If you want to collect better data, you need to ask better questions.
On the surface, this sounds simple. However, most people treat data like a Magic 8 ball.When in reality, they should be approaching data like a scientist.
In practice, this means starting with your hypothesis, and coming up with the questions that will help you validate or invalidate it. The answers you find are the foundation for more compelling business intelligence reports.
In this post, we’re taking a closer look at 7 data analysis questions.
- Tools Businesses Use for Monitoring and Reporting on Business Performance
- 7 Data Analysis Questions You Can Use to Improve Your Business Reporting Process
- Improve Data Analysis with Databox
Tools Businesses Use for Monitoring and Reporting on Business Performance
In a recent survey we ran, 93.10% of our respondents said their company culture is data-driven.
For additional context, 44.83% of our respondents are working in B2C services or products, 41.38% are working in B2B services and products. And, 13.79% work for agencies.
However, just because you say your company is data-driven doesn’t mean much. We wanted to find out exactly what kinds of tools and processes they are using to measure business performance.
In fact, more than half of our respondents have a centralized dashboard that aggregates data from different sources like Databox for monitoring and reporting on business performance.Still, more than a third of companies we polled for the recent state of business reporting research stated that they use between 11 and 25 tools for monitoring and reporting performance data, which is, indeed, a lot.
7 Data Analysis Questions You Can Use to Improve Your Business Reporting Process
One of the biggest mistakes we see both B2B and B2C businesses make is over-indexing on the tools they are using, and under-investing in the data they actually need to collect.
This usually happens when a company hoards all of the data that is easiest to collect. However, the easiest data to collect is rarely the most impactful for a business. Not to mention, if your dashboard or business intelligence reports are full of vanity metrics, most people will stop looking at them.
A better solution is to start by asking the right questions. When you ask the right questions, like the one outlined in this chart and section, it will lead you to the metrics that you most need.
Now let’s see how each of these questions can be helpful in your data analysis process.
- What exactly do I want to find out?
- Where will my data come from?
- How can I ensure data quality?
- Which KPIs will I use?
- What kind of software will help?
- Which statistical analysis techniques do I want to apply?
- Who are the final users of my analysis results?
1. What exactly do I want to find out?
One of the best questions to start with is to get clear on your business goal(s) and then ask yourself what data you need to make this clear.
“When analyzing data, we’ve found that asking the question “What do we want to find out?” can be incredibly helpful,” says Adam Rossi of TotalShield. “This question allows our team to narrow down exactly what they’re looking for and what the goal of the data analysis is. Is there an outcome that would signify success? An outcome that would signify failure?
We tend to start fairly broad, then “zoom in” as we go. Since using this question to begin our analysis, we’ve become more focused in our data reporting, and we’ve also been able to more effectively use our data to solve our business pain points.”
Claire Westbrook of LSAT Prep Hero adds, “The best way to ensure that your business reporting delivers meaningful data is to first establish a clear business strategy and then use key performance indicators (KPIs) to track how your company is performing against that strategy. We did a few key things to improve our analysis strategy using business reporting. First, we made sure to have a clear understanding of the goal of the report and what information is most important to achieving that goal.
Second, we focused on key areas of analysis that will provide the most insight into the data.
Lastly, we structured our report in a way that is easy to understand and makes it easy for others to find the information they need. By streamlining the data in our business reporting, we’re able to clearly see if we’re on track to meet our KPIs.”
One of the best ways to understand your goals, as well as your team’s progress towards them is by visualizing them. And that’s where Databox can help. See how goal tracking in Databox can make your performance more predictable.
2. Where will my data come from?
Once you know what data/metrics you need, you need to get clear on how you are going to dig up this information.
“We try to diversify our data sources through quantitative and qualitative methods,” says Albert Vaisman of Soxy. “This strategy allows us to broaden our perspective when drafting a business report. I have improved my analysis strategy through the use of visuals. Data can sometimes get too overwhelming if presented in a raw format. I use visualization tools such as graphs and pie charts to summarize how the business has performed over the last quarter.”
