The biggest lie you’ve been told about “clean” data.
The lie is that a “clean” dataset is one that is free of errors. A company had a dataset of customer transactions that was perfectly formatted, with no missing values or typos. It looked clean. But an analyst discovered a subtle bias: the data only included transactions from their website, completely ignoring the large volume of in-store purchases. The data was technically clean but factually incomplete, leading to flawed conclusions about customer behavior. Truly “clean” data isn’t just tidy; it’s a complete and accurate representation of reality.
I wish I knew this about the importance of tidy data when I started in data science.
My first dataset was a mess. Each row was a store, and the columns were “Sales_Jan,” “Sales_Feb,” etc. To calculate the average monthly sales, I had to write complex, clunky code. I wish I had known about the “tidy data” principle: every row should be an observation, and every column should be a variable. I spent a day restructuring the data so that each row was a single month’s sales for a single store, with columns for “Store,” “Month,” and “Sales.” Suddenly, every analysis became incredibly simple and intuitive.
I’m just going to say it: 80% of a data scientist’s time is spent on data wrangling.
A data scientist was hired to build a revolutionary machine learning model. Her company thought she would spend her weeks writing complex algorithms. The reality? She spent the first three weeks of her first project just trying to get the data in the right shape. She had to join data from three different databases, correct thousands of inconsistent category labels, and figure out how to handle a field that was sometimes a number and sometimes text. The glamorous work of modeling is only 10% of the job; the other 90% is the janitorial work of cleaning data.
99% of data analysts make this one mistake when cleaning their data.
The most common mistake is cleaning the data without first understanding its context. An analyst received a dataset with an “Age” column that had some strange values, like 200. Assuming these were errors, he deleted them. He later learned that for business customers, the “Age” column was used to store the number of employees, not a person’s age. By “cleaning” the data without understanding the business rules behind it, he had thrown away valuable information and invalidated his analysis.
This one small habit of documenting your data cleaning steps will change the way you work with data forever.
A developer wrote a complex script to clean and transform a dataset. Six months later, her boss asked her to explain a specific transformation she had made. She couldn’t remember. She had to spend a full day re-reading her own code to figure it out. She adopted a new habit: for every cleaning step, she would add a comment explaining why she was doing it. This small habit of documenting her process not only made her work understandable to others but also to her future self.
The reason your data analysis is flawed is because of inconsistencies in your data.
An analyst was trying to calculate sales by country. Her final report showed surprisingly low sales for the United States. The reason? In the “Country” column, the data had been entered in multiple ways: “USA,” “U.S.A.,” “United States,” and “America.” Her program was treating each of these as a different country. The analysis was flawed not because of a complex statistical error, but because of a simple data consistency issue. Standardizing these values was the first and most critical step.
If you’re still manually cleaning your data, you’re losing valuable time.
An analyst at Company A spent the first week of every month manually cleaning their sales data in Excel. An analyst at Company B, with the same data, invested one week to write an automated cleaning script. Now, at the beginning of each month, the analyst at Company B runs her script and gets clean data in five minutes. She spends the rest of the week performing valuable analysis, while the analyst at Company A is still VLOOKUP-ing and copy-pasting.
Data Visualization
Use interactive data visualizations to explore your data, not just static charts.
An analyst created a static PDF report with charts showing overall sales trends. An executive looked at it and asked, “But what about sales for my specific region?” The analyst had to go back and create a new report. A different analyst built an interactive dashboard. The executive could now explore the data herself, filtering by region, product category, and date range. This interactive exploration allowed her to uncover insights about her specific area of the business that a static report would have never revealed.
Stop doing cluttered and confusing charts. Do use clear and concise visualizations that tell a story instead.
A presenter showed a single slide with a line chart that had twelve different colored lines, a dual-axis, and no clear title. The audience was completely bewildered. The presenter had just dumped her data onto a chart. A better approach is to tell a story. She could have used multiple, simpler charts. The first could show the overall trend, and subsequent charts could highlight one or two key comparisons, using color and annotations to guide the audience’s attention and communicate a clear, understandable message.
The #1 secret for creating compelling data visualizations that will engage your audience.
The secret is to remove everything that isn’t data. This concept, known as maximizing the “data-ink ratio,” involves decluttering your chart. A designer took a standard bar chart and removed the unnecessary background lines, the heavy borders, and the redundant labels. He made the colors less distracting and integrated the legend directly into the chart’s title. The result was a clean, elegant visualization where the data itself was the hero. The message was instantly clearer and more impactful because all the noise had been stripped away.
The biggest lie you’ve been told about pie charts.
The lie isn’t that pie charts are always bad, but that they are a good general-purpose chart. A developer used a pie chart to show the market share of ten different competitors. It was impossible for the human eye to accurately compare the sizes of the different slices. A simple, sorted bar chart would have made the comparison effortless. Pie charts are only effective when you are comparing a few parts (ideally no more than three) of a whole, and the differences between them are large. For almost everything else, a bar chart is better.
I wish I knew this about the principles of visual perception when I started creating charts.
When I first started, I used a rainbow color palette for my charts because I thought it looked vibrant. I didn’t realize that the human brain doesn’t perceive these colors in a logical order and that some colors appear more prominent than others, unintentionally creating a false emphasis in my data. I wish I had known about visual perception principles, like using a single, sequential color palette to show magnitude or a diverging palette to show a difference from a central point. It would have made my visualizations far more accurate and honest.
