5 common self-service data analytics mistakes (and how to prevent them)

Self-service analytics empowers every team to get crucial business insights and make data-driven insights.

But with great power comes great responsibility. If implemented and used incorrectly, self-service analytics can lead to errors, overlooking the big picture, or mismanagement of the analysis process. 

Here are 5 common mistakes teams make when adopting self-service analytics—and how to prevent them.

1. Deploying platforms in isolation

The mistake: adding analytics tools in isolation, that is, without connecting them to the other apps your team uses every day, is a barrier to adoption. It becomes just another intimidating tool to manage, instead of a helpful resource. After all, the more effort it seems like it’s going to take to install, onboard, and use a new tool, the less likely that tool is going to be utilized well (or at all).

How to prevent it: to help people adopt self-service analytics across your organization, you need to make it a part of their daily workflow. 

Modern self-service analytics platforms like Contentsquare integrate directly with business intelligence (BI) tools, customer data platforms, and customer relationship management (CRM) systems, so teams can get insights in context. 

These integrations remove the roadblocks preventing self-service adoption, making it simple for users to quickly and effortlessly get the data they need.

2. Choosing visualizations based on their aesthetic appeal

The mistake: one of the most common self-service analytics mistakes is choosing the wrong chart type, based on design preferences instead of the character of your data.

Each analysis type requires a different visualization. For example, if you want to display a correlation between 2 metrics, you should use a scatter chart that can clearly showcase the relationship at a glance. 

This bubble chart fails to clearly show the relation between the data, and the annotation method is weak—for this data, a simple bar chart would work better

How to prevent it: the best way to prevent this mistake is to define your goals first. What are the relationships between the metrics you want to analyze? How much data do you need? Who’s the audience for this data visualization? Once you define your goals, you’ll have a clearer understanding of the best way to communicate your data.

Use your judgment when considering the available analyses and visualizations and choose only the points that can provide meaningful insights. Opt for straightforward visualizations to deliver quick, relevant, and accurate answers. 

3. Implementing procedures without standardized guidelines

The mistake: when an organization begins its journey into the world of self-service analytics, it is common for analytics and BI teams to create a lot of reporting user interfaces (UIs), data definitions, and one-off metrics. 

The problem with this is that it gets messy fast and leads to untrustworthy data. For example: 2 different users execute the same function, but label them differently. They end up with an analysis of 2 different metrics for the same outcome—leading to inconsistency and uncertainty that degrades trust in your data.

How to prevent it: as you roll out self-service analytics, create a set of standards for every stakeholder to adhere to. 

Store helpful guidelines and data definitions in a centralized digital hub that’s accessible to all teams. Each element added to this hub should contain information about where each data point was sourced, any changes applied, and any other information for the use of others. 

Similarly, set up user experience (UX) standards to make the look and feel of reports consistent. Making the process look and feel more standardized helps your users quickly interpret multiple datasets rather than having to relearn the base of each new set. Overall, users’ analysis will be quicker and their results of a higher quality.

4. Sticking to waterfall methodologies from start to finish

The mistake: many organizations adopt the waterfall methodology while they’re democratizing analytics and BI, but waterfall development is one of the main reasons why tasks take too long and don’t provide the expected value. 

Longer waterfall cycles also carry the risk of bad code and design mistakes going undiscovered until the project’s end, at which point it’s significantly more difficult to fix them.

How to prevent it: if your analytics projects are moving slowly, you should analyze whether the traditional waterfall approach is the cause—and whether an agile approach would be a better fit.

Agile development methods include shorter, incremental cycles, which allow mistakes to be identified and fixed more quickly, making them an ideal fit for many business applications where continuous refinements are expected. As you expand analytics and BI throughout your organization, an agile approach lets you consider new requirements and data so you can adapt as needed.

5. Proceeding without data governance

The mistake: getting the balance right between self-service analytics and data governance can be tricky.

Some businesses keep all data points under lock and key, which causes frustrations, especially when users want to merge datasets to uncover new insights. 

Other companies set up analytics in a way that completely neglects the need to supervise usage, so users can pull and analyze their data from any source. However, with several datasets created from differing sources floating around, it becomes challenging to figure out what the single version of the truth is.

How to prevent it: cultivate an environment that allows for data governance while encouraging self-service in a centralized manner. 

What does that mean in practice? As you deploy and configure your self-service tools, make sure to establish the right auditing measures and necessary controls that provide users with data access, but also provides your IT teams with the transparency they need to understand who uses what data. 

Taking some time to spell out permissioning and sharing capabilities can go a long way to helping you keep a handle on what represents your source of truth.

Improve how data is used across your business with self-serve analytics 

Self-service analytics delivers huge value for organizations, empowering every team to stay focused on the metrics that matter and become more customer-centric. But it needs to be implemented correctly to make sure your business gets the desired results.

Recognizing and preventing these common mistakes will help self-service analytics thrive across your organization, empowering decision-makers with the reports, visualizations, and datasets they need to make sound business decisions and improve the customer experience.