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Simple attribution models are single-click attributions. More advanced attribution models consider multiple or all touchpoints of the customer’s journey prior to the conversion. And very complex models account for more than just click touchpoints, and include impressions, usage of discount codes, and more.

So you know how to write SQL queries, can explain spreadsheets to a child, know how to bring numbers to life in engaging graphs, and can write a few lines of code in Python. But is that all there is to being a data analyst?

Being capable of doing what is expected of you and being great at what you do are two different things. And we want you to be the latter: excel at what you do, always. (No pun intended.)

That’s why, in this article, you’ll find five tips on how to become a kick-ass data analyst:

  • Check your data diligently
  • Think about speed
  • Understand the business logic
  • Master the craft of data storytelling
  • Use the right tools

Perhaps you’re not a data analyst just yet but would love to become one. If that’s you, then we’re already one step ahead. We prepared an article on the how-tos and whys around becoming a data analyst.

But for those of you who have gotten the basic skills down and know there’s always room for improvement, this article’s for you. Let’s dive in.

5 steps to improve your data analytics

1. Check your data diligently

A good data analyst checks their data sources once and then gets down to business. A great data analyst is very attentive to detail and double, or even triple-checks their data.

This might sound simple, but the truth is not everyone thinks triple-checking their data is necessary. Your rule of thumb should always be “there’s something wrong with this dataset” because more often than not, that will be the case.

People can be messy and overlook mistakes, or they might forget to update their sources in time and expect you to build them a dashboard with the wrong data. You need to be on your toes, scanning those datasets for any inaccuracies or errors and fixing them before jumping into the analysis.

That’s why the data transformation step is crucial. This is when you actually take the time to explore your data, clean it, filter it, and get it into shape before moving on to visualization.

However, you will need to make a judgment call on whether or not the errors are worth fixing. If fixing them will take hours out of your day, should you take this on? Or should someone else take care of this issue? Will it affect the end results if you overlook the problem? Depending on the use case you’re working on, you’ll know best which path to follow.

2. Think about speed

If you’re constantly working with large amounts of data, you need to learn how to navigate your datasets fast. The quicker you are, the more analysis you can produce, and the more business insights you can generate. Your speed moves the company forward, but this isn’t to say that you should overwork yourself. It’s all about working smarter.

How can you achieve this? By mastering the art of SQL to surf your databases and retrieve the data you need, and then using as little code as possible to model and visualize it. Learning how to write code as fast as possible is your best bet for finding the information you need. Bringing that information to life with just a few clicks, without having to double-think what the code should look like for that specific graph, is the recipe for lightning-fast deliveries. What’s better than being The Flash for your data team?

3. Understand the business logic

If the business team asks you to build them a dashboard and you have all the sources you need, you just have to apply the technical skills you’ve honed over the years to clean their data and visualize it.

That’s all well and good, but what if you don’t understand what the business team actually does in your company? What if you don’t know what market segmentation looks like? Who their ICP is? Who the competitors are? What MQLs, SQLs, and Opportunities are? In other words, what if you don’t understand the business logic behind the analysis you’re conducting?

Well, if you don’t understand how the business operates, if you don’t have a commercially driven mindset, if you don’t understand who the customers are and who the company is targeting, then you won’t be able to grasp and communicate the impact and importance of your analysis and how it can affect business decision-making.

If you just build dashboards when asked to do so, then you’re delivering insight to the teams. But if you build the dashboard knowing what the data is about and understanding the purpose behind the numbers, then you can deliver actionable insights — pieces of information that feed back into the teams’ strategies and operating plans and turn into steps to be followed.

However, delivering actionable insights doesn’t just come from understanding how the business functions. You also need to know how to communicate your findings in an effective way.

4. Master the craft of data storytelling

As a data analyst, you need to be well-versed in communicating both with computers and people. But why is communicating with people so important? You need to be prepared to present the results of your analysis, not only in a comprehensive way but in a compelling way that drives people to take action based on the learnings you share. You have to find the sweet spot to make sure your data discoveries don’t go unused.

So what do you do? You turn to data storytelling. You summarize and present the most important facts and learnings derived from your analysis. You’re concise and structured, prepare what you’re going to say based on the audience you’re talking to, and you aid your story with easy-to-digest visuals.

But you never (and we mean it — never) draw conclusions that go beyond the data. A great data analyst presents the facts and suggests a multitude of possible interpretations, but they don’t make up fairytales. You should simply learn to deliver those facts in a way that inspires people to come up with business hypotheses and test them:

  • “This is what the data says, and I suspect the reason behind this is…”
  • “The data shows this … and some possible reasons might be…”
  • “The results could serve as an inspiration for tests A, B, or C moving forward…”

5. Use the right tools

Getting into the habit of thoroughly checking and questioning your data, learning how to surf datasets as fast as possible, nailing your understanding of your company’s business logic, and becoming an expert at delivering data-driven business insights are all incredible tips for becoming the best data analyst you can be. But there’s only so much you can do on your own.

Honing your skills will only bring the results you want to see if you actually implement the right data analytics tools. Without a tool that enables you to smoothly integrate, model, and visualize your data, how are you going to become the Speedy Gonzales of data analytics? And let’s not even get started on automating your data workflows. What’s more efficient than not having to think twice about the actuality of your datasets and always being able to create up-to-date reports?

Our best practice solution here is to search the market for a full-stack, end-to-end data platform that not only helps you fulfill all the necessary steps to deliver stellar data analysis, but also enables you to shine as the kick-ass data analyst you know you can be.

One such tool is Y42. It’s a data platform that allows you to put your SQL knowledge into practice while taking advantage of the no-code interface that lets you integrate, orchestrate, and even visualize data in just a few minutes.


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