Self-Service Analytics Explained: Definition, Basics & Examples

What is self-service analytics? 

Self-service analytics is the processes and tools that empower everyone in a company to access and analyze datasets without relying on IT, engineering, or analytic specialists.

To better understand how self-service analytics speeds up the analytics lifecycle, consider how it differs from traditional business intelligence (BI).

In the usual scenario, a business user would have an ad hoc business question. To answer this query, they would need to involve these specialists across multiple stages:

1. Data engineers write custom code to extract raw data from data sources and transfer it to a data warehouse using programming languages like Python, Scala, or Java. 
2. Data engineers and data analysts use SQL or Python to clean data, compute metrics, and transform it into a format ready to be consumed downstream.
3. Data scientists then build data models to validate and compute additional metrics.
4. Data analysts or business intelligence analysts use traditional BI tools like Microsoft Power BI or Tableau to prepare dashboards with graphs and KPIs that address the initial question.
5. Finally, the business user receives the dashboard to analyze and find answers to their questions.

On the other hand, self-service analytics empowers business users to directly create datasets and conduct data analyses. This reduces the reliance on data specialists and speeds up the path from question to insight.

5 real-world examples of self-serve analytics 

Get inspired to build your solutions by checking out the following use cases:

1. Track performance on Shopify in real-time

As an example, Keboola offers a Data App that launches a real-time KPI dashboard for your Shopify (without zero coding skills). 

Users can connect the Shopify account with Keboola in just a few clicks and let the Data App extract metrics from various Shopify datasets (orders, sales, customers, etc.). The Data App will automatically build a filtrable and drillable KPI dashboard, full of useful BI visualizations and charts.

Additionally, you can set up Slack alerts and Jira tickets to monitor and respond promptly to any issues in your e-store.

2. Automate digital marketing reports

If you spend too much time downloading Excel files from Meta Business Suite and Google Ads just to understand where your budgets went, you’ll love this one.

Keboola launched a data app that automatically builds a digital marketing report. You give access to data by inserting your credentials, and the data app automatically collects data from each advertising platform (Meta Ads, LinkedIn Ads, Google Ads, and Bing Ads), cleans it, joins reports into a single table, and visualizes results and metrics (CPC, CPM, CTR, …).

This and other click-to-launch self-service analytics empower you with advanced reports without doing the heavy lifting. 

3. Increase the Average Basket Value

We all know that gin pairs well with tonic. But what other products could you recommend to upsell your customers?

By building a self-service report that analyzes your customer baskets, you can discover product demand, other products that pair well with the existing basket products, and other valuable shopping patterns.

Use this information to optimize product recommendations, set discounts, and streamline the checkout process to increase the average order size.

4. Create your target audience and export it into a third-party tool

Use self-service analytics tools to collect all your customer data in one place. 

For example, collect all their preferences from your e-commerce store, check their topic interests based on newsletter subscriptions, and link all the customer information into a single table.

Then, use the self-service analytics to build audiences based on interests and preferences. 

These audiences can be easily exported to another tool (CRM for sales, email software for marketing communication, Facebook ads for building lookalike audiences, etc.). 

5. Improve your bottom line with scenario-forecasting

How much better would your ROI be if you could lower the average cost of goods by 5%? And how much more would you sell if you could improve your email open rate by 3%?

Build a scenario-forecasting self-service app by integrating the data sources across your silos (marketing, sales, procurement, etc.) and start answering “what-if” questions to improve your finances.

📚Looking for more real-world examples to get inspired? Check ‘8 things you can do with Data Apps in Keboola’.

Why does your organization need self-service analytics? 

Self-service analytics brings many benefits to companies:

1. Save time

With self-service analytical tools, employees can create and customize their datasets and dashboards without involving IT, engineers, and data specialists.

This shortens the time between questions and insights for the data-dependant employee. And it also frees IT, engineering, and data resources for more strategic tasks.

For example, by using Keboola, Kindred was able to save 174 hours/month on report building, and Productboard empowered everyone at the company to build their dataset in hours or days instead of weeks (previous BI solution).

2. Increase data-driven decisions

Self-serve analytics empowers employees to build their data reports, which makes it faster and easier to access data for decision-making, instead of relying on untested opinions.

But informed decision-making isn’t just faster. It’s also more frequent. Without the traditional wait times, employees can ask and answer more business questions because the IT dependency bottleneck has been removed.

3. Adapt faster

Companies can adapt to changing business conditions more effectively with self-serve analytics. Users can quickly create new reports and dashboards to pinpoint issues and address them before it’s too late.

4. Enhance collaboration

These self-service analytics tools often support collaboration features, allowing teams to share reports, dashboards, and insights with colleagues. This fosters better communication and knowledge sharing within the organization.

With common and shared data, the communication silos between departments are easier to bridge. Non-technical teams and technical teams find it easier to speak the same language as their work on the same datasets and data initiatives.

5. Build custom solutions

Self-serve analytics tools can be tailored to individual user needs, enabling employees to focus on the specific data and metrics relevant to their roles and responsibilities.

The best self-service analytics platforms help the end users bridge the gap between insights and action, by turning stale reports into interactive data apps.

How do you set up self-service analytics? 

Most organizations fall for the trap of equating self-service analytics with the tool instead of the process. Without building the right processes, the tools will never be used to their full transformative potential. Make sure to follow these steps for success

1. Map the business needs 

Start by focusing on the desired business outcomes for the end users. Do you want to improve the email open rate? The conversion from add to purchase? Maybe you want to have better control of your cash flow. 

Once it’s clear what your business outcome is, you can start drafting the metrics and KPIs you need to follow, to implement that outcome in practice.

2. Pick the right self-service tool

The right tool will help you automate the entire data lifecycle needed to make your employees self-reliant. You’re looking for a tool that will streamline:

1. Data collection: Integrate raw data from various sources.
2. Data cleaning: Remove outliers, deduplicate data, …
3. Data modeling: Transform data and compute metrics.
4. Data visualization: Build dashboards, track KPIs, and the similar.

We’ll go into the details of what to look for in the tool below. But for now, beware of the self-service BI tool trap. 

Some data visualization tools offer self-service business intelligence features, such as drag-and-drop dashboard design. While these are great for empowering workers to generate reports on their own, they still require them to rely on IT and engineering for datasets.

Instead, pick tools that offer self-service data engineering and analytics features. 

3. Launch the first use cases

When setting up any new analytics initiative, two things matter:

• Start small then grow.
• Move fast.

Start with a single team and single use case (from point 1) that has a high chance of success. Getting quick wins will motivate the team to tackle harder challenges and motivate other teams to imitate them (as well as their tools and processes).

Make sure to move fast so you can collect feedback and course-correct until you provide actionable insights from your self-service analytics.

4. Scale as you need

Once you’ve collected the low-hanging fruits of success, scale your self-service analytics initiative. Empower your business teams to build their own data apps or launch more data-intensive use cases with ML/AI models. 

You can also onboard other teams on the self-service analytics platforms, and share data, reports, or insights.

Let’s look at the do’s and don’ts of picking the right self-service analytics tool.

How do you pick the right tool for self-serve analytics? 

Look out for these criteria when shortlisting your favorite self-service analytics tool:

1. Intuitiveness: It should be user-friendly. A good example is Keboola's drag-and-drop visual flow builder, allowing end users to build data pipelines without coding.
2. Flexibility: Opt for tools that cater to both no-code needs of business users and low-code requirements of tech experts. By providing flexible solutions for both, you increase the chances of different profiles collaborating on the same tasks. 
3. Integration coverage: Ensure the tool integrates data from all necessary sources. Some tools might only cater to specific niches (e.g. integrate only marketing APIs, but not databases or Excel spreadsheets), limiting their use.
4. Automation: Look for tools that automate as much work as possible. For example, Keboola’s data templates prepare analysis-ready datasets with a few clicks. These and other automations will save you time and free resources for more revenue-generating work.
5. Data product building: Go beyond reports; choose tools that allow you to create interactive data applications. 
6. Scalability & collaboration: The tool should be able to grow with your team, offering features like data sharing and cataloging.
7. Security and data governance: While accessibility is crucial, it shouldn't compromise data protection. Prioritize tools with robust security features.
8. Pricing: Ensure the tool's value justifies its cost, considering both the resources saved and time freed up.

Power up your business teams with Keboola 

Keboola is a Self-serve Data Operations Platform that’s ideal for launching your self-service analytics initiatives. It offers unmatched data integration and workflow orchestration capabilities:

1. Intuitive no-code features, like the visual-flow builder or no-code transformations.
2. Flexible architecture: suitable for both business users and technical experts.
3. 250+ out-of-the-box integrations.
4. Automated features like data templates.
5. Features that help you turn your raw data into interactive data apps.
6. Scalability and collaboration by design. 
7. Security and data governance out-of-the-box.
8. Competitive PAYG pricing with free minutes every month.

Try Keboola for free (no credit card required).