You are here:

Self-Service Business Intelligence: Definition and Overview

Contact - Thomas Hänig - AKQUINET

Thomas Hänig

Head of Development and Data Analyst

Consulting AnalyticsGate

+49 36 41 32 78 71-0
E-Mail

What is Self-Service BI?

Self-Service Business Intelligence (SSBI) is defined as a modern data management strategy for organizations that enables users to independently access, analyze, and transform data into reports without the need for IT department support. The core idea of this strategy is to provide users with easy access to data and metrics through user-friendly SSBI tools, thereby saving time and effort.

Key Facts

  1. SSBI process overview:
    The SSBI process chain encompasses ETL (Extraction, Transformation, Loading), data analysis, and reporting/visualization. Each of these steps plays a crucial role in enabling users to independently process data.
  2. The role of SSBI tools:
    SSBI tools such as Power BI, Qlik, or Tableau are central elements of the BI strategy. They offer intuitive interfaces and advanced analysis functions, helping users to efficiently and independently process and visualize data.
  3. Practical challenges:
    Practical challenges of SSBI include data security, ensuring data literacy among employees, and selecting the appropriate BI tool, all of which are crucial for successful SSBI implementation.
  4. Advantages of SSBI:
    SSBI leads to improved accessibility and user-friendliness of data, supports rapid and well-founded decision-making, simplifies data management, and reduces costs through lower initial investments and operational expenses.
  5. Future trends in SSBI for 2024:
    Future trends in SSBI include a stronger focus on data literacy, the integration of artificial intelligence into BI tools, and a shift towards storytelling in reporting.

Inhaltsverzeichnis

  1. Scientific definition of Self-Service B
  2. What is the difference between BI and Self-Service BI?
  3. Understanding the process chain behind Self-Service BI
  4. Self Service BI Tools
  5. Challenges of Self-Service BI
  6. Advantages of Self-Service BI
  7. Self-Service BI trends 2024

Scientific definition of Self-Service BI

In a scientific context and definition, Self-Service Business Intelligence focuses on four main objectives. These are:

  1. Facilitated access to source data for reporting and analysis: Making it easier for users to access source data for the purposes of reporting and analysis.
  2. Improved and Simplified Support in Analysis Functions: Enhancing and simplifying the support available for analysis functions.
  3. Faster Implementation Options: Providing quicker options for implementing SSBI solutions.
  4. Simpler, customizable, and collaborative user interfaces: Developing user interfaces that are easier to use, can be customized to individual needs, and support collaboration.

SSBI contributes to gaining competitive advantages and discovering new business opportunities. It expands the scope of BI applications to address a broader spectrum of business requirements and challenges. It creates a personalized and collaborative environment for decision-making, where the user-friendliness and usability of BI tools are of paramount importance to users (Imhoff & White, 2011).

What is the difference between BI and Self-Service BI?

Differences between BI and SSBI listed side by side on 3 main aspects

The traditional business intelligence approach is based on IT-centric data processing, whereas Self-Service BI allows for decentralized data processing. Traditional BI tools rely on complex and often monolithic BI systems that require intensive coordination between IT experts or data analysts and business department employees. SSBI enables direct access to data by users from various business departments, thus bypassing the need for data analysts.

Understanding the process chain behind Self-Service BI

Self-Service BI can be divided into three fundamental processes. An overview of these processes aids in comprehending Self-Service BI:

  1. Creating a data foundation: ETL (Extraction, Transformation, Loading
    If you are interested in this topic, read our article on Data Cleansing: Ensuring data quality in your company.
    • Extraction: This involves extracting data from various sources, such as databases, file systems, or cloud storage.
      Data sources:
      • Databases like MySQL, NoSQL, Oracle, CRM systems, ERP systems, or SQL servers.
      • Third-party APIs that provide access to external data sources like social media, weather services, financial market data, etc.
      • Cloud storage like Amazon S3, Microsoft Azure Blob Storage, or Google Cloud Storage.
    • Transformation: In this step, the extracted data is cleaned, normalized, and converted into a format suitable for analysis. This includes filtering, sorting, merging, and aggregating data.
      • Cleaning: Removing duplicates, correcting errors in the data (e.g., incorrect date formats, typographical errors).
      • Normalization: Adjusting data formats for consistency (e.g., converting all date formats to a uniform format).
      • Aggregation: Summarizing data, such as creating monthly sales figures from daily sales data.
      • Enrichment: Enhancing data with additional information, like adding geographical data to customer data.
    • Loading: Finally, the transformed data is loaded into a target data storage system, such as a data warehouse or a database, where it is available for analysis purposes.
      • Data Warehouse: Loading transformed data into a data warehouse like Teradata, Snowflake, or Google BigQuery for complex queries and analysis.
      • Databases: Transferring data to operational databases like Microsoft SQL Server for daily business operations.
      • Business intelligence tools: Directly loading data into BI tools like Qlik Sense, Tableau, or Power BI for immediate visualizations and analyses.
        If you are interested in this topic, read our article on Data Cleansing: Ensuring data quality in your company. (https://analyticsgate.com/blogbeitrag-details/datenbereinigung-im-unternehmenskontext.html)
  2. Data analysis:
    • The data analysis involves examining, modeling, and interpreting data to identify patterns and trends. The data basis for this is created through the ETL process. However, there is a clear distinction as the end users, who are to benefit from increased autonomy through SSBI, are not involved in the ETL process. This task remains with the IT experts and data analysts in the company.
    • For the end user and data analysis, only the SSBI tools are of interest. The software must first enable independent data analysis. Secondly, it should be user-friendly, and thirdly, it should enable faster analysis processes, e.g., in the form of ad-hoc analyses.
  3. Reporting and visualization:
    • Self-Service reporting: The results of the data analysis are presented in an understandable and accessible format, often in the form of reports, dashboards, and various visualizations such as diagrams and graphs. These visualizations assist end users in more easily understanding and interpreting data, enabling them to make informed decisions.
    • Visualization and clear presentation: SSBI tools allow users to effectively communicate complex data sets through visual storytelling. This includes:
      Developing storylines: Users can build a storyline or a narrative around the data that presents the key insights and trends in a logical and appealing way.
      • Custom visualizations: Instead of relying on standard diagrams, users with SSBI tools can create custom visualizations specifically tailored to the data and target audience.

You can find out more about successful BI reporting in our navigation compass for BI reporting.

Challenges of Self-Service BI

Implementing Self-Service BI in companies comes with a range of challenges that need to be addressed. A fundamental challenge is ensuring data quality and data security. The risk of data misuse and misinterpretation increases with the number of data users, particularly if they have limited data literacy.

Challenge - Data Security

Consequently, companies often implement robust data governance policies to ensure the accuracy, consistency, and security of data. This includes implementing access controls to ensure that only authorized users have access to sensitive information.

In our BI Guide for Companies, we also explain the specific steps you should take for successful implementation and integration of Self-Service BI in sales, controlling, management, and other departments.

Challenge - Data Literacy

The ability to understand, analyze data, and make decisions based on this analysis is known as data literacy, and it can be more challenging to learn than it initially appears. Incorrect data sets, inaccurate metrics, and interval distances, or the mixing of data sets can lead to faulty visualizations that present a distorted view of reality.

Data Literacy Basics in the Company

Not every employee has the necessary data literacy to effectively analyze and present data. It is important for companies to invest in data literacy training and continuing education programs to provide employees with the foundation for competent data handling. A pronounced level of data literacy at all levels of the company is a prerequisite for effectively utilizing the benefits of Self-Service BI.

Here you can find a detailed article about the problems of reporting after data analysis with concrete examples.

Training in Data Literacy

To develop the desired level of data literacy among employees, regardless of their previous experience or technical background, online courses, workshops, and seminars are used. It is important that these trainings are practical, as learning is best achieved through practical experience.

Therefore, successful implementation of Self-Service BI primarily means investing in practice-oriented training programs to ensure that employees have the necessary data competence and thus establish the basics of data literacy in the company.

When choosing the right training program, it is important to select the appropriate level. There are numerous providers that offer data literacy training for companies, from beginner courses to advanced courses. It is advisable to get well-informed advice beforehand. Conducting a status quo analysis is worthwhile. This assesses the current level of knowledge in the company and allows for the correct selection of a data literacy training program.

Challenge - Selecting the Right Self-Service BI Tool

Choosing the right Self-Service BI tool is a significant challenge, as it is not always easy to decide on the "right" SSBI tool from the plethora of available solutions.

An ideal SSBI tool is characterized by user-friendliness and offers seamless integration into existing workflows. It is important that users are able to create analyses and reports with a reasonable amount of effort.

Data Sources and Security Measures

A good SSBI tool provides flexible options for data connection and supports a variety of data sources. Equally important is strong support for data governance and security measures to ensure the integrity and protection of the data. Ensuring data security is a central concern of SSBI, as the susceptibility to errors increases with the number of users.

Important!
In practice, information security is resolved by the IT department through access control. This allows them to specifically assign or revoke data and access rights to users. Sensitive, customer, and personal data are thus protected in compliance with the law.

