Traditional BI vs Self-Serve BI: Which One Suits You the most?
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Traditional BI vs. self-serve BI: The difference?
Traditional BI is the conventional way of gathering, analyzing, and presenting business data using a centralized data warehouse and IT-driven reporting processes. On the other hand, self-service BI is a modern approach that enables business users to access and analyze data without extensive IT involvement.
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In this article, we will understand the differences between traditional BI and self-service BI, the different tools that come under them, which one to choose, and lots more.
Let’s dive in!
Table of contents
- Traditional BI vs. self-serve BI: The difference?
- What is traditional BI and its key components?
- 4 Key benefits of traditional BI
- What is self-service BI?
- Fundamental features of self-service BI tools
- 5 Key benefits of self-service BI
- Traditional BI vs. self-service BI: A deep dive
- Which one is better?
- Related reads
What is traditional BI and its key components?
Traditional Business Intelligence (BI) is the conventional way of using software, technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. The purpose of BI is to support better business decision-making.
In a traditional BI environment, IT departments would collect raw data from various sources, clean and organize it, and then store it in a data warehouse. Analysts would then use a range of tools to query this data, build reports, and generate insights.
Here are some of the common components of traditional BI that are vital to organizations:
- Data warehouse
- ETL tools
- Reporting and querying software
- Data dashboards
Let’s look into each of the above components quickly.
1. Data warehouse
A large store of data collected from a wide range of sources within a company and used to guide management decisions is called a data warehouse.
2. ETL tools
ETL stands for Extract, Transform, and Load. These are tools that extract data from different sources, transform it (like cleaning, combining, etc.), and then load it into a data warehouse.
OLAP stands for Online Analytical Processing. It is a computing method that enables users to easily and selectively extract and view data from different points of view.
4. Reporting and querying software
Reporting and querying software are essential components of Business Intelligence (BI) systems that facilitate data extraction, analysis, and presentation for decision-making purposes. These tools play a crucial role in transforming raw data into meaningful insights that business users can comprehend and act upon.
5. Data dashboards
A data dashboard is an information management tool that visually tracks, analyzes, and displays key performance indicators (KPI), metrics, and key data points to monitor the health of a business.
While traditional BI systems have been highly effective at helping businesses use data to drive decision-making, they are typically characterized by a significant level of complexity. They often require the involvement of IT professionals to manage data and generate reports, which can lead to delays and limit the ability of business users to access timely, relevant information.
Today, many organizations are shifting towards more modern BI tools and approaches, which are designed to be more user-friendly, offer real-time data analysis, and enable self-service capabilities. These include data visualization tools, cloud-based systems, and tools that leverage artificial intelligence and machine learning for advanced analytics.
4 Key benefits of traditional BI: Why it still matters?
The number of benefits your organization can derive from a traditional BI is endless. Yet, let’s have a look at the four key benefits of traditional BI.
- A controlled and managed environment
- complex data handling
- Scheduled reporting
Let’s look into each one of them in detail.
1. A controlled and managed environment
The data is managed by the IT department, which ensures that the data is accurate, reliable, and consistent across the organization. This is beneficial in reducing data discrepancies, avoiding misinterpretations, and ensuring that everyone in the organization is working with the same data.
2. Complex data handling
Traditional BI systems are equipped to handle complex data models, intricate calculations, and vast amounts of data. They are designed to perform heavy-duty processing tasks which are often needed in large organizations. These systems are capable of integrating data from various sources and preparing it for analysis in a manner that’s more efficient and reliable for large-scale operations.
Traditional BI systems often have stringent security measures in place to protect sensitive business data. This includes access controls to restrict who can access what data, audit trails to track how data is being used, and compliance features to meet legal and regulatory requirements.
4. Scheduled reporting
Traditional BI excels at providing scheduled, standardized reports that provide a regular overview of business operations. These reports can be automated, which means that once they’re set up, they can provide ongoing, timely insights without any further intervention.
Bottom line: Traditional BI provides a controlled, secure environment for managing complex data and generating scheduled reports.
What is self-service BI?
Self-service Business Intelligence (BI) is an approach to data analytics that enables business users to access and work with corporate data even if they don’t have a background in statistical analysis, BI, or data mining. Self-service BI tools allow decision-makers to use and interpret analytics on their own, with nominal assistance from IT.
The purpose of self-service BI is to make data and insights accessible to everyone in an organization, not just data scientists and analysts. It’s meant to reduce the delay in decision-making that often occurs when business users must rely on IT staff to generate reports and provide data insights.
With self-service BI, users can import data, create their own dashboards, generate visualizations, and perform ad-hoc analysis. It provides the flexibility to create personalized reports and perform in-depth data exploration.
4 fundamental features of self-service BI tools that make them unbeatable
Self-service BI tools typically have user-friendly interfaces and provide the ability to do the following:
- Data preparation
- Data analysis
- Data visualization
- Advanced analytics
Let’s take a closer look at each one of them.
1. Data preparation
This includes data extraction, transformation, and loading (ETL), which is made simple through automated processes and a graphical user interface.