I’m just going to say it: A good data visualization should not need a lengthy explanation.
A manager was presenting a dashboard to his team. He spent the first five minutes of the meeting just explaining what each chart meant and how to read it. If a visualization requires a manual to understand, it has failed. A good visualization is intuitive. The choice of chart type, the clear labels, the title, and the use of color should all work together to make the core message self-evident. The goal is for the audience to understand the insight in seconds, without needing a guided tour.
99% of people make this one mistake when creating a bar chart.
The most common and misleading mistake is truncating the y-axis (not starting it at zero). A marketer created a bar chart to show the conversion rates of two website designs. Design A had a 2% conversion rate, and Design B had a 3% rate. By starting the axis at 1.5%, the bar for Design B looked three times taller than Design A, dramatically exaggerating a minor difference. A bar chart’s length is its primary visual cue, and truncating the axis breaks this fundamental principle and misleads the viewer.
This one small action of choosing the right chart type for your data will change the way you communicate your insights forever.
An analyst wanted to show the relationship between two numerical variables: advertising spend and sales. He put them in a bar chart, which was confusing and didn’t show the relationship at all. He then took a moment to think about what he was trying to show—a correlation—and chose the right chart type: a scatter plot. The scatter plot instantly revealed a clear, positive correlation between the two variables. Choosing the right chart is like choosing the right word; it’s the key to clear communication.
The reason your data visualization is ineffective is because it’s not tailored to your audience.
A data scientist presented her findings to a group of senior executives. She showed them complex statistical plots and talked about p-values and confidence intervals. The executives were lost and disengaged. For her next presentation, she tailored her approach. She hid the statistical complexity and instead presented a simple, clear bar chart that summarized the key business takeaway. By understanding her audience and speaking their language, her message was heard and acted upon.
If you’re still using 3D charts, you’re losing data clarity.
A presenter, trying to make his report look “fancy,” used a 3D pie chart. The 3D perspective distorted the chart, making the slices in the foreground appear much larger than the slices in the background, even if their actual values were smaller. This “chartjunk” adds no information and actively misleads the viewer. A simple, 2D chart is always a more honest and effective way to represent data. The third dimension just adds confusion and makes it harder to accurately interpret the information.
A/B Testing
Use a rigorous statistical approach to A/B testing, not just looking at the conversion rates.
A marketer ran an A/B test. The new design (“B”) had a 10% conversion rate, while the old one (“A”) had 9%. He declared B the winner and spent a fortune implementing the new design. A data analyst looked at the results and pointed out that, given the small sample size, the 1% difference was not statistically significant. It was likely just random noise. A rigorous approach involves calculating confidence intervals and p-values to ensure you’re not making major business decisions based on chance.
Stop doing short-running A/B tests. Do run your tests long enough to get statistically significant results instead.
A team launched an A/B test on a Monday. By Tuesday, the new version was performing 20% better. Excited, they stopped the test and declared a winner. They didn’t account for the “novelty effect” or the fact that user behavior is different on weekdays versus weekends. After they permanently launched the new version, the uplift disappeared. Running the test for a full two weeks would have given them a much more reliable result, smoothing out daily fluctuations and providing a true measure of the change’s impact.
The #1 secret for designing A/B tests that yield actionable insights.
The secret is to test a clear, bold hypothesis, not just a minor, trivial change. A team spent weeks A/B testing the shade of blue on a button. The result was inconclusive and taught them nothing. A different team had a bold hypothesis: “Making our value proposition clearer on the homepage will increase sign-ups.” They tested a completely redesigned headline. The test was a huge success and, more importantly, it taught them a valuable lesson about their users. The goal of testing isn’t just to get an uplift; it’s to learn.
The biggest lie you’ve been told about A/B testing.
The biggest lie is that A/B testing is a magic wand that will always find a winner and continuously increase your metrics. The reality, as experienced by companies like Google and Netflix, is that the vast majority of A/B tests fail. Most new ideas, even those that seem brilliant, either have no effect or actually perform worse than the original. A/B testing is not a machine for generating wins; it’s a rigorous tool for validating ideas and, more often than not, for preventing you from launching bad ones.
I wish I knew this about the multiple comparisons problem when I was running multiple A/B tests.
I was so excited about A/B testing that I would test 20 different things at once on the same page: the headline, the button color, the image, etc. I was looking for anything that showed a “statistically significant” result. I didn’t realize that if you run 20 tests, you have a very high chance of getting at least one “false positive” just by random luck. I wish I knew about the multiple comparisons problem and the need to use statistical corrections, which would have prevented me from chasing after these illusory wins.
I’m just going to say it: Most A/B tests are not statistically significant.
Many companies proudly present their A/B test results: “We saw a 5% lift in conversions!” But when you dig into the data, you often find that the sample size was too small, the test was run for too short a time, or the result had a p-value of 0.20. In other words, the “lift” was most likely just random variation. A culture of rigorous statistical analysis reveals that a huge percentage of what people claim as “wins” from A/B testing are actually just statistical noise.
99% of marketers make this one mistake when interpreting the results of an A/B test.
The most common mistake is “peeking” at the results before the test has reached its required sample size. A marketer would check the A/B test dashboard every day. If he saw that the new version was “winning,” he might be tempted to stop the test early. This practice of stopping the test as soon as it looks significant dramatically increases the rate of false positives. You must commit to a sample size before the test begins and not stop until you’ve reached it, regardless of what the intermediate results look like.
