No Data Professional is an Island | by Christian Wanser | Aug, 2022
The necessary teamwork for data engagements
Teamwork is key as a data professional. I am not aware of any data experts that are able to operate in a vacuum without input or help from any other business function.
I am not aware of any data experts that are able to operate in a vacuum without input or help from any other business function.
In this article, I’ll specifically talk about the experience of data analysts and their engagements, but the processes, tips, and best practices I cover can be generalized across the field! Let me know your thoughts in the comments about the 9 tips on data engagements that I provide below.
Data analysts often effectively take on the role of project manager, coordinating the efforts and communications between the end users, engineering, and various departments required to deliver the final product. Managing these relationships is crucial to being an effective data analyst.
Data analysts often effectively take on the role of project manager.
For those that are interested in pursuing a career in data analytics, read on to learn about the necessary collaboration to be a great data analyst! And for those that are already practicing data analysts, continue reading to gain some tips or to perhaps confirm that you’re not alone in your dependence upon other business units!
Below, I’ll take you through the journey of data engagements with a handful of best practices along the way! Here are the steps we’ll cover:
- Field request from stakeholders
- Rough layout to confirm the path forward
- Reach out to the necessary data professionals
- Keep track of the project’s progress
- Deliver the project
- Check-in later
Data analysts must be able to handle numerous incoming requests where many stakeholders label their requests as ASAP. Not only do you have to deal with the sheer volume of requests, but you must also dive into the initial asks with the requesters rather than simply becoming an order taker.
In data analytics, the customer is not always right. But, you can reach a point of consensus amongst your stakeholders. Let’s talk about how you can achieve this.
In data analytics, the customer is not always right.
Clarify the Request for Everyone
One of the first steps you should take as a data analyst fielding requests is to ensure that everyone is on the same page. People will come to you with a problem and will often bring along a semi-thought-through solution. And that’s great! They’re already working to try and solve the problem.
Know the Problem Before You Try to Solve It
The issue with these semi-thought-through solutions is how they’re framed: people will often forget to include a description of the problem they’re trying to solve when they request a solution. As a thorough data analyst, you can’t let this slide! You need to know what problem you are addressing when you engage in data projects.
Maximizing the information you’re equipped with will only help you deliver better results. Moreover, through making sure that you understand the problem, you confirm that there is indeed a problem and you guarantee that your stakeholder has defined the business case.
Tip #1: Define the Business Case.
Make sure that you fully understand the request and that the requester fully understands what they really want.
Remember the Big Picture
It is important to remember that any dashboard, analysis, or data solution that you deliver will have to be maintained indefinitely. Solutions will often get “shelved” over time and collect dust, so this is important for data analysts to keep in mind.
Any dashboard, analysis, or data solution that you deliver will have to be maintained indefinitely.
Do not allow yourself to be complicit in providing superfluous projects. First, investigate and determine if any existing solutions may help to answer the questions that are being raised. Existing dashboards can be refigured to provide additional figures and insights. Reduce, reuse, recycle!
Tip #2: Reduce, Reuse, Recycle!
Look into existing solutions that can help address your current problem and consider recycling and revamping previous projects.
Please note that this is not encouraging you to be resistant in providing new solutions to your organization. It’s just healthy for your organization to look at requests with a critical eye and to ensure that the future is kept in mind. Keep a tidy house!
Expand Upon Initial Request When Appropriate
An ideal that is pulling in the opposite direction of “reduce, reuse, recycle”, but one that is equally important, is for data analysts to also know when to expand requests.
Tip #3: Expand the Ask
Don’t become an order taker! Instead, go the extra mile to provide more than the initial ask when appropriate and with minimalism/interpretability in mind.
When putting in this investment of time, make sure that it’s done right with an eye for improvement and look to the future for additional questions that may come up as your solution is put to use!
Future-Proofing Your Deliverable
Regarding the future, try to reduce the amount of rework you have to complete down the road!
Be it a dashboard, analysis, or other data solution, I strongly recommend that you implement dynamic solutions when time allows. Dynamic in that the data processes should be set up so that over time, over periods, across geographies, across business units, etc. the solution will still perform and require as little maintenance as possible.
Tip #4: A Dynamic Solution is a Happy Solution
When time allows, implement dynamic solutions that will stand the test of time and change.
When starting a new data engagement, creating a rough outline of the project to have stakeholders sign off on will make the path forward less tumultuous. This is a big step in reducing the amount of rework required!
What do these sketches look like? They can look however you want as long as they do a good job at explaining what the end product will look like and how it will be used. In the end, you know your stakeholders best! I’ll give some ideas below.
- Dashboard: Make a quick sketch of the various stats, KPIs, filters, graphs, etc. with a general layout.
- Analysis: Make a rough outline of the required data, the possible user inputs, as well as the various outputs (eg. charts, tables) and their formats (eg. PNG, HTML, CSV).
Analysis Paralysis
Note that this step should not consume a lot of time. It is certainly nice to have this confirmation from your stakeholders to establish a unified front when moving forward on a project. But, if it involves multiple people who are perhaps difficult to reach or this process of confirmation is just dragging along, strongly consider starting work on the engagement while waiting for this feedback.
Tip #5: Avoid Analysis Paralysis!
Don’t fall into the trap of endlessly hashing out the concept, to the detriment of starting on the actual product.
In the end, it is still better to get feedback while you are completing the project rather than upon the solution’s completion. This brings me to another important point: data analysts must have a disposition for action. Don’t work so hard and long only to produce outdated insights in the end.
As an analyst, if you are not an analytics engineer, you’ll most likely have to reach out to the people who bring the data to you. These wonderful people are often the data engineers in your organization. They’re your friends! They help you do your best work. Take them out for drinks sometime. Reach out on Thanksgiving!
Ensure that you maintain good relationships with these coworkers because you strongly rely on them to help you do your job well!
Clear, Tangible Requirement
When you reach out to your colleagues about a data engagement, it will serve you well to have a clear set of requirements prepared as well as the solution sketches mentioned earlier. This makes the project tangible and given the presentation of the data, the engineers can work to determine how to best bring the data in.
Tip #6: Know Your Stuff
Make sure that you know the engagement well enough to be able to explain the requirements thoroughly before reaching out to your fellow data professionals.
For most organizations, the data engineers are quite resource-constrained and it is important to remember that you are one of their many internal clients. More on this in the next section.
The engagement that you are currently working on is likely one of many you have on your plate. It is necessary to keep track of project progress and to inform your stakeholders of its development.
Document Progress
Documentation is the best way to keep track of project progress. This can be done through project coordination software or at the very least an Excel or Word document.
In maintaining documentation, you ensure that timelines are clearly communicated, negotiated, and agreed upon by the various groups involved in the engagement’s delivery. Documentation helps to hold all teams accountable for timelines as well as allot resources to ensure that your organization is distributing resources efficiently.
Tip #7: Documentation is Key
Documentation aids in establishing and maintaining timelines, keeping accountability, and distributing resources efficiently.
Check In with Stakeholders if Time Allows
Especially with longer engagements, it might be a good idea to check in with your stakeholders, inform them of the engagement’s progress, and ideally show them a draft of the solution as a work in progress. This allows for additional feedback and potential pivots that are better surfaced partway through the project than upon its delivery.
The finale! But not really… As I mentioned before, rarely does a project get handed off, never to be seen again. You can take extensive measures to reduce rework and revisiting projects, but it will still happen.
Rarely does a project get handed off, never to be seen again.
More Documentation!
I’m a big believer in documentation. It is certainly more work, but it has saved the day on many occasions.
Document for future users as well as other data professionals. Upon completing a dashboard or analysis, produce documentation to help users navigate your solution. For your fellow data professionals, include details on the data (eg. where it comes from, how it’s transformed, etc.), so that in the event that you move on from your current role, your organization can still use the solution. Don’t leave people high and dry!
Tip #8: Help Future You
Documentation will help future you or other data professionals to navigate your solution and minimize time wasted in attempting to understand how it works.
Ideally your dashboards, analyses, etc. should be self-explanatory — another great value-add of data professionals if done right, but documentation is key to going above and beyond in your role.
Walk Through the Solution
When you deliver the final product, don’t just leave it on the doorstep. Work with the users of your solution and help them understand how it works, inform them of any nuances they need to be aware of, and invite any questions or concerns they may have about it. This investment of time has big payoffs.
When you deliver the final product, don’t just leave it on the doorstep.
You have plenty of problems coming your way, so why would you willingly invite more problems?! Regarding follow-ups on delivered solutions, this may be what you’re thinking, but it is important to check-in with your internal clients.
It’s absolutely worthwhile to see how their use of the tools are going, what pain points they’re experiencing, potential areas for improvement, etc. and then work to fix any issues!
This is being a proactive contributor to your organization! Fix the problems before they accumulate and cause you and your team real headache. Future you will thank you.
Tip #9: Check In and Iterate
Check in with stakeholders often and iterate upon deliverables to achieve continuous improvement and growth of insights.
Iteration is where the cool stuff happens in analysis. Additional analysis or business questions may have surfaced as a result of your initial solution! Work to iterate on the original deliverable and provide insights on these follow-up questions.
The necessary teamwork for data engagements
Teamwork is key as a data professional. I am not aware of any data experts that are able to operate in a vacuum without input or help from any other business function.
I am not aware of any data experts that are able to operate in a vacuum without input or help from any other business function.
In this article, I’ll specifically talk about the experience of data analysts and their engagements, but the processes, tips, and best practices I cover can be generalized across the field! Let me know your thoughts in the comments about the 9 tips on data engagements that I provide below.
Data analysts often effectively take on the role of project manager, coordinating the efforts and communications between the end users, engineering, and various departments required to deliver the final product. Managing these relationships is crucial to being an effective data analyst.
Data analysts often effectively take on the role of project manager.
For those that are interested in pursuing a career in data analytics, read on to learn about the necessary collaboration to be a great data analyst! And for those that are already practicing data analysts, continue reading to gain some tips or to perhaps confirm that you’re not alone in your dependence upon other business units!
Below, I’ll take you through the journey of data engagements with a handful of best practices along the way! Here are the steps we’ll cover:
- Field request from stakeholders
- Rough layout to confirm the path forward
- Reach out to the necessary data professionals
- Keep track of the project’s progress
- Deliver the project
- Check-in later
Data analysts must be able to handle numerous incoming requests where many stakeholders label their requests as ASAP. Not only do you have to deal with the sheer volume of requests, but you must also dive into the initial asks with the requesters rather than simply becoming an order taker.
In data analytics, the customer is not always right. But, you can reach a point of consensus amongst your stakeholders. Let’s talk about how you can achieve this.
In data analytics, the customer is not always right.
Clarify the Request for Everyone
One of the first steps you should take as a data analyst fielding requests is to ensure that everyone is on the same page. People will come to you with a problem and will often bring along a semi-thought-through solution. And that’s great! They’re already working to try and solve the problem.
Know the Problem Before You Try to Solve It
The issue with these semi-thought-through solutions is how they’re framed: people will often forget to include a description of the problem they’re trying to solve when they request a solution. As a thorough data analyst, you can’t let this slide! You need to know what problem you are addressing when you engage in data projects.
Maximizing the information you’re equipped with will only help you deliver better results. Moreover, through making sure that you understand the problem, you confirm that there is indeed a problem and you guarantee that your stakeholder has defined the business case.
Tip #1: Define the Business Case.
Make sure that you fully understand the request and that the requester fully understands what they really want.
Remember the Big Picture
It is important to remember that any dashboard, analysis, or data solution that you deliver will have to be maintained indefinitely. Solutions will often get “shelved” over time and collect dust, so this is important for data analysts to keep in mind.
Any dashboard, analysis, or data solution that you deliver will have to be maintained indefinitely.
Do not allow yourself to be complicit in providing superfluous projects. First, investigate and determine if any existing solutions may help to answer the questions that are being raised. Existing dashboards can be refigured to provide additional figures and insights. Reduce, reuse, recycle!
Tip #2: Reduce, Reuse, Recycle!
Look into existing solutions that can help address your current problem and consider recycling and revamping previous projects.
Please note that this is not encouraging you to be resistant in providing new solutions to your organization. It’s just healthy for your organization to look at requests with a critical eye and to ensure that the future is kept in mind. Keep a tidy house!
Expand Upon Initial Request When Appropriate
An ideal that is pulling in the opposite direction of “reduce, reuse, recycle”, but one that is equally important, is for data analysts to also know when to expand requests.
Tip #3: Expand the Ask
Don’t become an order taker! Instead, go the extra mile to provide more than the initial ask when appropriate and with minimalism/interpretability in mind.
When putting in this investment of time, make sure that it’s done right with an eye for improvement and look to the future for additional questions that may come up as your solution is put to use!
Future-Proofing Your Deliverable
Regarding the future, try to reduce the amount of rework you have to complete down the road!
Be it a dashboard, analysis, or other data solution, I strongly recommend that you implement dynamic solutions when time allows. Dynamic in that the data processes should be set up so that over time, over periods, across geographies, across business units, etc. the solution will still perform and require as little maintenance as possible.
Tip #4: A Dynamic Solution is a Happy Solution
When time allows, implement dynamic solutions that will stand the test of time and change.
When starting a new data engagement, creating a rough outline of the project to have stakeholders sign off on will make the path forward less tumultuous. This is a big step in reducing the amount of rework required!
What do these sketches look like? They can look however you want as long as they do a good job at explaining what the end product will look like and how it will be used. In the end, you know your stakeholders best! I’ll give some ideas below.
- Dashboard: Make a quick sketch of the various stats, KPIs, filters, graphs, etc. with a general layout.
- Analysis: Make a rough outline of the required data, the possible user inputs, as well as the various outputs (eg. charts, tables) and their formats (eg. PNG, HTML, CSV).
Analysis Paralysis
Note that this step should not consume a lot of time. It is certainly nice to have this confirmation from your stakeholders to establish a unified front when moving forward on a project. But, if it involves multiple people who are perhaps difficult to reach or this process of confirmation is just dragging along, strongly consider starting work on the engagement while waiting for this feedback.
Tip #5: Avoid Analysis Paralysis!
Don’t fall into the trap of endlessly hashing out the concept, to the detriment of starting on the actual product.
In the end, it is still better to get feedback while you are completing the project rather than upon the solution’s completion. This brings me to another important point: data analysts must have a disposition for action. Don’t work so hard and long only to produce outdated insights in the end.
As an analyst, if you are not an analytics engineer, you’ll most likely have to reach out to the people who bring the data to you. These wonderful people are often the data engineers in your organization. They’re your friends! They help you do your best work. Take them out for drinks sometime. Reach out on Thanksgiving!
Ensure that you maintain good relationships with these coworkers because you strongly rely on them to help you do your job well!
Clear, Tangible Requirement
When you reach out to your colleagues about a data engagement, it will serve you well to have a clear set of requirements prepared as well as the solution sketches mentioned earlier. This makes the project tangible and given the presentation of the data, the engineers can work to determine how to best bring the data in.
Tip #6: Know Your Stuff
Make sure that you know the engagement well enough to be able to explain the requirements thoroughly before reaching out to your fellow data professionals.
For most organizations, the data engineers are quite resource-constrained and it is important to remember that you are one of their many internal clients. More on this in the next section.
The engagement that you are currently working on is likely one of many you have on your plate. It is necessary to keep track of project progress and to inform your stakeholders of its development.
Document Progress
Documentation is the best way to keep track of project progress. This can be done through project coordination software or at the very least an Excel or Word document.
In maintaining documentation, you ensure that timelines are clearly communicated, negotiated, and agreed upon by the various groups involved in the engagement’s delivery. Documentation helps to hold all teams accountable for timelines as well as allot resources to ensure that your organization is distributing resources efficiently.
Tip #7: Documentation is Key
Documentation aids in establishing and maintaining timelines, keeping accountability, and distributing resources efficiently.
Check In with Stakeholders if Time Allows
Especially with longer engagements, it might be a good idea to check in with your stakeholders, inform them of the engagement’s progress, and ideally show them a draft of the solution as a work in progress. This allows for additional feedback and potential pivots that are better surfaced partway through the project than upon its delivery.
The finale! But not really… As I mentioned before, rarely does a project get handed off, never to be seen again. You can take extensive measures to reduce rework and revisiting projects, but it will still happen.
Rarely does a project get handed off, never to be seen again.
More Documentation!
I’m a big believer in documentation. It is certainly more work, but it has saved the day on many occasions.
Document for future users as well as other data professionals. Upon completing a dashboard or analysis, produce documentation to help users navigate your solution. For your fellow data professionals, include details on the data (eg. where it comes from, how it’s transformed, etc.), so that in the event that you move on from your current role, your organization can still use the solution. Don’t leave people high and dry!
Tip #8: Help Future You
Documentation will help future you or other data professionals to navigate your solution and minimize time wasted in attempting to understand how it works.
Ideally your dashboards, analyses, etc. should be self-explanatory — another great value-add of data professionals if done right, but documentation is key to going above and beyond in your role.
Walk Through the Solution
When you deliver the final product, don’t just leave it on the doorstep. Work with the users of your solution and help them understand how it works, inform them of any nuances they need to be aware of, and invite any questions or concerns they may have about it. This investment of time has big payoffs.
When you deliver the final product, don’t just leave it on the doorstep.
You have plenty of problems coming your way, so why would you willingly invite more problems?! Regarding follow-ups on delivered solutions, this may be what you’re thinking, but it is important to check-in with your internal clients.
It’s absolutely worthwhile to see how their use of the tools are going, what pain points they’re experiencing, potential areas for improvement, etc. and then work to fix any issues!
This is being a proactive contributor to your organization! Fix the problems before they accumulate and cause you and your team real headache. Future you will thank you.
Tip #9: Check In and Iterate
Check in with stakeholders often and iterate upon deliverables to achieve continuous improvement and growth of insights.
Iteration is where the cool stuff happens in analysis. Additional analysis or business questions may have surfaced as a result of your initial solution! Work to iterate on the original deliverable and provide insights on these follow-up questions.
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