Related: How to Visualize Data: 6 Rules, Tips and Best Practices
3. How can I ensure data quality?
It is not enough to just have the data. Your team also needs to be able to trust it, if you actually want people to make data-driven decisions. Data quality is hugely important, and comes in the form of both accuracy and diversification of sources.
For instance, Daniel Neale of Kitty Cat Tips says, “We also try to use as many data sources as possible, including first-hand interviews and focus groups. Once we have collected the required data, we carefully examine it to extract the best possible results for our business. We have improved our analysis strategy by using data visualization. Data can become challenging to identify when presented simply through numbers. But when you use visuals like histograms and bar charts, you make it easier for people to identify new trends and patterns. Hence creating meaningful visuals is an exceptional way to improve your analysis strategy.”
David Clark of Basement Guides adds, “Reporting can be complex when it comes to business. We have still managed to instill great cultural values within our employees. Our functional strategy of simplifying the collected data has been working exceptionally. Complex terms of business reporting may prove to be detrimental if the data collected is hard to understand. This is because the sales department requires every piece of information in the simplest form. Employees are actively trained not to waste their time waiting for data collection at our company. Instead, we make do with the given resources and simplify the available information. Team members are advised to bring whatever feedback they can find, using the SWOT analysis strategy, may it be from customer reviews or any other quantitative information. It helps find weaknesses within the product or service before it is released and showcases its strengths.”
Related: Google Analytics Data: 10 Warning Signs Your Data Isn’t Reliable
4. Which KPIs will I use?
You also need to think about how the data you are collecting ties back to your overall business KPIs. This is another way to ensure that the data you are collecting is actionable instead of vanity metrics.
For example, Sandi Mazzeo of 301 Digital Media says, “One of the most important data analysis questions a company should ask in order to improve business reporting processes is: “What KPIs matter to your business?” Often, we find that businesses and clients focus on vanity KPIs like impressions, without an eye on the ultimate business objectives. Developing a focus on the KPIs that matter to a business’s overall success is really the best way to improve business reporting processes and, by extension, business performance.”
PRO TIP: How Are Users Engaging on My Site? Which Content Drives the Most Online Activity?
If you want to discover how visitors engage with your website, and which content drives the most engagement and conversions, there are several on-page events and metrics you can track from Google Analytics that will get you started:
- Sessions and % new sessions. How much traffic does your website receive on a daily or monthly basis?
- Sessions by channel. Which channels are driving the most traffic to your website?
- Average session duration. How long do visitors spend on your website on average?
- Pageviews and pageviews by page. Which pages on your website are viewed the most?
- Average time on page. What is the average time users spend on a specific webpage?
Now you can benefit from the experience of our Google Analytics experts, who have put together a plug-and-play Databox template showing the most important KPIs for monitoring visitor engagement on your website. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!
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5. What kind of software will help?
Chances are, you are not going to find all of the data that you need in just Google Analytics or HubSpot. So, making a list of the metrics you are tracking and what software you need to find this information is a great starting point.
Then, once you have the data, how you present it matters. That’s where dashboard software like Databox can be helpful.
“Good presentation is vital to ensure decision-makers are presented with the information that they require to make informed decisions,” says Leanna Serras of FragranceX. “Dashboards are a great way for conveying real-time data insights at a glance. Data reports should compare current performance to previous performance and highlight anomalies. A common mistake is to confuse audiences with excessive and irrelevant data. The best approach for presenting data is to focus on a few key areas.
We have found great success in monitoring our website performance with a simple dashboard powered by data from Google Analytics. This enables us to see our page views, time on page, and bounce rates. It also breaks down our traffic by source including organic, direct, and social media which helps us measure the effectiveness of our marketing campaigns.”
Alex Williams of Find this Best agrees, “Data visualization has proven to be the best way to ensure that my business reporting delivers meaningful data. I believe presenting data with stunning visuals not only piques the reader’s interest but also makes it easier to process the information. In this day and age of being bombarded with visuals in daily routine, audiences are more likely to understand visually appealing data.
