Six common problems types that data analysts work on every day
1. Making Predictions: Forecasting the Future
This involves using historical and real-time data to forecast future outcomes. Prediction problems often require a deep understanding of trends, behaviors, and key variables that influence what may happen next.
Example:
Imagine a hospital system using remote patient monitoring to predict health events for patients with chronic illnesses. Patients record their daily health metrics, such as heart rate and blood pressure, and combine that with other important factors like age and medical history. Using this data, the hospital can predict potential health problems before they occur, which could help reduce emergency room visits and hospitalizations. In this case, predictive analytics is saving lives and improving patient care.
2. Categorizing Things: Grouping Data for Better Understanding
Organize complex data sets into more manageable, understandable categories, making it easier to identify trends or make decisions.
Example:
A manufacturing company might analyze employee performance on the shop floor. By categorizing employees into groups such as “most effective engineers,” “best at repair and maintenance,” or “top performers in assembly,” the company can better understand employee strengths and weaknesses. This categorization allows managers to offer tailored training or reward high performers, thereby improving overall productivity.
3. Spotting Something Unusual: Identifying Outliers and Anomalies
This can help organizations identify problems before they escalate. Anomalies or outliers can often reveal insights that would otherwise go unnoticed.
Example:
Consider a school district noticing an unexpected 30% increase in student enrollment. A data analyst might dig deeper and discover that several new apartment complexes were built within the district’s boundaries, attracting new families. By identifying this anomaly early, the district can take proactive measures, such as hiring more teachers or expanding school facilities, to accommodate the influx of students and avoid overcrowding.
4. Identifying Themes: Grouping Data into Broader Concepts
Sometimes, data analysis goes beyond simple categorization and delves into identifying broader themes that encapsulate multiple categories. This step helps businesses or organizations understand the bigger picture and make decisions based on overarching concepts rather than just individual data points.
Example:
Returning to the manufacturing company, after categorizing employees by their specific tasks, an analyst could further group them into broader categories like high productivity or low productivity. By recognizing these patterns, the company can identify which departments or employees need additional support or training, and which ones are excelling and should be rewarded. This broader understanding can lead to better resource allocation and more efficient management.
5. Discovering Connections: Understanding How Different Entities Relate
Often, organizations face similar challenges across different departments or industries. By finding and understanding these connections, analysts can help companies work together and come up with innovative solutions.
Example:
Imagine a scooter company facing quality issues with the wheels it receives from its supplier. Meanwhile, the wheel manufacturer is also experiencing issues with the rubber it uses to make those wheels. If the two companies share data about the challenges they are facing, they might realize that both issues stem from a common supplier problem. By collaborating and finding a solution together, they can resolve the issue faster and more effectively. Discovering these types of connections can lead to more streamlined processes and better cross-functional collaboration.
6. Finding Patterns: Learning from the Past to Predict the Future
By analyzing historical data, analysts can uncover trends that help predict future behavior or events. This type of problem-solving is particularly useful in industries where consistency and predictability are crucial.
Example:
An e-commerce company may analyze customer buying behavior to identify patterns in purchasing. By examining past sales data, the company might recognize that certain products tend to sell more during specific times of the year, such as holiday seasons or after a marketing campaign. By understanding these patterns, the company can better plan for inventory needs, target marketing efforts, and optimize pricing strategies. In this case, recognizing patterns allows the business to stay ahead of the curve and maximize revenue.
SMART Framework : Crafting Effective Questions in Data Analysis
As a data analyst, you’re constantly confronted with new problems and data. The questions you ask shape the data you collect and analyze, influence the conclusions you draw, and ultimately determine the value of your insights.
Let’s imagine you’re working with a retail company that wants to optimize its customer experience. Before diving into analyzing sales or customer feedback data, you need to clearly understand the problem. The right question might be: “What are the most common reasons customers abandon their shopping carts?” This specific question sets the stage for targeted analysis and leads you to focus on cart abandonment metrics, behavioral data, and possible causes (like website navigation issues or pricing concerns). The wrong question, on the other hand, like “How can we improve customer satisfaction?”, is too broad and vague, making it harder to gather useful insights.
To craft questions that lead to meaningful answers, data analysts often rely on the SMART framework. This methodology ensures that the questions are Specific, Measurable, Action-oriented, Relevant, and Time-bound. Let’s break it down:
1. Specific: Focus on One Thing at a Time
A specific question narrows down the scope to a single issue or closely related ideas. This is important because broad questions often lead to vague answers. By making your questions specific, you gather data that is directly relevant to the problem at hand.
Example:
“Are kids getting enough physical activity?”, which is too general, you might ask “What percentage of kids meet the recommended 60 minutes of physical activity at least five days a week?” This question is focused, clear, and gives you measurable data that is directly relevant to the topic of interest.
2. Measurable: Ensure You Can Quantify the Answer
Measurable questions allow you to quantify the answers, which is crucial in data analysis.
Example:
“Why did our recent video go viral?” isn’t measurable. A more effective question would be: “How many times was our video shared on social media in the first week of release?” This gives you a number to work with and allows you to draw conclusions based on actual data.
3. Action-Oriented: Encourage Change or Decision-Making
Action-oriented questions are designed to drive change or inform decisions. These types of questions help businesses move from a current state to an ideal future state by highlighting areas for improvement or innovation.
Example:
“How can we get customers to recycle our product packaging?”, which is vague and doesn’t suggest a path forward, you could ask “What design features will make our packaging easier to recycle?” This type of question leads to specific insights that can directly inform product design or packaging decisions.
4. Relevant: Ensure the Question Matters
The questions you ask should be directly tied to the issue you’re trying to solve. Irrelevant questions lead to wasted effort and irrelevant data, which can cloud your analysis and confuse decision-makers.
Example:
If you’re working on the conservation of the Pine Barrens tree frog, a question like “Why does it matter that Pine Barrens tree frogs started disappearing?” is irrelevant because it doesn’t lead to actionable insights. A more relevant question would be: “What environmental factors changed in Durham, North Carolina between 1983 and 2004 that could explain the decline of Pine Barrens tree frogs?” This question helps you narrow down the factors that could have influenced the frog population and gives you direction for further investigation.
5. Time-bound: Define the Time Frame
Time-bound questions allow you to focus your analysis on a specific timeframe, reducing ambiguity and making your findings more actionable.
Example:
If you’re investigating sales trends, asking “How have our sales changed?” is too vague because it doesn’t specify the period in question. A more time-bound version of the question would be: “How did our sales perform during the last holiday season compared to the previous year?” This question sets a clear period for analysis, allowing you to focus on trends within a specific timeframe.
Fairness in Questioning: Avoiding Bias
In addition to crafting SMART questions, it’s crucial to ensure that your questions are fair and unbiased. An unfair question can lead to unreliable results, skewed data, or missed insights.
Leading Questions: These are questions that influence the respondent to answer in a particular way. For example, asking “These are the best sandwiches ever, aren’t they?” forces the respondent to agree, even if they don’t truly think so.
Closed-Ended Questions: While closed-ended questions can be useful for gathering simple data, they often limit the richness of the answers. For instance, asking “Did you enjoy growing up in Malaysia?” limits the respondent’s answer to a simple “Yes” or “No,” without delving into deeper insights.
Assumptive Questions: These make assumptions about the respondent’s experience. For example, a satisfaction survey that asks “What do you love most about our exhibits?” assumes that the respondent loves the exhibits, which may not be true.
Practical Examples of Effective Questions
Let’s look at some real-world examples of effective data analysis questions and how they align with the SMART framework:
Retail Analytics:
- Question: “What percentage of customers who abandoned their carts on our website returned to complete the purchase within 24 hours?”
- Why it’s effective: It’s specific, measurable, and time-bound. It helps the retailer understand cart abandonment and retention rates over a specific period.
Marketing Campaign Effectiveness:
- Question: “How many new customers were acquired through our Facebook ad campaign in the last quarter?”
- Why it’s effective: The question is measurable and time-bound, providing insights into the success of a specific marketing campaign.
Product Development:
- Question: “What design changes can we make to our app to reduce user churn by 10% in the next 3 months?”
- Why it’s effective: It’s action-oriented, specific, and time-bound, driving improvements that can directly impact user retention.
