Self-Service Analytics

What is self-service in business analytics?

In the context of business analytics, self-service refers to the ability of nontechnical users within an organization to access and use data without having to rely on data scientists or other technical personnel. Self-service functionality within an analytics environment means analytics can serve the needs of virtually anyone through their discovery. Users across an organization can become less dependent on others for insights and analysis; they can also become more agile and informed in their everyday decision-making through data access.

Self-service in analytics is a critical component of democratized analytics. This strategic analytics approach puts insights from analytics into the hands of anyone in a governed way as opposed to just data scientists and related technical experts. With self-service analytics, users have the tools they need to quickly and easily access and analyze data independently. This can help them better understand their business, spot trends and patterns in data, share those insights and make more informed decisions in the contexts of their unique roles.

How does self-service work?

Interfaces that support self-service analytics enable business users with no formal training to ask questions, make “what if” scenarios, and access insights practically without creating a support ticket for a data scientist or similar expert. This can be accomplished through self-service portals and self-service business intelligence (BI) tools that make analytics more intuitive, visual, and easy to use.

Even so, standard self-service tools, which may broaden data usability, often lack sophistication and tangible results. Emerging self-service interfaces remedy this through the use of purpose-built dashboards for each user’s role, artificial intelligence (AI), and user-friendly features and functionality, including:

• Natural language processing (NLP): BI platforms understand and respond to unstructured data inputs, such as text or voice queries.
• Visualizations: Users can quickly and easily visualize data in various formats, including charts and graphs, images, infographics, and maps, among others.
• Learning algorithms: BI platforms use machine learning algorithms to automatically generate insights from data without the need for predefined rules or models.
• Augmented analytics: AI and other advanced analytics capabilities are incorporated into self-service tools to automate data analysis, self-learn from user behavior, and make recommendations based on those learnings.

These functions contribute to a more advanced approach to business intelligence, called decision intelligence, which focuses on empowering employee decisions at all levels of an organization not only with analytics access but also with purpose-built, AI-driven analytics capabilities.

What are the benefits of self-service analytics?

Self-service analytics contribute to various individual, department, and overall business performance benefits. Notable benefits include:

• Better decision-making: By making data more accessible and self-service analytics capabilities more intuitive, self-service analytics serves as a powerful tool for improving decision-making at all levels of the organization.
• Greater agility and self-sufficiency: Employees can more easily access data, experiment with visualizations, uncover new insights, and leverage their unique analyses. Empowering individuals in these ways can lead to faster decision-making, more significant innovation, creative problem-solving, and improved productivity.
• Increased collaboration and knowledge sharing: Self-service tools make it easier for employees to share information and insights, encouraging knowledge sharing and collaboration throughout the organization.
• Achieving business KPIs: Companies can more easily track and meet key performance indicators (KPIs), such as revenue growth, profitability, customer satisfaction, and more.

How is self-service analytics used successfully today?

Many companies include self-service analytics as a critical component in emerging decision intelligence initiatives. By leveraging self-service and the enhanced capabilities of a decision intelligence platform, organizations can benefit from greater agility, faster time to insights, and more informed decision-making at all levels.

In practice, leading self-service platforms incorporate AI and ML to assist with data preparation, insight generation, model selection, and insights explanation. With these tools, users can enjoy a level of detail and interaction in data access that seamlessly contributes to any number of decisions—from the routine to the mission-critical.

How can Pyramid Analytics help?

Pyramid Analytics provides self-service functionality through a unified decision intelligence environment. This includes streamlined data discovery, augmented analytics, interactive visualizations, and more.

Contact us today to learn more about how we can help your business with business analytics.