What is Self Service BI?

Definition

Self-service BI (SSBI) means that insight creators and consumers can create their own reports and analyses. In contrast, full-service BI requires direct assistance from technical resources. They might include data engineers, data modelers, data architects, platform architects, and business intelligence engineers. Centralized, easy-to-use data infrastructure and governance make SSBI a reality. SSBI also requires no-code or low-code tools to enable the following:

• Ad hoc query 
• Data visualization
• Dashboard design
• Report generation capabilities
• Data preparation
• Metric creation 
• Data modeling / Dimensionalized data creation and integration
• Semantic layer modeling

Full-service BI (do-it-for-me) relies on IT resources to manage most or all activities to turn data into insights. Business users (sales, finance, HR, etc.) must ask analysts or IT colleagues to query and analyze the data. 

This traditional method requires IT to have much more control over data quality. However, it often creates bottlenecks because of resource constraints. Full-service BI prevents businesses from generating actionable insights at scale. 

Instead, SSBI focuses on enabling insights creators/consumers to partially or fully manage data product creation, usage, analysis, and presentation. Self-service BI doesn’t mean training everyone to be data analysts. It also doesn’t mean taking responsibility from your IT department. Instead, it means encouraging and educating your business teams to understand and interact with the data they generate throughout their work. It takes the proper education, processes, and technologies to facilitate SSBI.

Purpose

Self-service BI (SSBI) empowers business users to take over critical data preparation and insight creation activities. It frees up IT or other technical resources to work on higher-value projects. SSBI enables the following:

• Faster Speed-to-Insights – SSBI empowers the business user to create relevant insights directly (both pervasive/repetitive and ad hoc). These users have greater domain knowledge. So, this process happens much more efficiently when they have more control over insights creation, fostering a data driven culture. It also eliminates multiple hand-offs with IT and minimizes technical debt and wasted effort.
• Scalability – IT resource availability and centralized enterprise priorities no longer limit business users. As a result, more insights get created faster, leading to better business intelligence.

Primary Uses of a Self-Service BI

SSBI improves business performance with better business awareness, decisions, and actions based on data insights. Business intelligence supports a broad set of users and use cases. It aims to address the needs of almost every user in a business enterprise.

Business Intelligence addresses two primary uses:

• Reporting – Structured output of a consistent data set in a standard format. This consistency enables repeated, scheduled presentation of data to a standard group of users. Reporting typically focuses on what is happening.
• Analysis – Insights from data to address specific business questions, often focusing on the “why” more than the “what.” Analysis focuses on creating tailored queries of the data that are often ad-hoc to specific situations, users, and uses. Reporting queries, on the other hand, are more standardized and repetitive — intended for a wider audience.

Benefits of Self-Service BI

Businesses that use well-executed SSBI see several benefits. They ultimately see improved business performance through better data-driven awareness, insights, plans, decisions, and actions. As compared to other approaches, such as full-service BI, SSBI provides analysis with higher levels of:

• Relevance – Insights delivered from data are relevant to the business subject and user needs. Data are available, accurate, timely, understandable, and comprehensive to address the business users’ needs.
• Speed – Insights address business questions in a more timely and effective manner. This means that they lead to faster action.
• Clarity – The insights are more precise, compelling, and accurate. This level of clarity leads to more consistent conclusions across the board. It also means that data is sufficient to address questions posed from multiple interpretations of the data and insights.
• Alignment – The interpretation of the data is more consistent, improving alignment regarding which decisions and actions to take.
• Confidence – Enterprise staff better trust the insights created from data. As a result, the relationship between insights and effective, impactful decisions is direct, positive, and improving.

Typical Roles and Responsibilities for Self-Service BI

Self-Service BI involves the following key roles:

• Insights Creators – Insights creators (e.g., data analysts) are responsible for creating insights from data and delivering them to consumers. Insights creators typically design the reports and analyses and then develop them by reviewing and validating the data.
• Insights Enablers – Insights enablers (e.g., data engineers, data architects, BI engineers) make data available to insights creators. This includes helping them develop the reports and dashboards used by insights consumers.
• Insights Consumers – Insights consumers (e.g., business leaders and analysts) are responsible for using insights and analyses created by insights creators. They use these data insights to improve business performance through data-driven awareness, plans, decisions, and actions.
• Data Engineers – Data engineers create and manage pipelines that transport data from source to target. Their responsibilities entail creating and managing data transformations and ensuring that data arrives ready for analysis.
• Analytics Engineers – Analytics engineers support data scientists and other predictive and prescriptive analytics use cases. They focus on managing the entire data-to-model ops process. This can include data access, transformation, integration, DBMS management, BI and AI data ops, and model ops.
• Data Modelers – Data Modelers are responsible for each type of data model: conceptual, logical, and physical. Data modelers may also be involved with defining specifications for data transformation and loading.
• Technical Architect – The technical architect is responsible for logical and physical technical infrastructure and tools. This role ensures that various tools can access, query, and analyze the data model and databases, including source and target data.
• Data Analyst / Business Analyst – A business or data analyst is responsible for defining the uses and use cases of the data. This role also designs input for the data structure. They define metrics, topical and semantic definitions, business questions/queries, and outputs (reports and analyses). They also own the roadmap for enhancing the data to address additional business questions and bridge existing insights gaps.

Key Business Processes Associated with Self-Service BI

The processes for delivering self-service BI include the following:

• Accessibility – Make structured data available to approved users in a secure manner.
• Profiling – Review the data for relevance, completeness, and accuracy. Data creators and enablers should profile individual datasets and integrated datasets. In addition, they should review the data in raw form and ready-to-analyze structured form. 
• Preparation – Extract, transform, model, structure, and make data available in a ready-to-analyze form. This stage often uses standardized configurations and coded automation to enable faster data refresh and delivery. Data is typically made available in an easy-to-query form such as a database, spreadsheet, or Business Intelligence application.
• Integration – When multiple data sources are involved, integrate them into a single, structured, analysis-ready dataset. Integration starts with a single data model. Then, this stage focuses on extracting, transforming, and loading the individual data sources to conform to the data model. This process makes the data available for data insights creators and consumers to query.
• Extraction / Aggregation – Make the integrated dataset available for querying, including aggregation, to optimize query performance.
• Analysis – Use the data to create insights that address specific business questions. Often, this analysis aligns with queries made by business intelligence tools. These tools use a structured database that automates the queries and presents the data for faster, repeated use.
• Synthesis – Determine the critical insights that the data indicates. Then, decide how to convey those insights to the intended audience. 
• Storytelling / Visualization – Prepare the design of the data storyline, dashboards, and visuals. Then develop it based on the business questions to be addressed and the queries implemented. It is essential to think about how to present the data so that the insights are understood and addressed.
• Publication– Make query results available for consumption in multiple forms including datasets, spreadsheets, reports, visualizations, dashboards, and presentations.

Common Technologies Associated with Self-Service BI

Technologies involved with self-service BI are as follows:

• Data Engineering –  Moves data securely from source to target. This moved data must also be readily available and accessible.
• Data Transformation – Alters the data from its raw form to a structured form that is easy to analyze via queries. It also enhances the data by providing attributes and references that increase standardization and ease of integration with other data sources.
• Data Preparation – Enhances and aggregates data to make it ready for analysis — able to address a specific set of business questions. 
• Data Modeling – Creates structure, consistency, and standardization by adding dimensionality, attributes, metrics, and aggregation. Data models are both logical (reference) and physical. Data models ensure that data can be stored and queried transparently and effectively.
• Databases – Store data for easy access, profiling, structuring, and querying. Databases come in many forms to store many types of data.
• Data Querying –  Makes requests for slices of data from a database. Many organizations use Online Analytical Processing (OLAP) to automate data querying. Insights creators can manually query with standardized languages or protocols such as SQL. Queries take data as input and deliver a smaller subset of the data in a summarized form for reporting and analysis.
• Data Warehouses – Store data that is used frequently and extensively by the business for reporting and analysis. Data warehouses store the data in an integrated and secure way. This technology also makes data easily accessible for various standard and ad-hoc queries.
• Data Lakes – Data lakes are centralized data storage facilities that acquire, store, and make data available. They enable data to be profiled, prepared, modeled, analyzed, and published. Analysts often use cloud technology to create data lakes, making storage inexpensive, flexible, and elastic.
• Data Visualization – Data visualization is a method for visually depicting data slices formed through queries. Data visualizations are typically available in query tools (OLAP / BI Applications), as standalone applications, and as libraries.

Trends / Outlook for Cloud Data Warehouses

As technology continues to improve, keep an eye out for the following businesses trends in the self-service BI arena:

• Semantic Layer – A semantic layer is a consistent representation of the data used for business intelligence. It’s useful for reporting and analysis, as well as analytics.The semantic layer creates a standard way to define data in multi-dimensional form. It ensures that data across multiple applications gets queried with a standard definition. It is a better approach than creating data models and definitions within each tool, as it ensures consistency and efficiency. This level of consistency leads to cost savings and improved query speed/performance.
• Real-Time Data Ingestion and Streaming (CDC) – A CDC creates and manages data as live streams. This technology enables and supports modern, cloud-based analytics and microservices. It extends enterprise data into live streams, enabling modern analytics and microservices with a simple, real-time, and universal solution.
• Data Governance – Effective, centrally managed data governance manages secure access and usage. It often includes data usage policies, procedures, risk assessment, compliance, and tools for managing access. Governance efforts monitor usage across the semantic layer, metric stores, feature stores, data catalogs, etc.
• Automation – Vendors are placing increased emphasis on ease of use and automation to increase speed-to-insights. These new technologies include “drag and drop” interfaces for data-related preparation activities and no-code/low-code insights creation and queries. Many of today’s automation tools work well for repeated use and sharing.
• Self-service – As data grows, the availability of qualified data technologists and analytics becomes limited. The industry needs to address this gap and increase productivity without relying on IT resources to make data and analysis available. To do so, self-service is increasingly available for data profiling, mining, preparation, reporting, and analysis. Tools like AtScale’s semantic layer also enable business users/data analysts to model data for BI and analytics uses.
• Transferable – Several new technologies also make data easier to consume by making it available for earlier publication. They do so by using APIs and objects to store elements of the insights.
• Observable – Recently, a host of new vendors started offering services called “data observability.” Data observability means monitoring the data to understand how it changes and gets used. This trend, often called “DataOps,” closely mirrors the trend in software development called “DevOps.” It tracks how applications perform and is used to understand, anticipate, and address performance gaps and improve areas. Mainly, DataOps takes a proactive, not reactive, approach.

AtScale and Self-Service BI

AtScale is the leading provider of the universal semantic layer. Our platform delivers actionable insights and analytics with increased speed and scale. Research confirms companies that use a semantic layer improve their speed to insights by four times. So, a four-month project to launch a new data source with analysis and reporting capabilities can be done in just one. 

AtScale’s semantic layer supports rapid, effective self-service BI. We ensure that data used for AI and BI is consistently defined and structured with common attributes, metrics, and features. This includes automating the process of data inspection, cleansing, editing, and refining.

A semantic layer also enables the addition of new attributes, hierarchies, and metrics/features when needed. In addition, it can extract/deliver ready-to-analyze data automatically for multiple BI tools, including Tableau, Power BI, and Excel.

This process only requires one resource who understands the data and its analysis, eliminating complexity and resource intensity. Using a semantic layer minimizes multiple data hand-offs, manual coding, the risk of duplicate extracts, and suboptimal query performance.