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9 best practices for self-service analytics
Expecting business benefits from an analytics platform everyone can use? Take the right steps before putting the tool into users' hands.
Many organizations collect vast amounts of data, with the aim to improve their business. A self-service analytics platform enables nontechnical users to interpret and act on the data as fits into their workflows. The challenge is getting self-service data analytics implementations right.
Business intelligence with self-service analytics
Business intelligence (BI) initiatives have relied on IT, data teams and data scientists to create visualizations and reports. BI projects can take months, during which time a lot can happen: Requirements change, new challenges arise, or the information comes too late to be useful.
For organizations making data-driven decisions, that data needs to be in a format that is accessible, digestible and actionable. Self-service analytics enables nontechnical users to assess and respond to the available data quickly. Executives, product teams, operations and other business users can access the information they need without waiting for the results of a traditional BI project.
A self-service analytics platform provides user-friendly tools to generate reports and visualizations to share with stakeholders and other key players. These insights are used to evaluate operations, plan strategies and make data-driven business decisions.
To achieve the benefits of self-service analytics without compromising data security or privacy, follow these nine best practices:
- Understand end-user needs.
- Plan implementation with stakeholders.
- Assess all relevant tool options.
- Train, train, train.
- Improve data literacy.
- Govern data appropriately.
- Maintain high data quality.
- Focus on security.
- Know your limits.
1. Assess current needs and data quality
To decide how to implement self-service analytics, identify business users' short- and long-term needs and what they hope to achieve. Ask what questions they want to answer, the information necessary for timely decision-making, and the visualizations and reports that will increase productivity and efficiency.
As part of your assessment, identify gaps in existing data sources that would prevent users from achieving these goals. Assess the quality of existing data and how it can be improved. For the project to work, the data team might need to clean up and transform substandard data and augment what's there. In addition, review data workflows and transformation processes and determine how they can be improved and automated to accommodate self-service analytics.
2. Develop a plan to meet business needs
The plan for implementing the analytics platform must align with users' requirements. The project's stakeholders and end users should shape how the technical team rolls out the platform and prepares for its implementation.
A roadmap should:
- Describe the project's goals.
- Outline the steps to achieve those goals.
- Specify what success looks like.
- Include guidelines to scale up over time.
Focus initially on common BI use cases and quick wins that demonstrate the platform's potential and provide immediate value to stakeholders. This approach offers the opportunity to test and improve the system before moving on to more advanced use cases.
Users can benefit from templates and prebuilt reports to get started with self-service analytics. Set the projects up with a feedback loop that collects user input to ensure the platform's ongoing relevancy.
3. Choose a self-service analytics platform
Self-service analytics is an evolving sector of the data analysis tools market. Some of the many options include Domo, Looker, Microsoft Power BI, Pyramid Analytics, Qlik, Sisense, Strategy (formerly MicroStrategy), Tableau (now part of Salesforce), ThoughtSpot and Zoho.
Data analytics platforms designed for business users should not require a technical background in data science or advanced analytics. Many tasks can be performed through intuitive drag-and-drop or point-and-click operations. Users need only the data literacy skills necessary to make sense of and work with the underlying data.
The platform should also provide customizable dashboards and support the use of templates and prebuilt reports to accommodate different user types. Users should be able to create interactive visualizations and reports and share them with stakeholders and other key players.
Evaluate the technical aspects of platforms as well. Assess how they integrate with the organization's other tools, provide data security and scale.
Consider the product's total cost of ownership. The platform should provide the necessary features to justify the costs, along with an interface that's easy to understand and navigate. The selection process should be collaborative, involving the teams who will use the tools and work with data.
4. Train employees on needed skills
Expect to train two groups: the people who deploy the platform and those who work with it on a regular basis. Administrators and business users both need to know how the system works and how they can make the most of it, based on their roles within the organization. Administrators should understand how to optimize and scale the product, and protect data. Business users should know how to find data they need and build effective visualizations and reports.
