Data analytics tools can help deliver that value and bring that data to life. A lot of hard work goes intoextractingandtransformingdata into a usable format, but once that’s done, data analytics can provide users with greater insights into their customers, business, and industry.
There are three broad categories of data analytics that offer different levels of insight:
If you’re not using an analytics tool, you should be.Gartner predictsthat by 2019, self-service analytics and BI users will actually produce more analysis than data scientists. No matter what level of insight you need, here are 15 of the best data analytics tools to get you started on your journey, in no particular order.
Tableau features robust functionality with fast speed to insight. With connectivity to many different local and cloud-based data sources, Tableau’s intuitive interface combines data sourcing, preparation, exploration, analysis, and presentation in a streamlined workflow.Â
Tableau’s flexibility makes it well-suited to the three types of analytics discussed above. Tableau Server can easily house recurring reports. Power users will appreciate the integrated statistical and geospatial functionality for advanced self-service. And finally, Tableau uses application integration technologies like JavaScript APIs and single sign-on functionality to seamlessly embed Tableau analytics into common business applications.
Looker strives to provide a unified data environment and centralized data governance with heavy emphasis on reusable components for data-savvy users. Using an extract/load/transform (ELT) approach, Looker gives users the ability to model and transform data as they need it.
Looker also features proprietary LookML language, which harnesses SQL in a visual and reusable way. The reusability concept extends to Looker’s Blocks components, which are reusable utilities for data connections, analysis, visualization, and distribution. Finally, Looker is designed to easily integrate with popular collaboration and workflow tools such as Jira, Slack, and Segment.
BI360 offers modern, dynamic reporting with out-of-the-box integrations to many of the world’s most popular on-premise and cloud-based ERP systems. This easy-to-use report writer offers Excel, web, and mobile interfaces, and provides finance professionals with powerful financial and operational reporting capabilities in a variety of layouts and presentation formats.
BI360 also offers integrated budgeting workflow and analytics, including industry-specific templates. Once you connect data sources to the BI360 Suite, use these templates to access data, collaboratively develop a budget, and display results on predefined dashboards.
Available for cloud and on-premise deployment.
Dataiku DSS combines much of the data analysis lifecycle into one tool. It enables analysts to source and prep data, build predictive models, integrate with data mining tools, develop visualizations for end users and set up ongoing data flows to keep visualizations fresh. DSS’ collaborative environment enables different users to work together and share knowledge, all within the DSS platform.
With its focus on data science, DSS tends to serve deeply analytical use cases like churn analytics, demand forecasting, fraud detection, spatial analytics, and lifetime value optimization.
An open-source, enterprise class analytics platform, KNIME is designed with the data scientist in mind. KNIME’s visual interface includes nodes for everything from extracting to presenting data, with an emphasis on statistical models. KNIME integrates with several other data science tools including R, Python, Hadoop, and H2O, as well as many structured and unstructured data types.
KNIME supports leading edge, data science use cases such as social media sentiment analysis, medical claim outline detection, market basket analysis, and text mining.
RapidMiner emphasizes speed to insight for complex data science.
