Leading a Successful Data Analysis Project in 6 Steps
Leading a data analysis project has a unique set of challenges. Here are some steps to ensure your analysis is a huge success—even if your hands don’t touch the keyboard.
1. Begin with the End in Mind. What Questions Are You Trying to Answer?
Stephen Covey coined the phrase “begin with the end in mind” in his bestselling book The 7 Habits of Highly Effective People. Simon Sinek wrote a whole book with a similar theme, called Start With Why.
It can be tempting to just start going and figure it out as you go. Don’t. There is a time to refine your goals and ask more questions as you get further in the process and are exposed to more information. But you can’t refine what you don’t define. It doesn’t have to be high-tech or formal, but it should point you in the right direction.
2. Guide & Focus the Data Discovery Process
Even though you may not be gathering the data yourself, you should be an active participant in validating the work done by the technical teams as they locate possible data points. Be prepared to ask questions about what they find.
Here are some example questions:
Did you find information on how to split by different business units?
What values are there?
Why is the East Region not shown in your list of values?
Did you use Salesforce data or ERP data for product name?
How often is the data updated?
Keep in mind that this is not meant to be a combative discussion; instead, it should be collaborative and focused on the business questions you identified in Step #1.
As a leader, your job at this stage is to help focus the technical team. Are they exploring a lot of data points that don’t seem relevant? You should politely point that out and dig a little deeper to understand why that information is included.
Is that one business question you dreamed up going to cost months of work to collect data and process it? Can that be moved to a later phase? Having an open discussion with the teams can help the project succeed. Technical resources can get lost on this step (I have been there!) and need your guidance to stick to the defined goals. Just because you can pull other data points doesn’t mean you should if they don’t support the project goals.
Microsoft Excel and Google Docs can be great tools to communicate findings between the technical and non-technical teams for complex projects. This will ensure everyone is on the same page, understands what each data point means, and knows any limitations or “gotchas” in the data. If you don’t have the time or resources to document all variables, start with your most important items.
3. Evaluate & Prioritize the Data Standardization/Data Clean-Up Effort
A co-worker of mine would regularly use the phrase “garbage in, garbage out” when describing some of the poor results his technical solution was delivering. Without addressing them, data quality issues will jeopardize your entire analysis and could lead the team to make incorrect decisions. This step is one of the most challenging, time-consuming, and expensive steps of your analysis. Here are some tips to get you through.
It is inevitable that your team will come across some data points that don’t make sense or are clearly wrong. As a leader, your role is to request examples and analysis on the data quality issues the team has surfaced. Review the examples and quantity of occurrences. It may be impossible to eliminate all data quality issues. Thus, adding prioritization and focus to the project will be critical. As the technical team provides you this information, evaluate it using the following two criteria:
Importance of the metric to the analysis
Time and cost to resolve the quality issues
Based on these criteria, evaluate the technical team’s solutions and decide how to proceed. Maybe you wait to address the industry issue that only impacts three customers, but you tackle the issues that impact hundreds of clients. Be sure to suggest alternative solutions or resources that could be used to ensure the best end result is achieved.
4. Decide if Data Appends Are Needed
You may choose to explore options for supplementing the data points you have available. This can be achieved by leveraging external (paid or free) resources to append industry codes, contact information, or other data elements not native to your data source.
You can also partner with your team to simply utilize the data points you have to derive new data points. This allows you to better segment your data to answer your business questions. An example could be to create a new data point called “US region” based on a zip code mapping of your data.
5. Ask Questions. Does Everything Make Sense?
Your technical resources should be performing quality tests on the analysis to ensure everything makes sense and is explainable. Have an open discussion with the team about anything you find that doesn’t seem right. Ask questions and hold your team accountable for digging in and getting to the bottom of anything out of line.
6. Review the Results & Ensure Your Questions Are Answered
At this time, you will be reviewing the team’s findings and analysis. Here are some ideas for what do in this step:
Provide feedback on the visuals you find helpful and those that could be improved. Make sure you understand them, and ask questions if not.
Encourage the team to explore other variables or things they might not have presented.
Revisit the established goals from Step #1 and ensure the analysis provides the answers you were seeking.
Learn from the project and provide feedback to the team.
Request feedback from the technical team on what they felt you and the leadership could have done better for next time.
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Are you tired of losing valuable time sifting through mountains of data or trying to make sense of your number? Are time-consuming tasks eating into your ability to grow and nurture your company? Are you losing clients because you’re behind the eight ball when it comes to delivering information? We hear you. It doesn’t have to be that way. We’ll help you navigate your toughest data challenges like a pro with user-friendly data visualizations.
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