Here's how you can navigate conflicts between SEO analysts and data scientists when interpreting data.

LinkedIn cannot understand roles. I have no business talking about data science or SEO, except to observe that "garbage in = garbage out." Yet I am asked to "Contribute to 3+ articles in this skill with your unique perspective to be considered for a Top Organic Search Voice badge." I wonder if I will get this badge despite my utter lack of qualification.

Start by establishing a common understanding of the overall goals. Ensure both parties understand the data sources & their limitations. What are you hoping to achieve through data analysis? Is it improving website rankings, understanding user behavior or optimizing content performance? Collaboratively define KPIs that are both SEO-relevant & statistically sound, like - organic traffic segmented by keyword groups, conversion rates for specific landing pages or user engagement metrics for different content types. Present data in formats that cater to both perspectives. Utilize charts, graphs & visualizations that effectively communicate trends & patterns, while also providing underlying data points for detailed analysis by data scientists.

To navigate conflicts between SEO analysts and data scientists when interpreting data, establish clear communication channels, foster mutual understanding of objectives, leverage data-driven insights, and encourage collaborative problem-solving to reconcile perspectives effectively.

I think that many of us can find common ground on the idea that LinkedIn is getting "weird." So many of the things it tries to do to drive "engagement" are actually alienating and confusing. So many of the posts you see are bizarre inspirational filler or chest-pounding. I do celebrate the legitimate achievements of my friends and colleagues, but wow do I see some weird and frustrating stuff...

You should interpret my posts as horrible data. *Apparently I need 47 more characters after the end of my first sentence for this blurb to qualify. Character limits definitely enhance data and do not lead to a high noise:signal ratio at all. /s

I am communicating that I am totally unqualified to comment on this. Hopefully this leads to some insights regarding how LinkedIn solicits input.

I can't even... considering the AI derived "answers" that so many search engines now offer up at the top of the search results, an pastiche of information with fleeting chances of accuracy, especially for things that might have changed in the last few days/weeks/months. What's that Cory Doctorow line?

1. Shared Goals Understanding: Conduct joint workshops & review case studies to align on SEO objectives & data analysis approaches. 2. Collaborative Approach: Cross-train teams through workshops & collaborative projects to blend SEO & data science expertise. 3. Standardized Metrics: Establish common metrics & reporting frameworks to ensure consistency in data interpretation. 4. Continuous Learning: Encourage ongoing education in SEO trends & data analytics tools to enhance skills & knowledge. 5. Open Communication: Schedule regular meetings for discussions & provide constructive feedback to improve collaboration. 6. Adaptability: Stay updated on industry changes & adapt methodologies to leverage advancements in SEO & data science.