This one small action of calculating the required sample size before starting your A/B test will change the validity of your results forever.
A team ran an A/B test for a week and found no significant difference between the two versions. They concluded that their new design was a failure. The problem was, they had never calculated the required sample size. Given the small effect they were hoping to detect, they would have needed to run the test for a full month to have enough statistical power to see a result. By calculating the sample size upfront, they could have either committed to the longer test or decided that the potential gain wasn’t worth the effort.
The reason your A/B test results are inconclusive is because of a small effect size.
A team spent a month running an A/B test on a subtle change to their website’s font. The test results came back as “not statistically significant.” They were disappointed. The reason wasn’t that the test was run incorrectly, but that the change they made had a very small, almost non-existent effect on user behavior. To statistically detect such a tiny difference, they would have needed a massive sample size. Your ability to get a conclusive result is directly related to the magnitude of the change you are testing.
If you’re still making business decisions based on statistically insignificant A/B tests, you’re losing money.
A company ran an A/B test where the new version showed a 2% lift, but the p-value was 0.3. Despite the lack of statistical significance, the manager liked the new version and decided to roll it out anyway, investing a significant amount of engineering resources. After the launch, the overall site conversion rate didn’t change at all. They had spent a lot of time and money implementing a change based on what was essentially a coin flip, a classic example of mistaking noise for a signal.
Big Data
Use distributed computing frameworks like Spark for big data processing, not a single machine.
An analyst tried to process a 500GB log file on his powerful laptop. He started the script, and his computer’s fans spun up to maximum speed. After an hour, the program crashed because it ran out of memory. His colleague took the same file, loaded it into a distributed computing framework like Apache Spark, and wrote a similar script. Spark automatically distributed the work across a cluster of ten machines, and the job finished in five minutes. For data that doesn’t fit in memory, distributed computing isn’t an option; it’s a necessity.
Stop doing batch processing for everything. Do use stream processing for real-time data instead.
A credit card company had a fraud detection system that ran as a batch process every night. It would analyze the previous day’s transactions and identify fraudulent activity. The problem was, by the time the fraud was detected, the damage was already done. They switched to a stream processing model using a tool like Apache Flink or Kafka Streams. Now, every transaction is analyzed in real-time as it occurs. The system can detect and block a fraudulent transaction within milliseconds, not hours.
The #1 tip for optimizing your Spark jobs that will save you time and money.
The most important tip is to avoid “shuffles” whenever possible. A shuffle is an expensive operation where Spark has to redistribute data across the different nodes in the cluster. A developer wrote a Spark job with multiple, complex joins that caused a massive shuffle. The job was incredibly slow. By re-writing the code to use a “broadcast join” for a smaller table, he was able to send a copy of the small table to every node, completely eliminating the need for a shuffle and cutting his job’s runtime in half.
The biggest lie you’ve been told about the “3 V’s” of big data.
The lie is that the “3 V’s”—Volume, Velocity, and Variety—are what define big data. A company was proud of their massive volume of data. They had petabytes of it. But they weren’t doing anything with it. A much more important “V” is Value. It doesn’t matter how much data you have, how fast it’s coming in, or how many different types there are. If you are not able to extract business value from it, you don’t have a big data strategy; you just have a big data storage problem.
I wish I knew this about the trade-offs between different big data technologies when I started.
When I started in big data, I thought I had to use Hadoop for everything. I tried to use its MapReduce framework for an interactive query task. It was incredibly slow and cumbersome. I wish I had known that the big data ecosystem is a collection of specialized tools. For batch processing, Spark is often better than MapReduce. For interactive querying, a tool like Presto or Druid is a much better choice. There is no single “best” big data technology; the right choice always depends on the specific use case.
I’m just going to say it: You probably don’t have a “big data” problem.
The term “big data” is one of the most overused buzzwords in tech. A company spent a fortune building a complex big data platform with Hadoop and Spark to analyze their customer data. The reality was that their entire dataset was only 10GB. They could have loaded it into a single PostgreSQL database on a modest server and run their queries much faster and with far less complexity. Many companies that think they have a “big data” problem actually just have a “we haven’t tried using a database properly” problem.
99% of companies make this one mistake when starting a big data project.
The most common mistake is focusing on the technology before the business problem. A company’s IT department decided they needed to build a “data lake.” They spent a year and millions of dollars building a sophisticated platform. Then they went to the business units and asked, “What do you want to do with it?” The business units had no idea. The project failed because it was a solution in search of a problem. A successful big data project starts with a clear business question, not with a choice of technology.
This one small action of partitioning your data will change the performance of your big data queries forever.
An analyst was running a query to find the total sales for the last week. The query was scanning the entire 10-terabyte sales table, and it took an hour to run. A data engineer partitioned the table by date. Now, when the analyst ran the same query, the query engine knew it only had to scan the small partitions corresponding to the last seven days. The query time dropped from an hour to thirty seconds. Partitioning is a fundamental technique for making big data queries efficient.
The reason your big data project is failing is due to a lack of a clear data strategy.
A company was collecting every piece of data they possibly could. They had log files, sensor data, social media feeds, everything. Their data lake was growing by terabytes every day. But the project was delivering no value. They had no clear strategy for what they wanted to achieve. A data strategy isn’t about collecting data; it’s about defining what data is important, how it will be governed, and how it will be used to answer specific business questions and drive specific outcomes.
