In that way, \u201canalytics\u201d doesn\u2019t need to be isolated to traffic source reports in GA4, but could include qualitative insights, revenue and financial data, market data and more. <\/p>\n\n\n\n
There are two skills I\u2019ve deeply invested in throughout my career \u2013 writing and data \u2013 and to me, they have a lot in common. <\/p>\n\n\n\n
There\u2019s the surface level best practices \u2013 grammar, form, legibility. But to me, the teleology of both skills is the same: to exhibit clear thinking. <\/p>\n\n\n\n
There\u2019s levels to this, or why I hate the word \u201creporting\u201d<\/strong><\/h2>\n\n\n\nI never liked the term \u201creporting.\u201d <\/p>\n\n\n\n
In practice, it usually means copying and pasting an automated report or dashboard to show your boss whether the numbers are going up or down. <\/p>\n\n\n\n
If they\u2019re going up, you celebrate<\/a> and claim credit. If they\u2019re going down, you go on a hunt for causality and usually end up blaming something external, like \u201cseasonality.\u201d <\/p>\n\n\n\nIn this world, analytics is used for proving your value, which is not useless entirely, but is merely the tip of the iceberg. <\/p>\n\n\n\n
As Avinash Kaushik wrote<\/a>: <\/p>\n\n\n\n\u201cIf you are unique, why should you crack open a standard analytics tool<\/a> with its standard reports and metrics and get going?<\/p>\n\n\n\nOr why simply respond to a \u201creport request\u201d and start data puking? The person at the other end is probably uninformed about Analytics and Segmentation and what is possible (even as they are supremely qualified to do their job in Marketing \/ Sales \/ HR).<\/p>\n\n\n\n
You need business questions because:<\/p>\n\n\n\n
\n- Rather than being told what metrics or dimensions to deliver you want business context: What\u2019s driving the request for that data? What is the answer the requestor is looking for? Then you apply smarts because you have context.<\/li>\n\n\n\n
- Best practices are highly overrated. If this is your first day on the job, sure go ahead and puke out what \u201cindustry experts\u201d recommend. But know that it won\u2019t impress anyone because you don\u2019t actually know what the business is doing \/ cares about \/ is prioritizing.\u201d<\/li>\n<\/ol>\n\n\n\n
If you’re operating with little to no understanding of analytics and performance, reporting is a fine place to start. And it\u2019s very simple. Determine what metric you own and ask an analyst to help make you an automated Google Sheets report or Looker Studio chart that tracks this number over time. <\/p>\n\n\n\n
We can spend an hour there, automate it, and then move on to the more interesting stuff. <\/p>\n\n\n\n
Leaving aside technical skills like Python, R, or SQL<\/a>, the layers that I view when training analytics skills are as follows:<\/p>\n\n\n\n\n- Reporting and communicating results<\/li>\n\n\n\n
- Understanding the underlying data model and context<\/li>\n\n\n\n
- Being able to unearth insights that result in better decisions and actions<\/li>\n\n\n\n
- Asking intelligent business questions to guide your use of analytics<\/li>\n<\/ol>\n\n\n\n
![\"\"](\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\")
<\/figure>\n\n\n\n(:”Does he ever tired of the Vince McMahon meme?” No.)<\/p>\n\n\n\n
Essential data literacy and understanding context<\/strong><\/h2>\n\n\n\nData is just a bunch of information that you collect organized in a certain format. It\u2019s passive. There\u2019s no inherent value in \u201cdata.\u201d <\/p>\n\n\n\n
We as humans make sense of data. How we organize the data enables us to make sense of the information in different ways. <\/p>\n\n\n\n
For example, in Universal Analytics, data was collected according to \u201cscopes.\u201d It was hit-scoped, session-scoped, user-scoped, or ecommerce-scoped.<\/p>\n\n\n\n
A pageview was a \u201chit.\u201d So was an \u201cevent\u201d or a \u201cgoal.\u201d <\/p>\n\n\n\n