Training and education can come through demonstrations, video tutorials and in-person workshops. Plan to provide a support chat, online documentation, product-related FAQs or other materials as well. Target training to each relevant group within the organization.
5. Cultivate a data culture
Business users must recognize the value of data and actively engage with it to gain insights and make informed decisions. A data-driven culture is an organization-wide effort that prioritizes accessible and reliable data. Such a culture fosters a sense of shared responsibility for data quality, consistency and completeness, utilizing appropriate tools.
A data-driven culture strives to promote and improve data literacy among its business users. Data literacy gives them the skills to explore, understand, generate and communicate data. Data literacy is a tenet of data-driven culture, which in turn is essential to effective self-service analytics. The relationship is reciprocal, as self-service analytics can nurture a data-driven culture.
6. Implement a data governance strategy
Self-service analytics efforts are destined to fail without an effective data governance framework.
Governance provides the foundation on which self-service analytics is built. A data governance strategy:
- Formalizes the process to track and manage data throughout its lifecycle.
- Dictates standards of data quality, security and accessibility.
- Provides a structure for cataloging data, maintaining metadata and master data, and supporting data warehousing and BI.
- Delineates the business's overall data management and control approach.
Data governance is critical to self-service analytics because business users need to trust the data they interpret. Analytics based on flawed data leads to poor decision-making. Governance sets out how the business maintains data quality, makes data accessible and minimizes risk. This significant undertaking must be approached as an organization-wide effort, with clearly defined policies and roles.
7. Address data quality issues
An organization must invest time and resources to ensure that its data is current, accurate, complete, uniform and of the highest quality. Business users who do not trust the underlying data are unlikely to take advantage of an analytics platform and the benefits it provides.
To ensure data quality, the IT team must implement a management strategy that ensures continuous data readiness and reliability. Define and enforce data quality policies and practices that make data complete and well-formatted, while eliminating redundant, inaccurate and outdated entries. Automate data cleansing and transformation processes, implement data quality controls, and monitor and audit data for quality issues.
8. Prioritize security, privacy and compliance
Security is a critical pillar of data governance, especially in self-service analytics where more people have more access to more data than ever before.
IT teams must follow best practices to safeguard sensitive data and personally identifiable information. Administrators must control data access at a granular level, based on users' defined roles within the organization's security structure. Users should see only the data that is essential for them to do their jobs and nothing beyond that. No one else should be able to access any of the data.
IT and security teams must prevent unauthorized access to sensitive information. They can protect physical storage media, run antimalware software, implement firewalls, mask data, and encrypt data at rest and in motion. In addition, they must ensure compliance with applicable regulations like HIPAA and GDPR. Security management should also include regular monitoring and auditing to track how people and tools access data and flag any suspicious activity. As a best practice, keep software patched and up-to-date to minimize the risk of a data breach.
Regularly review security measures and access permissions.
9. Identify project limitations
Despite its benefits, self-service analytics doesn't fit every situation. For example, some data is too sensitive for broad access, while other data might require too much cleansing or transformation to justify the effort. Many organizations struggle with the vast amounts of data they collect and have no way to make all of it available for self-service analytics. Prioritize data for self-service analytics projects based on current business requirements.
Self-service analytics implementation requires teams to plan for negative scenarios. For instance, what happens when an individual user inadvertently shares sensitive information in their visualization? Report sprawl can occur when numerous users distribute reports -- how will you keep it in check?
Users have different levels of data literacy skills. Organizations might face resistance to self-service analysis adoption, especially if users aren't sufficiently trained on the tools or the data. Even with appropriate outreach, some might not fully understand the data, fail to choose the most relevant data, or interpret information inaccurately. Assess which types of analytics are best left to data scientists and analysts with specialized backgrounds.
Robert Sheldon is a freelance technology writer. He has written numerous books, articles and training materials on a wide range of topics, including big data, generative AI, 5D memory crystals, the dark web and the 11th dimension.
