Leveraging Self-service Business Intelligence Software For Departmental Growth – How can the modern data analyst enable their team and company to dominate the data decade? That is the question that today’s industry leaders, such as Walmart, General Motors, Hulu, and Schneider Electric, are asking.
Self-service analytics is a sort of business intelligence in which users can access and analyze data without the assistance of IT or BI experts. This implies that your analytics department will have less manual reporting to do, giving them more time to spend in strategic initiatives.
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Self-service provides front-line business users with access to data and valuable insights, allowing them to make data-driven decisions rather than depending on views or gut reactions. Given its benefits, self-service analytics is quickly becoming a must-have for enterprises of all sizes.
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So, what exactly are these benefits, and what are some best practices to follow? Continue reading to find out.
Users may access data, get answers, and make reports quickly and easily without having to wait for someone else to do it.
This is especially true for Factory 14, a European consumer goods company focused on enhancing the profitability of Amazon-sold brands. Spreadsheets are drowning the brand management and operational staff. These data silos are interfering with their capacity to meet product demand.
They opted to implement a true self-service analytics system at that point. The increased efficiency of self-service analytics was described by Leon Tang, then vice president of analytics, as follows:
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“It’s fantastic that someone like [CBO] can just go in, type a few words into the search bar, and get the exact information he needs.” Excel is being used far less frequently these days. Instead, they’re looking for specific information.”
Factory 14 has since been acquired, and Leon Tang has moved on to a new position as Director of Data at JobandTalent. However, the impact of self-service analytics on his employees cannot be denied.
Self-service analytics decreases the possibility of errors that can occur when data is manually input, transferred to a machine, or analyzed by allowing users to access it. While this is true in practically every industry, data quality and reliability are especially important in healthcare.
Consider Gilead Sciences, a biopharmaceutical business that does research. Murali Vriddhachalam, head of enterprise data and analytics, explains how self-service analytics has helped him improve the impact of his insights:
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“I noticed a variety of teams working on siled data and analytics.” Gilead now takes a comprehensive strategy to launching data-driven [insights]. It is not a cliche….Many items, for example, rely on a single BI team to generate reports. We are attempting to change this culture by offering self-service analytics.
The growth of self-service analytics and the democratization of data have the potential to improve patient outcomes in healthcare.
Users have more control over their data and may develop reports and dashboards that are tailored to their individual requirements using self-service analytics.
Snowflake, a data cloud pioneer, has direct experience with the extreme customization that self-service analytics can offer. Because Snowflake’s entire business is based on data, not simply high-quality data products.
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“As a company, we believe that collecting the right data leads to better business outcomes.” As a data platform company, we must keep that promise.” -Sunny Bedi, Snowflake’s CIO and CDO
Sunny understood he needed to bring in more resources or work smarter to meet the growing demands of his extremely successful business. That’s when he discovered self-service analytics. One of the motivating forces behind that choice was consumerism.
“Pre-packaged reports are insufficient. When you live in that world, you are not taking use of all of the opportunities that your data can provide.” -Sunny Bedi, Snowflake’s CIO and CDO
Read the entire case study to learn how self-service BI and customization helped Snowflake cut their IT backlog by 20%.
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The first step in implementing self-service analytics is to ensure that all of your data is freely accessible to individuals who require it. This entails having a central repository where data can be kept and accessible, such as a cloud data warehouse, and ensuring that data is correctly categorized and organized.
Another critical part of self-service analytics is providing a user-friendly and engaging interface for data exploration. It should be simple to use and browse, and users should be able to customize their experience.
With so many users providing data insights, it’s critical to encourage collaboration so that no one is recreating the wheel. This includes enabling business users to share reports and push insights to technologies such as Google Sheets, Microsoft Teams, Slack, and email, allowing insights to be shared more widely.
Because self-service analytics is dependent on quality data, it is critical to adhere to data governance best practices. This entails making certain that your data is appropriately maintained and controlled. Your self-service analytics efforts are jeopardized without effective data governance.
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Finally, self-service analytics programs must be iterated on a regular basis. This entails soliciting feedback and making modifications and enhancements on an ongoing basis so that the software may continue to satisfy the demands of users. Furthermore, metrics must be tracked so that progress can be tracked and problems can be corrected in real time.
A self-service analytics platform should be simple to use and allow users to obtain and evaluate data rapidly. It should have a user-friendly interface that is simple to use. Users must be able to access data without having to go through a series of stages.
Your self-service analytics platform should be scalable as your company grows. Platforms that are unable to keep up with the changing needs of organizations as they grow might quickly become obsolete.
A self-service analytics platform should be flexible enough to allow users to build highly configurable reports without requiring support from IT or other departments. This degree of adaptability is required for making data-driven judgments rapidly.
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Unauthorized data access can be disastrous for any firm. As a result, it’s critical that your self-service analytics platform has strong security safeguards in place. Self-service analytics platforms should have SOC 2 Type II, ISO 27001, Privacy Shield Framework, and GDPR certifications at the very least. You may help protect your data and prevent unauthorized access by verifying that your platform fulfills these criteria.
Self-service analytics is essential if you want to take your business to the next level. Being able to swiftly evaluate data and generate personalized, actionable insights will set you apart from competition.
It offers strong search and AI-powered self-service analytics capabilities to help you find and develop insights. Are you ready to begin? Begin your free trial now! As huge data collection challenges enterprises of all sizes, ensuring that all business activities are under control becomes increasingly difficult. Finally, organizations and businesses require assistance in making long-term and profitable decisions. Every difficulty may be swiftly answered by any business user using current and professional business intelligence solutions (BI tools), without the requirement for extensive IT participation.
These technologies collect, analyze, monitor, and predict future business scenarios by providing a unified picture of all data that an organization maintains. Identifying patterns, enabling self-service analytics, utilizing powerful visualizations, and providing professional BI dashboards are all essential tools in corporate operations, strategy formulation, and ultimately profit growth. Furthermore, the self-service nature of these solutions allows users of all levels to access all of the features we’ve outlined without the need for any technical skills or specific training. As a result, they are the ideal answer for democratizing data analysis and improving corporate performance.
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In this section, we define a BI tool, discuss the primary benefits that a company may obtain from employing them, and present a list of the top BI tools on the market, along with an evaluation of each. We used two prominent websites for software comparisons and user reviews to get the most accurate list possible. On one side, we have Capterra, which is a subsidiary of Gartner, the world’s largest consulting and research firm. G2Crowd, on the other hand, is one of Gartner’s main competitors and has been a key participant in the review business for over a decade. We chose the best-rated business intelligence software solutions with at least 50 reviews on both websites for our analysis.
BI tools are software applications that collect, process, analyze, and visualize data from the past, present, and future in order to develop actionable business insights, generate interactive reports, and streamline decision-making processes.
Key elements of these business intelligence platforms include data visualization, visual analytics, interactive dashboarding, and KPI scorecards. Furthermore, they enable consumers to leverage self-service automated reporting and predictive analytics features in a single package, making the analytics process efficient and accessible.
But what really are the advantages?
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