Adding relevant visuals to my business reports helps my investors and employees easily identify patterns and comprehend difficult information. I’ve improved my business analysis by adopting the BPM (business process mapping) strategy. With this strategy, my team and I can map out business processes with the help of visuals. We include flow charts, diagrams, and mind maps to understand our weak points and develop practical solutions.”
Editor’s note: Make comprehensive and easy-to-read quarterly reports for your investors with automated reporting software. This will allow you to automate the process of collecting and sending data and send it to recipients whenever you are ready.
6. Which statistical analysis techniques do I want to apply?
Data quality isn’t the only important thing. You also need to gut-check your data analysis models. After all, it is easy to cherry-pick stats or data points to tell the story you want to tell. That story may or may not be what’s really going on.
In ensuring that business reporting delivers quality data to my business, there are strategies I have adopted in analyzing data,” says Maria McDowell of EasySearchPeople. “First is identifying the data source and size of the dataset that my business is working with. Secondly is ensuring the completeness and accuracy of the data. Lastly, determining the usefulness of the data to my business. These strategies have helped my company’s data analysis procedures which have, in turn, proved of great value in business reporting.”
Nick Drewe of Wethrift adds, “The best way to deliver meaningful data is to ensure you’re working from a strong foundation of accurate, relevant, and complete data, so it’s crucial that you start by asking: how can we guarantee we’re collecting high-quality data? There’s no single worse reporting mistake than working from inaccurate data – every other step you take is potentially useless if you aren’t 100% certain that you’re analyzing good quality data.
Unfortunate as it is, bad data means bad analytics and flawed insights, leaving your business reporting on incredibly shaky ground. Clean and organized data is truly the bedrock of reporting meaningful data, and much attention needs to be paid to controlling and profiling incoming data. We’re lucky in this day and age that there are a wealth of digital tools to help with this task, and that it needn’t be a tedious manual process.”
Related: How to Present Qualitative Data in a Business Report? A Step-By-Step Guide
7. Who are the final users of my analysis results?
A final question to ask is who is going to be reading this report. The metrics you share and how you present the data in your data analysis report or dashboard will vary depending on if you are sharing with just the executive team, your manager, or the entire company.
“Data analysis is an iterative cycle – so we ask questions, explore the answers and then create further questions based on those outcomes,” explains Gary Warner of Joloda Hydraroll. “To understand the best questions to start with, we need to know what the relevant stakeholders want to know.
From there, we can create a report to deliver. Once they’ve had time to review this, we can discuss whether it covered everything that they wanted to know. If not, we need to ask further questions and adapt the reporting accordingly.
This is a forever moving process, as data will be used to inform decisions, and the outcome of those decisions may highlight areas that your stakeholders need further detail on.
We regularly produce case studies, and these give us the opportunity to analyze data relating to particular projects. This helps us to understand our process from start to finish, and the data that can impact decisions at each stage.
Specifically, we have been able to help our clients in analyzing their own data to help them understand the difference that our solutions can make for their business.”
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Getting clear on the data you need, finding that information in your tech stack, and analyzing it is hard enough as it is.
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What are the 7 data analysis process? ›
Diagnostic Analysis, Predictive Analysis, Prescriptive Analysis, Text Analysis, and Statistical Analysis are the most commonly used data analytics types. Statistical analysis can be further broken down into Descriptive Analytics and Inferential Analysis.Which data analysis questions should a company ask in order to improve its business reporting? ›
The Key To Asking Good Data Analysis Questions
Or even better: “Which marketing campaign that I did this quarter got the best ROI, and how can I replicate its success?” These key questions to ask when analyzing data can define your next strategy in developing your company.
