What is self-service analytics?

Leaders and frontline business users can use key internal data sources in real time to make more accurate predictions, gain actionable insights, streamline workflows and provide better customer service.

Self-service BI tools are a key aspect of an effective data strategy. With reliable data, decision-makers can improve forecasting, set accurate key performance indicators (KPIs) and make critical data-driven decisions. Efficient self-service analytics platforms empower organizations to increase efficiency and enhance data literacy across all levels.

Self-service analytics platforms are powered by a network of data pipelines built and managed by IT teams and data engineers. These data pipelines feed organizational data into self-service analytics tools through which business users can access data.

The data teams behind an organization’s self-service analytics adhere to strict data governance and data observability metrics to maintain data security and quality.

• Data pipelines collect, store and transport organizational data across an enterprise. They are built and maintained by teams of data engineers.

• Self-service analytics platforms receive data from the data pipelines and convey it to end users.

Data pipelines are the networks that store and move data through an organization. They contain 3 key phases of data management and processing:

• Data integration: Data is transferred from various data silos and sources, such as data warehouses and data lakehouses, into a single unified data system.

• Data transformation: Data is sanitized to improve data reliability and formatted into ready-to-use data sets.

• Data serving: Self-service analytics tools bring digestible data to nontechnical users. Real-time data modeling and data visualization are 2 common examples of data serving that simplify complex data for end users.

Self-service data platforms conclude an organization’s data pipelines. They feed relevant data into intuitive interfaces with powerful analytics capabilities that make sense of key business data.

Many analytics solutions, such as Tableau, Microsoft’s Power BI and IBM® Cognos® Analytics, offer several of these common features: 

• Augmented analytics: AI-automated data analysis.

• Data modeling: Identifying relationships between data.

• Data visualization: Creating graphical representations of data.

• Data monitoring: Real-time data quality assurance.

Augmented analytics is a specialized development in AI analytics that streamlines the process of distilling insights from large data sets. It is a type of advanced analytics that automates the big data analytics typically performed by data scientists and analysts.

Driven by powerful machine learning (ML) algorithms and natural language processing (NLP) models, augmented analytics turns complex data sets into digestible, actionable insights.

Data modeling is the process of structuring a relational database based on relationships between data points. It requires a high-level overview of the contents of the database to help ensure accurate relationship-mapping along with data representation and storage. Data modeling is critical when crafting schemas for data warehouses and lakehouses.

Accurate data representation makes it simpler for BI tools to convert natural language queries into structured query language (SQL) when searching a database.

Data visualization is the practice of creating graphical representations of data to make ad hoc analysis and data exploration more intuitive. Tables, graphs and charts are 3 commonly used data visualization techniques that reveal trends and patterns in data sets.

The ability to render complex data sets easily understandable by business users is one of the primary benefits of self-service business intelligence platforms. Drag-and-drop data loading streamlines the creation of custom data visualizations as needed.

Data monitoring is the ongoing evaluation of an organization’s data reliability, accuracy and consistency. Strong data monitoring leads to accurate forecasting and better trend detection. Data governance—the practice of data security—is equally important as it shields organizational data from unwanted access or changes.

Self-service analytics paves the way for more informed decision-making, more efficient workflows and greater agility in response to changing markets. When correctly implemented, the benefits of self-service analytics include: 

• Better data-driven decisions: Data provides an informative context for strong decisions.

• Greater efficiency: All personnel can access the data as needed.

• More cross-team collaboration: Teams can work together in the same platform.

• Improved accuracy: Automated data-serving prevents personnel from having to manually input data.

• Increased flexibility: Organizations can respond quickly to changing conditions.

• More customization: Workers can create their own custom data workspaces.

Data-driven decision-making is perhaps the strongest use case for self-service analytics. When reliable data is universally accessible and delivered in easy-to-understand visual formats, business users can make informed decisions for optimal outcomes.

Self-service BI tools transform internal data into one of an organization’s greatest assets. Every business decision across all levels can be made with the relevant information at hand.

Self-service BI tools enable all personnel to work without having to wait for someone to give them the data they need. Business users can create reports, perform ad hoc analyses, take appropriate actions and make independent decisions.

Before self-service analytics, data teams were responsible for preparing and delivering data, resulting in an organizational bottleneck that has now been removed. The increased efficiency brought about by self-service data analytics empowers organizations to create data workflows with high scalability and resilience.

With an entire organization under the umbrella of a single self-service analytics platform, diverse teams can work together within the same environment. A centralized data workspace keeps all personnel on the same page, aligning priorities and removing data siloes that act as barriers to effective collaboration.

Intuitive data tools empower business users and data analysts to make collaborative and informed decisions yielding stronger overall outcomes.

Pan-organizational data delivery removes the need for personnel to manually enter data as they work, in turn raising accuracy. Automated data serving prevents business users from mistakenly entering incorrect data and causing further downstream inaccuracies.

When good data is always available, business leaders and employees can perform ad hoc analysis and respond quickly to changing circumstances. They act and decide based on past trends and patterns while benefiting from accurate forecasts. Meanwhile, teams can freely engage in what-if scenarios and create action plans for hypothetical futures.

Self-service analytics tools enable users to create custom data workspaces based on the information they need. Drag-and-drop interfaces and automated data visualization populate a user’s dashboard with all the data relevant to their role, without the distraction of unneeded information.

The field of data analytics can be divided into 4 main categories. Each is made simpler with the use of self-service data analytics. The primary types of data analytics are:

• Descriptive analytics: What happened in the past? 

• Diagnostic analytics: Why did these events or trends happen? 

• Predictive analytics: What will happen next? 

• Prescriptive analytics: What should be done next? 

Descriptive analytics seeks to identify previous trends and events, answering the question, “what happened?” This information can then be used to inform future decisions. For example, a large restaurant chain can identify popular food items, uncover seasonal trends or discover which items customers are likely to buy together. A self-service analytics platform feeds this data into visual dashboards for more intuitive data analysis powered by automation.

Diagnostic analytics is the study of causes and correlations within complex data sets, answering the “why” behind events and trends. Stakeholders can drill down into the events revealed through descriptive analytics and learn what caused them to happen. These insights can then be applied to improve upon past successes and learn from mistakes.

Predictive analytics identifies patterns in the past to make educated guesses about the future, forecasting trends and outcomes. Some self-service analytics platforms offer built-in predictive modeling powered by AI, giving business leaders reliable forecasts to inform strategic decision-making.

Prescriptive analytics provides suggestions on how business leaders should react in a specific situation. Business teams can consider these prescriptions when formulating action plans based on current or theoretical scenarios.