So according to this data model, a pageview can not have a conversion rate, because conversions occur within a session. Therefore, to have understood how a page converted, you needed to know that you would have to view session-based reports and report on something like \u201clanding page conversions.\u201d <\/p>\n\n\n\n
As Matt Gershoff<\/a> from Conductrics<\/a> once told me, quoting his college math professor, \u201cHow can you make cheese if you don\u2019t know where milk comes from?!\u201d<\/em><\/p>\n\n\n\nAs SEOs, you have a lot of domain expertise that business stakeholders broadly don\u2019t have. You understand Google Search Console metrics and how they are tracked, from clicks to impressions and position and click-through-rate. <\/p>\n\n\n\n
I\u2019d argue that we, as marketers, could all benefit from a great understanding of our specific telemetry. What constitutes a \u201cbounce\u201d that makes up the engagement metric \u201cbounce rate.\u201d How is an event logged and when does it count as a \u201cconversion?\u201d What is the variance in our data?<\/p>\n\n\n\n
So after teaching basic reporting templates and building out organic growth dashboards, the next place I start when training analysts is in our particular platforms. <\/p>\n\n\n\n
At Workato, where I ran experimentation, we had to learn about website analytics generally, but also A\/B testing statistics and how our tool randomized and calculated metrics, and then we also had to plug into our B2B attribution modeling tools, namely Marketo \/ Bizible, because we were logging qualified leads as well as general conversions. <\/p>\n\n\n\n
Beyond the platform specifics, it\u2019s important to understand some basic data literacy topics<\/a>, such as:<\/p>\n\n\n\n\n- Categorical data vs Numerical data<\/a><\/li>\n\n\n\n
- Nominal vs ordinal data<\/a><\/li>\n\n\n\n
- Discrete vs continuous variables<\/a><\/li>\n\n\n\n
- Correlation vs causation<\/a><\/li>\n<\/ul>\n\n\n\n
<\/figure>\n\n\n\nImage Source<\/a><\/p>\n\n\n\nOr like, if you\u2019re running A\/B tests, you should probably know what a P-Value actually means<\/a> and how we infer causality based on randomized controlled trials. Note: you don\u2019t have to explain this to stakeholders, and probably shouldn\u2019t bring out the nerdy stuff<\/a> at the executive meeting, but it\u2019s important to understand what the data you\u2019re analyzing actually means. <\/p>\n\n\n\n<\/figure>\n\n\n\nA great way to learn this stuff, through trial by fire, is by learning SQL<\/a> (or R or Python).<\/p>\n\n\n\nThis is obviously a heavy lift from a learning perspective, but these tools force you to understand what data type and format you\u2019re dealing with, because that will essentially limit what you can do with it and how you can merge or compare it with other data types. <\/p>\n\n\n\n
Do, Watch Talk<\/strong><\/h2>\n\n\n\nAnalytical thinking requires both conceptual knowledge as well as practical understanding. <\/p>\n\n\n\n
That\u2019s why I like this model for coaching<\/a>: I do, you watch, we talk. You do, I watch, we talk. <\/strong><\/p>\n\n\n\nLet\u2019s say, for example, that a consultant at Omniscient is used to working with PLG SaaS companies who measure last click conversions based on freemium user signups. <\/p>\n\n\n\n
Then, they take on a client who has a more complex, sales-led model. The best way to credit value to content and SEO is not last click user signups, but rather something like first click or linear attribution to lead conversions, and from there we need to tie together lead and CRM data to attribute revenue from our program.<\/p>\n\n\n\n
This is a novel paradigm, so the first time we\u2019d implement it, I would walk through how to set this attribution model up in our web analytics tool, how we\u2019d work with their team to connect GA4 to Salesforce, and how to build a dashboard<\/a> that reports on assisted pipeline. <\/p>\n\n\n\nBut the next time, our consultant would take a crack at it, I\u2019d watch, and we\u2019d talk through any discrepancies or skill gaps. <\/p>\n\n\n\n
In this way, you\u2019re not just throwing someone to the deep end and saying \u201clearn to swim,\u201d but you\u2019re also not absolving someone of the privilege of learning how to do this by practicing. <\/p>\n\n\n\n
Practice plus theory <\/strong><\/h2>\n\n\n\nThe way I like to learn is to have a project with some stakes, a bit of skin in the game.<\/p>\n\n\n\n
That\u2019s how I learned <\/a>R. My manager, Peep, gave me 6 weeks to complete a project where I built data-driven personas<\/a> using qualitative and quantitative behavioral data and clustered product user attributes using R. <\/p>\n\n\n\nIn other words, you could read the AP Style guide, or you could try to write an article. <\/p>\n\n\n\n
Like, I knew in abstract what COGS (cost of goods sold) and OpEx (operating expenses) were. But until I was actually running a business, they were merely concepts in my mind. Now I can see strategy and opportunity in the numbers when I read a P&L. <\/p>\n\n\n\n
Statistics didn\u2019t mean much to me, despite taking classes in college and running many t-tests. Then I built a career in A\/B testing<\/a>, and everything made so much more sense to me. <\/p>\n\n\n\nSo yes, listen to the podcasts and read the articles I\u2019ll recommend below. But also, pick a project that helps you develop analytical skills. <\/p>\n\n\n\n
Try to implement a custom event in GA4. Turn it into a Key Event. Build a Looker studio dashboard<\/a> that automates the reporting of this metric.\u00a0<\/p>\n\n\n\nInstead of getting into LinkedIn debates about attribution, compare a few different models and see the differences in the credit they assign your marketing channels and campaigns. <\/p>\n\n\n\n
Additionally, have a burgeoning analyst attempt to scope out and estimate their own project timelines<\/a>. Having a sense of ownership, for some psychological reason, helps you learn things much faster and makes the lessons stick. <\/p>\n\n\n\nThe ultimate aim: curiosity and asking better questions<\/strong><\/h2>\n\n\n\nUltimately, a good analyst is like a good journalist. It\u2019s all about asking better questions. <\/p>\n\n\n\n
The question you ask in analytics dictates the landscape of possible answers.<\/p>\n\n\n\n
Asking, \u201cwhat\u2019s a good bounce rate?\u201d for example has several assumptions embedded within it – that bounce rate is important, that there is a \u201cgood bounce rate,\u201d and that you should benchmark your bounce rate to this \u201cgood\u201d bounce rate. <\/p>\n\n\n\n
If you want to get more utility from data, then, as Rustin Cohle said on True Detective<\/a>, \u201cstart asking the right f*cking questions.\u201d <\/p>\n\n\n\nSometimes the right question is as simple as \u201chow is our organic traffic trending?\u201d But more often, I find that the surface layer question is sitting on top of something more important \u2013 especially when a surface level question comes from a business stakeholder or client. <\/p>\n\n\n\n
I\u2019ll give you a specific one from our own GTM.<\/p>\n\n\n\n
On a very simple level, we can ask, \u201chow much revenue are we bringing in every month?\u201d which we can back out one step and ask, \u201chow many clients did we close this week \/ month?\u201d <\/p>\n\n\n\n
But if you think about it for longer than a few seconds, you realize that whatever the answer is, the action you take is \u201cclose more deals.\u201d It doesn\u2019t really matter if you\u2019re ahead or behind of your goal. <\/p>\n\n\n\n
But if we reframe the question and start to think more about the system that results in revenue and new client deals, we can ask, \u201cwhat leading indicators predict the revenue we attain or clients we close?\u201d <\/p>\n\n\n\n
Okay, so we can start to run regressions, or we can start with a hypothesis: pipeline predicts clients and revenue. <\/p>\n\n\n\n
So how do we increase pipeline? Again, we can run regressions on historical data, or we can start to map out an acquisition channel matrix and calculate our current saturation versus the potential of the channel. We can plot ourselves on a custom built S Curve<\/a>.<\/p>\n\n\n\nThen, instead of berating ourselves and saying \u201cclose more deals,\u201d we can say, \u201cSEO represents the best possible investment due to reasons A, B, and C. Because of this, we will take actions X, Y, and Z.\u201d <\/p>\n\n\n\n
