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Alex | 29 April 2021

7 Steps for Approaching Real-World Data Analysis

As data becomes more integrated into your planning and decision-making, you need a directional blueprint for approaching analysis as it can be easy to get lost along the way.

Here are seven steps to help keep your data projects on track.


1. Identify a clear question or problem you wish to address

Make sure that the analysis you are about to conduct has practical relevance. The best way to confirm this is to ask, “if we had this data or these results, what would we do differently because of it?”. Having clarity around this question will help others understand the importance of your project and add more impact to the results you eventually present.

2. Speak with others who have a vested interest in the content or potential results

A typical mistake when conducting analysis is to take the initial concept and then lock yourself in a room until it is complete. But it is incredibly important that others in your organization - and potentially outside of it - have a chance to understand and provide feedback on project objectives and developments.

This will undoubtedly help refine your core set of questions. Giving others a voice will also make them more receptive to the final results due to their early involvement, reducing “why didn’t you think of x, y, and z” questions at the culmination.

3. Collect the required information

Once you have clarified purpose based on these conversations you are ready to start collecting data. Although the techniques you’ll take depend on the nature of the project, be sure to document what is being collecting and store it in clean and accessible ways.

4. Explore what is available

Now the fun begins. You’re able to dig into what you’ve uncovered to explore relationships, trends, and anything else that will get you closer to your project objectives. In this stage, you may also find that the original goals or approaches need revision. This is completely ok. Just be sure to renew conversations with people from step two so that everyone remains on the same page.

5. Determine the practical implications of what you have found

This is the pivotal step in your data project. It is likely that no one else will have either the skill or energy to sift through all the raw data that your uncovered and connected. The only items that make it into the final report or presentation will therefore be guided by the original objectives, internal conversations, and your unique business perspective.

6. Clearly communicate findings to key stakeholders

After you’ve identified what matters most, you still need to package the findings in a way that resonates with a wider audience. This means balancing important detail with digestibility.

Few decision-makers will have the time to read hundreds of pages filled with methodologies and comprehensive cross tabs. You need to create a limited number of impactful visuals and talking points that guide others towards action.

7. Push for a decision or change in behavior

Don’t let your analysis go to waste. Most data projects these days are not designed as simple fact-finding missions or nice-to-have industry overviews. They are started with the intention to make operational changes that improve the status-quo.

Positioning your data findings along with explicit recommendations will increase the perceived value of your work.


An opportunity to lead with data

As organizations craft strategic plans to become more data-driven, employees with data skills and mindsets have an opportunity to align themselves with these ambitions.

As more people acquire baseline data skills, however, differentiating yourself will require more robust abilities. In other words, your data-centered career can’t be limited to data retrieval alone.

Moving up the data value chain

You move up the data value chain by providing more than what was initially requested. How have things changed over time? What are the unexpected drivers? What should the company do differently based on current internal and external realities?

This progression should also make working with data more rewarding as you get to be involved in higher-level conversations, which in turn makes it even easier for you to provide deeper insights in the future.

Working with data today is a professional development opportunity. It’s also just happens to be a lot of fun.

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