- Who is the audience that will use the results from the analysis? (board members, sales people, customers, employees, etc)
- How will the results be used? (make business decision, invest in product category, work with a vendor, identify risks, etc)
data analysis process follows certain phases such as business problem statement, understanding and acquiring the data, extract data from various sources, applying data quality for data cleaning, feature selection by doing exploratory data analysis, outliers identification and removal, transforming the data, creating ...What is an example of an analysis question? ›
Here is an example of an analysis question: "How did the various causes of the French Revolution combine with one another to lead to an eventual collapse of the ancien regime?"What are the four questions of data analysis? ›
The four questions of data analysis are the questions of description, probability, inference, and homogeneity. Any data analyst needs to know how to organize and use these four questions to be able to obtain meaningful and correct results.What are six steps of data analysis discuss briefly the main objectives of each step? ›
According to Google, there are six data analysis phases or steps: ask, prepare, process, analyze, share, and act. Following them should result in a frame that makes decision-making and problem solving a little easier.What are the 10 steps in analyzing data? ›
- Collaborate your needs. ...
- Establish your questions. ...
- Harvest your data. ...
- Set your KPIs. ...
- Omit useless data. ...
- Conduct statistical analysis. ...
- Build a data management roadmap. ...
- Integrate technology.
This program is split into courses, six of which are based upon the steps of data analysis: ask, prepare, process, analyze, share, and act.What are the 5 steps of data analysis? ›
- STEP 1: DEFINE QUESTIONS & GOALS.
- STEP 2: COLLECT DATA.
- STEP 3: DATA WRANGLING.
- STEP 4: DETERMINE ANALYSIS.
- STEP 5: INTERPRET RESULTS.
What are the 7 steps in the quantitative analysis approach? ›
- Method selection.
- Solution preperation.
- Sample pre treatment.
- Analytical measurement.
- calculation of the analytical result.
- Statistical evaluation of the result.
The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis. A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision.What are the basic data analysis methods? ›
The two primary methods for data analysis are qualitative data analysis techniques and quantitative data analysis techniques. These data analysis techniques can be used independently or in combination with the other to help business leaders and decision-makers acquire business insights from different data types.What are the three 3 kinds of data analysis? ›
There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.What are the four 4 types of analysis? ›
- Descriptive Analysis.
- Diagnostic Analysis.
- Predictive Analysis.
- Prescriptive Analysis.
- String (or str or text). Used for a combination of any characters that appear on a keyboard, such as letters, numbers and symbols.
- Character (or char). Used for single letters.
- Integer (or int). Used for whole numbers.
- Float (or Real). ...
- Boolean (or bool).
- Observation. Observational methods focus on examining things and collecting data about them. ...
- Survey. Survey methods focus on gathering written or multiple choice answers about various subjects from individuals. ...
- Focus group. ...
- Interview. ...
- Design thinking. ...
- User testing.
7 Key Questions: Who, What, Why, When, Where, How, How Much? - Consultant's Mind | Change management, Business analysis, Business case.What are the six levels of questions? ›
The original taxonomy: (1) knowledge, (2) comprehension, (3) application, (4) analysis, (5) synthesis, and (6) evaluation.What are the 4 E's of big data analytics? ›
However, this does not necessarily mean that we are talking about “Big Data”. IBM data scientists break it into four dimensions: volume, variety, velocity and veracity. This infographic explains and gives examples of each.
What are the 5 types of data analytics? ›
- Predictive data analytics. Predictive analytics may be the most commonly used category of data analytics. ...
- Prescriptive data analytics. ...
- Diagnostic data analytics. ...
- Descriptive data analytics.
- Take a look at your top survey questions.
- Determine sample size.
- Use cross tabulation to filter your results.
- Benchmarking, trending, and comparative data.
- Crunch the numbers.
- Draw conclusions.
