Self-service Business Intelligence Tools: Exactly Just What The Gartner Magic Quadrant States

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Self-service Business Intelligence Tools: Exactly Just What The Gartner Magic Quadrant States – Business Intelligence (BI) has been known since the 1960s. However, at that time, the choice between different BI tools was practically non-existent and only available to a small number of IT professionals. As a result, business intelligence methods were also hardly applicable to daily management decisions.

Since then, a lot has changed: managers at every level are turning to one of the BI methods to master the challenges of their daily work life, and business departments are also using BI techniques to get the most out of data. take your However, in most cases, standard office software in combination with database queries works as a universal solution for BI questions, and only a few use the powerful self-service BI tools (SSBI tools) available in the market.

Self-service Business Intelligence Tools: Exactly Just What The Gartner Magic Quadrant States

Therefore, this paper explains about the potential of BI in general and SSBI tools in particular. In addition, we summarize the advantages and disadvantages of the various methods and compare them to the well-known and highly discrete MS Excel & Access dashboard solutions.

What Is Self Service Business Intelligence (self Service Bi)?

An umbrella term that includes applications, infrastructure and tools and best practices that enable access and analysis of information to improve and optimize decisions and performance.

[1]. Therefore, it summarizes any means of discovering, analyzing and visualizing data that is needed to support management decisions to optimize business processes, define strategic goals and extrapolate data of any kind.

With the introduction of artificial intelligence (AI) components into daily business processes, especially the extrapolation aspect of BI analytics has become increasingly important over the years: most SSBI tools benefit from improved predictive features due to machine learning algorithms and high-quality statistics. models [2].

BI tools or the use of BI tools can be divided into two parts, technical and business. The technical part of BI analysis is usually performed by the IT department following the definitions and requirements of the business department. The commissioned report is presented to the business department, who interprets and analyzes the charts and data, possibly modifying previous requirements and thus starting the process over.

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In contrast, SSBI tools are designed as do-it-yourself tools and are used entirely in commercial sectors. Thus, the extra loop of providing requirements specifications to IT, receiving a report, reworking the requirements, and receiving another report – is eliminated so that the entire BI analysis process is as lean as possible. To achieve this goal, the full potential of SSBI software must be accessible without deeper knowledge of statistical methods, programming hacks, etc., without reducing the BI performance of the tools. Different providers of SSBI tools found different ways to meet these requirements.

Therefore, the main question is how business intelligence is realized or should be realized in a particular business environment, because there are as many tools to choose from as there are aspects to analyze.

One of the very simple solutions to realize business intelligence is certainly to use existing resources. Therefore, MS Office software often suggests itself for the first stages of BI analysis and dashboard compilation. However, on closer inspection, there are several disadvantages to this solution and you may want to consider a dedicated SSBI tool instead.

In the following, we first discuss several pros and cons of “traditional” and usually self-built MS Office BI solutions.

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Costs and Setup: In most cases, MS Office Suite programs are standard software equipment in a corporate environment. Whenever a new BI analysis is started, all components are available and ready to go.

Software operation: In connection with the availability of the MS Office suite, the fact that users know how to analyze with MS Office programs, the training and adaptation steps for new tools are obsolete. Also, in terms of employee satisfaction, force of habit often makes Excel & Co the BI solution with the least resistance among users.

Available pre-analytics: Data analytics of any kind are the bread and butter activities of employees and managers. As a result, there are many existing analyzes that do the groundwork for each aspect that will be covered in the next BI report.

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Example. Suppose an analysis requires the integration of multiple data sources, a set of diverse transformations, and multiple analysis steps with distinct graphical representations. Completing this work with MS Office programs leads to a very complex and cumbersome effort to transfer data from main databases or data warehouses to Access, Excel and finally perhaps Power Point to achieve the desired BI-Analysis in The template becomes a dashboard.

Prone to (manual) errors. Due to different data transfers, possibly different people performing data conversions between transfers, there are many potential sources for errors to occur.

A lot of effort to implement, operate and customize. During the final analysis and creation of the dashboard, the first implementation, the subsequent modifications as well as the regular operation of the self-built process chain, all steps are relatively time and resource intensive:

The limited performance of analyzing large data sets often reaches the limits of Excel & Co., although the xlsx format allows for about a million rows. Alas, really large datasets imported from e.g. Data warehouses can easily exceed this number. To analyze the data, the original set must be divided and analyzed into groups, which is likely to obscure correlations or patterns within the data. Furthermore, even if the data set remains within this limit, complex analysis soon becomes difficult to track, and processing becomes slower as the number of rows increases.

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In summary, the advantages of custom built MS Office BI solutions quickly outweigh the disadvantages mentioned. This is especially true when the data sets to be analyzed are of considerable size, if the analysis is to be shared and modified by multiple team members, or if the report appears to be “quick and dirty”. be used And it has been evolving for a longer time. The more sensitive the data, the more important are highly automated, transparent, and stable data transformations that can be easily documented and, if necessary, debugged.

Therefore, this article continues by explaining SSBI tools that are a serious alternative to self-built MS Office BI solutions.

SSBI tools provide non-technical business users with tools to collect, analyze, visualize and share data. Providing a relatively intuitive management, advanced data analysis functionality and modular visual elements, end users can easily assemble the required dashboard or report or review their data without significant effort. In more detail, SSBI tools offer the following benefits:

Considering these advantages, in the next section we will discuss the detailed steps of making the report.

Data Engineering: Data Warehouse, Data Pipeline And Data Engineer Role

Regardless of the BI tool in use, the main steps leading to a new report or dashboard are always the same, see figure 1 :

The first step in BI report development is to collect relevant data. Identifying the necessary data sources, extracting and importing data sets and thus creating a data model for further analysis forms the basis of the next steps.

Here, one of the important features of BI tools is evident: by having a variety of predefined interfaces, connections to different data sources such as files, flat files, databases or online services can be established without further discussion. Once the data is available in the BI tool, the data model can be instantly modified and configured to suit the user’s needs with the help of data filters, transformations and column deletions. The main goal of this step is to keep the data model as pure and transparent as possible, thereby reducing unnecessary redundant data and unnecessary complexity for the following steps.

To build a dashboard, present and provide a concise and comprehensive overview of your data, the next step is a thorough analysis. Therefore, further changes and for example merging different datasets of the data model are done before the data is presented.

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In the next step, it is necessary to choose the appropriate form of visualization to be applied and to relate the graphs to the data model. SSBI-Tools a

Loads of predefined graphics and charts that can be customized individually. Since the data and the graphical representations, and – depending on the design – also the graphs are interconnected, a specific modification only needs to be done once and then it is rendered in real time.

It is important to remember that the charts themselves do not have to represent the final level of analysis: each chart is equipped with additional filters and options and still contains a set of information that the end user can access by pressing a few buttons. They are easily accessible. buttons. In this sense, the goal of pre-analysis and dashboard aggregation is to summarize the flood of data into relevant aspects and digest these aspects with better visualization. By applying filters and options to the compiled graphs, key parameters and conclusions can be immediately inferred.

: A report should be prepared for each business segment. Among other things, it should contain information

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