4 Reasons Why Self-Service Analytics Is Here to Stay

Scaling AI Bhawna Krishnan

Self-service analytics refers to decentralized ownership of the insight production process (and isn’t just “pulling numbers from dashboards”). For example, in an ideal world, line of business professionals or analysts would be able to work with data to generate insights and data visualizations with little direct support from data scientists, IT, or the larger data team (though the data product itself and its greater platform should be supported by these profiles).

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Self-service analytics is important because it enables individuals and organizations to access and analyze data without relying on IT or data analysts. This results in faster, more accurate, and more flexible decision-making, as well as greater insight into key business metrics. 

There are plenty of compelling reasons that self-service analytics — while its naming convention may continue to evolve over time — is not going away:

1. Self-Service Analytics is Essential to the Future of Working

If organizations don’t adopt a culture of data sharing (where one “route” of sharing is through self-service analytics), they are setting themselves up for failure as future-proof organizations. In the future, organizations that excel in their data initiatives will not limit data sharing to sporadic, one-off instances. Instead, data sharing will be seamlessly integrated into daily business operations. By promoting data accessibility across various departments and teams, these organizations will enable quick and informed decision-making, encourage a culture of reuse, and unlock the potential for new use cases from data that was previously locked up in silos.

2. Self-Service Analytics Reaffirms the Narrative That, to Truly Scale AI Efforts, We Need More Than Just Tech Experts 

In an ideal world, the self-service analytics data product is built by a combination of technical data people and business people. The product is co-built and is more robust to being taken apart, analyzed under the hood, and reused in other ways.

Business users are gaining more power to collaborate with experts in building analytics workflows due to their increased access to data and the willingness of experts to work with them to incorporate the necessary business context and subject matter expertise. This collaborative effort between business users and technical professionals, along with acquiring new skills and access to effective tools like Dataiku, can result in a quicker path to realizing impactful outcomes.

With self-service analytics specifically, organizations can give everyone (with proper access rights) the ability to discover and use data, prepare that data, and create a data product. They can also enable data product creators to share their work with other colleagues across teams and departments, as well as enable non-data teams to improve access to better data insights, understand critical metrics, and streamline processes.

At Dataiku, we’ve always believed that data projects require involvement and alignment from both data and domain experts and wholly support the notion that organizations won’t scale AI without enlisting non-experts to the cause.

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3. The Next Level of Business Value Can Only Be Generated By Bringing in Non-Data Experts, Too 

Self-service analytics is a critical component for achieving success in data and analytics. However, there's often a disconnect between implementing self-service analytics and realizing its full business value. Some organizations may view self-service analytics as a failed initiative or one with limited value. Additionally, business users may feel unsupported and unsure if they are utilizing the data correctly, leading to a negative perception of self-service analytics.

To overcome these challenges and derive tangible business results, organizations must ensure that their self-service analytics efforts:

  • Are rooted in a thorough understanding of the business and its needs, taking into account the specific challenges and obstacles being faced and leveraging data to find solutions that align with the business's objectives and KPIs
  • Encourage collaboration and engagement between IT and business teams
  • Have proper governance and established guidelines
  • Are sustainable and repeatable for future projects, freeing up resources to identify new value creation opportunities

4. Self-Service Analytics Can Help Mitigate Silos and Promote Best Practices Around Trust and Credibility

When it comes to self-service analytics, various aspects of trust and credibility must be taken into account:

  • The enterprise must have confidence in its employees' capability to use data in a self-service environment effectively.
  • Business users utilizing self-service analytics must have faith in the data they are using. It's crucial to have someone continuously overseeing the data's quality, ensuring that it is regularly updated, formatted correctly, and utilized appropriately.
  • Both managers and executives need to trust the insights derived from self-service analytics initiatives.

To ensure strong governance practices, organizations need to strike a balance between control and agility. They need to build a solid (yet flexible) strategy that allows lines of business access to the data they need while also restricting any access they have no business need to access. They also need to maintain a workable feedback strategy that enables users to gain access they don’t have (but need) to avoid those data access problems from ultimately killing the data project. This balance is best struck in a centralized environment (like Dataiku) where roles and rights can be managed and updated easily as obligations and priorities evolve over time.

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