Top 10 Key Features of BI Tools in 2020
Nowadays, the business intelligence market is heating up. Both the investment community and the IT circle are paying close attention to big data and business intelligence. But do you know what problems BI tools can solve and what kind of BI tools can be considered good?
Based on the study of the evaluation criteria of Gartner Magic Quadrant for analytics and Business Intelligence Platforms, I have summarized top 10 key features of BI tools for your reference.
Overall, as users’ data sources become more extensive, their preferences for BI are changing. They prefer self-service development, interactive dashboards, and self-service data exploration. To put it bluntly, users increasingly want to do their own data analysis without having to find support from the IT department.
1. Management, security and architecture of the BI platform
Good BI tools can achieve platform security, manage platform users, monitor access and usage, optimize performance, support operation in different operating systems, and ensure system’s high availability and disaster recovery.
As a part of enterprise informationization, there are many reasons for BI platform to do separate management and disaster recovery. On the one hand, governments, Internet companies, and large enterprises attach great importance to informatization construction and require separate maintenance. On the other hand, BI systems have gradually become the support of business management decisions and play an increasing role. Enterprises require BI systems to provide stable services throughout the day.
2. Metadata management
Users can centrally manage metadata, including searching, extracting, processing, storing, sharing metadata, and publishing metadata externally.
The metadata here is focused on the dimensions, indicators, hierarchies, measures and other data required for business analysis. It also includes some processed data, such as KPI, personal sales, single product sales and other data. At the same time, the system supports administrators to associate and integrate metadata processed and stored by users with the underlying data connected to the BI platform.
3. Analytics dashboards
Create highly interactive dashboards and content with visual exploration operations and embedded advanced geospatial analysis.
The analytics dashboards can also be understood as interactive chart components, such as common bar charts, line charts, scatter plots, etc., as well as advanced rectangular tree charts, multi-layered donut charts, administrative geographic maps, and custom maps, heat maps, flow maps, and more. The main point here is that these charts should be built in the BI tool, and at the same time it supports business staff to simply drag and drop to achieve chart display. From the time being, this trend is quite obvious.
4. Interactive visual exploration
Explore and analyze data with a series of common and special charts.
These conventional charts are mainly pie charts, line charts, etc., while special charts refer to special visual effects such as heat maps, flow maps, rectangular tree maps, and GIS geographic information maps. Of course, in addition to the richness and beauty of charts, you also need to pay attention to the interactive operation.
If you want to learn more about chart types, this article is for your reference: Top 16 Types of Chart in Data Visualization.
5. Support mobile display
Users can publish the analysis content of BI tools to mobile terminal devices, and can use the mobile device’s own functions to implement touch operations, photos, videos, positioning, etc. of BI pages.
Mobile BI is a big bright spot in the BI market right now. Different companies have different needs. Some people pay attention to multiple operating systems, such as Android and IOS versions, which must be supported synchronously. Some people pay attention to functions and interaction effects, such as data collection, image and video collection, positioning, linkage and drilling on the mobile devices. However, please pay more attention to the security of mobile terminals, and mobile BI must ensure the security of corporate data.
6. Embed analysis content
Support the seamless integration of BI analysis pages into business processes or business systems, and support the direct creation and modification of analysis content in business software, and management of the BI platform.
The seamless embedding of BI analysis content mainly considers several aspects. The first is to achieve single sign-on, which means that users do not have to log in to the business system, and then log in to the BI system again, and the system can automatically complete multi-platform authentication. The second is permission integration. The BI platform must provide an integration solution that allows users to view BI analysis content within the permission in the business software interface. The third is UI integration. As part of the components embedded in the business system, the BI platform itself should have the ability to easily modify the UI so that the embedded BI interface is integrated into the business system.
7. Embedded advanced analytics
Users can easily use the advanced analysis functions built into the BI platform, or they can import and integrate advanced analysis models developed externally.
In popular understanding, the BI platform comes with an advanced analysis model and algorithm model, which allows users to drag data and automatically runs the model to reach a conclusion. Judging from the current BI product strategy on the market, most BI products have not yet supported embedded advanced analytics, and a few support integration with R language. A common scenario is that the user develops an algorithm model by himself or has accumulated advanced analysis models for many years, and then connects the data processed by the analysis model to a BI system for visual analysis and display. That is, BI tool is still for data analysis and graphical display.
8. Self-service data preparation
Users themselves drag and drop data from different sources to create analysis models, and then the system automatically processes the data through intelligent analysis and automatic correlation, including structured data and unstructured data.
Self-service data preparation is essentially letting the BI system automatically handle the logical association between data. At present, it is difficult for many BI tools to achieve this, but the BI reporting tool like FineReport on the market have opened up new solutions. In addition to automatically associating and escaping data, you can also manually set the association. At the same time, after the IT staff initially processes the data, the business people can process the data again through the SPA spiral analysis function.
9. Publish and share analysis content
Allow users to publish and manipulate BI analysis content through various file output types and distribution methods.
Users can share the content and decision of BI analysis through BI platform. In short, the BI analysis page I made can be shared with others, and I can also edit and modify the content shared by others, and we can communicate with each other. The biggest value here is the reuse of BI analysis. That is, the BI analysis data and conclusions that you have made can be shared, and your own analysis model can also be shared, to improve the collaboration efficiency of enterprise employees.
10. Ease of use and visualization
It is easy to manage and deploy BI platform, create and share BI analysis, and easy to visualize data.
The professionalism and ease of use of BI software are two aspects that are difficult to balance. The ease of use of BI software must take into account the software operation level of the business staff and the acceptable training costs in this regard. I think ease of use is more about providing operational tips and easier interaction. However, the necessary training is still needed. After all, data analysis itself is a professional matter and requires corresponding skills.
In the end
In the research and practice of the field of business intelligence, more and more managers are expected to solve the difficulties encountered in enterprise management and decision-making by implementing data analysis projects and purchasing business intelligence software. Some companies explore from the three levels of data sharing, data analysis, and business forecasting. More often, they adapt to the actual operation of the enterprise and make business adjustments.
Others pay more attention to the management of the enterprise, taking the three stages of analysis, operation and strategy. This creates a fundamental difference in the demand for BI. We don’t necessarily have to change the way companies manage their businesses. Instead, we use proper BI software to make management more effective and make decisions more scientific.
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