Self-service Business Intelligence Tools: The Development Of Computer-mediated Information Expedition

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Self-service Business Intelligence Tools: The Development Of Computer-mediated Information Expedition – 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 tasks such as data mining and statistical analysis. Self-service BI tools allow users to filter, sort, analyze and visualize data without involving the organization’s BI and IT teams.

Organizations implement self-service BI capabilities to make it easier for employees from executives to front-line workers to gain actionable business insights from data collected in BI systems. The primary goal is to make more informed decisions that result in positive business outcomes, such as increased efficiency, better customer satisfaction and higher revenues and profits.

Self-service Business Intelligence Tools: The Development Of Computer-mediated Information Expedition

With traditional BI tools and processes, the BI team or IT performs 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 request new reports and BI dashboards, typically through a requirements gathering process initiated by BI staff.

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Once the project is approved—which can take weeks in some cases—the BI team prepares the necessary data or, if necessary, works with IT to extract, transform and clean it from source systems, and load it into the data. warehouse or other data store. The BI team creates 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’s imperative that self-service analytics software has a user interface (UI) that’s intuitive and easy to use. But self-service BI systems must meet the needs of both casual users, who just want to look at data, and power users with more technical skills.

Self-service users should be trained 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 lets business users access, model and analyze data, which can lead to faster, more agile responses to data insights than is possible with traditional BI.

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The expanded data access and analytical capabilities that self-service BI provides can benefit businesses in a variety of ways. Potential benefits include:

Self-service BI deployments also pose various challenges for organizations. Obstacles and obstacles to a successful self-service initiative include:

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

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

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Tableau, Qlik and Tibco were among the first vendors of Spotfire self-service BI and data visualization tools. Now, software vendors that offer traditional BI tools for skilled analysts also offer self-service. 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 analytic features designed to help users find, prepare and help. Data analysis.

Microsoft Power BI is another leading self-service BI platform. Many other self-service options available to users come from vendors including IBM, Oracle, SAP and SAS, as well as 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 that has now been integrated into the Tableau product line. Information Builders was also a notable BI vendor before Tibco bought it in early 2021.

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 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 generation capabilities. The software can be used as a relatively simple self-service reporting tool by executives and operational workers who only need to view 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 tools 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 key components of self-service BI platforms. They include natural language query capabilities that do not require writing queries in SQL or other programming languages, as well as AI and machine learning algorithms that can identify relevant data, interpret the meaning of data elements, automate the data preparation process and suggest appropriate types. . of data visualization. Gartner predicts that by 2022, augmented analytics features will be “ubiquitous” in BI tools.

Other notable trends include the rollout of low-code and no-code development tools by vendors to simplify the process of creating BI applications, as well as the addition of support for multi-cloud environments to BI platforms. Overall, use of the cloud 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.

The Business Applications Research Center (BARC), an analyst firm that focuses primarily on BI and data management software, said that self-service BI ranked fifth on its list of the most important BI trends among 2,865 users, consultants and vendors it surveyed in 2020. . According to BARC’s “BI Trend Monitor 2020” report, data discovery and visualization and establishing a data-driven culture, both closely related to self-service BI, were No. 2 and No. 3. Data quality and master data management were first on the list, while data governance was fourth. Enterprises have adopted self-service analytics to promote innovation – self-service tools are ubiquitous. While data democracy improves productivity, self-service analytics also brings a fair amount of chaos. Enterprises are looking for ways to control self-service users from a governance perspective without stifling innovation. CDOs find themselves managing a delicate balance between centralization and independence.

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Centralization enables data organizations to control access to sensitive information, standardize metrics, clean/sanitize data, provide data and analytics at the enterprise level, and control operations. However, centralized development teams cannot keep up with the never-ending flow of requests and ideas from business users.

The advent of self-service tools has created many-to-many relationships between analytics development and production (Figure 1). Users and analysts across the enterprise develop and deploy analytics for a variety of consumers. A major brokerage firm has provided 16,000+ employees with self-service analytics tools and has seen significant growth in the creation of analytics. Recognizing the benefits of empowering users with data, the enterprise must still manage all those grass-roots innovations to protect personal information, comply with regulations and keep practices and definitions consistent. No small task and if it is not done correctly,

Figure 1: Most analytics organizations have a mix of self-service and centralized tools that combine to deliver insights to their end customers. Decentralized development creates many-to-many relationships between development and production, but that activity must be governed.

If enterprise impedes freedom, users revolt. They will copy the data to their laptop, data science tool, or cloud service to use their preferred toolchain. If they create analytics on an island, the source code cannot be reintegrated into centralized source control. After all, users tend to focus on their immediate goals – compliance with governance and policies is secondary. Users love the creative aspect of projects. Many of them don’t want to own the repetitive deployment phase of analytics and the ongoing documentation required for proper governance.

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From a CDO’s perspective, self-service analytics spurs innovation, but can be difficult to manage. Data flowing across unregulated workspaces complicates security and governance. Without visibility into decentralized development, the organization loses track of its data sources and data catalog, and cannot standardize metrics. A lack of integration makes collaboration more difficult, adds latency to workflows, creates infrastructure silos, and complicates analytics management and deployment. It’s hard to keep the trains running on time amid the creative chaos of self-service analytics.

Good governance and security are not optional. Existing and emerging regulations now require enterprises that collect customer data to carefully manage it. Unruly behavior simply cannot be allowed. Below are some example rules:

Apart from the two rules mentioned above, there are many more

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