Self-service Business Intelligence Tools: A Guide On Computer-supported Business Analytics

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Self-service Business Intelligence Tools: A Guide On Computer-supported Business Analytics – Self-service business intelligence (BI) is an approach to data analytics that enables business users to access and explore data sets, even if they do not have a background in BI or related functions such as data mining and statistical analysis. Self-service BI tools allow users to filter, sort, analyze, and visualize data without involving an organization’s BI and IT teams.

Organizations implement self-service BI capabilities to make it easier for employees ranging from executives to frontline workers to derive useful business insights from data collected in BI systems. The primary goal is to promote more informed decision making that results in positive business outcomes, such as increased efficiency, improved customer satisfaction, and higher revenue and profits.

Self-service Business Intelligence Tools: A Guide On Computer-supported Business Analytics

With traditional BI tools and processes, the BI team or IT performs the data analysis for business users. In this approach, users request new analytical queries, which a BI analyst or other BI professional writes and runs for them. Similarly, users ask for new reports and BI dashboards, typically through a requirements gathering process initiated by BI staff.

Online Analytical Processing (olap)

Once a project is approved—which can take several weeks in some cases—the BI team prepares the necessary data or, if necessary, works with IT to extract it from source systems, transform and clean it, and load it into a data warehouse or other data store. The BI team then builds queries to produce the requested analysis results and designs a dashboard or report to display the information.

In contrast, a self-service BI environment enables business analysts, executives, and other users to run queries themselves and create their own data visualizations, dashboards, and reports. Because some of those users may not be tech-savvy, it is essential that self-service analytics software has a user interface (UI) that is intuitive and easy to use. But self-service BI systems must meet the needs of both casual users who just want to view data, and power users with more technical skills.

Self-service users should be given training to help them understand what data is available and how it can be queried and used to make data-driven business decisions. In many cases, BI team members support users as needed on an ongoing basis and promote BI best practices throughout the organization.

Self-service BI gives business users the ability to access, model, and analyze data, which can lead to faster, more agile responses to data insights than traditional BI.

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The expanded data access and analysis capabilities provided by self-service BI can benefit businesses in a variety of ways. Potential benefits include:

Self-service BI deployment also creates various challenges for organizations. Barriers and hindrances to a successful self-service initiative include:

To avoid or overcome such challenges, an organization must begin with a well-planned BI strategy, which includes a solid BI architecture that establishes technology and governance standards. Those foundational elements can help ensure that the organization has the right data sets and infrastructure in place to support enterprise-wide use of self-service BI tools.

Additionally, a BI training program should educate workers not only on how to use self-service systems, but also on how to find the business data they need and how to create effective data visualizations, dashboards, and reports. Meanwhile, the data governance policy should define key data quality metrics; data management, access and use policies; processes for sharing reports and dashboards; and how data security and privacy protection will be maintained.

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Tableau, Qlic and Tibco Spotfire were among the first vendors of self-service BI and data visualization tools. Now, software vendors that once offered traditional BI tools for skilled analysts also offer self-service tools. In fact, consulting firm Gartner characterizes a modern analytics and BI platform as a set of easy-to-use tools that support a complete data analysis workflow with an emphasis on self-service capabilities and enhanced analytics features designed to help users discover, prepare, and analyze data.

Microsoft Power BI is another leading self-service BI platform. Some of the many other self-service options available to users come from IBM, Oracle, SAP and SAS as well as vendors including AWS, Domo, Google’s Looker unit, MicroStrategy, Pyramid Analytics, Sisense, ThoughtSpot and Yellowfin. Salesforce, which acquired Tableau in 2019, also offered its own BI software, but it is now integrated into the Tableau product line. Information Builders was also a notable BI vendor before it was bought by Tibco in early 2021.

The ease of use, sophistication, and features vary for each vendor’s self-service BI tools. For example, some platforms may be used primarily for simple dashboards and visualizations, rather than for more complex data analysis and related tasks, such as self-service data preparation, data discovery, and interactive visual exploration.

Key features of self-service BI software include ad hoc querying, data visualization, dashboard design, and report building capabilities. The software can be used as a relatively simple self-service reporting tool by executives and operational staff who only need to look up specific information, while more advanced users can take advantage of its query and design features to share analysis results with others.

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Self-service devices also offer a variety of other features, either as standard items or as optional add-ons. Some of those items include:

Augmented analytics technologies are increasingly becoming a core component of self-service BI platforms. They include natural language query capabilities that eliminate the need to write queries in SQL or other programming languages, as well as AI and machine learning algorithms that can identify relevant data, explain the meaning of data elements, automate the data preparation process, and suggest the appropriate types of data visualizations. Gartner estimates that enhanced analytics features will be “ubiquitous” in BI tools by 2022.

Other notable trends include the rollout of low-code and no-code development tools by vendors to simplify the process of building BI applications, as well as the inclusion of support for multi-cloud environments across BI platforms. Overall, cloud use for BI and analytics is on the rise – in its 2021 “Magic Quadrant for Analytics and Business Intelligence Platforms” report, Gartner said the “vast majority” of new spending on BI systems is for cloud deployments.

Business Applications Research Center (BARC), an analyst firm primarily focused on BI and data management software, said it surveyed 2,865 users, consultants and vendors in 2020, ranking self-service BI fifth on its list of most important BI trends. According to BARC’s “BI Trend Monitor 2020” report, data discovery and visualization and the establishment of a data-driven culture, both closely related to self-service BI, were No. 2 and No. 3. Data quality and master data management ranked first on the list, while data governance was ranked fourth. All businesses operate with data – information generated from many sources internal and external to your company. And these data channels serve as a pair of eyes for the executives, providing them with analytical information about what is happening with the business and the market. Accordingly, any misunderstanding, inaccuracy, or lack of information can lead to a distorted view of market conditions as well as internal operations – which can then lead to bad decisions.

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Making data-driven decisions requires a 360° view of all aspects of your business, even the ones you didn’t think about. But how do you turn chunks of unstructured data into something useful? The answer is Business Intelligence.

In this article, we will discuss the actual steps to bring business intelligence to your existing corporate infrastructure. You’ll learn how to set up a business intelligence strategy and integrate the tools into your company workflow.

Business Intelligence or BI is a set of practices for collecting, structuring and analyzing raw data in order to transform it into actionable business insights. BI considers methods and tools that transform unstructured data sets, compiling them into easily understandable reports or information dashboards. The main purpose of BI is to support data-driven decision making.

Business Intelligence is a technology driven process that depends heavily on inputs. Technologies used in BI to transform unstructured or semi-structured data can also be used for data mining, as well as front-end tools for working with big data.

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, Descriptive and diagnostic analytics – or BI – allows businesses to study market conditions in their industry, as well as their internal processes. A historical data overview helps to find problem points and opportunities for growth.

Based on data processing of past and present events. Instead of observing historical events, predictive analytics makes predictions about future business trends. It also enables scenario simulation and comparison. To make this possible, complex data architectures involving advanced ML techniques must be built by a professional data science team.

So we can say that predictive analytics can be considered as the next phase of Business Intelligence. Meanwhile, prescriptive analytics is the fourth, most advanced type that aims to find solutions to business problems and suggest actions to solve them.

Is a broad concept that can include organizational aspects (data governance, policies, standards, etc.), but in this article, we will focus primarily on technical infrastructure. Often, this includes

Business Intelligence (bi) Software

We’ll now examine each of the infrastructure elements individually, but if you want to expand your knowledge of data engineering, check out our article or watch the video below.

To begin with, a key element of any BI architecture is a data warehouse. Warehouse is a database that usually holds your information in a predefined format

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