2. Data analysis
Users can run queries, create derived metrics, and perform various statistical analyses.
3. Data visualization
Users can create interactive dashboards and data visualizations to easily interpret the results and share them with others.
4. Advanced analytics
Some self-service BI tools offer more advanced capabilities, such as predictive analytics, machine learning algorithms, and more.
Self-service BI has many advantages, including increased flexibility, reduced time to insight, and the democratization of data. However, it also presents challenges such as data governance and data quality issues. It requires a culture of data literacy and proper governance frameworks to ensure that data is used responsibly and accurately.
5 Key benefits of self-service BI
Embracing self-service BI empowers organizations with the tools they need to thrive in a data-driven landscape. Here are five key benefits you need to know:
- Democratization of data
- Agility and speed
- Increased engagement
- Personalized reporting
Let’s understand each benefit in detail.
1. Democratization of data
In the era of data-driven decision-making, self-service BI tools put data in the hands of the people who need it most - the business users. By making data more accessible and understandable, self-service BI promotes a culture where everyone uses data to inform their decisions, not just data scientists or IT professionals.
Learn more about → What Is Data Democratization : Definition, Benefits & Strategy!
2. Agility and speed
Self-service BI reduces the time it takes to get insights from data. Traditional BI often involves a cycle of business users requesting reports, IT creating those reports, and then sending them back. With self-service BI, business users can create their own reports and analyses, greatly speeding up this cycle.
By reducing the reliance on IT for generating reports and conducting analyses, organizations can save costs. It also allows IT to focus more on strategic tasks, such as improving the data infrastructure, instead of spending time on ad hoc reporting requests.
4. Increased engagement
With self-service BI, business users take a more active role in working with data. This can lead to a better understanding of the business, more informed decision-making, and increased engagement as users see the direct impact of their work.
5. Personalized reporting
Since each business user or department might have different needs, self-service BI allows for greater flexibility and personalization in reporting. Users can create reports and dashboards that are tailored to their specific needs, leading to more relevant and actionable insights.
With faster access to insights, reduced dependence on IT, and personalized reporting, self-service BI paves the way for an agile and cost-effective approach to harnessing the full potential of data, driving growth, and staying ahead of the competition.
Traditional BI vs. self-serve BI: A deep dive
Traditional BI and self-service BI differ primarily in their approaches to data handling and user interaction.
|Primarily IT professionals and data analysts.
|Primarily business users across the organization.
|Managed by IT, ensuring consistency and integrity.
|Managed by end users, requiring robust data governance.
|Ease of Use
|Typically requires more technical expertise.
|Designed to be user-friendly for non-technical users.
|Speed of Access to Insights
|Can be slower due to dependence on IT for reports.
|Faster access to insights as users create their own reports.
|Less flexible due to predefined models and reports.
|Highly flexible with ad hoc queries and customized reporting.
|Security and Control
|High level of security and control.
|Requires a strong governance framework to ensure proper use.
|May require more resources for IT support.
|Can be more cost-effective by reducing reliance on IT.
|Scope of Analysis
|Suitable for complex, large-scale data analysis.
|Ideal for personal, department-level, or less complex analysis.
|Democratization of Data
|Limited due to technical barriers.
|Promotes data literacy across the organization.
However, it requires strong data governance to ensure accuracy and consistency across the organization. The choice between traditional and self-service BI often depends on the specific needs, resources, and data culture of an organization.
Traditional BI vs. self-service BI: Which one is better?
Whether traditional BI or self-service BI is “better” really depends on the specific needs and context of an organization. Traditional BI may be better for organizations that need a high level of control over data and reporting, have complex data integration needs, or require a high degree of consistency and security.
On the other hand, self-service BI may be more suitable for organizations seeking more agility, and faster insights, or those wanting to foster a data-driven culture among non-technical users. In many cases, a balanced approach that combines elements of both can be the most effective solution.
Recap: What have we learnt so far?
- Understanding BI types: Both of traditional and self-service BI are used to analyze and interpret data to aid decision-making.
- Traditional BI pros and cons: Traditional BI is typically managed by IT departments, making it more controlled and secure. It’s great for complex data scenarios but can slow down decision-making due to its reliance on IT for reports.
- Self-service BI pros and cons: Self-service BI allows non-technical users to analyze and interpret data, leading to quicker insights and fostering a data-driven culture. However, it needs a strong data governance framework to maintain data accuracy and consistency.
- Choosing the right BI: The choice between traditional and self-service BI depends on an organization’s needs. Factors to consider include data complexity, the need for control and security, desired speed of insights, and the goal of fostering a data-driven culture.
- Balanced approach: Many organizations find value in maintaining a balance between traditional and self-service BI, leveraging the strengths of both based on specific needs.
Traditional BI vs Self-Service BI: Related reads
- BI & Metadata Management: The Rhino and the Oxpecker
- Enrich user experience in BI tools with additional metadata
- Power BI Data Governance: Elements, Features & Approach
- Data Catalog: The Must-Have Tool for Data Leaders in 2023
- AI Data Catalog: It’s Everything You Hoped For & More
- Data Management 101: Four Things Every Human of Data Should Know
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