Dashboards are snapshots of Key Performance Indicators (KPI's) and metrics. Managers and their teams use them to track a particular business objective or goal. With the input of others, Data analysts are often asked to build and update dashboards. There are several tools that can be used to build dashboards including Tableau, Power BI and even Excel. In addition to these well-known tools, there are many free tools available online. If you do not have experience building dashboards, talk about how your data visualization skills can help you in this area. Do not get too detailed describing your experience, but include the purpose of the dashboard, types of data visualizations you used and a few metrics that were included. If the interviewer would like more detail, he/she will ask additional questions.
"I have experience creating dashboards that included Marketing metrics. The metrics included brand awareness, customer satisfaction and sales by quarter. I used pie charts, bar and line graphs, as well as tables to present the data in the dashboard. I have created dashboards using both Power BI and Excel."
Interviewers would only pose this question if you do not have industry-specific experience related to their company. Without this experience, they want to know how you can apply what you learned from previous positions to the one for which you are interviewing. Be ready to answer this question by conducting research ahead of time.
"There are a few similarities between the financial services and healthcare industries, specifically related to data and the work of a Data Analyst. One of the most important is the security of customer or patient data. Both industries work with highly personal and sensitive data that must be kept secure at all times. Because of this, access to data may be more restricted and analyses may require more time to complete as you navigate through all the security. To be successful as a Data Analyst in these industries, you will need to be, not only organized, but able to present a clear case for the data your analyses require."
Hiring managers look for Data Analysts with strong presentation skills - someone who can present analyses and answer questions clearly and with confidence. It would be ideal if you had experience presenting to an executive level audience, but this is not always necessary. When describing the types of audiences, include approximate size, whether it included executives and possibly what departments within the company were present. For example, in many cases, hiring managers look for candidates that can clearly present their findings to people with different backgrounds. More specifically, experience communicating with both technical and non-technical audiences is a highly valued skill. With the rise of remote work, some hiring managers will want to know if you have experience presenting analyses via phone or video conference calls, since these types of presentations can present unique challenges.
"As a Data Analyst, I have presented to a wide variety of audiences made up of people with differing backgrounds. The groups ranged in size from one person to 25 people, with the larger ones composed of co-workers from different departments in the company. Most of these presentations were conducted in person, but I have presented a few analyses remotely via video conferencing to smaller audiences. In addition, about one-third of my presentations have had audiences made up of senior managers."
Data Analysts should use all the data available to them to conduct the most impactful analyses. This could include both quantitative and qualitative data. The hiring manager wants to know how much experience you may have marrying qualitative to quantitative data. Sometimes it is straightforward, as is the case when working with survey data that has both qualitative and quantitative questions. Other times, it may take some creativity to find applicable qualitative data to use in conjunction with your quantitative data. If you have several projects to choose from, share about the project where you used the most creativity in merging the two types of data.
"If possible, I always try to incorporate qualitative data to support what the quantitative data is telling me. I have been fortunate enough to have conducted several analyses where qualitative survey data was readily available to me. However, when working with survey data, I don't think you should limit yourself to the qualitative data from one survey. When appropriate, I have found that there can be valuable qualitative data from other surveys or external sources. For one particular marketing analysis dealing with a new product evaluation, I reached out to the operations department to utilize qualitative data they had collected from distributors. Using this qualitative data strengthened the validity of my recommendations to the product development group."
Soft skills are personal attributes that help people work well with others and perform their jobs at a high level. Many refer to these skills as 'non-tangible' and 'non-technical'. As with most jobs, it is important that Data Analysts have strong soft skills, because they do not work in isolation, and therefore their work habits and performance affect others on their team. Interviewers want to know that you understand the importance of these types of skills. Use your past work experiences to support why you find a particular soft skill important and try to include how you have developed it over time.
"Personally, as a Data Analyst, I have found leadership skills to be one of the most important soft skills. In my experience, exercising leadership skills does not require you to be in a managerial role. In a team environment, leadership skills are displayed when you take initiative to guide and help others. In many cases, Data Analysts are in a position where they need to educate others on the data and how to interpret it. I have found it has been crucial for me to speak up and become an expert in interpreting the company data. Being able to take the initiative has become easier for me over time. I have been able to strengthen this skill by educating myself and finding more opportunities to share my learnings with my team. As a result, I became more confident and built myself up as a leader in this area amongst my team members."
Although Data Analysts spend much of their time working with numbers, having strong writing skills is very important as well. They will need to interpret the results of their analyses into words to present to stakeholders. Data Analysts should be able to tell the 'story' with words and numbers together. If you find that you have not had many opportunities to use or strengthen your writing skills, state the extra measures you are willing to take to change that, whether it be through training or by proactively seeking opportunities.
"I have a high level of confidence in my writing skills. As a Data Analyst, I surprisingly have had many opportunities to strengthen these skills. Whether it is through email communications with team members or more formal analytical summaries, I find that I am able to get my point across in a clear and concise manner. I continually look for ways to exercise and strengthen my writing skills."
As a Data Analyst, you will likely have opportunities to work on cross functional teams. Members of these teams have various backgrounds, differing priorities and varied skill sets. Of course, there are general skills that would be helpful in any team environment. However, hiring managers would be interested in skills that may be somewhat unique to a Data Analyst facing a team environment. If possible, think of skills that go beyond basic ones such as communication.
"I believe Data Analysts have the somewhat unique challenge of communicating technical and statistical concepts that many others on the team may not understand. So it is important that Data Analysts are able to explain these types of concepts in a way that that is easily understood by everyone. In order to successfully do so, Data Analysts need to have the ability to apply these technical and statistical concepts to the business - specifically to those parts of the business represented on your team. This is possible when Data Analysts are able to have a more holistic view of the company."
An increasing number of Data Analyst job postings include web analytics experience as a preferred or required skills. At times, companies may choose to separate the roles, but in many cases, they would prefer Data Analysts to have a holistic view, and therefore, choose to integrate web analytics into their job description. When sharing your web analytics experience, give some detail on the types of measurements you were tracking and the general scope of the project.
"Using Google Analytics, I have used web analytics as part of a larger marketing campaign evaluation project. The web metrics I tracked included open rate, click-through rate, average time on page and conversion rate. In addition, I was able to build funnels within Google Analytics to measure where visitors were dropping off before converting. By tracking these web metrics in conjunction with non-web marketing efforts, I was able to recommend the best marketing channels to use to target specific segments."
When considering Data Analysts' skills, creativity is not top of mind for many. Instead, plenty of people would consider technical and math or statistical skills to be at the top of the list. However, Data Analysts use their creativity in a variety of ways including developing analytical plans, finding solutions to data issues and presenting data visually. Creativity is about 'thinking outside the box'. Be prepared to share in more detail how you used your creativity for a specific project.
"As a Data Analyst, there is no question that creativity is an important skill to have. Creativity is what has gotten me past the data roadblocks in past projects. It has also helped me find new and interesting ways to present analytical results to clients. More specifically, I find that creativity is important when validating data before analyzing it. There have been a few times when I began analyzing data only to find there were some 'abnormal' results. I stepped back and created new and 'non-routine' data checks in order to identify issues causing the atypical results."
If you are an Excel expert, it would be difficult to list all the functions you have experience using. Instead, concentrate on highlighting the more difficult ones, particularly statistical functions. If you have experience utilizing the more challenging functions, hiring managers will presume you have experience using the more basic ones. Be sure to highlight your pivot table skills, as well as your ability to create graphs in Excel. If you have not attained these skills yet, it is worthwhile to invest in training to learn them.
"As a Data Analyst, I have used Excel almost on a daily basis. It has become an essential tool for me in all phases of my analytical projects. I have used Pivot tables to check and clean data sets, as well as analyze them. In the analysis phase, I have also used statistical functions to calculate standard deviations, correlation coefficients, percentiles and quartiles. In addition, I have used the graphing function in Excel to develop visual summaries of the data. As an example, I regularly worked on customer satisfaction surveys and received raw data from external vendors. I would take this data and bring it into Excel and use sort functions and pivot tables to verify the data was clean and loaded correctly. As part of the analysis phase, I always worked with pivot tables to segment the data. In addition, if the analysis called for it, I used the statistical functions I mentioned earlier. Building tables and graphs in Excel allowed me to tie my analyses together in visually. Many times, I could complete the tasks in one file, making everything I worked on easily accessible."
SQL is the most well known scripting language, and many believe it is the easiest to learn. To be marketable, Data Analyst should make sure to learn SQL and gain experience using it. This question is straightforward - the interviewer wants to gauge the strength of your SQL programming skills. If you only have a few years of experience, emphasize how your skills have grown with each subsequent project.
"I have 7 years of experience programming with SQL. Over this timespan, I have used SQL to some degree for over 90% of my projects. At times, I have used multiple languages for different phases of projects, but find SQL to be the language I turn to the most."
Working with large data sets can present some challenges, so hiring managers want to know that you have the experience to handle them if they arise. Share any challenges you might have faced and how you successfully overcame them. If you have been fortunate enough not to face any challenges, stick to the details of your project and the steps you took while working with the data.
"I have had experience working with large data sets delivered to us from outside vendors. Many times these data sets were survey responses for Marketing Research projects with a large sample size. Upon first receiving the data set, I checked the validity of the data by running predetermined frequencies and queries. Doing so would often reveal issues such as missing data, data type issues and errors in skip patterns within the survey. I would work with the vendor to correct these issues before beginning further analyses on the data. Once the data issues were resolved, I would load the data into a data analysis tool to begin my analysis. Sometimes I would work with a Data Engineer to load it into an appropriate tool that could handle the size of the data set."
At the minimum, Data Analysts should have knowledge and experience using basic statistics including mean, median and mode and be able to conduct significance testing. A more advanced level of statistics may be required, but this would be specified in the job description. Data Analysts should not only know how to calculate these basic statistics but should also be able to interpret them in relation to the business.
"I use statistics on a regular basis as a Data Analyst. For the majority of my work , I calculate basic statistics such as the mean and standard variances. I also conduct significance testing frequently to determine if measurement differences between two populations is statistically significant and worth highlighting for further investigation. In addition, for a few projects I have worked with correlation coefficients to determine the relationship between two variables in a data set."
Hiring managers would like to see that you have confidence in your knowledge and experiences as a Data Analyst and that you would take the initiative to recommend a change that would benefit the company. When sharing the recommendation you made, include as many details as possible, particularly the reason you made it. Do not be hesitant to share a change you recommended that may have not been implemented. It still shows your ability to take initiative and that you are continually considering process improvement.
"I believe Data Analysts in non-technical departments are usually the most familiar with the company's data. However, I have worked in companies where data was accessible to several people who were not in an analyst role. This caused much confusion over the interpretation of the data. While data dictionaries can be helpful in these situations, I believe it provides a limited explanation. I recommended that those in non-analytical jobs rely on Data Analysts for data access. This ensured that data was not misinterpreted which could negatively affect strategies being created. I built a case by identifying examples of when data was misinterpreted. The company implemented my recommendation and was even willing to hire more Data Analysts to make sure that there were enough resources to execute my plan."
At some point, Data Analysts will run into data obstacles when conducting analytical projects. Hiring managers want to know how you would deal with these type of situations, particularly when working directly with stakeholders who may not have a strong understanding of the data. Your answer to this question will also reflect your ability to problem solve.
"Years ago, I worked at a company where the executives wanted to initiate a customer segmentation project. However, the data collected in the customer data warehouse was not robust enough to create a meaningful customer segmentation plan. Understanding the importance of this project to the stakeholders, I was able to work with the data warehouse team to outline a handful of data initiatives that would move us closer to a customer segmentation plan. By the time I moved on from this company, the initiatives were progressing well towards its ultimate goal."
It is acceptable to prefer completing one task over another. However, in many cases, it would be expected that you have experience performing all these types of tasks. Avoid showing aversion to any of the steps in the process. When indicating which step you enjoy performing the most, include an explanation as to why it's your favorite. This may illustrate your strengths to the interviewer.
"If I had to select one step as a favorite, it would be analyzing the data. I enjoy developing a variety of hypotheses and searching for evidence to support or refute them. Sometimes, while following my analytical plan, I have stumbled upon interesting and unexpected learnings from the data. I believe there is always something to be learned from the data, whether big or small, that will help me in future analytical projects."
The technical complexity of your work as a Data Analyst may vary depending on the size of the companies you have worked at in the past. Strong technical skills is an important attribute of a Data Analyst's background. Having experience retrieving data from multiple data warehouses demonstrates your understanding of databases, data structures and programming languages.
"In the larger companies I have worked at as a Data Analyst, I have had to work with multiple data warehouses to retrieve the appropriate data. For a particular corporate-wide initiative, I queried against 4 different data warehouses. Once I retrieved the records and variables I needed, I built one large dataset I worked off of to complete my analysis."
Not all Data Analysts will have experience working with statistical models. Interviewers, in most cases, will only be asking this question if statistical modeling was included in the job description. In the case where you are surprised with this question, be upfront about your experience. If you have not had any direct involvement with statistical modeling work, attempt to highlight what you know about it and any training or exposure you may have had to it. Remember, statistical modeling work can include tasks such as building, using or maintaining it.
"Working as a Data Analyst, I have had experience working with Statisticians to help them build their models. Although I do not have direct experience building the model itself, I have aided them by analyzing data and ensuring they have access to the appropriate data. The model was built to help the sales team identify customers who were most likely to purchase additional products and services and when they would be most apt to make that decision. This model increased the sales team's efficiency so that time wasn't wasted with customers who were unlikely to purchase again in the near future. I aided in identifying the appropriate variables used in the model as well as evaluating the efficacy of the model upon completion."
When launching an analysis, most analysts have a prediction on the outcome based on learnings from past projects. However, there will likely be times when the results were unexpected. Your answer to this question will give the interviewer a glimpse of not only the type of analytical projects you have worked on, but also your enthusiasm for them. When describing your project, be sure to show some passion about the learnings you drew from it. Also consider including what action was taken by you and/or stakeholders as a result of the unexpected results.
" In my experience working with customer profiling projects, analyses usually do not show notably surprising results, particularly for established brands. However, while conducting one routine analysis, I was able to identify a customer subsegment that had the potential to provide additional value to the company if it was offered the right product and services with a relevant message. For me, it felt as if I struck gold - the opportunity to add value to a subset of an existing customer base through new products and services was invaluable. It was surprising to everyone involved that we could identify a subsegment from this customer base. From there we began strategizing with Product Development and Brand Managers to develop a plan for this new subsegment."
As with any job, communication is a key skill for Data Analysts. However, communicating with co-workers from different departments takes a different type of skill than communicating with co-workers within your department. It may require using less technical terms and a larger portion of time listening to their questions and concerns, which sometimes requires patience. The experience you share should reflect how you were able to adapt to working with people who may not have spoken your 'language'. Many times this requires you to have the ability to look at a situation from different perspectives and beyond just your own.
"I have run into this situation frequently as a Data Analyst. Each situation is similar to others to a certain degree. In most cases, stakeholders want answers to questions that are not available, because the limitations of the data that is collected or of the database structure. In these situations, I worked with the stakeholder to develop an analysis to answer related questions that may give them an answer as close to what they are looking for as possible. In the process, I tried to offer them a basic understanding of the data available and of the database structures - nothing too detailed as I thought this may confuse them. In the long-term, we developed a project to investigate whether we are able to collect the unavailable data. This ensured them that I understood their needs and was willing to work hard at trying to get them what they needed."
Many hiring managers look for Data Analysts who have the capability to deal with massive data sets with a considerable number of variables and rows. This question is relatively straightforward and you should not feel compelled to review details about the background of the project and any processes you might have gone through. With questions like this, they are interested in size and type of data.
"The largest data set I have worked with was built for a corporate strategy project that required the combined efforts of various departments. This data set had over a million records and 500-600 variables. Included in this data set was Marketing and Operational data that was eventually loaded into an analytical tool for exploratory analysis."
The Data Analyst is tasked with researching, gathering, analyzing, and reporting the data sets requested by Managers or Executives of the Company.
To be successful as a Data Analyst, it is imperative to understand what exactly the End User is wanting to study. The Data Analyst must understand the request or risk spending hours researching and reporting on the wrong requirements.
The Data Analyst must be able to communicate to Managers and Executives how they arrived at the results of their findings. They must be ready to explain how they determined the data to gather and research. What did they include and exclude from the report and why. They must be able to show the trending, forecasting and critical impacts that could negatively impact the end results.
In some companies, the Data Analyst will submit their reports and findings to the Business Analysts or Project Managers, but in many cases the Data Analyst must attend the meeting with the Executives to explain their report, how they went about creating it, describing each step and then explaining the results. The Executives may not like the output of the report and ask if you have ideas as to how to improve the results, so it is important to understand the data sets for each Data Analyst request to which assigned.