If you’re still trying to process terabytes of data with Excel, you’re losing your sanity.
An analyst was given a dataset with 50 million rows and was asked to analyze it. He tried to open it in Excel. The application froze, and after ten minutes, it crashed. Excel is a fantastic tool, but it is not designed for big data. For datasets that have more than a million rows, you need to use tools that are designed for the job, whether it’s a database, a programming language like Python with Pandas, or a distributed computing framework like Spark. Using the wrong tool for big data is an exercise in frustration.
Predictive Analytics
Use machine learning models for predictive analytics, not just traditional statistical models.
An insurance company used a traditional logistic regression model to predict which customers were likely to churn. The model was interpretable but not very accurate, as it could only capture linear relationships in the data. They switched to a machine learning model like a gradient boosted tree. This model could capture complex, non-linear interactions between the features, and its predictive accuracy was 20% higher than the old model, allowing them to more effectively target their retention efforts.
Stop doing one-time predictions. Do create a system for continuous prediction and monitoring instead.
A data science team built a predictive model, handed off a spreadsheet of predictions to the business team, and moved on to the next project. A month later, the model’s predictions were no longer accurate because the underlying data had changed. A better approach is to build a system. This involves creating an automated pipeline that can continuously retrain the model on new data, serve predictions via an API, and, crucially, monitor the model’s performance in production to detect when it needs to be updated.
The #1 secret for building an accurate predictive model.
The secret isn’t a complex algorithm; it’s high-quality feature engineering. A team was trying to predict house prices. They started with basic features like square footage and number of bedrooms. The model was mediocre. Then, they started engineering new features. They created a “school district quality” score, a “distance to nearest park” feature, and a “crime rate” feature. These new, context-rich features provided the model with a much deeper understanding of what drives house prices, and its accuracy soared. The best model can’t overcome bad features.
The biggest lie you’ve been told about the accuracy of predictive models.
The lie is that a model with “99% accuracy” is a great model. A data scientist built a model to predict a very rare type of manufacturing defect. The model achieved 99.9% accuracy. But upon inspection, the model was simply predicting “no defect” for every single item. Because the defect was so rare, this strategy was almost always correct. This is the “accuracy paradox.” For imbalanced datasets, accuracy is a terrible metric. You need to use metrics like precision, recall, and F1-score to understand a model’s true performance.
I wish I knew this about feature engineering when I was building my first predictive model.
When I built my first model, I just threw all the raw data I had into the algorithm. The results were poor. I wish I had known about the art of feature engineering. This is the process of using your domain knowledge to create new input variables for your model. For a customer churn model, instead of just using the “last purchase date,” I could have engineered a “days since last purchase” feature. This kind of creative transformation of raw data into meaningful features is often the most important factor in a model’s success.
I’m just going to say it: A simple predictive model is often better than a complex one.
A junior data scientist spent weeks building a highly complex deep learning model. It was a marvel of engineering. A senior data scientist on the same team spent an afternoon building a simple logistic regression model. The complex model was only 1% more accurate than the simple one, but it was a “black box” that no one could understand. The business chose to deploy the simple model because it was interpretable, easy to maintain, and “good enough” for their needs. Don’t let complexity be the enemy of a practical solution.
99% of data scientists make this one mistake when evaluating their predictive models.
The most common mistake is evaluating the model on the same data that was used to train it. A developer trained a model on his entire dataset and was thrilled to see it achieved 98% accuracy. But when he used the model on new, unseen data, the accuracy dropped to 60%. He had “overfit” the model; it had just memorized the training data instead of learning the underlying patterns. The correct approach is to split your data into a training set and a testing set, and only evaluate the final performance on the unseen test data.
This one small action of understanding your business problem before building a model will change the impact of your predictive analytics forever.
A data science team built a technically brilliant model to predict which customers were most likely to click on an ad. The business team was not impressed. The business problem wasn’t about clicks; it was about lifetime value. A different team started by deeply understanding the business goal. They built a model to predict a customer’s future value. This model, though perhaps technically simpler, had a massive impact because it directly addressed the problem the business actually cared about.
The reason your predictive model is not working is because you’re not using the right evaluation metric.
A team built a model to predict which patients were at risk for a serious disease. They optimized for accuracy. Their model was very accurate, but it had a low “recall,” meaning it missed a large number of the patients who actually had the disease. For this business problem, a false negative (missing a sick patient) was far more dangerous than a false positive. They should have been optimizing for recall, not accuracy. Choosing the right evaluation metric depends entirely on the business context.
If you’re still making business decisions based on gut feeling, you’re losing a competitive advantage.
The manager of an e-commerce company would decide which products to recommend based on his own personal taste and what he thought was popular. His recommendations were often wrong. A competing company used predictive analytics. They built a machine learning model that analyzed a user’s past behavior to predict which products they were most likely to buy. Their data-driven recommendations were far more effective, leading to a significant increase in sales and customer satisfaction.
Data Storytelling
Use data to tell a compelling story, not just present a bunch of numbers.
An analyst presented a slide with a table of numbers showing a 15% decline in user engagement. The audience nodded politely and forgot about it. Another analyst presented the same data as a story. She started with a line chart showing the sharp decline, creating tension. She then showed data that revealed the decline started right after a recent app update. She concluded by recommending a specific fix. Her story provided context, identified a cause, and proposed a solution, compelling her audience to take action.