Self-Service BI tools

An ideal Self-Service BI tool should be intuitive to use, accessible, and scalable to meet the needs of different user groups. It should also have robust data management functions to ensure data integrity and security.

Here is a brief list of the most common Self-Service BI tools:

  • Power BI
  • Qlik
  • Tableau
  • Sisense
  • Zoho Analytics

Advantages of Self-Service BI

Accessibility and user-friendliness

Intuitive interface

Self-Service BI tools are designed to be easily accessible even for users without a technical background. Their intuitive interface allows users to quickly become proficient with the software, significantly lowering the barrier to entry in data analysis. This enables more employees within a company to gain and use data-driven insights, regardless of their technical know-how.

Simple data manipulation

The drag-and-drop functionality in Self-Service BI tools greatly simplifies data manipulation. Users can intuitively move and adjust elements, making the creation of reports and analyses much more efficient and user-friendly. This function makes it easier for users to explore and visualize complex datasets without getting lost in complicated query processes.

Quick results

Pre-built templates and dashboards in Self-Service BI tools enable users to quickly create insightful reports. These templates serve as a starting point that can be individually customized and expanded to meet specific business needs, allowing companies to react faster to market changes and make timely decisions.

Mobile analytics and unlimited access

The ability to use BI tools on mobile devices and access data anytime revolutionizes decision-making. This constant access allows users to view current business data on the go and make informed decisions, significantly enhancing the agility and responsiveness of the company.

Decision-making

Immediate insights

Self-Service BI tools allow users to directly access and analyze data, leading to faster and more informed decisions. Immediate insight into current business data enables quicker recognition of trends and early addressing of potential issues.

Dynamic ad-hoc analysis

The flexibility of Self-Service BI tools to perform spontaneous ad-hoc analyses allows users to quickly respond to unforeseen questions. This type of dynamic analysis is especially valuable in fast-paced business environments where the ability to rapidly adapt to changes is a crucial competitive advantage.

Promotion of collaboration

Self-Service BI encourages collaboration within the company by allowing teams to analyze and interpret data together. This collaborative approach leads to a better understanding and integrated decision-making, as it incorporates diverse perspectives and expertise into the process.

Data management

Customizable data access

Individual access options to data strengthen the autonomy of users. Each employee can independently retrieve and analyze data relevant to them, leading to more efficient use of resources and stronger involvement in data-driven processes.

Customizable dashboard design

The ability to tailor dashboards allows users to configure reports precisely according to their needs. This not only improves the relevance of the information presented but also the acceptance and use of BI tools within the company.

Data literacy

Self-Service BI tools support the development of basic analytical skills among users. By providing resources and training materials, employees can enhance their data analysis skills, leading to a stronger data culture within the company.

You can find out how and in which business areas you should build up data literacy in our article: Simplifying data management in your company.

Cost benefits

Lower initial investments

Using cloud-based Self-Service BI solutions can significantly save on initial investments. High expenses for software licenses and the purchase of expensive server hardware are eliminated, making it particularly attractive for small and medium-sized enterprises.

Time and resource savings

The quick and independent analysis capability of Self-Service BI saves companies time and resources. Decision-making processes are accelerated, and employee productivity is increased as they no longer rely on the IT department's support.

Scalability and flexibility

Cloud-based Self-Service BI solutions offer high scalability, enabling companies to flexibly adapt their BI functionalities to changing business requirements without significant additional investments, as scaling in the cloud is simple and cost-effective.

Self-Service BI trends 2024

Trend: Focus on Data Literacy as a Core Strategy

The year 2024 marks a crucial phase in the evolution of Self-Service BI, with a clear focus on promoting data literacy. Companies recognize that merely providing Self-Service BI tools is not enough. It is essential that employees have the necessary skills and knowledge to effectively use these tools. A strong trend towards comprehensive training and continuing education programs is emerging to enhance data literacy at all levels of the company.

Trend: Artificial Intelligence

By 2024, the integration of artificial intelligence in Self-Service BI tools will have advanced significantly. AI algorithms primarily aid in automating tedious tasks that consume a lot of time. Many BI tool providers are incorporating AI into their programs. AI can also actively assist in analyzing corporate data through automatic pattern recognition and predictive analysis. What the future holds remains an exciting question.

Trend: Storytelling in Reporting

Instead of merely presenting data, Self-Service BI tools are increasingly focusing on providing a narrative analysis. This means that data is embedded in a contextual framework that makes it easier for users to understand the significance and impact of the data and make informed decisions based on it.

Sources: