15 Data Analysis Questions for Efficient Analytics

Explore 15 pivotal data analysis questions to transform raw data into actionable business insights! Dive into strategic analytics with our comprehensive guide.

February 14, 2024
Written By
Rand Owens
Founding team member at Motive (Formerly KeepTruckin) and passionate about all things Marketing, RevOps, and Go-To-Market. VP of Marketing @ Polymer Search.

15 Data Analysis Questions for Efficient Analytics

Data analysis is a quest for truth—and it best starts with a question. 

In business, this question may arise from problems, untested ideas, and previous results. Regardless of its origin, the true, reliable answer lies within your data. 

But before initiating your truth-finding process, it's important to ask internal questions that revolve around data analysis. 

Table of Contents

  • Importance of asking the right data analysis questions
  • 15 data analysis questions examples
  • Setting data hypotheses
  • Use Polymer for efficient data analysis

Importance of asking the right data analysis questions

Data is meaningless without a well-defined purpose. And to find this purpose, you need to ask the right questions

Remember, raw data and actionable insights are completely different. 

Raw data pertains to unstructured information collected from data sources, like analytics tools, survey results, case studies, and recorded customer interactions.

Actionable insights, on the other hand, are readable, meaningful interpretations of data that empower business decision-making.

In order to turn raw data into actionable insights, set research goals around relevant data analysis questions. Answering these questions gives direction to every step in your data analysis process, including data collection, storage, optimization, and presentation.

15 data analysis questions examples

Below are 15 important data analysis questions you should answer:

1. What is your research about?

What prompted the need for data analysis?

Research goals often revolve around problems, hypotheses, and performance evaluation. 

Problems in business, for one, require a data-driven approach. Rather than relying on rumors or hunches, decision-makers need to let numbers tell the story. 

Suppose you received significantly more complaints over the last quarter than in previous years. 

It's easy to pin the blame on strategic decisions, changes, or recent events in and out of your organization. However, smart businesses aim to uncover the whole truth using data. 

For example, transitioning to a fully remote workforce may have negatively impacted the customer experience—or there could be a lot of unmet expectations that stop customers from buying again.

Whatever the reason, effective data analysis paired with the right research questions will shed much-needed light. 

While data analysis can identify the root cause(s) of a problem, it can also prove a hypothesis. 

A hypothesis pertains to an assumption or speculation based on inconclusive evidence. In relation to the example above, your hypothesis could be:

  • Moving the workforce from fully remote to hybrid can improve the customer experience and reduce complaints.
  • Revising the product landing page copy to set more accurate customer expectations will improve satisfaction.
  • Rolling out a missing feature that previous customers expect will increase the customer experience and repeat purchases.

Apart from problems and hypotheses, data analysis is also done for performance evaluation

The primary goal is to assess Key Performance Indicators (KPIs) to find improvement opportunities, like productivity bottlenecks to be addressed and successful strategies to be expanded. 

A few other reasons to conduct research are risk management, competitor research, cost reduction, and customer behavior analysis. 

2. What data analysis techniques should you use? 

Identify the data analysis techniques you need to accomplish your research goals. 

Data analysis techniques are activities that focus on cleaning, organizing, optimizing, and interpreting data to extract valuable insights. Since data comes in many types and structures, different data analysis strategies work best for certain research objectives. 

Suppose your goal is to evaluate the performance of your salespeople. Some of the data analysis techniques you should consider are:

  • Sales Pipeline Analysis: Focus on the efficiency and performance of your salespeople's pipelines. This analysis targets aspects like how fast leads move through the pipeline stages (pipeline velocity), the percentage of leads that convert into paying customers (win rate), and so on. 
  • Sales Metrics Analysis: Evaluate your salespeople's individual performance metrics outside of sales pipelines. This includes sales volume, average deal size, churn rate, upsell/cross-sell rate, and quota attainment. 
  • Customer Feedback Analysis: Analyze qualitative data by investigating customer feedback and complaints. Most sales teams incorporate advanced KPIs like Customer Satisfaction (CSAT) and Net Promoter Score (NPS) to evaluate performance. 

3. Which KPIs will you track?

With your research goals and data analysis techniques figured out, it's time to define the KPIs you'll use to measure results. 

Identifying priority KPIs eliminates the potential problem of having too much information in data processing. More importantly, it allows you to pick the data collection, analysis, and reporting tools. 

A KPI is a classification of metrics selected to measure meaningful insights, progress, and outcomes. These should align with your data analysis or research objectives. 

Let's say your research goal is to determine factors that set your top salespeople apart. 

Although metrics like conversion rate and sales revenue help you identify top performers, they don't help you fulfill your research objectives. Instead, focus on the following KPIs:

  • Sales by lead source
  • Calls per day
  • Average call time
  • Lead response time

4. Which data sources should you use?

Specify the data sources that track the KPIs you need to measure. 

Data sources can either be primary or secondary. 

Primary sources generate raw data that your organization collects directly. Secondary sources, on the other hand, include data gathered by third parties—even if you initiated or requested their collection. 

Either data source type can be used to measure different KPIs. 

For example, customer feedback via phone interviews is a type of primary data. They provide qualitative insights that help you spot strengths or weaknesses in your customer-facing activities. 

The same can be said for customer feedback submitted to third-party review platforms. But since the information comes from third parties, these pieces of data are considered secondary. 

5. Who or what is your sample?

In data analysis, it's important to define sample population or data for benchmarks. 

It could be as simple as defining target demographics for your research. Or, it may involve data grouped under categories, like: 

  • Sales in the past 90 days
  • Leads by device type
  • Website visitors by traffic source

Your sample population or data set is crucial in determining the scope of your data analysis. It helps control your budget as well as ensure you collect quality, relevant data.

Most data sources, like CRM software, analytics tools, and Business Intelligence (BI) software, let you use filters to siphon sample data from a larger data set.

Polymer, for example, lets you create global filters to extract specific sample data. 

6. How long will your data collection last?

Understand your sample data to determine how long your data collection should last until you have sufficient information for analysis. 

Clarifying the length of data collection is not only important for managing research resources. It also ensures your data analysis project stays in line with your original research goals. 

Defining a scope for research also helps address ethical and privacy concerns. 

According to the GDPR, organizations can keep personal data until it achieves its intended purpose. After that, data holders are obliged to delete all records, including copies or backups. 

Lastly, setting a duration for data collection preemptively reduces the likelihood of "scope creep." This is when the scope of data collection and analysis goes beyond the original goal after the project has already begun. 

Scope creep offers little to no advantages but comes with several downsides.

It can bloat the costs of a research project, dilute data quality or relevance, and more. 

7. What tools do you need? 

The scope, sources, KPIs, techniques, and goals of your data analysis will help you decide the tools you'll need for your research. 

It doesn't take a data scientist to find solutions that match your data analysis requirements. But first, you need to understand the different types of tools in data analysis: 

  • Data integration tools: While not always required, data integration tools streamline data imports and exports between multiple platforms. A number of BI and dashboard solutions like Polymer are equipped with built-in integrations with multiple data sources. 
  • Database Management Systems: A Database Management System or DBMS  allows businesses to manage large amounts of data. It enables various data management activities, including but not limited to storage, retrieval, and cleansing.
  • Backup tools: Data backup tools keep secure copies of your data to protect against data loss. Modern backup and recovery tools include automation features so you can allocate invaluable IT resources to other activities.
  • Web analytics tools: To measure and optimize the performance of your online channels, use web analytics tools that track metrics like clicks, conversion rate, bounce rate, and monthly traffic. Popular options include Google Analytics along with in-app insights tools from services like Facebook Ads and Shopify.   

In addition to the tools above, businesses leverage visual reporting and dashboard software to consolidate, analyze, and represent data from various sources. 

Drag-and-drop tools that convert data into shareable visualizations and dashboards help make insights readable to non-technical team members. Multi-purpose platforms like Polymer also include data integration, automation, and analysis tools alongside visual reporting features. 

8. How will you present your data?

To help stakeholders understand and utilize data for decision-making, it's important to use the appropriate data visualizations to represent information. 

You can present different types of data with elements like:

  • Pie Charts: A pie chart is a popular data visualization that presents a breakdown of a data set according to groupings or units. For example, use a pie chart to visualize the share of leads per source (email, social media, search engines, walk-ins, etc.). 
  • Bar graphs: To compare subsets of data or visualize changes over time, use a bar graph to highlight key information. They help stakeholders grasp takeaways quickly, such as the most profitable product categories or sales revenue growth over the months.
  • Pivot tables: A pivot table is an interactive visualization used for tasks like data summarization, filtering, and aggregation. Businesses often use database tables or spreadsheet software like Google Sheets to create pivot tables for presenting data insights.
  • Heatmaps: Heatmaps use color codes to visualize patterns and relationships between two data sets or dimensions. A heatmap, for example, can quickly spotlight the best and worst-selling products through multiple quarters. 

A few other data visualization types to explore are:

  • Line charts
  • Scatter plots
  • Basic data tables 
  • Bubble charts
  • Dependency wheels

Most data management and analysis tools use pre-defined visualizations to refine data into actionable insights.

Some BI solutions also allow stakeholders to configure custom visualizations to accomplish data analysis goals. 

For example, Polymer features a drag-and-drop dashboard builder that uses data visualization "blocks." Within a few clicks, it lets you create pivot tables, pie charts, bar graphs, scorecards, and other interactive elements. 

9. Is your data clean?

Data cleaning techniques are essential for ensuring quality and accuracy.

Messy data from multiple sources, for instance, need to be cleansed for consistent formatting and reliability. This is important for seamlessly generating accurate data visualizations.

Data cleansing also prevents skewed analysis due to incomplete or erroneous data sets. 

Below are a few examples of data cleansing methods you should remember:

  • Formatting standardization: Data from multiple sources are often formatted differently. Decimal points, extra spaces, symbols, and other special characters may also cause your data management tools to read data incorrectly. 
  • Cleaning duplicate data: Use spreadsheet software or sorting features to organize your data and spot duplicates. These can arise from the use of multiple data sources, incorrectly configured data integrations, human error, software glitches, and synchronization problems. 
  • Address missing data: Incomplete data may also occur for the same reasons as duplicate data. To address this issue, consider deleting entries with incomplete information or filling in the gaps using data imputation techniques.
  • Data aggregation: Make your data more manageable by "zooming out" and focusing on the bigger scale. For example, rather than calculating daily averages, aim for weekly or monthly values instead.  

10. Are there any outliers?

Outliers are values that significantly deviate from the rest of the data set. In most cases, they're due to uncommon situations, extreme events, or deliberate manipulation. 

If left disregarded, outliers could pull high-level metrics in a specific direction, compromising the accuracy of data reports. 

To spot outliers, a straightforward approach is to use data visualizations like scatter plots, bar charts, and histograms. This will highlight outliers by visually separating them from the rest of your data.

More advanced methods of identifying outliers include calculating the z-score for data points, comparing data entries against the Interquartile Range (IQR), and using outlier detection tools in statistical software. 

11. How should you optimize your data?

Data optimization is one of the pillars of effective data analysis. 

It is crucial for data stakeholders to understand what is data optimization and why optimization is important. 

In simple terms, data optimization covers activities that enhance the quality, accuracy, and efficiency of data. It overarchs multiple areas of data management, like data cleansing, visualization, aggregation, transformation, and governance. 

Data optimization ensures you obtain reliable, straightforward insights out of your data. Apart from data cleansing, other examples of data optimization techniques are: 

  • Tracking data quality metrics. Data management comes with its own set of metrics to track and use for optimization purposes. This includes data error rate, validity, completeness, and timeliness. 
  • Data indexing. To streamline data retrieval, implement a data index structure that lets you find specific data types faster. Data infrastructure tools like ZettaBlock and Zebra equip organizations with the tools necessary to build index structures for big data. 
  • Storage tiering. Another way to optimize data storage and retrieval operations is to divide data into storage layers. For example, businesses may store rarely-accessed data in low-bandwidth, "archive-tier" storage, whereas frequently-accessed data are stored in high-performance storage devices.  

12. How will you handle the Extract-Transform-Load (ETL) process?

The ETL process is an indispensable area of data management that pulls information from a database, converts it into another form, and loads it into a different destination. 

It sounds simple, but ETL is an intricate process that directly affects the effectiveness and overall value of data analysis.

Some data reservoirs require specific methods for data extraction, whereas destination systems may have strict data formatting rules for imports—complicating the process's "transform" phase. 

The good news is, organizations can invest in affordable, user-friendly tools that simplify the ETL process. 

BI tools like Polymer let businesses implement an ETL process that any team can understand. 

Polymer's frontend makes ETL procedures accessible to anyone regardless of data management experience. Within the drag-and-drop editor, users can take advantage of AI recommendations to instantly turn data into nuggets of wisdom.

13. Who will be granted access to your data?

Data analysis projects involve multiple users with varying access privileges. 

To avoid confusion and prevent disruptions, identify the individuals who will become part of your data analysis workflow.

Flesh out details like their data needs, goals, tools, and level of competency with data management. 

Knowing who gets to access your data will also help you pick your data analysis software. Pay attention to the tool's interface, reporting, and sharing features. 

For example, Polymer is designed to help everyone across an organization utilize data for decision-making.

Aside from the streamlined user interface, sharing analytics dashboards and insights is as easy as pasting a direct link or embed code.

14. What processes can you automate?

Some data management operations can be automated to save time and increase efficiency.

Automated data analysis encompasses several tasks, including: 

  • Integration and synchronization: Data integrations usually include automated synchronization features to keep your data up-to-date across multiple data management platforms. This allows you to save time on manual data exports, imports, and cleanups
  • Preprocessing: In data analysis, preprocessing is the practice of preemptively cleaning, transforming, and standardizing data to suit a destination platform. These tasks, including other ETL procedures, can be automated using a centralized data management solution, including BI platforms and spreadsheet software. 
  • Report generation: One of the most basic forms of automation in data analysis is automated reporting. Most data analysis tools let you create an automated reporting schedule, keeping data stakeholders on the same page with minimal human intervention. 
  • Visualization: Analytics tools and dashboard builders automatically translate data into visual charts, tables, and a slew of other interactive elements. Certain tools like Polymer are even capable of automatically generating an entire data dashboard.

To check out Polymer's automated data processing, open your data set and switch to the 'Boards' tab. Look for the "AI Generated Board" already prepared for you. 

AI-generated boards on Polymer are fully customizable using the drag-and-drop block editor. Just click 'Edit' to launch the editor, add new elements, or configure existing blocks—all without writing code.

15. How can you improve?

The very nature of data analysis is about measuring and building on previous successes or failures. 

Use past data analysis examples and results to identify inefficiencies in your data management approach. At the same time, figure out what works by looking at the steps leading to successful data-driven decisions.

Double down on strategies that yielded positive results while refining or pulling the plug on methods that fell short. 

Setting data hypotheses

Ready to start your data analysis project? 

One of the fundamentals of data analysis is setting an effective hypothesis to guide your research. 

Hypotheses originate from a number of things, be it your personal observation, industry report, or team brainstorming sessions. Whichever the source, remember the following tips when setting data hypotheses:

  • Brainstorm with your team. Round up insights and educated guesses from team members to craft a hypothesis out of the consensus. 
  • Use measurable variables and KPIs when formulating your hypothesis. For example, instead of "hybrid working will lead to happier customers," think "hybrid working can improve support ticket response times and boost CSAT scores." 
  • Refer to existing, passively collected data to detect potential anomalies or congruencies with your ideas. This may include primary data or secondary data from competitors, industry reports, and analytics services.
  • Create the null and alternative hypotheses. A null hypothesis states that there's no statistical significance or relationship between the data sets in your research, whereas an alternative hypothesis proposes a different outcome or idea from your original hypothesis.

Use Polymer for efficient data analysis

Polymer is a powerful Business Intelligence solution that optimizes the entire data analysis process.

It allows you to compile and transform data from multiple sources to create readable, interactive visualizations in minutes.

Our platform also uses the power of AI to automate time-consuming data analysis tasks, including data integration, summarization, and visualization.

Build your single source of truth by starting a free trial today.

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