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Add Augmented Analytics to Your Business Data Practices

Michael Singer
Director, Product Marketing, Oracle Analytics

How helpful are instruction manuals? Just think back to the last time you needed to assemble furniture or install a kitchen faucet. Chances are you also turned to an online video, a chat group or some other user group to make the task go quickly and smoothly. Either way, without a series of clear steps, you might have a hard time figuring out how to fix that leaky faucet in your home.

The same can be true of adding augmented analytics to your business' best practices. While most companies have gotten over their dependence on single-use spreadsheets, siloed data, and antique business intelligence tools, the current wisdom is to be more proactive. Leveraging predictive analytics and machine learning can address the needs of the business with speed and agility. Oracle provides a single platform for data analysis, paired with intelligent search and data discovery capabilities that collect, analyze, and interpret data from a variety of sources.

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But don't just take our word for it. Based on the G2 Crowd Summer 2019 Grid Report for Business Intelligence Platforms, Oracle Analytics recently outlined how companies can benefit from empowering users with augmented analytics to find the information and insights they need, when they need it.

Why Augmented Analytics?

Whereas the first wave of business intelligence tools was great at interpreting data, the tools were typically regulated by the IT department, required manual data preparation, and produced static visualizations or predesigned reports. Certainly, this is no way to run a business.

Most business intelligence solutions are built for power users and data analysts, not the business end user. Typically, they are developed for (and delivered at) a departmental level inhibiting company-wide data access that limits the end user's ability to find cross-functional, business-critical insights. Some common end user use cases of traditional business intelligence tools are employees in finance operations that need to track cash flow and expenses, sales managers forecasting revenue, or customer service professionals tracking tickets and agent performance. However, without simple self-service capabilities for data exploration and analysis, are these end users actually able to take advantage of the data and tools provided to them?

(G2’s Summer 2019 Grid Report for Business Intelligence Platforms)

The evolution of business intelligence tools is augmented analytics. Born of the cloud-based and hybrid-cloud software era, these tools offer a self-service experience that provides data enrichment powered by machine learning. The mere fact that there is no need for IT assistance to pull critical data is worth the shift. The self-service nature of augmented analytics means you can get automated chart recommendations for visualizations along with point-and-click visual exploration, drag-and-drop features, and recommended fields, columns, data sets, and visualizations that draw attention to insight.

Additionally, augmented analytics systems like Oracle Analytics provide natural language or voice queries for quick analysis. They also allow for predictive analytics to suggest future outcomes and indicate where the company is headed.

Leveraging Intelligent Features

There are many features within Oracle Analytics that make it a great value for data-hungry business leaders. Here are our five favorites:

  1. Intelligent search. Seamless search with natural language queries, offering the ability to search all aggregated data sources while providing the most relevant visualizations based on the search.
  2. Smart data discovery. Automatically analyze and generate explanations of data, including key drivers of the results
  3. Smart data preparation. Augments, enhances, heals, and creates richer data that can lead to improved business insights and sharper understanding.
  4. Natural language. Provides a truly self-service experience, where users can search in written text or via speech using NLP and Oracle Analytics Cloud will provide analytical insights in plain, written language with the help of NLG.
  5. Mobile capabilities. A combination of speech or natural language search capabilities and proactive notifications for data changes or the availability of new data sets, all to discover new insights on the go

Stacking up Against the Competition

As we've said, the best endorsement of our recommendations for adding augmented analytics into your business' data strategy is the independent feedback of our peers. In a survey of G2 Crowd reviewers, 75 percent of those surveyed favored Oracle Analytics for "Predictive Analytics Feature Satisfaction" compared to 68 percent who either favored Microsoft Power BI or Tableau desktop products.

Similarly, when asked about services, 78 percent of survey respondents put Oracle at the top of the list of vendors whom they felt satisfied their needs for Big Data Features. Contrast that with Microsoft and Tableau, which scored 76 percent and 73 percent respectively.

By connecting with big data sources such as those that leverage Hadoop, users can analyze unstructured data like text, videos, and image datasets, among others. This enables businesses to monitor and dig insights out of nontraditional datasets—like social media posts, emails, or IoT sensors, to name a few—that provide streaming data.

Not only do these advanced features provide previously undiscovered insights, they offer relief to organizations that are not able to hire large teams of data analysts through true self-service functionality delivered via natural language.

By embracing augmented analytics, businesses are able to leverage their data like never before.

Check out the whole story in this infographic: https://www.oracle.com/a/ocom/docs/oracle-analytics-cloud-storyboard.pdf

 

To learn how you can benefit from Oracle Analytics, visit Oracle.com/analytics.

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