Starting with a clear objective is an essential step in the data analysis process. By recognizing the business problem that you want to solve and setting well-defined goals, it'll be way easier to decide on the data you need.What are 3 key things you need to start analyzing the data set? ›
To improve how you analyze your data, follow these steps in the data analysis process: Step 1: Define your goals. Step 2: Decide how to measure goals. Step 3: Collect your data.What are two important first steps in data analysis? ›
The first step is to collect the data through primary or secondary research. The next step is to make an inference about the collected data. The third step in this case will involve SWOT Analysis. SWOT Analysis stands for Strength, Weakness, Opportunity and Threat of the data under study.What makes good data analysis? ›
A good data analyst must have a firm understanding of the business operations. In any organisation, the analyst must be commercially aware of the customer, people within his or her team, different departments and the line of business.What are the four steps in preparing data for analysis? ›
- Collect data. Collecting data is the process of assembling all the data you need for ML. ...
- Clean data. ...
- Label data. ...
- Validate and visualize.
The five C's pertaining to data analytics soft skills—many of which are interrelated—are communication, collaboration, critical thinking, curiosity and creativity.What 3 skills are involved in data analysis? ›
- Data Visualization. As the term suggests, data visualization is a person's ability to present data findings via graphics or other illustrations. ...
- Data Cleaning. ...
- MATLAB. ...
- R. ...
- Python. ...
- SQL and NoSQL. ...
- Machine Learning. ...
- Linear Algebra and Calculus.
Qualitative research methods include observations, one-on-one interviews, case study research, focus groups, ethnographic research, phenomenology, and grounded theory.
What are the 7 characteristics of quantitative research? ›
- Contain Measurable Variables. ...
- Use Standardized Research Instruments. ...
- Assume a Normal Population Distribution. ...
- Present Data in Tables, Graphs, or Figures. ...
- Use Repeatable Method. ...
- Can Predict Outcomes. ...
- Use Measuring Devices.
In computer science, a list or sequence is an abstract data type that represents a finite number of ordered values, where the same value may occur more than once.Why is data analysis important in business? ›
Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.How do you write a data analysis example? ›
- Overview. Describe the problem. ...
- Data and model. What data did you use to address the question, and how did you do it? ...
- Results. In your results section, include any figures and tables necessary to make your case. ...
- Defining the question.
- Collecting the data.
- Cleaning the data.
- Analyzing the data.
- Sharing your results.
- Embracing failure.
Three Rules for Data Analysis: Plot the Data, Plot the Data, Plot the Data.What are the 6 phases of data analysis? ›
This program is split into courses, six of which are based upon the steps of data analysis: ask, prepare, process, analyze, share, and act.What are the stages in data analysis? ›
These steps and many others fall into three stages of the data analysis process: evaluate, clean, and summarize.What are the 6 steps to analyzing the data? ›
According to Google, there are six data analysis phases or steps: ask, prepare, process, analyze, share, and act. Following them should result in a frame that makes decision-making and problem solving a little easier.What are the five 5 key steps of data analysis process? ›
- STEP 1: DEFINE QUESTIONS & GOALS.
- STEP 2: COLLECT DATA.
- STEP 3: DATA WRANGLING.
- STEP 4: DETERMINE ANALYSIS.
- STEP 5: INTERPRET RESULTS.
What are the 6 most common qualitative data analysis methods? ›
- Qualitative content analysis.
- Narrative analysis.
- Discourse analysis.
- Thematic analysis.
- Grounded theory (GT)
- Interpretive phenomenological analysis (IPA)
Starting with a clear objective is an essential step in the data analysis process. By recognizing the business problem that you want to solve and setting well-defined goals, it'll be way easier to decide on the data you need.What are the 5 levels of analysis? ›
Using five levels of analysis (explicit, implicit, theoretical, interpretive, and applicable) addresses this concern by challenging students to comprehend the central ideas of texts, interrogate in terms of social justice, connect concepts to their immediate realities and extrapolate useful ideas to apply to their ...What are the four techniques for data analysis? ›
In data analytics and data science, there are four main types of data analysis: Descriptive, diagnostic, predictive, and prescriptive.What are six techniques used to gather data and information? ›
The most commonly used methods are: published literature sources, surveys (email and mail), interviews (telephone, face-to-face or focus group), observations, documents and records, and experiments.What is data analysis with example? ›
The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis. A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision.