Self-service Data Analytics tools are software programs that let people access and look at data without help from technical staff. They empower business users with the ability to make data-driven decisions by providing them with insights into their business operations, customer behavior, and market trends.
With self-service Data analytics tools, users can explore, manipulate, and visualize data in real-time to gain insights and make data-driven decisions. These tools often include features such as drag-and-drop interfaces, data visualization tools, and natural language processing to help users easily analyze data and create reports.
Self-service data analytics tools have become increasingly popular in recent years as companies seek to empower their employees with data-driven decision-making capabilities. By providing users with the tools they need to analyze data on their own, companies can reduce their reliance on IT and data experts, save time and money, and foster a culture of data-driven decision-making.
Data from Forrester Analytics Global Business Technographics® Data And Analytics Survey, 2019, shows that 53% of global data and analytics decision-makers are implementing or expanding self-service business intelligence for analytics and reporting, while 54% are working on offering data preparation tools for self-service data management by end users.
When choosing a self-service analytics tool, it is important to know which features are most important for your organization.
Considerations When Choosing a Self-Service Data Analytics Tool
Business Needs: When you look for the right self-serving tool for your business, you must first identify your business needs. You should also check your company’s data variety and what analytic tools will suit it.
Budget: You will also need to consider the budget for your selected tool. Some tools are free, while others charge a fee per query. Check the cost of your data acquisition, storage, and management costs to find a solution that fits your budget.
User experience: Familiarity with the tool and its user interface will help you easily navigate it and get results quickly. You should also consider your business domain, data types, volumes, and how users will interact with the tool.
Security: The self-service analytics tool should have strong security features to keep hackers from getting into the data and the platform. You can check for security certifications like ISO 27001, which show that the tool is secured against vulnerabilities and threats.
Features: It should have advanced ways to show data, like charts, graphs, and tables, so that users can quickly figure out what complex data sets mean.
Integration: The analytics tool you choose should be easy to connect to different datasets and sources of information so you can get a complete picture. It should be able to integrate with popular databases like Oracle, SQL Server, and MySQL, as well as cloud-based storage solutions like AWS S3 and Google Cloud Storage.
Support: You must carefully check the level of support a self-service analytics tool gets before choosing one that gets the best support. Some tools may only offer limited help, but others offer full training programs and technical support to help users get through difficult data analysis tasks. Depending on your team’s expertise levels and requirements, opting for a more supportive tool could make all the difference in achieving accurate insights from your data.
At SCIKIQ, we understand businesses’ challenges in choosing the right self-service analytic tools to meet their unique needs. Our team of experts has a lot of experience choosing, implementing, and optimizing a wide range of self-service analytical tools, such as data visualization, predictive analytics, and business intelligence software. We can help organizations reach their business goals by using our expertise and tried-and-true methods to help them make smart decisions and quickly implement the right tools.
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