How to separate your analysis

How to separate your analysis

A cat stuck in a birdhouse

Photo: Thomas Toma | unplash

Sometimes people hit a wall when dealing with data. Bottlenecks arise and the next steps are unclear. In this post, I’ll look at bottlenecks that can make you feel stuck in your analysis, as well as share tips on how to get rid of the problem.

When Analytics Maturity Slows You

Oftentimes, analysts get stuck due to the stabilization stage in their capabilities and resources. Analysts move through a continuum of analytics maturity throughout their careers, as their skills and capabilities grow. People have studied this phenomenon over the years, with one well-known maturity model coming from Stefan Hummel in 2009 (he has an updated version of the model here). When the analytics professional reaches that stage, it leads to this stuck feeling – when this continuous streak of improving skills and abilities stops.

How do you break down? The starting point is to realize that stumbling is not a failure in the workflow. It is an opportunity to rethink how best to deploy resources and skills. Realizing that you are stuck means that you realize that communication exists and that better processes are possible. It’s a reflection of one skill that I feel analysts should have: curiosity.

To improve this curiosity and turn it into useful processes, use the following ideas to get past the obstacles.

Challenge the assumptions hidden in your analysis

Part of the faltering can come from setting up an analytics project too close to legacy processes, which fail to give the project the necessary momentum forward.

Check the assumption you made that supports the intended data model. Is the starting point the same as the previous analysis? How do the initial assumptions compare to current conditions? Are there new data conditions that did not exist before? Even differences in data cleaning techniques, such as processing NA notes, can be a reason to reevaluate assumptions.

A prolonged default can drive projects into technical bankruptcy, a type of technical debt that can be recognized by the lack of agency going forward. To avoid this, visualize the milestones of your project and ask what you need to reach these milestones. Doing so will help you identify smaller tasks that can be accomplished. See this as a reactionary, not as an opportunity to blame something that may have been undone.

Related Article: What Analytics Trends Marketers Should Expect in 2022?

Avoid ‘Data Vomit’ – put your analysis back in focus

Scattered analysis work seems to contradict the organizational nature of analytics, but it does happen. Not bringing order to this mess will eventually confuse your audience with “data vomit,” when you are reporting anything and everything unnecessarily.

When you get confused, take a step back and see if you can focus on a subset of the data where reasonable answers seem to be taking shape. Ask an analytical question: What is the question: How does this data detect correlation with the customer journey or goal of the analysis? Focusing on a subset reduces repetitive tasks that lead to fatigue with aimless, persistent tasks.

Another aspect is looking at the framework that is applied to advanced analytics modeling. It’s easy to get lost in a sea of ​​frameworks and lose sight of the purpose they serve in building a relationship within the data.

Related article: How to choose the right data visualization

Rubber duck with end users

Often times, the audience for your analytics results will not be as skilled as they are with the technical elements of analytics and data modeling. For example, they will know what SEO is, but are unable to look at the code and see that metadata or analytics tags are missing. A common scenario is when a colleague needs insights, but is unable to describe the technical elements of where to get the ideas. This is not the same as going to the doctor and describing the pains, but not knowing the medical terminology for the parts of the body that hurt. If the end user is stuck in the quicksand of analytics, you are also stuck.

To climb your way out of quicksand, go through point by point review. The developers call this review process “rubber bending” – a way of expressing a problem in a non-technical language. A review of rubber ducks can sometimes help determine how the results can better talk to your colleague and lead to modification of important steps in completing the analysis. It’s also a great learning opportunity, a blame-free “internal check” to see when to refine the analysis workflow.

Related article: How to improve data knowledge among non-members of your organization

Building a supportive resource community

Throttling sometimes arises when a solution tool or feature is used ineffectively. If you think this is the case, do a safety check for people who have encountered similar issues. Analysts often share questions or concerns about certain tools in online communities. Forums are often hosted by these solution providers, including IDEs like RStudio or dashboard platforms like Tableau. You can also find independent help online, whether it’s through a hashtag on Twitter or through groups hosted on Discord. The analytics communities have been around specific platforms for a while, so they usually know all the ins and outs regarding updates and past feature bug history.

You can also use this process to determine the potential documentation needs of the software you are using. Documentation is sometimes overlooked with upgrades and changes. A documentation review can highlight whether functional questions regarding existing software are being properly addressed and keep resources up to date.

Is automation in your platform stuck?

Analysts have a number of automation options for ingesting data, such as macros in Excel, API libraries for R and Python programs, and aggregation features within tools such as Google DataStudio, Tableau, and Power BI. So, look at any existing automated workflows if a bottleneck arises. Ensure that data sets and visualizations are updated with the latest information. When implemented correctly, collecting data from automated steps should consume less effort over time so that you can pay deeper attention to the analysis.

Review your frameworks to see if there are better feature solutions. At the very least, the review will lead to questions that will enrich your research. To learn how to configure these questions, see my post “How to Create Dashboard Frameworks That Support Marketing Analysis.” There is no one perfect solution, but the time invested in choosing and improving the solution will help you separate your analysis.

Use bottlenecks as a time to address privacy, security, and accessibility needs

While bottlenecks can be a hindrance, you can use these slowdowns as an opportunity to review data security, privacy compliance, and accessibility for data generated from the web user interface – since you’re already reviewing the data and data sources. The inputs to these operations correspond to the data ingest and processing steps. For example, adding analytics tags to a website page is an opportunity to verify the accessibility of the content of that page.

Remember to celebrate your victories

If you feel like you still have a lot of work left after putting in all that effort to decipher your analytics, celebrate the victories you have from your accomplishments completely. The last two years, dominated by the pandemic, can feel like an ongoing exercise in avoiding burnout. But any effort you make to prevent a small analytic problem from expanding into a larger problem is good cause for joy.

Pierre Dubois is the founder of Zimana, a small business digital analysis consultancy. It reviews data from web analytics and social media dashboard solutions, then makes web development recommendations and actions that improve marketing strategy and business profitability.

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