Better questions bring better answers. <\/p>\n\n\n\n
Data and epistemological humility <\/strong><\/h2>\n\n\n\nOne paradoxical result of getting better at analytics is an increase in expressed ignorance. <\/p>\n\n\n\n
Someone with little to no understanding of data may have undue confidence. They may read a headline in the news talking about bad science<\/a> and take it at face value. They may see a correlation and run with it. <\/p>\n\n\n\nThe more you learn about data, the more the word \u201cuncertainty\u201d comes up. You can reduce uncertainty, but you can never eliminate it entirely.<\/p>\n\n\n\n
That\u2019s why, in the project stage of some candidate interviews, we include a portion where they analyze a live data dashboard. The point is not to have answers. It\u2019s to have good questions and self-awareness. Or really, it\u2019s to identify the following traits:<\/p>\n\n\n\n
\n- Curiosity<\/li>\n\n\n\n
- Attention to detail<\/li>\n\n\n\n
- Data literacy<\/li>\n<\/ul>\n\n\n\n
What you\u2019re looking for is an inclination to say, \u201chuh, that\u2019s interesting,\u201d when you see something in a chart that sticks out. \u201cWe should research this further. I wonder if\u2026.\u201d <\/p>\n\n\n\n
Learning more about data also helps you understand when you shouldn\u2019t rely on data to make decisions. In some, perhaps many, cases, you\u2019re operating on low information and simply need to move forward. <\/p>\n\n\n\n
It\u2019s the mark of a great analyst and operator to know when that is the case.<\/p>\n\n\n\n
Want more insights like this? Subscribe to our <\/a>Field <\/a>Notes<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"This essay, I\u2019ll admit, is almost too broad to tackle. …<\/p>\n","protected":false},"author":3,"featured_media":6627,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[24,184],"tags":[],"wf_post_folders":[],"class_list":{"0":"post-4600","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-content-strategy","8":"category-field-notes"},"yoast_head":"\n
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To be pedantic, what does \u201canalytical\u201d even mean, and how can one determine how \u201canalytical\u201d","og_url":"https:\/\/beomniscient.com\/blog\/how-to-improve-analytical-skills\/","og_site_name":"Omniscient Digital","article_published_time":"2024-06-20T16:20:41+00:00","article_modified_time":"2026-04-02T14:52:17+00:00","og_image":[{"width":1536,"height":864,"url":"https:\/\/beomniscient.com\/wp-content\/uploads\/Field-Notes-082.png","type":"image\/png"}],"author":"Alex Birkett","twitter_card":"summary_large_image","twitter_creator":"@beomniscient_","twitter_site":"@beomniscient_","twitter_misc":{"Written by":"Alex Birkett","Est. reading time":"11 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/beomniscient.com\/blog\/how-to-improve-analytical-skills\/#article","isPartOf":{"@id":"https:\/\/beomniscient.com\/blog\/how-to-improve-analytical-skills\/"},"author":{"name":"Alex Birkett","@id":"https:\/\/beomniscient.com\/#\/schema\/person\/163f626994fa8a754f56bc9bdf747249"},"headline":"How to Improve Analytical Skills","datePublished":"2024-06-20T16:20:41+00:00","dateModified":"2026-04-02T14:52:17+00:00","mainEntityOfPage":{"@id":"https:\/\/beomniscient.com\/blog\/how-to-improve-analytical-skills\/"},"wordCount":2252,"publisher":{"@id":"https:\/\/beomniscient.com\/#organization"},"image":{"@id":"https:\/\/beomniscient.com\/blog\/how-to-improve-analytical-skills\/#primaryimage"},"thumbnailUrl":"https:\/\/beomniscient.com\/wp-content\/uploads\/Field-Notes-082.png","articleSection":["Content Strategy","Field Notes"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/beomniscient.com\/blog\/how-to-improve-analytical-skills\/","url":"https:\/\/beomniscient.com\/blog\/how-to-improve-analytical-skills\/","name":"How to Improve Analytical Skills - 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In this world, analytics is used for proving your value, which is not useless entirely, but is merely the tip of the iceberg. <\/p>\n\n\n\n
