10 things I wished I knew when I began my data career (Part II)
This post is a continuation of my previous post here.
Data is power. Watch out for people who want to use it to manipulate the narrative
Data can be extremely powerful. As a data steward, it’s important to be mindful of how data can be used to push a narrative.
In most companies, access to data is limited to a few key individuals, and those who can narrate a story with data can influence decisions; that is power.
Be alert when people want access to raw data without wanting any analysis from you whatsoever. Ask what they hope to do with the data, and ask them to explain the context of the data request.
Be proactive and offer to help the person analyze and interpret the data to prevent bias that may go into analyzing and presenting data.
If the person requesting the raw data is at a significantly higher level than you, consult with your manager so you two can manage expectations.
Create presentations that a 10-year old can understand. Keep the details in the appendix
It took me two years in my career to realize that data-driven presentations should be built for everyone in the company.
Building a deck aimed at the right audience is a core skill to be an effective Data Analyst.
My early presentations contained too many details and superfluous graphs that did not help drive my narrative. If a graph does not directly drive your narrative, put it in the appendix.
Write succinctly, and use language that a 10-year old can understand; this not only helps convey your message more effectively, but it also forces you to focus your presentation on a few key points.
Know the context of the business as much as possible
Always keep in mind how your analysis can affect the business. Every assumption and recommendation you make must be rooted in knowing the business.
A common mistake Data Analysts/Scientists make is analyzing data and making recommendations on only the data itself.
The recommendation then turns out to be ineffective because the analysis was missing the greater business context.
For example, an analyst concludes that a marketing promotion did not increase sales compared to the same day last year. The analyst recommends that the company discontinue the promotion.
If the analyst had known that the business makes profit by returning new customers from marketing promotions, the analysis should have focused on measuring long-term sales on repeating new customers instead of immediate sales on the promotion day.
Make an effort to know the business and understand the industry your company is competing in.
Deliver bad/critical news to stakeholders in private first
If you are tasked to analyze a program or product and the analysis does not turn out favorable, give the key stakeholders a preview before you officially present the results to the greater organization.
This preview allows key stakeholders to process the information ahead of time, and give you feedback on your analysis; they may give you more context about the program, which could change the results.
Presenting bad news in private can help you avoid emotional responses and challenges to your analysis in the official presentation.
Once people challenge your assumptions about your analysis, you will not recover from your presentation. This has happened to me before and the experience was very jarring.
Delivering unfavorable news to key stakeholders first will also allow you to build allies.
People will appreciate you for giving them a preview of the results, as this gives them an opportunity to figure out a strategy to manage the bad news before an official presentation.
Build trust with key stakeholders and you will become their trusted advisor.
During your presentations, guide people to reach the same conclusions you have
This tip is the best method to influence decisions. Analysts and Data Scientists ultimately do not make the final decisions for the company, but they can give insights and influence people to make the best decision.
Therefore, consider crafting your presentations so that people reach the same conclusions (on their own) as if they were you.
This is where guidance and storytelling play the biggest factor in helping people conclude things on their own. Present in a way to guide people through the thought process it takes to reach your conclusion.
Every slide you present should get them one slide closer to your conclusion. There should be no logical gaps between slides.
Throughout your presentation, add in context and solicit light discussions from your audience so they can take part in the analysis themselves.
Data analysis can be thought of as a journey of finding insight through data.
Therefore, be everyone’s guide as you take them on the journey and they’ll all eventually reach the same conclusions as you did.
Once your audience reach the same conclusions, you can then guide the group into making the best decisions because everyone is now focused on what to do next and not focused on interpreting the data.
Ultimately, your job is to help everyone see insights within the data and guide them towards making the best decisions from your insights.
The best data analysts I’ve seen empower people and enable them to make smart decisions. They don’t merely provide data; rather, they advise people.
Most importantly, they empathize with their colleagues and are always making an effort to understand the business.
Find the opportunities to empower and understand your colleagues and you will excel in your data career.