Solutions Review highlights the most common data analytics use cases you need to know about so you can select the best software.
Evaluating data analytics software is growing increasingly complex. These complexities are growing even wider when organizations consider emerging analytic capabilities like AI, machine learning, augmented analytics, predictive modeling and the cloud. In data analytics, users want to be able to inspect and understand the steps and models involved in decision making. Given the pervasive nature of AI-powered business intelligence tools entering the marketplace, this technology is quickly becoming mainstream.
AI will continue to be a game-changer for BI users, especially those without technical data science skills. However, the best AI-focused data analytics tools can explain the processes behind each prediction. Business user ease of use and complexity of analysis are two top-of-mind considerations for buyers in the current space. Common support packages include assistance for non-technical users who require self-service, but there’s also sometimes deployment assistance, dedicated business intelligence use cases representatives, and user training modules that can be a great help.
With these things in mind, our editors have compiled this list of the most common data analytics use cases you need to know.
Self-service analytics enables non-technical users (see business analysts) the ability to connect directly to a number of data sources so they may analyze and build data visualizations of blended datasets. Successful self-service strategies incorporate elements of data governance as well. This is because data governance ensures that the information shared is accurate and exhibits quality control. It is recommended for users to form partnerships to ensure that data is in place and that it has integrity. Business definitions also need to be accurately laid out for efficient report consumption and creation.
Data source connectivity is a key consideration if you are evaluating data analytics and BI software and require self-service. The same goes for data preparation functionality and the ability to for non-technical users to create data visualizations from the data they curated.
Embedded analytics software provides analytic functionality within the confines of a business application. Some self-service BI platforms provide the ability to embed analytic dashboards into commonly used applications to make data analysis more convenient. Embedding analytics into existing workflows helps business users gain access to the capabilities they need without having to go outside of the environments they use daily to do so. Users are often rewarded with faster, more informed and more efficient decision-making, which can lead to more actionable insight.
The process for buying embedded analytics or a standalone tool are very different. Buyers should be aware that embedded BI requires analysis flexibility and ease of analysis for non-technical users. Other major factors include the processing of embedding seamlessly into the host application, lifecycle management, and distribution at scale.
Augmented analytics uses machine learning to change how analytic content is developed and used. The technology encompasses other modern analytical capabilities like data preparation, data management, business process management, process mining and data science. Organizations can also embed insights from augmented analytics into their own applications. Augmented analytics automates these processes to eliminate the need for data scientists.
It is expected that augmented analytics will make data science and machine learning model building accessible to new citizen data science roles, while making expert data scientists more productive. We recommend exploring opportunities to complement their exiting initiatives with augmented analytics for extension projects and other tasks with a high degree of manual analysis.
The cloud has become a major disruptor in many of the key enterprise software categories, with none impacted more than BI and data management. As adoption of cloud technologies becomes more prevalent, we expect this trend to continue. This data analytics use case refers to organizations that seek cloud BI products that support hybrid and multi-cloud deployment methods. Like the self-service use case above, data connectivity is a major consideration. So are governance and security.
Some providers are more apt to offer full-fledged cloud analytics support than others. In fact, vendors develop, organize and market their product portfolios with this fact in mind. If cloud analytics is an important use case for your organization, we recommend initially evaluating only cloud-first platforms.
Where traditional analytics is the process of using historical data to make more informed decisions in the future, predictive models attempt to predict the future. Organizations that use predictive tools wind up creating a simulation of future conditions in order to run through different scenarios in hopes of coming to conclusions before their competitors. The accuracy and usability of predictive analytics is dependent on how granular the analysis is and the type of assumptions that are being made.
It could be argued then that advanced analytics are a product of the success businesses have with traditional BI. Predictive modeling is especially useful in a real-time environment where the data that is being analyzed is created in the present time.
Learn more about the key data analytics use cases and how to solve for them with our Buyer’s Matrix Report below.
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