As Avinash Kaushik wrote<\/a>: <\/p>\n\n\n\n \u201cIf you are unique, why should you crack open a standard analytics tool<\/a> with its standard reports and metrics and get going?<\/p>\n\n\n\n Or why simply respond to a \u201creport request\u201d and start data puking? The person at the other end is probably uninformed about Analytics and Segmentation and what is possible (even as they are supremely qualified to do their job in Marketing \/ Sales \/ HR).<\/p>\n\n\n\n You need business questions because:<\/p>\n\n\n\n If you’re operating with little to no understanding of analytics and performance, reporting is a fine place to start. And it\u2019s very simple. Determine what metric you own and ask an analyst to help make you an automated Google Sheets report or Looker Studio chart that tracks this number over time. <\/p>\n\n\n\n We can spend an hour there, automate it, and then move on to the more interesting stuff. <\/p>\n\n\n\n Leaving aside technical skills like Python, R, or SQL<\/a>, the layers that I view when training analytics skills are as follows:<\/p>\n\n\n\n (:”Does he ever tired of the Vince McMahon meme?” No.)<\/p>\n\n\n\n Data is just a bunch of information that you collect organized in a certain format. It\u2019s passive. There\u2019s no inherent value in \u201cdata.\u201d <\/p>\n\n\n\n We as humans make sense of data. How we organize the data enables us to make sense of the information in different ways. <\/p>\n\n\n\n For example, in Universal Analytics, data was collected according to \u201cscopes.\u201d It was hit-scoped, session-scoped, user-scoped, or ecommerce-scoped.<\/p>\n\n\n\n A pageview was a \u201chit.\u201d So was an \u201cevent\u201d or a \u201cgoal.\u201d <\/p>\n\n\n\n So according to this data model, a pageview can not have a conversion rate, because conversions occur within a session. Therefore, to have understood how a page converted, you needed to know that you would have to view session-based reports and report on something like \u201clanding page conversions.\u201d <\/p>\n\n\n\n As Matt Gershoff<\/a> from Conductrics<\/a> once told me, quoting his college math professor, \u201cHow can you make cheese if you don\u2019t know where milk comes from?!\u201d<\/em><\/p>\n\n\n\n As SEOs, you have a lot of domain expertise that business stakeholders broadly don\u2019t have. You understand Google Search Console metrics and how they are tracked, from clicks to impressions and position and click-through-rate. <\/p>\n\n\n\n I\u2019d argue that we, as marketers, could all benefit from a great understanding of our specific telemetry. What constitutes a \u201cbounce\u201d that makes up the engagement metric \u201cbounce rate.\u201d How is an event logged and when does it count as a \u201cconversion?\u201d What is the variance in our data?<\/p>\n\n\n\n So after teaching basic reporting templates and building out organic growth dashboards, the next place I start when training analysts is in our particular platforms. <\/p>\n\n\n\n At Workato, where I ran experimentation, we had to learn about website analytics generally, but also A\/B testing statistics and how our tool randomized and calculated metrics, and then we also had to plug into our B2B attribution modeling tools, namely Marketo \/ Bizible, because we were logging qualified leads as well as general conversions. <\/p>\n\n\n\n Beyond the platform specifics, it\u2019s important to understand some basic data literacy topics<\/a>, such as:<\/p>\n\n\n\n Image Source<\/a><\/p>\n\n\n\n Or like, if you\u2019re running A\/B tests, you should probably know what a P-Value actually means<\/a> and how we infer causality based on randomized controlled trials. Note: you don\u2019t have to explain this to stakeholders, and probably shouldn\u2019t bring out the nerdy stuff<\/a> at the executive meeting, but it\u2019s important to understand what the data you\u2019re analyzing actually means. <\/p>\n\n\n\n A great way to learn this stuff, through trial by fire, is by learning SQL<\/a> (or R or Python).