Stop doing data dumps. Do create a narrative around your data instead.
A junior analyst was asked to present his findings on customer churn. He put 20 different charts on 20 different slides and walked through them one by one. It was a “data dump.” The audience was bored and confused. A senior analyst, coaching him, helped him find the narrative. The story was: “Our most valuable customers are churning at an alarming rate. The data shows this is because of a recent price increase. We recommend creating a loyalty program to retain them.” This narrative turned a boring presentation into a persuasive argument.
The #1 secret for effective data storytelling that will persuade your audience.
The secret is to know your audience and what they care about. A data scientist was presenting to the marketing team. She started by talking about the technical details of her model. They were bored. She changed her approach. She started by saying, “I’ve found a way to identify our most valuable customers so you can target your campaigns more effectively.” She connected her data to their goals. By framing the story around what the audience cared about, she captured their attention and got their buy-in.
The biggest lie you’ve been told about data speaking for itself.
The biggest lie is that if you just show people a chart, the data will “speak for itself.” Data has no voice. A chart of rising sales could be interpreted as a success. But what if the context is that the entire market grew by twice that amount? In that context, the same data tells a story of failure. Data without context and interpretation is just numbers. It is the storyteller’s job to give the data a voice and a clear message.
I wish I knew this about the art of storytelling when I started as a data analyst.
When I first started, I thought my job was to find interesting patterns in the data and present them. I would show my manager a chart and say, “Look at this interesting correlation!” He would ask, “So what?” I wish I knew then that my job wasn’t just to find insights, but to weave them into a story that answered that “So what?” question. It’s about explaining what the insight means for the business and what action should be taken as a result.
I’m just going to say it: The most important skill for a data scientist is communication.
A data scientist could be a technical genius who can build the most accurate and complex machine learning model in the world. But if they cannot explain what the model does, why it’s important, and how it can be used to a non-technical audience, then their work is useless. The most valuable data scientists are not the ones with the deepest technical skills, but the ones who can bridge the gap between the technical world and the business world. They are translators and storytellers.
99% of data professionals make this one mistake when presenting their findings.
The most common mistake is leading with the “how” instead of the “what.” A presenter will start by describing the complex data sources and the sophisticated methodology they used. By the time they get to the key finding, they’ve already lost their audience’s attention. The correct approach is to start with the single most important message or recommendation. Lead with the headline. You can always provide the details of your methodology later for those who are interested.
This one small action of starting with a question will change the way you tell stories with data forever.
An analyst used to start his presentations by saying, “Here is the data I looked at.” It was boring. He changed one small thing. He started his presentations by posing the key business question he was trying to answer, for example, “Why did our sales decline last quarter?” This one small action immediately engaged the audience. It framed his entire presentation as the answer to a compelling mystery, turning a data review into a detective story.
The reason your data presentation is boring is because it lacks a clear narrative arc.
A boring data presentation is just a series of disconnected facts. A compelling one has a narrative arc, just like a good story. It starts by setting the context and introducing the problem (the “inciting incident”). It builds tension by exploring the data and uncovering conflicts or challenges (the “rising action”). It presents the key insight or discovery (the “climax”). And finally, it concludes with a clear recommendation or call to action (the “resolution”).
If you’re still just showing dashboards without any context, you’re losing the attention of your audience.
A manager would start his weekly meeting by putting a dashboard on the screen and asking, “Any questions?” Usually, there was silence. The team was looking at a collection of numbers and charts with no story to connect them. A better approach is to use the dashboard as a visual aid to support a narrative. The manager could say, “As you can see from this trend line, our user sign-ups have been flat. Let’s discuss why and what we can do about it.” The dashboard becomes the evidence for the story, not the story itself.
Data Governance
Use a proactive data governance framework, not a reactive approach.
A company suffered a major data breach. In the aftermath, they scrambled to figure out what data they had, who had access to it, and how it was protected. This reactive approach was chaotic and stressful. A different company had a proactive data governance framework. They already had a catalog of their critical data, clear ownership, and access control policies in place. While no company is immune to breaches, their proactive posture allowed them to respond to incidents in a calm, organized, and effective manner.
Stop doing data governance in a silo. Do involve business stakeholders instead.
A company’s IT department tried to implement a data governance program on their own. They created a set of technical rules that the rest of the business found confusing and impractical. The program failed due to a lack of adoption. A successful data governance program is a partnership between IT and the business. Business stakeholders must be involved in defining the data, setting the quality rules, and determining who should have access, because they are the ones who understand the data’s meaning and context.
The #1 secret for successful data governance that fosters trust and collaboration.
The secret is to treat data governance as an enabler, not a gatekeeper. A company’s data governance committee was seen as the “data police,” a bureaucratic hurdle that slowed everyone down. A more successful company’s governance team saw their role as making it easier for people to find, understand, and use high-quality, trusted data. They built a data catalog, created clear documentation, and worked to provide a “paved road” for data usage. Their focus was on enablement, not enforcement.
The biggest lie you’ve been told about data governance being just about compliance.
The lie is that data governance is a boring, back-office function that is only about complying with regulations like GDPR or CCPA. While compliance is a critical component, it’s not the whole story. The true purpose of data governance is to increase the value of your data assets. Good governance leads to higher data quality, which leads to better analytics. It leads to greater trust in the data, which leads to more confident decision-making. It’s not just about risk mitigation; it’s about value creation.
