5 Technical Skills You Need To Be A Successful Digital Analyst
What does a digital analyst do?
Digital analytics is a rapidly growing and changing industry, both in scope of work, and in industry size. This field originally started with a focus on analyzing user behavior on websites, but today’s digital analysts are often also responsible for analyzing digital marketing trends - email, search, social, and other acquisition channels, not to mention drawing connections between customers offline and online. This means the roles of a “Web Analyst” and a “Digital Marketing Analyst” are now usually blurred at most organization. Here is an article explaining the difference between Web Analytics vs. Digital Marketing Analytics, and how they are both subsets of Digital Analytics.
The technical vs. business career path
Digital analysts often find themselves contemplating two distinct career paths: Do they go to the technical side and deliver implementations that allow for the collection of digital data, or do they go to the business side, delivering analysis/insights/recommendations that drive business strategy? This juncture is where it takes some serious self-awareness and perhaps some guidance from a boss, colleague, or teacher.
My advice is to know what you’re good at and play to those strengths. Here is an article from Optimize Smart that provides great career advice for this industry, and will help you understand whether the technical or non-technical side of digital analytics is right for you.
Here is a general overview of the required skills by pathway:
Digital Analytics professionals on the technical path focus on the implementation of tags and other analytics solutions that allow for the tracking and collection of web data. These professionals are required to have extensive knowledge of Tagging, Quality Assurance (QA), and the set up of A/B tests.
Digital Analytics professionals on the business path focus on translating the collected data into insightful reports, dashboards and powerpoint presentations. These professionals are required to have knowledge of data extraction & transformation, data visualization, story telling with data, and A/B test analysis.
While tagging, QA and A/B test set up are not required for analysts on the business side, I recommend that you have at least a basic understanding of these skills as they will allow you to trouble shoot any data collection issues you run into. Having these “technical” skills will also make you more valuable to any organization as you will be more adept at translating business requirements into technical requirements, and can complete a simple technical task if necessary.
If you’re interested in the business career path within digital analytics, here are 5 technical skills you’ll need to be successful:
1. Tagging
What is Tagging?
Tagging is the process of adding a snippet(s) of JavaScript to the source code for a web page. Tags power online marketing and analytics, and can:
Instruct web browsers to collect data
Set cookies
Extend audiences between multiple websites
Integrate third-party content into a website (e.g. social media widgets, video players, ads, etc.)
Even though most organizations have their tagging implemented by the Engineering team or a digital analyst on the technical career path, I would recommend that all digital analysts have at least a basic understanding of how tagging works, and if possible work on a few tagging implementation projects. This will make you a well rounded analyst as you’ll understand how to efficiently communicate technical requirements to the development team, troubleshoot tagging issues, and can implement tagging updates yourself when the technical team is busy working on other projects.
Tagging Tools
The 2 most popular tagging tools are Adobe Launch (Formerly Adobe DTM), and Google GTM. All tools within the Adobe Experience Cloud can only be practiced on when you are part of a company as it costs quite a bit of change to purchase, but you can learn and practice Google GTM for free. To practice Google GTM, you can set up a personal website using Squarespace, use the Google GTM learning guide below to implement tags, and analyze traffic trends on your own site !
Tagging Learning Resources:
Google GTM - Google Tag Manager Tutorial
Adobe Launch - Adobe Launch Implementation Tutorial
2. Quality Assurance (QA)
What is QA?
QA is the process of going through a website/app’s analytics implementation (tags and analytics solutions), and making sure they’re working properly so that you can trust your data-based decisions. QA should become an integral part of the web analytics strategy and put in place at the same time as the implementation is crafted.
Analytics Implementations can break for a variety of reasons:
Inadvertently breaking dependent components (one change breaks another)
Changes in the functionality of the website itself
Tags were removed by accident or duplicated
Broken implementations result in incorrect data, which is known to:
Erode organization confidence in web analytics
Hinder analysis and decision-making
Prevent proper execution of optimization opportunities
Lead to bad decisions and result in lost opportunity value
While QA is typically done by the technical teams, you will run into many instances where you’ll need to understand how to QA to trouble shoot a data collection issue.
QA Tools
Here are few debugging tools you can download, and use to QA your personal/business website:
QA Learning Resources
3. Data Extraction & Transformation
What is Data Extraction & Transformation?
Once a business has gone through the QA process and is sure the web data is accurate, it’s time to create some dashboards! Some data may be located in databases, and need to be extracted and transformed before you can create meaningful dashboards.
Data extraction is the process of collecting or retrieving disparate types of data from one or more sources, many of which may be poorly organized or completely unstructured. Once the data has been successfully extracted, it is ready to be refined. During the transformation phase, data is sorted, organized, and cleansed. For example, duplicate entries will be deleted, missing values removed or enriched, and audits will be performed to produce data that is reliable, consistent, and usable.
Data Extraction & Transformation Tools
The most common data extraction/transformation programming languages used at most corporations are SQL, Python & R. If you are new to data science, I recommend learning SQL first as it’s used at pretty much all corporations, while Python and R have not been universally adopted yet.
Data Extraction Learning Resources
Here are links to Data Camp courses for each programming language:
SQL For Business Analysts Track
Data Camp is a great resource as it’s used for upskilling at many large corporations, so hiring managers recognize the certifications as a good measure of your knowledge. Data Camp also does a great job of guiding you through the nuances of each programming language, so no prior coding experience is needed to be successful in completing the courses.
Upon completion of each Data Camp course, you’ll get a certification that I recommend adding to your Linkedin profile and resume. I also recommend sharing the completion of any course with your network. This is a great way for recruiters to notice your drive and determination to learn, and encourage them to reach out to you with an opportunity.
4. Data Visualization
What is Data Visualization?
Now that your data is transformed and clean, you are ready to build insightful reports and dashboards. Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from. The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets.
Data Visualization Tools
Analysts have a large number of data visualization tools to choose from, such as Tableau, Adobe Analytics Workspace, Adobe Analytics Report Builder, Google Analytics, and Power BI.
Data Extraction Learning Resources
Here are links to learning resources for the most popular digital data visualization tools:
Tableau Fundamentals Skill Track
Adobe Analytics YouTube Channel
Google Analytics Full Course By Simplilearn
5. A/B Testing
What is A/B Testing?
A/B testing is a method of comparing two versions of a webpage, app or campaign against each other to determine which one performs better. A/B Testing is a critical piece of digital optimization and personalization strategy. It helps ensure that a company can zero in on the experience that nudges customers to buy, read, download, or take whatever other action is needed to meet business goals. Here is a video from Adobe that explains how A/B tests can be used to optimize business results.
A/B Testing Tools
The most common A/B testing tools are Google Optimize and Adobe Target.
A/B Testing Learning Resources
Here are a few learning resources so you can learn more about how to set up A/B tests:
