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The adoption of business analytics doubled between 2013 and 2016, according to the BARC Research and Eckerson Group study BI and Data Management in the Cloud: Issues and Trends. The most popular tools are those that are designed to improve data exploration, including visual discovery. Additional factors that differentiate cutting-edge business analytics tools include extensive support for dashboard reporting (76%), ad-hoc analysis and exploration (57%), and dashboard authoring (55%). But there are dozens of new business analytics tools released into the market every year, making it a daunting task to select the best choice for any organization. Blogger Louis Columbus, of SelectHub, has tried to simplify the process, by identifying six key criteria to use in making a selection. His advice follows.
1. Intuitive, easy-to-use interface that’s customizable by business analysts.
“Business analysts and other power users value certain features, like flexibility, in their software,” notes Columbus. “For example, defining workflows, model creation and user interfaces are some of the features they value above others. The most cutting-edge business analytics tools are capable of guiding even non-technical users through the model creation process. They provide a rich contextual experience regarding data analysis options.”
2. The option of using a natural language interface to complete analysis, while also providing more API-driven, automated approaches to data analysis.
“IBM’s Watson natural language interface is the most well-known among the crowd and one people choose often,” Columbus explains. “However, there are many smaller, faster-moving companies that are breaking new ground today by using NLP. Amazon QuickSight, Attivio, Endor and Microsoft Power BI are examples of business analytics tools that are actively developing applications. They use NLP and Natural Language Generation (NLG) responses for queries. These advanced business analytics tools don’t require a data scientist to get them running either.”
3. Support for advanced analysis algorithms capable of finding patterns in data, and then recommending visualization options.
3. Support for advanced analysis algorithms capable of finding patterns in data, and then recommending visualization options.
4. Cutting-edge business analytics tools also can combine multiple sources of complex data, scaling from the transactional to the unstructured.
“Big data can scale from the transactional to the unstructured,” Columbus says. “During a recent conversation, one CIO remarked that over 60 percent of his company’s data is semi-structured and unstructured. He was looking for tools that allow flexibility to analyze structured, semi-structured and unstructured data. He needed to find tools that wouldn’t require an IT analyst or data scientist.”
5. The ability to test advanced statistical models iteratively using machine learning algorithms.
“Another criterion business analysts -and those who are a part of the decision making process- mention is the ability to define test parameters for analytics models,” Columbus explains. “They said it was crucial to have machine learning-based algorithms seek optimal outcomes. This functionality is typically found in higher-end, enterprise-wide BI platforms. New tools are beginning to incorporate this functionality into apps designed for mainstream business users.”
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6. Support for large-scale data analysis techniques, including Hadoop, R and others, while also supporting intuitive graphical analysis and queries.
“The depth of functionality and scope of support for advanced business analytics tool features are becoming comparable to full Business Intelligence suites and platforms,” Columbus explains. “The progression of advanced features begin with business analytics tools, then to BI suites, applications and platforms.”