10 Items to Know When Starting A New Data Project

Are you a data professional looking to start a new data project?

Then you need to review my 10-point checklist to make sure you’re on the right track. Starting a new data project can be overwhelming. But don’t worry, with my 20 years of experience, I’m here to guide you through it.

Typically when I start to perform a new data related task or analysis for a project, I have to make sure that I meet the expected objectives, which often include identifying patterns, trends, and insights that can be used to drive business decisions.

10 Point Checklist

Point 1: First things first, you need to understand the nature of the deliverable that’s being asked for. Is it a new report, database table, column, data visualization, calculation, or a change to any of the above? In a similar fashion you also need to understand the technologies in play that you have to work with. This could be anything from Tableau, Power BI, SQL Server, Oracle, Teradata or even Microsoft Access (yes, people still use this tool).

Point 2: It’s crucial to know the desired delivery time frame for your project. You don’t want to end up with a longer timeline than what the project manager or client had in mind. Communication is key in this situation.

Point 3: Who is the intended audience for this deliverable? If it’s for an executive audience, you may need to roll the numbers up and take out some detail. If it’s for an analyst or operational audience, you may want to leave in more detail.

Point 4: How much historical data is required? What is the anticipated volume of data that your deliverable is going to generate? Don’t get caught in a situation where your solution can’t handle the trending analyses for a 2 year time frame when you only pulled data for the last 6 months.

Point 5: Understand the volume of data that your solution will generate. For example, a 5 million row output is not conducive to a 100% Excel approach. You will definitely be in the land of database analyses. However you may later present the data at an aggregated level (see point 3) via Excel but hopefully using a real data visualization tool .

Point 6: You need to understand if there’s any Personally Identifiable Information (PII) or sensitive data that you need to access in order to carry out the request. This could include social security numbers, passport numbers, driver’s license numbers, or credit card numbers.

Point 7: It’s important to understand the business processes behind the request. As data people, we tend to focus only on the data piece of the puzzle, but understanding more about the relevant business process can help you deliver the better results for your end users.

Point 8: Try to find and understand any relevant KPIs associated with the business processes on which your data project/task is affecting.

Point 9: Perform data profiling on your datasets! This can’t be stated enough. Profiling leads to understanding data quality issues and can help lead you to the source of the issues so they can be stopped.

Here are a few data profiling videos I’ve created over the years to give you a sense of data profiling in action.

Point 10: Understand how your solution will impact existing business process. By changing a column or calculation, how does this impact upstream or downstream processes? Keep your email inbox clean of those headache emails that are going to ask why the data looks different than it did last week. Most likely there was not a clear communication strategy to inform everyone of the impact of your changes.

Bonus Considerations:

Here are a few bonus considerations since you had the good fortune of reading this blog post and not just stopping at the video.

Bonus Point 1: Consider any external factors that could impact your data project. For example, changes in regulations can impact the data that you collect, analyze, and use. If the government imposes stricter regulations on data privacy (see point 6 above), you may need to change your data sources or analysis methods to comply with these regulations.

Bonus Point 2: Consider internal organizational politics when starting on a project. If you work in a toxic or siloed organization (it happens), access to data can be a challenge. For example, if the marketing department controls customer data, accessing that data for a sales analysis project may be challenging due to internal strife and/or unnecessary burdensome roadblocks.

Internal politics can also lead to potential conflicts of interest, such as when stakeholders have different goals or agendas. For example, if your data analyses could impact a department’s budget, that department may have an incentive to influence your work outcome to their advantage (or try to discredit you or your work by any means necessary).

Bonus Point 3: Finally, make sure to document everything. This includes the business requirements, technical requirements, saved emails, and any changes that were made along the way.

When I started my first office position as an intern at a well known Fortune 500 company, my mentor told me the first rule of corporate life was to C.Y.A. I’m sure you know what that means to cover. Having solid documentation of your work and an email trail for decisions made along the way can keep you out of hot water.


And there you have it, my 10-point checklist for starting a new data project. By following these steps, you’ll be well on your way to delivering high-quality results. Don’t forget to like and subscribe for more data-related content!

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Keep doing great things with your data!

Anthony B. Smoak


Keys for a Successful Career as a Data Analyst

Congratulations, you have just started working as a data analyst in a corporate environment. You think that hard work and performance are enough to get noticed for promotions and salary increases. Well, this is not the case. In this post I am happy to share some knowledge with you that I have painstakingly gained as a career data professional that will help you succeed. It is the kind of advice I wish I had when I started my first job out of college. In actuality, this advice can be tweaked by anyone trying to navigate corporate America. The reality is that hard work and technical acumen are important, but they need to be supplemented. Here are some keys to the game.

Be aware of the P.I.E Theory as coined by Harvey Coleman. Make sure to perform exceptionally well (10%), cultivate the proper image (30%) and manage exposure so the right people will know who you are (60%).

Focus and become an expert in your area of interest. Learn the technical skills required to solve problems others cannot. Do not just be “good enough” to not lose your job. Be exceptional! As companies cut back on training programs, you must close the training gap on your own time.

Learn from “Virtual Mentors” online. The internet and more specifically YouTube can be a true Library of Alexandria when it comes to technical resources.

Show what you know. As you learn and gain new experiences, share your knowledge with an online audience and promote yourself. Not in an obnoxious “smarter than thou” way, but in a way that is sincere in helping others. If you produce high quality content, you will get noticed and opportunities will follow.

Build a reputation. In the words of industrialist Henry J. Kaiser, “When your work speaks for itself, don’t interrupt.”

Be Aware of the P.I.E Theory as Coined by Harvey Coleman

  • Perform exceptionally well (10%)
  • Cultivate the proper image (30%)
  • Manage exposure so the right people will know who you are (60%)

I first learned of Harvey Coleman’s P.I.E. Theory while working on my MBA at the Georgia Institute of Technology Scheller School of Business. As a pure technical computer science undergrad, the political and organizational rules for success were not apparent to me.

Mr. Coleman in his book “Empowering Yourself: The Organizational Game Revealed” assigns a weight to each one of the principles that indicates its impact on long term success. One would think that performance would constitute most of the weight, but curiously it does not. Performance is simply the price of admission, table stakes and the minimum expectation for employment. If you are not performing, your image and exposure will not help you (unless you are royalty or your last name matches the owner of the company). Nothing else matters if you are not good at your job.

Image at 30% has three times the weight as performance with respect to climbing the ladder. It serves as an important tiebreaker amongst a high performing candidate pool. Image can consist of the clothes you wear, your demeanor, your manner of speaking, ability to be a team player, etc.

Be aware that you are sending signals at all times of your appropriateness for success at the next level. If you are a hoodie and jeans diehard while the next level is sport coats and slacks, you may want a fashion makeover if you have higher aspirations.

Exposure will have the greatest impact on your career at 60%. You cannot ascend on your own. Someone at the next level or higher (i.e., sponsor) will have to champion you for a disruptive career enhancing move. The risk to the sponsor is having to explain to others at their level why they championed an unqualified individual, which can reflect poorly on their judgement.

You can minimize your would-be sponsor’s risk by volunteering for internal projects, assuming more responsibility within your job role, being a team player and learning new skills. Make your manager feel comfortable advocating on your behalf when she is speaking with the other power players in your organization.

Focus and Become an Expert in your Area

We look to experts to solve our problems and provide advice within certain domains. If we have vision problems, we might seek advice from an optometrist, not necessarily an auto mechanic. The same dynamic is in play when we look for people within an organization to solve a particular problem.

If you have a reputation as one of the few SQL experts or Tableau experts (or any other in-demand skill) that can relieve specific pain points, then you will increase your reputation. People will begin to seek you out for assistance in solving their problems which enhances your image and can lead to the proper exposure.

Data analyst appropriate technical skills include knowledge of database languages such as SQL, R, and Python. Let me emphasize that you MUST learn SQL if you consider yourself a data professional. Master Microsoft Excel because it is not going anywhere anytime soon. When the next 50 mile wide asteroid impacts Earth, the only things left will be crocodiles and spreadsheets.

Sometimes all a company has for “reporting” tools is the basic combination of Excel and PowerPoint. Therefore you better be ready to pivot, VLOOKUP and write customized VBA functions at a minimum. Sometimes we go to war with the tools we have, not the tools we might want or wish to have at a later time, to paraphrase a former government official.

Learn data visualization software such as Tableau (solid visuals and strong community), Power BI (Microsoft stack) or Qlik (data load scripting). It does not matter if your organization uses the tools or not. Learn them now and use them later to solve problems in a novel way that your peers may not consider. All three tools have free versions that are available for experimentation.

Other useful data analyst skills include Alteryx, SAS and statistical skills. As a data analyst your role is to help gather, organize, analyze and report data. Deep expertise will help you stand out from others and build your internal reputation.

Learn tools and skills even if you do not currently use them in your role. When I first started learning Tableau I was turned down for opportunities because I did not have any Tableau project experience on my resume. I made it a point to skill myself up on nights and weekends and combined that effort with visible displays of credibility (my blog and YouTube channel).

I learned basic Tableau skills using Tableau Public because I did not have a current Tableau license at the time! Now that I have acquired recognition as a Tableau authority, opportunities come to me from within and outside my organization without me explicitly seeking them out.

Do not just be “good enough” to not lose your job. Be exceptional! If that means learning skills on nights and weekends, commit yourself to that goal. Learn as much as you can as fast as you can to make an impact.

In Michael Porter style parlance, you want differentiation to be your competitive advantage. You do not want “low cost” to be your differentiator, at least not for a substantial period of time.

Find “Virtual” Mentorship

There is always someone or a group of people that we admire for their proficiency at a given skill. When I started work on my first job, I had a wonderful manager who took me under his wing and taught me the technical skills I needed to succeed at the job. Shoutout to John Jarosinski!

Sometimes we are not lucky enough to establish personal connections and mentorship at the same time. However, thanks to the internet we can follow experts online. When I decided to start learning Tableau, I learned much from Andy Kriebel. I have never met the man, but I count him as a virtual mentor in learning the Tableau game. As my proficiency increased, I followed others in the Tableau community like Luke Stanke, the Flerlage Twins, Ryan Sleeper and Lindsay Betzendahl.

On the Power BI side of the house consider Guys in A Cube, Sam McKay, Ruth Pozuelo Martinez, Parker Stevens and Spencer Baucke (who is also excellent in Tableau). For Qlikview I seek out Udemy classes by Shilpan Patel. Excel standouts include Leila Gharani and Oz du Soleil.

The internet and more specifically YouTube can be a true Library of Alexandria when it comes to technical resources. Learn what you can and support creators through their paid online classes and merch when available!

The reality is that you will need to learn more and more on your own as companies have pared back on generous training programs for this generation of workers. A number of organizations believe that training dollars are wasted on employees who will simply jump to the next company after they have received training. If you work for one of these companies, you will need to skill yourself up on your own time to standout.

Show What You Know (Promote Yourself)

In past roles, I performed well but outside of my specialized cohort, no one knew. As typical with data roles, if reports were generated on time, there was no issue. If there were interruptions, then everyone suddenly took notice. Don’t remain underappreciated!

One of my talented ex-co-workers who I still consider a friend started publishing and sharing what he knew online for others to consume. He developed training courses and started posting articles regularly on LinkedIn. He is a humble person but highly skilled. His self-promotion activities were not vanity endeavors, he genuinely wanted to help people learn.

I noticed that people both inside and outside of the organization took notice and his star began to rise (he was a high performer as well). He was able to leverage his performance and the subsequent image and exposure boost to obtain a significant raise. He eventually moved on from the organization into another organization with a greater increase in salary. The reputation he built from performing at a high level AND establishing visible displays of credibility online smoothed the path for his transition to greener organizational pastures.

Meanwhile I was underpaid and underappreciated, yet diligently performing my tasks. As the saying goes, “If you don’t have your own plan, you will fall into someone else’s plan.” I’ll add, “You may not like the alternate plan.”

From that point on, I decided to follow my friend’s blueprint and started publishing what I knew on social media. We all have something to say in our unique voice. There are gems that you know that you take for granted, but others would benefit from that knowledge. So share them!

Start a WordPress blog, post to Medium, LinkedIn or Twitter. Create visualizations on Tableau Public or collaborate on GitHub projects. If you have the discipline and afterwork/weekend time commitment start a YouTube channel!

As you learn and gain new experiences, share your knowledge with an online audience and promote yourself. Not in an obnoxious way, but in a way that is sincere in helping others. If you produce high quality content, you will get noticed and opportunities will follow.

Combine your social media efforts with certifications in your desired area to establish your bona fides.

This is exactly what I did. Today I am in a new organization, I make more financially (greener pastures, pun intended) and have been promoted multiple times. Combined with excellent performance (i.e., table stakes), I picked up recognized business intelligence and Tableau certifications. I also learned visualization skills that helped my manager look successful. I made everyone I could be aware of my new Tableau and visualization skills by leveraging social media to exhibit my passion for data. Thus, I mitigated the risks associated with my manager’s sponsorship.

Currently, my YouTube channel has just under 11 thousand subscribers and 1.4 million views. That’s not a bad subscriber count for a niche data channel. Leverage social proof to your advantage!

If you are searching for your first or next position in data, recruiters (or hiring managers in your current organization) will search for your online body of work. If they cannot find any evidence of your credibility, you are at a disadvantage. When your portfolio of work can be found online, it affords you an advantage against others in the candidate pool. Your reputation speaks for you before your initial conversations.

In Conclusion

Data analysis is a fun and interesting career for those who have the technical chops and dedication to continually better themselves. Technology does not stand still, and minimal training and work effort do not move the needle. Learn as much as you can as fast as you can, earn certifications, promote yourself (this is key) and give your would-be sponsor a reason to advocate for your disruptive career progression.

If you agree or disagree, let me hear it in the comment section.

Do Great Things with Your Data

Anthony B. Smoak

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