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SeattleDataGuy
SeattleDataGuy

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5 Tips On How To Become a Better Data Analyst

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Becoming a data analyst is one of the most common early career moves for people educated in STEM fields. Not to mention according to theΒ Bureau of Labor StatisticsΒ the demand for data analysts will grow about 20% over the next few years.

Working as an analyst allows you to put skills learned in college to work while also giving you an opportunity to develop new skills on the job. Average salaries ranging from $60,000 to $80,000 also make data analyst positions lucrative starting points for younger professionals.

As companies have become more data-driven, the skills that go into working as an analyst have grown more technical. Many analysts today are skilled in programming languages like Python and R that are suitable for processing large data sets. Before developing these highly technical skills, however, there are some basicΒ tips and tricks that all data analysts should learn. Here are some of the fundamentals you should focus on in order to become a better data analyst or data engineer.

1. Set Up a Clear Data Analytics Process

One of the fundamental parts of becoming a successful data analyst is to have a clear process set up for your projects. This will save you the time and trouble of approaching each project in an ad hoc manner. A simple data analytics process is outlined below:

  • Define the Question:Β Fully define the question you're trying to answer and the goals of your data analytics project.
  • Collect Data:Β Work with data engineers or other data professionals to gather relevant data for your project.
  • Clean the Data:Β Standardize the data you've collected and remove any incorrect or irrelevant entries.
  • Analyze the Data:Β Employ data analysis techniques to understand the data and drive answers to your question. This step can take many different forms, depending on the question you're trying to answer.
  • Share Your Results:Β Create data visualizations and resources that will help others understand the insights you've produced.

With this simple framework, you'll have a clear road map for outlining and completing data analytics projects. Following this basic process will also keep you from getting sidetracked as you conduct your analysis.

2. Don't Bury the Lede

When your analysis is finished, it's important that you're able to communicate your findings to others in an effective way. A key part of this is to keep your reports simple and concise. While it may be tempting to show all of your findings, it's better to condense your results down to a simple, understandable message.

For optimal communication, consider telling the story of your data with a few carefully selected charts. These should be relevant to the core question and easy for your audience to understand. Sum up your findings with a conclusion that answers the question and drives value for your audience. By doing this, you'll avoid confusion and keep your messaging focused on what your analysis has produced.

3. Data Analytics Peer Review

Putting a peer review process in place for your analysis is one of the best ways to ensure your work is sound and accurate. Getting a second set of eyes on your analysis can help you find potential errors or room for improvement. If a fellow analyst confirms your analysis, you'll know that your work is ready for presentation.

Peer review is especially important for less experienced data analysts. If you can get a more experienced analyst to review your work, you'll be able to learn from their insights and comments. It's also helpful for analysts with more technical roles who mayΒ be working on projects that were once primarily the domain of data engineers.

4. Triple-Check Your Data

Whenever you're working with data, it's good practice to assume that the datasets have at least some flaws. These flaws can range from simple organizational errors to completely erroneous pieces of data. For this reason, you should get into the habit of triple-checking your data as you conduct your analyses.

Finding flaws or inaccuracies in your datasets will help you provide a better analysis. In many cases, you'll be able to simply fix the issues you find and then proceed with your work. In others, though, you may discover much larger problems that require substantially more work to resolve.

There are times when it's even advisable to ignore flawed data altogether. While you might lose some information, you have to know when a problem is too labor-intensive to be worth fixing. This decision will depend on the project and the nature of the problems with your data.

While checking your data, it's a good idea not to assume that anything is accurate.Β Even something that seems foolproof may have errors that could throw your analysis off. Columns in which data are entered from a dropdown menu, for example, seem like they should be free from errors. If there are invalid options in the menu, though, you could end up with flawed data as a result. Assume that there are errors in any dataset and conduct a thorough search to find them. By doing this during the data cleaning phase of your project, you can save yourself from having to backtrack and fix mistakes later on.

5. Know When To Stop Your Analysis

A final critical skill that is frequently overlooked is knowing when to stop your analysis. Having a set endpoint is a key part of the data analytics process. When you reach that endpoint, you need to be able to stop and finalize your analysis.

Without a clear endpoint, you can easily think of new questions to ask and find yourself going down rabbit holes that aren't relevant to your project. While there are times when further exploration delivers useful insights, endless data analysis frequently fails to produce valuable results.

Knowing when to stop relates directly to the first step of the data analytics process outlined above. If you don't know exactly what question you're trying to answer, it's very difficult to know where to stop. With a clearly defined question, you should have a natural endpoint beyond which there's no need for further analysis.

Conclusion

TheΒ tools and resources available to dataΒ analysts and engineers are constantly changing as technology evolves. The fundamental role of an analyst, however, remains the same. As a data analyst, your primary task will always be to provide valuable, data-driven insights that help your business or organization achieve its goals.

By focusing on these fundamental skills, you can give yourself an extremelyΒ strong foundation as an analyst. From there, you can build your technical skills to expand your capabilities. Whatever tools you're using, though, keeping these basic principles in mind will help you improve as an analyst and create more value for your employer.

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