Self-Service BI — No Longer a Luxury But a Business Necessity
Self-Service Analytics and Business Intelligence (BI) have remained on the wish lists of many enterprises for several years. The key features of today’s Self-Service BI platform benefit newbies as well as those with years of experience. Needless to say, many users are leveraging Self-Service BI tools for a while now without explicitly using the term “Self-Service.”
Today, the requirements in many organizations for Analytics may range from subjective data analysis to tweaking reports to modifying data models. With the help of Self-Service Analytics & Business Intelligence (BI), naïve users can succeed in conducting routine analytics tasks with minimal help from IT. On the other hand, savvy BI users will enjoy the great freedom of self-service functionality.
Without access to Self-Service Analytics & BI tools, business users will have to depend on IT for insights to conduct analysis, tweak reports, or make the required modifications in the data. The IT resources in an organization are scarce and overburdened. The flooding requests from a large number of business users can put extra strain on their functionality, thereby affecting efficiency.
Many times, it is difficult or non-profitable for business users to wait for weeks, or even months for IT professionals to respond to their data requests. Because by the time they get the requested data, the issue may have become irrelevant, or the data may have become outdated.
Progressive enterprises realize the need for the ability to build customized reports and dashboards for business users for the implementation of business intelligence.
Why is BI No More Seen As a Nice-to-Have Option But a Necessity?
The following are the major reasons:
- Interactive and user-friendly interface that eliminates the need for specific expertise for resolution of a query
- The ability to respond to user’s queries in real-time without the help of IT
- Reduced processing and turnaround time for users to receive answers
- Browser-based access for all users dispersed at various locations to interact and obtain the data they require
- Minimal to no training needed to understand and operate Self-Service BI tools
- Negligible support costs
How Does Self-Service Business Intelligence Benefit Users and Organizations?
Single Version of the Truth
Self-service Analytics capabilities offer all decision-makers access to a refined and updated version of data. Unlike spreadsheets, which account for unwanted delays and complexities by becoming incompatible and outdated data representation tools for a variety of systems, Self-Service Business Intelligence ensures data doesn’t lose compatibility or flow with time.
Self-service BI tools also combine data collected from disparate sources such as accounting, ERP, HR, CRM, and others to add value. With Self-Service BI, data is automatically captured from originating applications and converted into a working data model. Moreover, the ability of Self-Service BI to constantly update data offers decision-makers a ‘single version of the truth.’
By leveraging modern BI tools, business users can get a consolidated, accurate, and precise view of data to collaborate with other users, analyze insights to make meaningful decisions, and eliminate waste by optimizing existing products and processes. Self-service BI removes complexities associated with disparate data silos and ensures consistency, transparency, and flow, thereby making users more efficient and productive.
Reduced Dependency on IT and MIS
Business data is usually spanned across multiple areas such as finance, ERP, HR, and other systems that are not effectively integrated. This generates an incomplete and incomprehensive view of business operations, causing users to take ununiformed decisions. To obtain information that can help informed decision-making, users turn to the IT and Finance teams. This inundates the teams with the burden of responding to requests.
Self-Service Business Intelligence pulls data from multiple disparate sources and converts it into a single rolled-up report, reducing the need for intervention from the finance and IT departments.
Cost Control
The higher adoption rate of the Self-Service BI Analytics framework allows adding additional server capability without significant help from IT. Also, the implementation of a BI system eliminates the requirement for the data warehouse and costs related to licensing. As a result, organizations save a significant amount of time and expense using BI projects.
Higher Flexibility
As the name implies, Self-Service Business Intelligence comes with tremendous flexibility for users. When used efficiently, decision-makers can generate customized reports to solve critical business problems using historical as well as real-time data quickly and effectively.
Effortless Decision-Making
Business operations and revenue are subject to decisions made by the key enablers of business. However, the overwhelming frequency of decision-making in an organization may exhaust the decision-makers — possibly resulting in poor decision-making and therefore, reduced productivity and missed opportunities. By introducing a powerful Self-Service BI strategy, organizations can optimize and simplify the decision-making landscape. With intuitive dashboards, reports, and visualization, users can convert data into meaningful insights to make prompt and effective decisions effortlessly.
What are the Most Critical Use Cases of Self-Service BI?
As we discussed above, business users may leverage the Self-Service capabilities of modern business intelligence systems in several ways. To cater to the unique range of requirements of today’s business users and decision-makers, Self-Service BI can fit into the following use cases.
Modification of Reports and Dashboards
Self-service BI tools allow modifications in reports and dashboards so that users can apply necessary filters and customize outputs according to their key indicators in the most meaningful way. They can visualize components that address their specific requirements and help them derive value from relevant business processes.
Ad-Hoc Business Reporting
To ensure efficiency and productivity, IT shouldn’t be overburdened with tasks such as creating reports and dashboards. Thanks to the intuitive interface with predefined templates and dashboard objects, ad-hoc reporting can be done by power users independently for other end users under the organization.
Integration of Local Data
Another crucial use case of modern business intelligence is its capability of integrating local enterprise data into existing records, for instance, reports, analyses, or data models. Such data can be in raw form and can come from a variety of sources such as excel documents, flat files, and more. Local data can be utilized to relay the insights delivered by the data warehouse, reducing the workload and associated stress on data management.
Modification and Creation of Data Models
Business analysts and power users must be armed with the ability to create and modify data models without depending on other teams to make way through specific decision-making scenarios. In such situations, business users can play a vital role by creating new models or making modifications to the existing ones without depending on technical teams. The authority can be given to users at the metadata layer, a database, or a confined environment. Each organization should draw a practical roadmap for data management to ensure the best approach.
Key Takeaways
Effective decision-making requires effortless and exceptional reporting and data exploration tools. While business intelligence introduces modern techniques to overcome severe analytics challenges, Self-Service BI tools shift power from technical teams such as IT, HR, and MIS to business users. Self-Service Business Intelligence systems facilitate Self-Service data culture by enabling business users to tackle the toughest situations in real-time without the need to call IT teams for support or help. The key features of Self-Service BI include the ability to create and modify reports and dashboards by breaking down data in multiple ways, applying various filters, switching the data visualization, and drawing a comparison between large numbers of data components.