I wish I knew this about the importance of data ownership when I started working with enterprise data.
When I first started, I found two different reports with two different numbers for “total customers.” I spent a week trying to figure out which one was correct. There was no clear owner for the “customer” data entity. I wish I had known that establishing clear data ownership is the foundation of good governance. When a specific person or team is officially accountable for a critical data asset, it ensures that there is a single source of truth and a point of contact for resolving these kinds of issues.
I’m just going to say it: Data governance is not a one-time project; it’s an ongoing process.
A company spent a year and millions of dollars on a “data governance project.” They created a massive set of rules and documents, declared victory, and then disbanded the team. A year later, the rules were outdated, the documentation was ignored, and the data was a mess again. Data governance is not something you can “finish.” It’s an ongoing, living process that must adapt to new data sources, new regulations, and new business needs. It’s a program, not a project.
99% of organizations make this one mistake with their data governance initiatives.
The most common mistake is trying to govern everything at once. An organization will try to create a massive, all-encompassing governance framework that covers every single piece of data in the company. This “boil the ocean” approach is doomed to fail because it’s too complex and takes too long to show value. A much better approach is to start small. Identify one critical data domain—like “customer” or “product”—and establish a solid governance model for that first. Success in one area will build momentum for the next.
This one small action of creating a data catalog will change the way you manage your data assets forever.
A new analyst at a large company spent her first month just trying to figure out what data was available and where to find it. It was a frustrating and inefficient process. The company then implemented a data catalog. The catalog was a searchable inventory of all their data assets, with clear descriptions, owners, and quality metrics. This one tool transformed their data landscape from a hidden swamp into a well-organized library, dramatically improving the productivity of every data professional in the company.
The reason your data governance program is failing is due to a lack of a clear vision and roadmap.
A company’s data governance program was just a collection of disconnected rules and policies. Nobody understood the purpose, and there was no executive support. The program withered. A successful program starts with a clear vision: “We will empower our employees with trusted data to make better decisions.” This vision is then supported by a practical roadmap that outlines the specific steps, priorities, and metrics for success. A clear vision provides the “why” that is necessary to drive a complex, cross-functional initiative.
If you’re still not investing in data governance, you’re losing the value of your data.
Two companies had a similar amount of data. Company A had no data governance. Their data was a messy, untrusted swamp that nobody could use effectively. Company B invested in data governance. Their data was clean, well-documented, and trusted. They were able to use their data to optimize their operations, personalize their marketing, and create new products. Data without governance is a liability. Data with governance is a valuable strategic asset.
Business Intelligence (BI)
Use self-service BI tools, not just static reports.
A sales manager used to receive a static, 50-page PDF sales report every Monday. It was overwhelming and usually didn’t have the specific slice of data he was looking for. His company switched to a self-service BI tool like Tableau or Power BI. Now, he could log into an interactive dashboard, filter by his specific region and sales reps, and drill down into the data to answer his own questions in real-time. This empowered him to make better, faster decisions without being dependent on a report generator.
Stop doing data requests to your IT department. Do empower your business users to explore data themselves instead.
A marketing manager had to submit a ticket to the IT department every time she wanted a simple report. The process would take two weeks. By the time she got the data, it was often too late to be useful. A smarter company’s IT department focused on enabling self-service. They built a reliable data warehouse and gave the marketing manager access to a user-friendly BI tool. Now, the marketing manager could build her own reports, and the IT department could focus on high-value data engineering, not on being a report factory.
The #1 tip for choosing the right BI tool for your organization.
The most important tip is to focus on user adoption and ease of use, not just the length of the feature list. A company chose a BI tool that was incredibly powerful and had hundreds of advanced features. But it was so complex that only a few highly-trained analysts could use it. The business users found it intimidating and stuck with their spreadsheets. A better choice is often the tool that your non-technical users can easily learn and adopt, because a tool that isn’t used provides zero value, regardless of its power.
The biggest lie you’ve been told about the “single source of truth”.
The lie is that a BI tool will magically create a “single source of truth” for your entire organization. A company implemented a new BI platform, but the sales and marketing teams still had different numbers for “new customers.” The reason? The tool was just a visualization layer. The underlying problem was that the two teams were pulling from different source systems with different definitions. A BI tool can only surface the data; creating a single source of truth requires a deeper data governance effort to standardize definitions and create a unified data model.
I wish I knew this about the importance of a well-designed data model for BI.
When I built my first BI dashboard, I connected the tool directly to the messy, transactional production database. The dashboard was incredibly slow, and the data was confusing to work with. I wish I had known about the importance of a proper data model. By creating a separate data warehouse with a clean, simple “star schema” specifically designed for analytics, I could have made the dashboard lightning fast and the data intuitive for business users to explore. The quality of the BI experience is determined by the quality of the underlying data model.
I’m just going to say it: Most BI dashboards are not used.
A BI developer spent months building a beautiful, complex dashboard with 25 different charts. He launched it with great pride. A year later, he checked the usage logs and was shocked to see that only three people had ever looked at it. The reason? He had built the dashboard he thought the users needed, without ever actually talking to them. He didn’t understand their business problems or the specific questions they were trying to answer. The most common reason dashboards fail is because they don’t solve a real user need.
99% of BI developers make this one mistake when creating a dashboard.
The most common mistake is cramming too much information onto a single screen. A developer will try to show every possible metric the business might ever want to see, all on one page. The result is a cluttered, overwhelming “data vomit” dashboard that is impossible to interpret. A better approach is to create multiple, focused dashboards. Each dashboard should be designed to answer a specific set of related business questions, providing a much cleaner and more guided analytical experience.
This one small action of understanding your users’ needs will change the adoption of your BI solution forever.
A BI team was frustrated by the low adoption of their new sales dashboard. They decided to take one small action: they sat with the sales team for a day and just watched them work. They realized the sales reps didn’t care about the 20 high-level KPIs on the dashboard. They cared about three things: their personal quota, their commission, and their top leads for the day. The BI team created a new, simplified dashboard that showed just those three things. Adoption skyrocketed overnight.
The reason your BI project is failing is due to a lack of user adoption.
A company spent a million dollars on a state-of-the-art BI platform. The technology was brilliant. But the project was a failure. They never invested in training their business users, they didn’t get executive buy-in, and they didn’t build a community of practice around the new tool. The platform sat unused. A BI project is not a technology project; it’s a change management project. Success depends less on the features of the software and more on your ability to get people to actually use it.
If you’re still using spreadsheets for your business intelligence, you’re losing agility.
The finance team at a company would spend the first week of every month manually exporting data from multiple systems, copy-pasting it into a master Excel spreadsheet, and creating a set of charts. The process was slow, prone to copy-paste errors, and the data was out of date the moment it was created. A competitor using a modern BI tool had access to real-time, automated dashboards. They could make decisions based on what was happening today, while the first company was still trying to figure out what happened last month.
Data Engineering
Use modern data stack tools, not legacy ETL tools.
A data engineering team was using a traditional, on-premise ETL (Extract, Transform, Load) tool. It was rigid, expensive, and required specialized skills. Creating a new data pipeline would take weeks. They switched to a modern, cloud-based data stack using tools like Fivetran for extraction, Snowflake for warehousing, and dbt for transformation (an ELT approach). They could now build, test, and deploy new data pipelines in hours, not weeks, dramatically increasing their team’s velocity and agility.
Stop doing brittle, hand-coded data pipelines. Do use a workflow orchestration tool like Airflow instead.
A data engineer had a series of data processing scripts that were scheduled with cron jobs on a server. When one script failed, the subsequent scripts would still run, leading to corrupted data. There were no automatic retries or alerting. It was a brittle nightmare. She migrated her workflow to an orchestration tool like Apache Airflow. Now, she could define her pipeline as a Directed Acyclic Graph (DAG), clearly see the dependencies, and get automatic retries and failure notifications, making her pipelines robust and reliable.
The #1 secret for building scalable and reliable data pipelines.
The secret is to make your pipeline tasks idempotent. Idempotency means that running the same operation multiple times produces the same result as running it once. A data pipeline that wasn’t idempotent once ran twice by accident due to a network error. This created duplicate records in the final database table, corrupting the data. An idempotent pipeline, in contrast, could run a hundred times, and the final state of the data would still be correct. This makes your system incredibly resilient to failures and retries.
The biggest lie you’ve been told about data lakes.
The biggest lie is that you can just dump all of your raw, unstructured data into a data lake and magically derive value from it later. A company spent millions building a massive data lake. They poured in every piece of data they could find, with no governance, no cataloging, and no clear purpose. The result wasn’t a pristine lake; it was a polluted, unusable “data swamp.” A successful data lake requires a deliberate strategy for data governance, quality, and metadata management from day one.
I wish I knew this about the importance of data quality testing in my data pipelines.
I built a data pipeline that pulled data from an API, transformed it, and loaded it into our data warehouse. It ran perfectly for months. Then, one day, the team using the data complained that their reports were all wrong. I discovered that the source API had made a subtle change to its data format three weeks ago, and my pipeline had been silently loading corrupted data ever since. I wish I had built automated data quality tests into my pipeline to validate the data at every step.
I’m just going to say it: Data engineering is the unsung hero of the data world.
Everyone loves to talk about the data scientists who build glamorous machine learning models. But none of that work would be possible without the data engineers. They are the ones who build the reliable, scalable pipelines that clean the data, transform it, and load it into a state where it can actually be used. Data engineering is the unglamorous, foundational work—the plumbing—that makes all of the sexy data science and analytics possible. It’s the most critical and often the most underappreciated role in the data ecosystem.
99% of data engineers make this one mistake when designing their data pipelines.
The most common mistake is creating a single, monolithic pipeline that does everything. A data engineer will build one giant script that extracts data from five sources, performs 20 different transformations, and loads it into ten different tables. This pipeline is impossible to debug, maintain, and test. A much better approach is to break the pipeline down into a series of smaller, modular, and single-purpose tasks. This makes the overall system more resilient, flexible, and easier to manage.
This one small action of implementing idempotency in your data pipelines will change their reliability forever.
A data pipeline was designed to process daily orders. One day, the job failed halfway through and had to be re-run. Because the pipeline was not idempotent, the re-run processed the orders that had already succeeded a second time, resulting in duplicate sales figures in the final report. By redesigning the pipeline to be idempotent (for example, by using an UPSERT command instead of INSERT), the engineer ensured that re-running the job would always yield the correct final result, making the pipeline resilient to failure.
The reason your data pipeline is failing is due to a lack of monitoring and alerting.
A critical data pipeline that refreshed the company’s main sales dashboard failed silently over the weekend. Nobody noticed until the CEO looked at the dashboard on Monday morning and saw stale data. The engineering team had built the pipeline but had not set up any monitoring or alerting. A simple alert, configured to send a Slack message if the pipeline didn’t complete successfully by a certain time, would have notified the on-call engineer and allowed them to fix the problem before it impacted the business.
If you’re still not treating your data pipelines as code, you’re losing maintainability.
A data engineer built a complex data pipeline using a drag-and-drop graphical ETL tool. When he left the company, nobody could figure out how the complex web of transformations worked. There was no version control and no way to easily test changes. A modern data engineer treats pipelines as code, using tools like dbt or Spark. This allows them to use software engineering best practices like version control (Git), automated testing, and code reviews, making their data pipelines maintainable, reliable, and collaborative.
The Modern Data Stack
Use a modular and composable modern data stack, not a monolithic platform.
A company was locked into a single, monolithic data platform from one vendor. It was expensive, and they were stuck with the vendor’s mediocre visualization tool. A competitor adopted a modular “modern data stack.” They used best-in-class tools for each part of the process: Fivetran for data ingestion, Snowflake for warehousing, dbt for transformation, and Tableau for visualization. This modular approach allowed them to pick the best tool for each job and swap out components as better technologies emerged.
Stop doing on-premise data warehousing. Do use a cloud data warehouse instead.
An organization’s on-premise data warehouse was a constant headache. Query performance was slow, it was constantly running out of storage, and scaling it required a massive, months-long hardware procurement project. They migrated to a cloud data warehouse like BigQuery or Snowflake. Now, they could scale their storage and compute resources independently and instantly, paying only for what they used. Their queries ran in seconds instead of hours, and the data team could finally focus on analysis, not infrastructure management.
The #1 tip for building a modern data stack that will scale with your business.
The most important tip is to choose tools that decouple storage and compute. This is the architectural magic behind modern cloud data warehouses. A traditional data warehouse ties storage and compute together. If you need more computing power to run complex queries, you also have to buy more storage, and vice versa. By decoupling them, you can scale your compute resources up to handle a heavy analytical workload and then scale it back down to save costs, all without affecting your stored data.
The biggest lie you’ve been told about the modern data stack being a silver bullet.
The lie is that adopting the new tools of the modern data stack will automatically solve all of your data problems. A company spent a fortune on Snowflake, dbt, and Fivetran. But their analytics were still a mess. The reason? They didn’t have a clear data strategy, their data culture was poor, and they lacked the skilled people to use the tools effectively. The modern data stack is a powerful set of tools, but tools are only as effective as the people and processes that surround them.
I wish I knew this about the integration challenges of different tools in the modern data stack.
I was so excited to build my first modern data stack. I chose what I thought were the best tools for ingestion, warehousing, transformation, and visualization. Then I spent the next month just trying to get them all to talk to each other correctly. I wish I had known that while a modular stack is flexible, it also introduces integration complexity. Making sure the different pieces work together seamlessly, handle authentication correctly, and have a coherent workflow is a significant challenge that is often underestimated.
I’m just going to say it: The modern data stack is constantly evolving.
A team built what they thought was the perfect modern data stack in 2021. By 2024, half of the tools they had chosen were being displaced by newer, better alternatives. The concept of the “modern data stack” is not a fixed set of technologies; it’s a rapidly evolving ecosystem and philosophy. The key to success is not to pick one stack and stick with it forever, but to build a culture of continuous learning and be willing to adapt and evolve your stack as the landscape changes.
99% of companies make this one mistake when building their modern data stack.
The most common mistake is focusing exclusively on the technical implementation while ignoring the “people” and “process” aspects. A company can have the best cloud data warehouse and the most sophisticated transformation tools, but if their analysts aren’t trained on how to use them, and if there are no clear processes for data governance and quality, the project will fail. A successful data stack implementation is a socio-technical problem; it requires just as much focus on change management and user enablement as it does on technology.
This one small action of choosing tools that follow open standards will change the flexibility of your data stack forever.
A company built their data stack using a set of proprietary tools from a single vendor. When they wanted to switch their visualization layer to a different, better tool, they found it was impossible because the vendor used a proprietary data format. A different company made a point of choosing tools that used open standards, like standard SQL for querying and Parquet for storage. This small decision gave them immense flexibility, allowing them to easily swap components in and out of their stack without being locked into a single vendor.
The reason your modern data stack is so expensive is due to a lack of cost optimization.
A company loved the power and speed of their new cloud data warehouse. Their developers were running massive queries without thinking about the cost. Their monthly bill was enormous. The reason? The pay-as-you-go model of the modern data stack means that inefficient queries directly translate to a higher bill. By implementing best practices—like training developers to write optimized SQL, materializing common tables, and setting up billing alerts—they were able to cut their costs significantly without sacrificing performance.
If you’re still not leveraging the modern data stack, you’re losing a competitive advantage.
Company A was stuck with a legacy on-premise data warehouse. Getting insights was a slow, cumbersome process that took weeks. Company B had adopted a modern data stack. Their business users could explore real-time data with self-service BI tools, and their data scientists could quickly build and deploy machine learning models. Company B could understand their customers better, react to market changes faster, and make data-driven decisions while Company A was still waiting for a report to run.