<\/p>\n\n\n\n This is obviously a heavy lift from a learning perspective, but these tools force you to understand what data type and format you\u2019re dealing with, because that will essentially limit what you can do with it and how you can merge or compare it with other data types. <\/p>\n\n\n\n Analytical thinking requires both conceptual knowledge as well as practical understanding. <\/p>\n\n\n\n That\u2019s why I like this model for coaching<\/a>: I do, you watch, we talk. You do, I watch, we talk. <\/strong><\/p>\n\n\n\n Let\u2019s say, for example, that a consultant at Omniscient is used to working with PLG SaaS companies who measure last click conversions based on freemium user signups. <\/p>\n\n\n\n Then, they take on a client who has a more complex, sales-led model. The best way to credit value to content and SEO is not last click user signups, but rather something like first click or linear attribution to lead conversions, and from there we need to tie together lead and CRM data to attribute revenue from our program.<\/p>\n\n\n\n This is a novel paradigm, so the first time we\u2019d implement it, I would walk through how to set this attribution model up in our web analytics tool, how we\u2019d work with their team to connect GA4 to Salesforce, and how to build a dashboard<\/a> that reports on assisted pipeline. <\/p>\n\n\n\n But the next time, our consultant would take a crack at it, I\u2019d watch, and we\u2019d talk through any discrepancies or skill gaps. <\/p>\n\n\n\n In this way, you\u2019re not just throwing someone to the deep end and saying \u201clearn to swim,\u201d but you\u2019re also not absolving someone of the privilege of learning how to do this by practicing. <\/p>\n\n\n\n The way I like to learn is to have a project with some stakes, a bit of skin in the game.<\/p>\n\n\n\n That\u2019s how I learned <\/a>R. My manager, Peep, gave me 6 weeks to complete a project where I built data-driven personas<\/a> using qualitative and quantitative behavioral data and clustered product user attributes using R. <\/p>\n\n\n\n In other words, you could read the AP Style guide, or you could try to write an article. <\/p>\n\n\n\n Like, I knew in abstract what COGS (cost of goods sold) and OpEx (operating expenses) were. But until I was actually running a business, they were merely concepts in my mind. Now I can see strategy and opportunity in the numbers when I read a P&L. <\/p>\n\n\n\n Statistics didn\u2019t mean much to me, despite taking classes in college and running many t-tests. Then I built a career in A\/B testing<\/a>, and everything made so much more sense to me. <\/p>\n\n\n\n So yes, listen to the podcasts and read the articles I\u2019ll recommend below. But also, pick a project that helps you develop analytical skills. <\/p>\n\n\n\n Try to implement a custom event in GA4. Turn it into a Key Event. Build a Looker studio dashboard<\/a> that automates the reporting of this metric.\u00a0<\/p>\n\n\n\n Instead of getting into LinkedIn debates about attribution, compare a few different models and see the differences in the credit they assign your marketing channels and campaigns. <\/p>\n\n\n\n Additionally, have a burgeoning analyst attempt to scope out and estimate their own project timelines<\/a>. Having a sense of ownership, for some psychological reason, helps you learn things much faster and makes the lessons stick. <\/p>\n\n\n\n Ultimately, a good analyst is like a good journalist. It\u2019s all about asking better questions. <\/p>\n\n\n\n The question you ask in analytics dictates the landscape of possible answers.<\/p>\n\n\n\n Asking, \u201cwhat\u2019s a good bounce rate?\u201d for example has several assumptions embedded within it – that bounce rate is important, that there is a \u201cgood bounce rate,\u201d and that you should benchmark your bounce rate to this \u201cgood\u201d bounce rate. <\/p>\n\n\n\n If you want to get more utility from data, then, as Rustin Cohle said on True Detective<\/a>, \u201cstart asking the right f*cking questions.\u201d <\/p>\n\n\n\n Sometimes the right question is as simple as \u201chow is our organic traffic trending?\u201d But more often, I find that the surface layer question is sitting on top of something more important \u2013 especially when a surface level question comes from a business stakeholder or client. <\/p>\n\n\n\n I\u2019ll give you a specific one from our own GTM.<\/p>\n\n\n\n On a very simple level, we can ask, \u201chow much revenue are we bringing in every month?\u201d which we can back out one step and ask, \u201chow many clients did we close this week \/ month?\u201d <\/p>\n\n\n\n But if you think about it for longer than a few seconds, you realize that whatever the answer is, the action you take is \u201cclose more deals.\u201d It doesn\u2019t really matter if you\u2019re ahead or behind of your goal. <\/p>\n\n\n\n But if we reframe the question and start to think more about the system that results in revenue and new client deals, we can ask, \u201cwhat leading indicators predict the revenue we attain or clients we close?\u201d <\/p>\n\n\n\n Okay, so we can start to run regressions, or we can start with a hypothesis: pipeline predicts clients and revenue. <\/p>\n\n\n\n So how do we increase pipeline? Again, we can run regressions on historical data, or we can start to map out an acquisition channel matrix and calculate our current saturation versus the potential of the channel. We can plot ourselves on a custom built S Curve<\/a>.<\/p>\n\n\n\n Then, instead of berating ourselves and saying \u201cclose more deals,\u201d we can say, \u201cSEO represents the best possible investment due to reasons A, B, and C. Because of this, we will take actions X, Y, and Z.\u201d <\/p>\n\n\n\n Better questions bring better answers. <\/p>\n\n\n\n One paradoxical result of getting better at analytics is an increase in expressed ignorance. <\/p>\n\n\n\n Someone with little to no understanding of data may have undue confidence. They may read a headline in the news talking about bad science<\/a> and take it at face value. They may see a correlation and run with it. <\/p>\n\n\n\n The more you learn about data, the more the word \u201cuncertainty\u201d comes up. You can reduce uncertainty, but you can never eliminate it entirely.<\/p>\n\n\n\n That\u2019s why, in the project stage of some candidate interviews, we include a portion where they analyze a live data dashboard. The point is not to have answers. It\u2019s to have good questions and self-awareness. Or really, it\u2019s to identify the following traits:<\/p>\n\n\n\n What you\u2019re looking for is an inclination to say, \u201chuh, that\u2019s interesting,\u201d when you see something in a chart that sticks out. \u201cWe should research this further. I wonder if\u2026.\u201d <\/p>\n\n\n\n Learning more about data also helps you understand when you shouldn\u2019t rely on data to make decisions. In some, perhaps many, cases, you\u2019re operating on low information and simply need to move forward. <\/p>\n\n\n\n It\u2019s the mark of a great analyst and operator to know when that is the case.<\/p>\n\n\n\n Want more insights like this? Subscribe to our <\/a>Field <\/a>Notes<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" This essay, I\u2019ll admit, is almost too broad to tackle. …<\/p>\n","protected":false},"author":3,"featured_media":6627,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[24,184],"tags":[],"wf_post_folders":[],"class_list":{"0":"post-4600","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-content-strategy","8":"category-field-notes"},"yoast_head":"\n\n
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<\/figure>\n\n\n\nEssential data literacy and understanding context<\/strong><\/h2>\n\n\n\n
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![\"\"](\"https:\/\/beomniscient.com\/wp-content\/uploads\/image-42.png\")
<\/figure>\n\n\n\n![\"\"](\"https:\/\/beomniscient.com\/wp-content\/uploads\/image-43.png\")
<\/figure>\n\n\n\nDo, Watch Talk<\/strong><\/h2>\n\n\n\n
Practice plus theory <\/strong><\/h2>\n\n\n\n
The ultimate aim: curiosity and asking better questions<\/strong><\/h2>\n\n\n\n
Data and epistemological humility <\/strong><\/h2>\n\n\n\n
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