Self Serve Business Intelligence – Allowing each user to access analytics and reports based on their data is the next process improvement many companies are looking to implement. Our data and analytics guide explores what self-service business data and analytics are and some things to consider before diving in.
As more organizations move their data architecture to the cloud, they need to improve their processes to improve their analytics. One of the most common improvements is to make information more accessible throughout the organization so that employees and teams make decisions based on relevant and accurate information. This has led to a growing movement in business intelligence and self-service analytics. In this article, we provide an overview of what self-service business intelligence is and some important factors to consider when implementing it.
Self Serve Business Intelligence
As noted above, the goal of business intelligence and self-service analytics is to make information widely available to all users in an organization. In this way, users can be empowered to answer their own questions with available data and tools instead of relying on IT to generate a new report every time a new question is asked. The two biggest benefits of this are that it accelerates business analytics and frees dedicated data analysts from creating new reports for every ad-hoc business request.
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Take Microsoft Power BI as an example of what self-service business intelligence looks like. Power BI allows data sets created by data controllers to be easily shared for collaboration, allowing business users to connect from Excel, a tool they may already be using. Users can drag and drop the metrics they want and analyze them by dragging and dropping columns on pivot tables, creating charts and more.
Often in a self-service business intelligence model, two distinct user profiles must be defined and understood: the data controllers who make the data available and the business users who use the data. Data controllers typically write queries, design data models, simplify data sets, and manage access to data sets. They do this by collaborating with subject matter experts and team members who help provide database requirements. Business users will have broader BI experience, data knowledge and can account for different user profiles, which is critical when considering your options.
With that background provided, there are a variety of ways to perform business intelligence and self-service analytics, depending on your organization’s needs. I would like to outline some of the key pillars that need to be established in a self-service business intelligence model.
Self Service Business Intelligence (bi) Dashboards And Reports
One of the key requirements is the data access layer, where data is collected, verified and shared. This can take different forms depending on what your organization needs. It could be a data virtualization tool (eg, Kyligence, Denodo, or AtScale), an online data source repository for your analytics tool (eg, Tableau Server Data Sources, Power BI Online Sources, etc.) , or it could be. A unique database in your warehouse containing views ready for user interaction. The goal is to make it easy and intuitive to find information in your system.
This tool should have the ability to manage publishing and manage access rights for users. It is important that only certain accounts have the ability to update existing data sources and publish new ones. It is also important to ensure that access to sources with more sensitive information is strictly controlled.
Another important requirement is the training and knowledge base for users. In almost all cases, business users will have different backgrounds and skills, and not everyone will be ready to start their own analysis. Making training and knowledge bases easily accessible to users can help bridge this gap.
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Part of the training may be technical training on how to use the tool (downloading data, creating images, applying filters, etc.). The other part will be hands-on training so that users can understand where the data comes from, how it is formatted and how it should be used. Much of this practical information can be managed through data dictionaries/catalogs as well as documents that can be viewed by users when the data owners are not available to provide their own explanations.
One last requirement I would like to mention is the importance of data management. For business intelligence and self-service analytics to work well, data sources must be accurate. This seems obvious, but to successfully implement this requires organizational alignment of what should be the source of “truth” for the various systems.
Procedures need to be in place to ensure data integrity, as well as settings enabled at your data access layer to ensure that data controllers can properly manage users’ data access rights. Working with the business to understand what a certified data set means and what each certified data source should have is a critical step in achieving data integrity and self-service analytics.
Best Practices In Self Service Business Intelligence In 2022
One of the main goals of self-service analytics is to enable your business users to perform their own data analysis because they know their own requirements and the questions they want to ask the data.
There may be some situations where different departments in an organization have different analysis requirements and want to use different tools to perform their analysis. Situations like these make it important to consider whether your tool can be agnostic to a particular BI tool. It may be better for your organization if everyone uses the same tool so everything is standardized. Either approach can work, but it’s important to discuss this when developing the solution.
It is clear that data management is a very important aspect when it comes to the entire enterprise. For datasets such as sales and costs, the data must be carefully managed, and each update to these datasets must go through multiple rounds of review to ensure accuracy.
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However, there may be situations where a small group must create a resource for ad-hoc reporting requirements. If the data is not sensitive, it may not have the same level of scrutiny and number of views as the previously mentioned sources of sales and expenses. Your data management should ensure accuracy, but be flexible enough to allow ad-hoc reporting.
In order for data controllers to design data models and publish sources, they need a solid understanding of data from various system sources. Often, however, the source systems are owned by different business departments (marketing, operations, HR, etc.) rather than the IT department. This means that the data owners, the people most familiar with the data generated by those systems, are often in the business, not the IT department. This means that there can be some form of collaboration between these two user groups, and you need to think about what that collaboration should look like.
As you can see, there are many requirements and considerations when it comes to business intelligence and self-service analytics. That’s why it’s important to know these before implementing your solution. By clicking Sign in with Social Media, you consent to Pat Research storing, using and/or disclosing your social media profile and email address in accordance with Pat Research’s Privacy Policy. And agree to the terms of use.
Self Service Analytics
Self-service business intelligence is an approach to data analysis that empowers end users to design and deploy their own reports, queries, and analytics within an architecture approved and supported by the enterprise.
Self-service business intelligence saves an organization’s business intelligence and information technology teams the trouble of creating the largest number of reports so that these teams can focus on other activities that help the enterprise achieve its goals.
Self-service business intelligence facilities focus on four main goals: providing easy access to source data for reporting and analysis, improved and simplified support for data analysis features, rapid deployment options such as on-premises and cloud computing, and simple end-to-end user interface.
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In today’s economic environment, enterprises need business intelligence to make intelligent and fast decisions. On the other hand, end users need access to critical information at the right time and in the right format for easy understanding. The importance of BI cannot be underestimated, this model gives businesses a competitive advantage and allows them to find new business opportunities. Businesses, along with their employees, must embrace innovation to be effectively competitive.
Companies are turning to business intelligence as an alternative to leverage innovation and improve time to value. One such innovation is establishing an architecture where data workers can create and access specific BI reports, queries and analytics – without IT staff.
The main goal of this approach is to address a wide range of BI applications.
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