Tips to choose the best self service data analytics tools

The data-driven revolution is in full swing and businesses everywhere are realizing the power of putting insights in everyone’s hands. As the call for self-serve analytics solutions that let employees fly solo with data grows louder, picking the right tool for your crew is no easy feat. Beyond features and figures, other factors come into play. But fear not – we’ve got you covered! This guide on 10 top tricks to help you hit the analytics jackpot and pick a platform for self service data analytics tools. Self service data analytic tools can benefit a person in many ways. From visual wizardry and model mastery to support superstars and more, read on for pro tips that’ll have you feeling like a self-serve sensei in no time flat.

Tip 1: Define Your Use Cases and Requirements

The first step is to clearly identify the types of analyses, reports and dashboards required. Do you need basic visualizations or advanced predictive modeling capabilities? Will the tool be used for operational, financial or customer analytics? How many concurrent users will access it? What data sources need to be connected – databases, data lakes, CRM systems etc. Take the time to map out very specific use cases with screenshots or examples wherever possible to help visualize requirements. Documenting clear requirements upfront with this level of detail will help effectively shortlist options during the evaluation process and select a platform that provides the best fitment for your unique business intelligence needs.

Tip 2: Assess Data Integration and Management

Ease of connecting to various data sources, moving data in and out, data wrangling capabilities, governance controls etc are important to evaluate. Consider the effort required to prepare and maintain the underlying data on an ongoing basis. Opt for tools that integrate seamlessly with data residing in databases, data lakes, cloud storage and other systems in use. Look for self-service options to define and manage connections without extensive coding. Can the tool handle streaming, real-time and large volume datasets efficiently? What types of transformations like filtering, sorting, joins, aggregations etc. can be performed within the tool itself? Centralized metadata and data lineage tracking features that provide visibility into data flows and transformations add significant value. Check supported file formats, ability to schedule and monitor ETL/ELT jobs, version control utilities and overall ease of ongoing data management.

Tip 3: Evaluate Visualization and Exploration Options

Look for rich and customizable visualization types like charts, graphs, maps, dashboards etc. that clearly present trends, patterns and outliers in the data. Check if visualizations can be embedded in reports, dashboards or shared externally on platforms like email, collaboration tools or websites. Assess self-service capabilities for exploration through filters, drill downs, what-if analysis etc. without relying on IT. Advanced features like augmented analytics that provide recommendations for insights accelerate knowledge discovery. Ensure visualizations render well across devices and screen sizes like desktop, laptop, tablet or mobile for universal access and sharing. Opt for solutions with an intuitive, interactive interface for visualization customization and exploration to gain actionable insights from your data.

Tip 4: Consider Collaboration Features

Collaboration is key for self-service analytics success. Look for features that facilitate teamwork like commenting and annotation on visualizations, dashboards and reports. See if insights can be shared via links or embedded across departments. Consider capabilities for collaboratively exploring and analyzing data in real-time through filters and selections. Advanced tools offer version control and review workflows for governance. Social features for following colleagues, liking insights and discussing discoveries foster communities of learning. Evaluate security and access controls for collaborative projects and folders. Integration with productivity suites and collaboration tools extends value. The right collaboration capabilities streamline knowledge transfer, accelerate decision making through consensus and break data silos across distributed teams.

Tip 5: Examine Analysis and Modeling Capabilities

Gauge supported techniques for statistics, forecasting, predictive modeling, optimization, text/image analysis etc. Check for an intuitive interface to develop and deploy models. Consider model governance capabilities. Advanced tools provide pre-built algorithms, custom modeling and code-free workflows. Look for capabilities to automate feature engineering, model selection, parameter tuning and deployment. The solution should support retraining and updating models on incremental data. Ensure model explanations are available to understand prediction logic and variables influencing outcomes.

Tip 6: Assess Deployment Flexibility

Choose a tool that can be deployed on-premises, on cloud or both as per your infrastructure needs. Cloud-based SaaS solutions are low maintenance but on-premises provides more control. Hybrid options balance flexibility and governance. Ensure seamless access from any device as well.

Tip 7: Evaluate Pricing and Licensing Models

Pricing depends on user/concurrent licenses, data volumes, additional services. Perpetual licenses may be costlier initially but provide ownership. SaaS subscriptions are affordable for most budgets and include upgrades. Over the long term for on-premises infrastructure as well. Free trials help compare features versus cost.

Tip 8: Check Support Commitments

Robust support directly impacts adoption and success. Assess availability of documentation, online resources, forums, implementation services and support SLAs. Evaluate certifications for the sales and services team as well. Managed services provide additional assistance for complex deployments.

Tip 9: Assess Ease of Administration and Governance

Centralized administration and configuration management eases maintenance. Role-based access and activity monitoring enforce governance. Audit trails and version control features aid compliance. Integration with user directories simplify user management. Regular product updates and enhancements ensure long term usability.

Tip 10: Evaluate Integrations and Ecosystem

Look for open APIs and connectors to extend the tool. Check partner ecosystem for complementary solutions, services and expertise. Integrations with BI platforms, data warehouses and other line of business applications enhance value. Future-proofed extensibility protects investments.

Conclusion

Carefully evaluating requirements, features, pricing, support, governance and ecosystem fitment helps choose the right self service tool usa. While popular tools suffice basic needs, complex deployments require assessing advanced capabilities as well. Free trials, references, certifications and managed services complement the selection process. With the right tool, organizations can empower users across functions to glean actionable insights from data.