Self-Service Analytics: Keys for Execution

Scaling AI Bhawna Krishnan

Self-service analytics is a critical aspect of modern data-driven decision-making. When executed successfully, it empowers users across an organization to access, analyze, and act on data in a timely and informed manner. This leads to several benefits, including increased business agility, improved data literacy and democratization, and better alignment between data and business goals. A successful self-service analytics program enables users to explore data and identify data-driven insights relevant to their needs. As a result, businesses can make data-driven decisions more quickly, adapt their strategies to shifting market conditions more successfully, and ultimately provide better results.

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For self-service analytics to be effective, enterprises must establish a system that streamlines the journey from business questions to data-supported answers, reducing obstacles such as delays, false starts, and roadblocks. This shortens the time it takes to get insights from data.

Trust plays a crucial role in the effective execution of self-service analytics. Trust and credibility in various aspects of self-service analytics must be established, including:

Data and AI Governance

Governance, when properly implemented, can improve trust in data at all levels of an organization, allowing employees to be more confident in the decisions they are making with company data. It can also improve trust in the analysis and models produced by data scientists, along with greater accuracy that results from improved data quality. 

Governance isn’t just keeping the company safe; data and AI governance are essential components to bringing the company up to today’s standards, turning data and AI systems into a fundamental organizational asset, and building confidence in employees' ability to handle data in a self-service setting. 

Governance should facilitate, not hinder, innovation. Teams need to differentiate between different stages of development, such as proof-of-concepts, self-service data projects, industrialized data products, and the corresponding governance requirements. While allowing for exploration and experimentation, teams must also determine when self-service projects or proof-of-concepts are ready for funding, testing, and assurance to be transformed into an industrialized, operationalized solution.

Playing the long game when it comes to organizational change brings more solid governance practices, considering how decisions about data access and use will affect the organization long term and building a governance program to match (and, not to mention, to reduce liability). For more on data and AI governance, check out this ebook.

Data Quality

Business users working on self-service analytics need to trust the data that they’re working with, and there needs to be someone continually responsible for its quality, making sure it’s regularly updated, formatted, and being used appropriately. Teams need to decide who will be in charge of what and assign the role of setting clear definitions, metrics, categorization rules, and goals to specific individuals. For example, who will evaluate data quality, and will the evaluation be based on completeness, validity, timeliness, etc.? The first step to accuracy and consistency is to define these roles and responsibilities clearly.

Many organizations impart these tasks to a data steward, someone responsible for the management and oversight of the organization’s data assets to help business users with high-quality data that is not only easily accessible in a consistent manner but also compliant with policy and/or regulatory obligations. For specific insights for CDOs and data executives on how to get data quality right, check out this ebook.

Bring in Non-Data Experts

In an ideal scenario, the self-service analytics data product is created through collaboration between technical data professionals and business individuals. It should not be developed in a one-sided manner but rather in a way where the product is co-created and can be easily analyzed and reused. With increasing access to data, business users are able to work alongside experts to ensure proper subject matter expertise and context, leading to faster results. Collaboration between technical professionals, business individuals, and advanced data experts, along with adequate training and appropriate tools, can lead to faster business outcomes, improved data insights, a clearer grasp of critical metrics, and efficient processes.

Trust Insights

To fully realize the benefits of self-service analytics, it's important for managers and executives to have confidence in the insights it provides. This requires proper implementation, maintenance, and validation of the processes and tools used, as well as clear communication and transparency about the data sources, methods, and limitations of the analysis. Building and maintaining trust in the insights from self-service analytics will help drive informed decision-making and increase the overall impact and value of the projects.

In order to drive tangible business value, organizations need to be sure that their self-service analytics efforts:

  • Are driven by intimate business knowledge (i.e., they consider the specific problems or roadblocks the business is facing and explore how data can help solve them) and always tie back to business objectives and KPIs

  • Don’t exist in a vacuum but rather encourage collaborative discourse and engagement between IT and business people

  • Are governed and have guardrails

  • Are sustainable and reproducible for other projects and requests down the road (and, notably, free up key resources to support the identification of key value creation opportunities for operationalization).

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