Announcing new Oracle Analytics Cloud innovations
Automating
the analytics workflow to help users predict outcomes through richer
interactions with data
T.K. Anand Senior Vice President, Analytics
I’m very happy to announce a set of new capabilities in Oracle
Analytics Cloud to provide all employees
—not just data specialists— with easy-to-use, self-service analytics. Customers
can now experience interactions with their data via maps, visual market basket
analysis, and mobile devices to more quickly identify patterns and
relationships that allow them to predict outcomes and make faster decisions.
The enhancements to Oracle Analytics
Cloud include explainable machine learning, data preparation for transforming
customer-specific data into quality information, built-in text analytics,
affinity analysis, custom reference knowledge, graph analytics, custom map
analytics, natural language queries, and narratives, as well as a new mobile
app.
“Oracle’s innovation in AI-based
automation in all phases of the analytics and business intelligence continuum
delivers insights, recommendations, and actions in the context of
enterprise-wide business activities that accelerate desired outcomes,” said Dan
Vesset, Group Vice President, Analytics and Information Management, IDC.
New
Innovations in Oracle Analytics Cloud
Key new capabilities include:
- Explainable Machine Learning: Any user can now see simple explanations of the
factors that influenced a machine learning model to predict a certain
outcome. In addition, they can interact with a model, adjusting factors to
fine-tune the results. For example, of all the factors that influenced the
denial of a bank loan application, users can quickly see which were the
most determinant and why?
- Automated Data Preparation: A data profiling engine samples and scans data
to identify and proactively prompt users about potential data quality
issues, like automatically suggesting the obfuscation of sensitive credit
card information or social security number. It can enrich zip codes with
city, population, income, ethnicity, and payment data to provide more
in-depth location analysis. Users can further enrich data by uploading
more business-specific data, such as sales regions, delivery zones, or
product categories.
- Text Analytics:
Text analytics enables you to extract words from unstructured data, count
them, visualize the results, and then join that analysis with your
original data so you can drill into any level of detail. For example,
sentiment analysis uses text analytics to determine whether comments are
negative, positive, or neutral, enabling users to understand how their
brand is perceived or how a product launch is performing based on text in
surveys or social media.
- Affinity Analysis:
Discover relationships in your data by identifying sets of items that
often appear together. This data mining technique is also known as association
rule learning. A common and useful application of it is market basket
analysis in consumer goods or retail banking, where users can obtain the
probability of different products being purchased together to make
marketing decisions. When developing promotions, retailers often look at
popular combinations to develop their strategies for increasing product
sales. For example, shoppers who buy cereal often also buy milk at the
same time. Understanding this co-occurrence of items in a collection helps
retailers better manage store layout, coupon offers, and cross-selling,
and is valuable for direct marketing, sales promotions, and discovering
business trends.
- Graph Analytics:
Graph analytics show data relationships visually, such as how people and
transactions are connected or the shortest distance between two hubs in a
network. Using Oracle Analytics Cloud, anyone can easily analyze
graph data in the Autonomous Data Warehouse. This has powerful
applications in a variety of various domains, from marketing and social
media to security and compliance. For example, pathfinding lets a user
find the shortest path between two points; another common use of graph
analytics is for ranking and measuring the importance of website pages.
- Custom Map Analytics:
Map analytics gives users the ability to apply custom images as map
backgrounds and create map layers to enhance data visualizations. For
example, doctors can visualize data on an image of the human body to
identify areas that require attention and visually track the impact of
medication or other treatments. Maps can be loaded into OAC or hosted on a
web server as a dynamic background using the Web Map Service (WMS)
protocol and XYZ tile layers. This enables customers to use map
information they might not have access to in their enterprise, such as
weather and building schematics, and easily present it with their business
data.
- New Oracle Analytics Mobile App: The new Oracle Analytics mobile app lets users find
data quickly and easily, all with a consistent user experience across
Oracle Analytics Cloud and the app. It lets users interact with data
visualizations, explore dashboards, and share information across teams for
further collaboration. Users can also listen to natural-language generated
audio narratives of the most salient points from reports, dashboards, and
visualizations.
- Natural Language Processing: Oracle Analytics Cloud allows users to query their
data in natural language using a simple search-like experience—using text
or voice— and obtain spoken narratives of the results. It supports 28
different languages and various language constructs such as synonyms,
abbreviations, dynamic filters, and calculations. Users can type, text, or
speak aloud to ask business questions, such as, “what’s our employee churn
this month?” and get an employee attrition dashboard in return. Oracle
Analytics Cloud not only accepts natural language as input, but it also
outputs natural language narratives that explain the results of the query.
It has an embedded natural-language generation engine that understands the
context of the data a user is looking at and automatically updates the
narrative as the user adds data, changes filters, or otherwise changes the
context as in a typical data discovery and analysis process.
In summary, we now offer a richer
set of capabilities that power the entire analytics workflow, from connecting to
a data source, transforming and preparing the data, modeling the data, to
exploring it, and sharing results with other users —all within a rich and
engaging user experience.
These latest updates are reflective
of our focus on innovation and responding to our customers’ needs. This is why
we are particularly excited to branch out into new ways our customers can share
data and analyses in the form of “data stories” —all through a single,
extensible cloud analytics platform that allows everyone to create custom
visual experiences with little or no code.
Source: Oracle media announcement